Reviewer #2 (Public review):

Public summary

Summary:

The authors wanted to investigate how cells defend against intracellular pathogens, such as Shigella and Burkholderia species, that coopt the host actin machinery to spread from cell-to-cell. Previous work has identified IFNg-inducible GTPase of the Guanylate Binding Protein (GBP) family in cytosolic defence against Gram-negative bacteria. By forming a coat around Shigella, human GBP1 suppresses actin-based motility by displacing IcsA, which is the actin-polymerising virulence factor present at bacterial poles. In addition, GBP1 recruits the cytosolic LPS-sensor, caspase-4, to the bacterial surface, which results in the removal of bacterial replicative niches via pyroptotic cell death. Here they followed up their finding that GBP1 can reduce actin-based motility of Shigella in HeLa cells and surprisingly fails to do so during Burkholderia infection. In contrast, in T24 bladder epithelial cells, GBP1 is competent in blocking Burkholderia actin-tails. They therefore wanted to identify the GBP1-independent factor that blocks actin-based motility in IFNg-treated cells that is absent in HeLa cells.

Major strengths and weaknesses of the methods and results:

The authors report a second IFNg-dependent pathway involving the protein product of the gene GVIN1, which was previously thought to be a pseudogene. GVIN1 (GTPase, very large interferon inducible 1) is thus the first human member of this family of ~250 kDa putative GTPases to be demonstrated to be functional and have potential antimicrobial roles. The discovery that GVIN1 is indeed functional, forms coats on Burkholderia in an LPS O-antigen-dependent manner, and limits actin-dependent motility are the main strengths of this paper. The authors use CRISPR/Cas9-based knockouts in HeLa and T24 cells, and complement them, to demonstrate GBP1 and GVIN1 are both required to inhibit actin-based motility.

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

The authors achieved their main goals through well-planned experiments and unbiased screens. They succeeded in finding the factor that blocks actin-based motility independently of GBP1. This is driven by GVIN1, which coats bacteria and limiting actin-tail formation by reducing the expression of BimA through currently unknown mechanisms. Further, they found that an O-antigen mutant can escape coating by GVIN1, indicating the requirement for these polysaccharides in GVIN1-dependent bacterial sensing. However, the authors have not investigated whether GVIN1, which has two GTPase-domains, does indeed have GTPase activity and whether GVIN1 and GBP1 together completely block cell-to-cell spread by Burkholderia and thereby restrict bacterial numbers over the infection time course. They also do not show whether GBP1 and GVIN1 target the same bacterial cell or different populations of bacteria.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

This work uncovers the antimicrobial actions of a member of yet another family of IFNg-induced GTPases, which potentially acts against other intracellular pathogens. GVIN1 appears to operate independently and in parallel to GBP1, pointing to the breadth and complexity of the IFNg-inducible GTPase families.

Comments on revised version.

They have addressed my comments.

Reviewer #2 (Public review):

Summary:

This paper attempts to examine how rare, extreme events impact decision-making in rats. The paper used an extensive behavioural study with rats to evaluate how the probability and magnitude of outcomes impact preference. The paper, however, provides limited evidence for the conclusions because the design did not allow for the isolation of the rare, extreme events in choice. There are many confounding factors, including the outcome variance and presence of less-rare, and less-extreme outcome in the same conditions.

Strengths.

(1) The major strength of the paper is the significant volume of behavioural data with a reasonable sample size of 20 rats.

(2) The paper attempts to examine losses with rats (a notoriously tricky problem with non-human animals) by substituting time-outs as a proxy for losses. This allows for mixed gambles that have both gain and loss possible outcomes.

(3) The paper integrates both a behavioural and a modelling approach to get at the factors that drive decision-making.

(4) The paper takes seriously the question of what it means for an event to be rare, pushing to less frequent outcomes than usually used with non-human animals.

Reviewer #2 (Public review):

Summary:

This article by Bhattacharya et al. investigates how neural stem cells (NSCs, NBs) in Drosophila integrate spatial and temporal cues to activate neuron-specific terminal selector (TS) genes. Prior to this work, it was understood that NSCs utilize spatial transcription factors (STFs) and temporal transcription factors (TTFs) to determine lineage identity and birth order, but the mechanisms of integration were not fully elucidated. The authors employed chromatin profiling techniques to analyze the binding of STFs and TTFs in two specific neuroblast lineages, NB5-6 and NB7-4. They found that Gsb (an STF) binds both accessible and less-accessible chromatin in NB5-6, while En (another STF) binds only to pre-accessible chromatin in NB7-4. The findings support an "STF code" where the combination of pioneer and non-pioneer spatial factors, along with temporal factors, triggers neuroblast-specific enhancer activation and determines lineage identity.

Comments on revised version.

The authors have made extensive revisions and addressed the concerns raised in my previous review. Overall, I find the revised manuscript substantially improved, both experimentally and conceptually. In particular, the additional locus-level and genome-wide analyses, the motif analysis of En occupancy, the attempted endogenous Gsb depletion, and the revised interpretations and distributed STF-code model have strengthened the manuscript considerably.

(1) Adding Gsb occupancy profiles at specific gene loci such as lbe, ap, and eya, along with chromatin accessibility, Hb binding, and expression data at ems, enhances the link between TaDa results and lineage phenotypes. Although these data do not prove direct regulation of each locus, they strongly support a correlation between Gsb binding, chromatin state, and neural stem cell identity. Therefore, this aspect is mostly addressed, as long as the manuscript does not present these correlations as conclusive evidence of direct regulation.

(2) We appreciate the additional genome-wide analysis distinguishing genomic loci that gain versus lose accessibility following Gsb misexpression, as well as the comparison with two independent Gsb occupancy datasets and the corresponding Hb occupancy analysis. These data strongly support the conclusion that Gsb has chromatin-remodelling capacity and that accessibility loss occurs at loci associated with Gsb occupancy.<br /> We also appreciate the authors attempt to examine endogenous Gsb function using early CRISPR depletion and their explanation of why this approach could not subsequently be coupled to lineage-specific TaDa. The inability to directly assess chromatin consequences of Gsb loss in native NB5-6 remains a limitation, but the authors have now acknowledged this appropriately. We therefore consider this comment substantially addressed.<br /> We would, however, encourage the authors to distinguish clearly between demonstrated chromatin-remodelling capacity and the stronger, physiologically unresolved claim that Gsb establishes chromatin priming in NB5-6.

(3) The response to the question about "En not being a pioneer factor" is particularly satisfactory. The new motif analysis directly tackles the concern that the lack of En occupancy at NB5-6-specific sites might just be due to the absence of En binding motifs. The similar presence of En motifs at NB5-6- and NB7-4-specific sites strengthens the conclusion that differing occupancy is unlikely caused by motif absence. Additionally, comparing the accessibility dependence of En and Gsb occupancy helps argue against the idea that TaDa cannot detect binding at less accessible loci. Importantly, the authors have removed the categorical claim that "En is not a pioneer factor" and now limit their conclusion to the specific developmental and cellular context studied. This response adequately addresses the comment.

(4) The revised distributed STF-code model is clearer and provides a useful framework for explaining the complementary roles of Gsb, En, and other spatial factors. I would encourage the authors to clarify that the proposed stepwise model is a conceptual framework rather than an experimentally established temporal sequence. In particular, the data do not directly demonstrate that Gsb-dependent chromatin priming is causally followed by Hb recruitment at the same loci. Similarly, the division of labour among STF components should not be interpreted as mutually exclusive, since individual STFs could contribute to both chromatin remodelling and TTF-dependent enhancer regulation. The schematic and accompanying text should therefore distinguish the demonstrated differences in Gsb and En chromatin engagement from the broader proposed model.

(5) The authors correctly recognise that the factors determining the NB7-4 accessibility landscape are still unknown. Pinpointing Msh and other motif-enriched candidates clarifies this issue and offers valuable leads for future research.

Overall, the authors have responded constructively to the major concerns raised in the review. The revised manuscript presents a substantially more coherent model in which STF-code components have distinct but complementary roles in shaping the chromatin landscape and enabling lineage-specific Hb engagement.

Reviewer #2 (Public review):

Summary:

This important manuscript presents solid evidence that the major contribution of KMT2A to ribosomal gene transcription is through promoting histone acetylation rather than H3K4 methylation. It is suggested this occurs in part through interactions of KMT2A with PCAF.

Strengths:

This manuscript advances an important concept that chromatin modifying enzymes can also function to promote transcription through interactions with other transcriptional regulators independently of their catalytic functions. The data are overall supportive of this conclusion for KDM2A.

Weaknesses:

(1) Care needs to be taken regarding interpreting the lack of effect of depleting KMTs or HATs on histone acetylation or methylation levels. There are several overlapping HATS or KMTs that generate these modifications. The most that can be said from these negative results is that the depleted HAT or KMT is not the sole enzyme generating these modifications at these loci.

(2) The biochemistry supporting an interaction between KDM2A and PCAF is under-developed and not strong enough to support this conclusion at this point.

(3) It is concluded that acetylation causes increased rDNA transcription. However, acetylation may be a consequence of transcription instead, especially over transcribed regions.

(4) Some conclusions, for example implicating PCAF, are based on overexpression experiments that are very artificial and difficult to draw conclusions from.

(5) Several figures are generated using publicly available data sets that were reanalyze using updated reference genomes, sometimes from different cell lines/types. This seems to be a useful exercise but the methods should include information regarding the robustness of these data sets.

Reviewer #2 (Public review):

Summary:

Kašnik et al. set out test whether the previously reported requirement for the inner nuclear protein Samp1in myogenic differentiation and its also previously reported role in chromatin organization at the nuclear periphery are functionally linked. The authors confirm using a previously characterized shRNA system in cultured C2C12 cells that Samp1 loss leads to myogenic differentiation defects. Loss of Samp1 results in gene expression changes, including of genes associated with myogenesis and also interfered with the formation of peripheral heterochromatin and peripheralization of chromosome 8 which has previously been reported to be associated with myogenic differentiation. Finally, many of the downregulated genes by Samp1 loss were found in chromatin domains bound by lamin B.

Strengths:

The strength of this study is that is represents a rather straightforward analysis of the loss of Samp1 in myogenic differentiation in vitro. The RNAseq data is informative and is in line with the observed differentiation defects. In addition, the experiments are clearly designed and cleanly performed.

Weaknesses:

The major weakness of the study is that major conclusions are strongly overinterpreted and the degree of advance compared to prior studies is limited.

The study does not - as claimed by the authors - functionally link Samp1 to myogenic differentiation via its role in genome organization. As outlined by the authors there is already considerable evidence for a role of Samp1 in myogenic differentiation. There is also prior data to show that Samp1 loss prevents peripheralization of chromosome 8. The data reported in this study add further data to a possible functional link between these two events, but similar to the currently available data, the evidence remain circumstantial and they fall short of the authors' claim - and stated goal - of directly demonstrating a functional role of Samp1 in myogenesis via genome organization. The presented data does not rule out that the two effects represent unrelated functions of Samp1. The authors have not shown as claimed that Samp1 exerts its effect on myogenesis via genome reorganization.

The most relevant piece of data to point to such a link is the DamID data in Figure 3 showing that many downregulated Samp1-sensitive genes reside in lamin B associated chromatin. However, in this experiment, critical controls are missing to demonstrate that this effect represents an enrichment of Samp1-sensitive genes (see Recommendations).

While the RNAseq data offers the opportunity to speculate on pathways involved in myogenesis, none of the proposed pathways are experimentally tested to validate their functional roles. They remain hypothetical but are presented as demonstrated facts.

Finally, many of the findings are confirmatory in nature, such as the finding that Samp1 inhibits myogenic differentiation, that differentiation is accompanied by chromatin peripheralization or affects MAPK signaling.

PatientID: None

KindredID: 1

Case: M, 2Y0M, Danish

DiseaseAssertion:Asymptomatic

FamilyInfo: The patient's father had the p.P2P VHL mutation as well as another VHL mutation (p.P81S) and presented with renal cell carcinoma. The patient's uncles had this p.P81S mutation but were both asymptomatic. The patient's paternal grandmother also had this mutation and had breast cancer. See Figure 1 for family pedigree.

CasePresentingHPOs: N/A

CaseHPOFreeText:N/A

CaseNotHPOs: N/A

CaseNotHPOFreeText: N/A

CasePreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: N/A

Variant: p.P2= (c.6C>G)

LegacyVariant: N/A

CaseProblemVariantFreeText: N/A

ClinVarID: 456593 https://www.ncbi.nlm.nih.gov/clinvar/variation/456593/

CAID: N/A

gnomAD: 0.000 https://gnomad.broadinstitute.org/variant/3-10183537-C-G

VariantEvidence: N/A

MutationType: synonymous_variant

CivicName: P2= (c.6C>G)

MultipleGeneVariants: N/A

PatientID: None

KindredID: 1

Case: M, 31Y0M, Danish

DiseaseAssertion: VHL

FamilyInfo: The patient's brother carried the mutation. The proband’s paternal uncle died from metastasizing RCC at 46 years of age, and the proband’s father died from urosepsis in relation to bladder cancer in situ at the age of 56. Neither of these men were able to undergo genetic testing. The patient's mother was found to carry the mutation and had breast cancer. The maternal grandmother as well as a half brother were found to harbour the mutation but were asymptomatic. The proband's child was found to have a different VHL mutation (p.P2P). See Figure 1 for family pedigree.

CasePresentingHPOs: HP:0005584 (renal cell carcinoma)

CaseHPOFreeText: The patient presented with renal cell carcinoma at age 29.

CaseNotHPOs: N/A

CaseNotHPOFreeText: N/A

CasePreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: N/A

Variant: p.P2= (c.6C>G)

LegacyVariant: N/A

CaseProblemVariantFreeText: N/A

ClinVarID: 456593 https://www.ncbi.nlm.nih.gov/clinvar/variation/456593/

CAID: N/A

gnomAD: 0.000 https://gnomad.broadinstitute.org/variant/3-10183537-C-G

VariantEvidence: N/A

MutationType: synonymous_variant

CivicName: P2=(c.6C>G) and P81S(c.241C>T)

MultipleGeneVariants: Germline, VHL, c.241C>T (p.Pro81Ser)

Reviewer #2 (Public review):

Summary:

This manuscript presents an MspA nanopore-based approach for investigating the conformational dynamics and interactions of the intrinsically disordered transactivation domain of p53. The authors resolve multiple current states associated with trapped p53-TAD, identify differences between the wild-type protein and the N29K/N30D variant, and extend the approach to examine MDM2 binding and the effects of EGCG. The combination of single-molecule recordings, hidden Markov modeling, and molecular dynamics simulations provides a useful framework for studying disordered proteins under nanopore confinement.

Strengths:

The experimental observations are compelling, and the manuscript makes a valuable methodological contribution.

Weaknesses:

The analysis and interpretation could be strengthened.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors demonstrated that the amino acid transporter CD98hc (SLC3A2) undergoes fast endophilin-mediated endocytosis (FEME), followed by rapid β1-integrin-dependent recycling back to the plasma membrane. The latter is essential to maintain CD98hc membrane levels and counteract bursts of endocytosis in both endothelial and osteosarcoma cells.

Strengths:

The authors used pharmacological inhibitors, siRNA-mediated downregulation and CRISPR approaches to identify the regulators of CD98hc trafficking in different cell systems, strongly supporting their conclusions. In addition, they complemented their experimental results with computational models, which further validate their conclusions

Weaknesses:

The mechanistic details of how the interaction between CD98hc and b1 integrin determines CD98hc recycling have not been explored in the current version of the manuscript. Moreover, despite the title mentioning metabolic homeostasis, there is limited insight into how changes in CD98hc recycling affect intracellular amino acid content and cell metabolism, and how this impacts cell growth.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors aim to assess how the functional organisation of the hippocampus is related to the geometry and neurobiological differences of the hippocampus. In particular, the authors focus on the first three eigenvectors of hippocampal-cortical functional connectivity, based on non-linear dimensionality reduction on resting-state functional MRI data. Furthermore, the work aims to describe changes in these functional axes and their relation to other factors throughout youth and evaluate whether they are predictive of individual variations in cognition.

Strengths:

A major strength of this study is the attempt to replicate key findings across multiple developmental cohorts.

Weaknesses:

The major weaknesses of the manuscript center on gaps in technical transparency and several conceptual inaccuracies. The machine learning methodology used for cognitive prediction is scarce, leaving little means to evaluate whether the behavioral results suffer from data leakage or overfitting. The introduction sets up an oversimplified historical premise regarding the field's understanding and appreciation of hippocampal connectivity, and contains several incorrect references that throw doubt on the argumentation. Additionally, T1w/T2w signal intensity is incorrectly used as synonymous with myelin, despite gold-standard histological validation showing a non-significant correlation between T1w/T2w and myelin staining (Sandrone et al., 2013).

Appraisal of Aims and Conclusions:

The authors partially achieve their aims by illustrating certain age-related changes in hippocampal function; however, the correlative study design is not equipped to examine how these changes are "shaped" by geometry, myelination, or gene expression (especially the latter two). Furthermore, conclusions were often overstated based on small effect sizes.

Context and Field Impact:

This work adds to a growing body of literature focused on gradient-based representations of hippocampal topology. By applying these methods across a wide developmental age bracket, it provides a useful reference point for how the hippocampus and wider cortex interact during maturation. To improve utility to the neuroimaging and cognitive neuroscience communities, the nesting of subfields within the eigenvector topology should be addressed, too.

Reviewer #2 (Public review):

Summary:

Niu et al. develop a biochemical pipeline using systematic co-immunoprecipitation from cell lysate coupled with in vitro binding studies to test hypotheses generated based on structure prediction of how the kinesin heavy chain is autoinhibited through intramolecular interactions, as well as interactions with the kinesin light chain. Additionally, the authors provide insight into kinesin heavy chain binding to the cargo adaptor TRAK. Structural understanding of kinesin regulation has been elusive due to apparent flexibility of the motor. The advent of protein structure prediction, validated through cross-linking mass spectrometry (XL-MS), has provided an inroad to understanding kinesin regulation (Tan et al., Weijman et al.). Most recently, Ashaduzzaman et al. have used cross-linking and local refinement to obtain a reconstruction of the autoinhibited KHC:KLC tetramer. The biochemical analysis presented here extends previous studies and has enabled the authors to define the hierarchy of competing KHC interactions with KLC and the TRAK2 adaptor.

Strengths:

While other studies relied predominantly on XL-MS to validate autoinhibitory contacts in the KHC:KLC autoinhibited tetramer, the present study used in-cell interactions to define minimal interacting units, and measured the effects of prediction-guided mutagenesis on their interaction in vitro. This complementary approach stratifies the multitude of predicted autoinhibitory interactions and offers a functional test for reported structures and structure predictions.

Weaknesses:

The work is not appropriately placed in the context of previous literature. The KHC:KLC assembly interface (Figures 1 and 2) was previously predicted by Weijman et al., supported by XL-MS in Tan et al., and subsequently visualized by cryo-EM in Ashaduzzaman et al. Contacts between the regions designated in this study as KHC CC1 and CC4 (Figure 6) were also reported by Tan et al. and Ashaduzzaman et al. Similarly, a structural model of TRAK2:KHC (Figures 3-4) was previously reported and tested by co-immunoprecipitation and mutagenesis by Gladkova et al. Tan et al. additionally mapped binding of the related adaptor TRAK1 to KIF5B using XL-MS. These results should be discussed throughout to help the reader understand whether biochemical observations are consistent with prior work, or whether they hint at previously unknown regulatory mechanisms.

To test the KHC:KLC and KHC:TRAK2 predicted interfaces, mutations of hydrophobic residues were introduced in KHC (Figures 2 and 4), and TRAK2 (Figure 4). However, both KHC and TRAK2 exist as homodimers in solution. Are these mutations specific to destabilizing the heterotetramers, or do they also affect the KHC and TRAK2 homodimers? Such an effect could provide an alternative explanation for reduced binding and impaired mitochondrial recruitment of KIF5C by TRAK2 mutants, such as L217Q/L220Q. Retained mitochondrial localization of TRAK2 mediated by the C-terminus is not a good proxy for functionality of the N-terminal motor-binding region. Mutants should be assessed for stability and dimerization propensity, and their effects fully described.

Several structure-prediction models are introduced without pLDDT and PAE confidence plots. These are necessary to assess structure prediction confidence. Additionally, residue number labels would help orient the reader as to the boundaries of the protein fragments modelled.

Reviewer #2 (Public review):

Summary:

Nakatani et al. developed a conductance-based, morphologically realistic computational model of an astrocyte soma and its perisynaptic processes (PAPs) to examine how extracellular potassium shapes the astrocytic membrane potential across spatial scales. The model builds on previously published morphological and electrophysiological data, as well as experimental measurements of transporter and ion-channel expression levels. It incorporates the astrocytic potassium channels Kir4.1, the Na⁺/K⁺-ATPase, the K2P leak channels TREK-1 and TWIK-1, and gap-junctional coupling to neighboring processes via connexin-43. Glutamate transporters (GLT-1), NMDA receptors, and GABA_A receptors are also included to examine neurotransmitter-mediated depolarization.

The authors propose that a compartmental model that explicitly distinguishes the soma from the PAPs, provides important insights into realistic potassium uptake and the electrophysiological properties of these distinct compartments.

Strengths:

(1) Clearing extracellular K⁺ is one of the most fundamental functions of astrocytes. Failures of K⁺ buffering are implicated in epilepsy, spreading depolarization, and several neurodegenerative conditions. Yet how K⁺ handling is divided between perisynaptic processes and the rest of the cell, and how this shapes local membrane potential, remains poorly understood. The study gives a mechanistic account. The soma behaves as a low-resistance K⁺ "sink" with little voltage changes for local K⁺ elevations around the PAPs, while producing much smaller, than expected by simply by Nernst equation. The local membrane potential deviates from E_K because current escapes to the rest of the cell. This framing separates physiological, synapse-scale K⁺ handling from global, pathological K⁺ load.

(2) The model described here fills an important gap in computational modelling of astrocytes. While computational models are now central to neuroscience, astrocytes and their interactions with neurons remain relatively underrepresented in mainstream computational modeling. Existing models mostly treat either whole-cell K⁺ buffering or the isolated perisynaptic compartment. This is novel in that it connects the two within one framework and provides a tool to address how astrocytes integrate local and global signals.

(3) Including realistic morphology shows that spatial heterogeneity matters. A key conceptual strength is that the model does not treat the astrocyte as a single, well-mixed, isopotential compartment. Using an EM-informed morphology with perisynaptic astrocytic processes, it shows that stimulus location and spatial extent shape the membrane response. Fine processes, constricted segments, and local conductance ratios generate responses that cannot be predicted from whole-cell properties alone. These questions are difficult to address experimentally because fine processes are below the resolution of conventional electrophysiology, and somatic recordings are limited by space-clamping. An in silico approach is therefore especially valuable, as it allows controlled manipulation of individual variables, such as stimulus location, channel density, and extracellular volume, that cannot be varied independently in tissue.

(4) It is built using standard, open tools, and therefore the model can be extended with ease by future studies. The model is implemented in NEURON, the most widely used simulator for biophysically detailed cells, with mechanisms written in NMODL and driven through Python. It also uses a previously published astrocyte morphology. Parameters are listed, and the authors state that the model files are in a repository. Together, these make the model accessible and reproducible if the repository link is also provided.

(5) The model is anchored to experimental data. Kir4.1 and K2P leak conductance were fitted to experimental current-voltage data (Figure S2), and other channel parameters were constrained by the literature. Notably, the authors avoided channel formulations with built-in mechanisms that fix the resting potential, which are common in earlier astrocyte models. The resting potential and K⁺ responses therefore emerge from the conductance rather than being imposed. The model also reproduces established whole-cell behavior, including Nernstian responses to bath K⁺ application, before being used for new predictions.

(6) The study tests several candidate mechanisms for peripheral depolarization within one framework and offers a way to reconcile conflicting results: K⁺ through Kir4.1, GLT-1, Na⁺/K⁺-ATPase, K⁺/glutamate spillover, and astrocytic GABA_A and NMDA receptors, all of which in principle can produce responses of the observed depolarization under various conditions. The model therefore offers a biophysical explanation for discrepancies in the astrocyte literature.

(7) The work makes several testable predictions:<br /> a) the local [K⁺]o required for a given PAP depolarization;<br /> b) local depolarization depends on the spatial extent of activation;<br /> c) somatic recordings would not detect PAP depolarizations.

Weaknesses:

(1) The Nernst equation itself does not appear to break down; local E_K should still follow it. Rather, the membrane potential deviates from E_K because low-resistance electrical coupling allows current to spread to the rest of the cell. I suggest rewording this statement for accuracy.

(2) The Methods mention an uploaded repository, but no link is provided. The authors should include a public link and deposit the model in ModelDB or a comparable repository.

(3) The deployed morphology is hippocampal, not cortical. But the paper's conclusions are for cortical somatosensory astrocytes based on morphological similarity. That deserves additional explanation, especially since PAP geometry is central to the conclusions.

(4) Astrocytic NMDA receptors best fit the observed depolarizations. However, because the experiments in Armbruster et al. were performed in the presence of APV, an NMDA receptor antagonist, the authors suggest that the responses may involve APV-insensitive NMDA receptor subunits. This explanation would be stronger if supported by evidence that these subunits are expressed in cortical astrocyte PAPs.

(5) For activation of GABA_A receptors on astrocyte PAPs, the source of GABA is unclear, since the stimulated ascending axons are glutamatergic. The model shows these receptors could explain the data, not how they might be activated. This deserves an explanation.

(6) I also recommend adding a discussion paragraph on the functional implications of heterogeneous astrocytic control of potassium homeostasis. Do the results suggest that astrocytes efficiently buffer K⁺ under normal conditions but fail during widespread K⁺ accumulation?

Reviewer #2 (Public review):

Tian & colleagues generate and characterize six novel humanized mouse models engineered to express precursor human antibody repertoires for inducing multiple lineages of broadly neutralizing antibodies (bnAbs) against HIV-1. Building on prior "VRC01-class" proof-of-concept work, this manuscript advances the field by demonstrating the value and suitability of these sophisticated models for testing the ability of vaccine candidates to induce bnAbs. By utilizing flow cytometry and HGTS sequencing techniques, the authors demonstrate that the targeted bnAb minigenes successfully undergo physiological V(D)J recombination and junctional diversification. Despite phenotypic heterogeneity in B-cell development, they show that these models reproducibly produce diverse repertoires enriched for functional humanized B-cell receptors expressing knock-in-derived bnAb precursor rearrangements, a subset of which yield the long, correctly charged CDRH3 loops required to target conserved but glycan-shielded bnAb epitopes. This achievement successfully overcomes a major vaccine testing bottleneck by providing a pool of patient-deduced precursor configurations in sufficient amounts to reliably study how subsequent somatic mutation events are induced by candidate vaccine protocols (with the potential exception of the PG9 model). Crucially, the comparative utilization of Kymab data serves as an excellent control to demonstrate the extreme rarity of bnAb rearrangements in fully locus-humanized mice, beautifully illustrating the design limitations of humanized models without adjusting for the much smaller repertoires of mice. Finally, the discussion is nuanced and well-written, including articulating the models' free availability and highlighting why these small-animal systems are complementary-and sometimes essential-in relation to rhesus macaque systems.

The paper's conclusions are robustly data-supported, with the only slight weakness being that the Kymab data were extrapolated rather than derived from actual head-to-head analyses. The comments below predominantly aim to provide further context and clarity-mainly in the abstract, results, and discussion sections-to this already excellent, highly detailed study. Refining the text clarity and phenotypic observations will further polish this impressive manuscript.

(1) Abstract Clarity:<br /> Concluding sentences should incorporate the discussion's explanation: finding natively low-frequency precursors in standard humanized locus mice is statistically prohibitive due to their smaller absolute B-cell compartment. These models bypass this restriction by maximizing relevant segment expression to expand the functional CDR3 search space.

(2) Results (Line 183, BG18 expansion):<br /> The intriguing expanded B1 cell population warrants more conservative commentary regarding genuine autoreactivity versus other possibilities, such as hyperactive baseline signaling via autophosphorylation (as shown by Hassan Jumaa for certain HC/LC clonotypes). While beyond this manuscript's scope, these mechanisms are differentiable via calcium flux assays with general anti-BCR signaling reagents.

(3) Results (p15/Line 324, PCT64/CH235 Vκ Allelic Architecture):<br /> The text states that lacking a recombination cassette caused hVκ3-20 to account for only a fraction of rearrangements. While Figure 1B's general engineering feature (tandem reverse hJκ-RSS 23 and 12 RSS hJκ) implies human Vκ should predominate everywhere, Figure 1C suggests hVκ3-20's low representation in CH235 and PCT64 models stems from being introduced differently-specifically via pre-rearranged minigenes rather than unaltered RSS orientations in an unrearranged cassette. The authors should clarify this point in the text and/or Figure 1.

(4) Results (p14, Clever Engineering of λLC rearranging mice):<br /> Replacing mCκ with hCλ3 is a clever genetic trick and an important proof of concept for stably accommodating a human lambda rearranging light chain cassette within the mouse kappa locus. This excellent methodological highlight deserves prominent recognition as a template for other model developers. I suggest giving this further play in the discussion.

(5) Discussion (Model Comparisons & Baseline Resolution):<br /> The authors aptly compare these humanized mice to macaques; in addition to their superior scalability, they point out that macaques don't recapitulate human-specific bnAb-relevant configurations in certain instances (like lacking the VH1-2 minigene for VRC01-class responses, or utilizing germline D segments that favor V2 apex responses, potentially overestimating human responses). To further enhance nuance, referencing other prior review KI model literature on this topic (some published by these authors) would be beneficial. For context, KI-derived adoptive transfer models offer more potential diversity (thus, 'higher bar' off-target response testing) and frequency control but are restricted to short priming studies. Conversely, the rearranging models in this study support full vaccine regimens and generate multiple variant bnAb precursors to compare potential in vivo response plasticity.

(6) Discussion (Baseline phenotypes):<br /> One limitation of the cellular flow data, albeit beyond the manuscript's current scope, is that the baseline phenotypes predominantly reflect variations of a fixed pair of V(D)J rearrangements, rather than bona fide patient-derived UCA bnAb precursors with correct charges/lengths. To help resolve pre-antigenic functional signaling boundaries and thus potentially aid immunogen design, the discussion could note how future work could aim to use techniques like single-cell transcriptomics to map expressed selection signatures of bnAb precursor paired V(D)J's to rule out anergy or survival/death gene signatures.

(7) Discussion (PG9 Model & HCDR3 Length Constraints):<br /> While the PG9 model's failure to produce bona fide precursors is unsurprising given its long HCDR3, the authors should provide a bit more detail as to whether a subset of PG9 variants with slightly shorter HCDR3s are permissive for bnAb production (analogous to DH511/10E8). Providing the specific HCDR3 sizes generated by this model in relation to recently discovered monkey antibodies-which are shorter/different but function like PG9 bnAbs (e.g., Guenega et al Nature 2026) - would offer excellent context. Such related lineage and/or lineage precursor variants could serve as structural alternatives, and highlighting how these length constraints impact selection provides vital boundary conditions for the immunogen design field when planning germline-targeting regimens.

GroupID: 2

DiseaseAssertion: VHL

Group size: 1

GroupNumberMales: N/A

GroupNumberFemales: N/A

GroupEthnicity: N/A

GroupAgeRange: N/A

Group Info: Retrospective analysis of a longitudinal cohort of 21 VHL germline mutation carriers and retinal haemangioblastomas. All patients were categorized in two genotypic categories: missense mutations and truncating mutations.

GroupPresentingHPOs: HP:0009711 (retinal capillary hemangioma)

GroupHPOFreeText: The patient presented with retinal capillary hemangioma.

GroupNotHPOs: N/A

GroupNotHPOFreeText: N/A

GroupPreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: Table S1

Variant: Exon 1-2 deletion

CaseProblemVariantFreeText: N/A

LegacyVariant: N/A

ClinVarID: N/A

CAID: N/A

gnomAD: N/A

VariantEvidence: cDNA level information: c.(?_1)_463+?del

MutationType: exon_loss_deletion

CivicName: Exon 1-2 deletion

MultipleGeneVariants: N/A

Reviewer #2 (Public review):

Summary:

Breuig et al. studied the effect of Dnm2 conditional knockout in the developing neocortex. The authors generated CNS-specific Dnm2 conditional knockout (cKO) mice using Nestin-Cre, Emx1-Cre, and Nex1-Cre drivers. Confirming existing findings of previous research, they reported that Dnm2 was the predominant dynamin isoform in the embryonic forebrain by immunoblot, as well as by analysis of published single-cell and spatial transcriptomic atlases. The authors went on to demonstrate that deletion of Dnm2 in progenitors causes disorganization of the ventricular zone, depletion of Sox2+ progenitors, an excess of Tbr2+ intermediate progenitor cells and Tuj1+ immature neurons, as well as periventricular hemorrhage and perinatal lethality. On the contrary, deletion of Dnm2 in postmitotic neurons produces no detectable phenotype. In the mutant cortex, Dnm2 conditional knockout led to accumulation of Dll1, reduced cleavage of NICD, and reduced Hes1/Hes5 expression. To separate ligand- from receptor-side effects, they utilized a neural stem cell co-culture assay. They tested dynamin function in the in vitro assay using a variety of molecules and probes, including a dominant-negative Dnm2 (K44A), an shRNA, and the tamoxifen-inducible deletion in reprogrammed Dnm2-floxed neural stem cells. They further performed postnatal electroporation with Hes5-EGFP and Dll1-mOrange2 reporters and an in vivo dual-luciferase assay to test the effect of receptor side effects. In summary, they showed that inhibiting or downregulating Dnm2 increases Notch signaling levels, while the involvement of Dll1 remains unclear depending on the experimental condition.

Strengths:

The topic of the manuscript, which is the molecular mechanisms and the signal transduction of neural stem cell maintenance, is of fundamental importance to the field of neurodevelopment. The conditional knockout strategy is compelling.

Weaknesses:

The central claims of the current manuscript are not sufficiently supported by experimental results. The rigor of the experiments conducted also needs to be significantly improved.

(1) Quantification and statistical analyses of the core in vivo phenotypes are essential to establish the central claim of progenitor depletion and premature neurogenesis. Only representative images of Sox2, Ki67, Tbr2, Ngn2, Tuj1, and cleaved Caspase-3 are shown (Figures 2, 3, 4, S2, S3), as is the EM observation of increased clathrin-coated vesicles (Figure 3G-H).

(2) The authors report that "the brain of mutant animals appears indistinguishable from control littermates". However, based on the representative images of Figure 2, at 11.5 the disorganization of the neocortex and the disruption of the radial columns have already manifested. The authors should carefully describe the aspects of the brain that they deemed indistinguishable between control and mutant animals, and by what criteria.

(3) The transition from Dnm2 conditional knockout (cKO) to Notch signaling appears somewhat abrupt. The authors should provide additional experimental evidence or literature precedents to logically bridge this gap.

(4) The claim that dynamin inhibition in receptor-expressing cells increases Notch signaling is not supported by the comparisons shown. In Figure 7D, the significant receptor-side comparison is K44A-receiver + Dll1 versus K44A-receiver without ligand, which tests whether ligand-driven activation survives dynamin inhibition, not whether it is enhanced. The in vivo results are marked p = 0.07 (Fig. 8F), p = 0.07 (Fig. 8L), and n.s. (Fig. 8Q), yet the text states the increase was significant, and the Figure 8 title asserts it. This is the paper's most novel claim and the basis of the ligand/receptor dichotomy in the title and Abstract. As stated, it is not supported at conventional significance.

(5) Since the prominent phenotype of Dnm2 cKO was a reduction in radial glial cells, the subsequent reduction of signaling molecules (Hes1, Hes5, etc.) may be due to the reduced progenitor pool, rather than cell-autonomous reduction of Notch signaling. Quantitative results, such as the ones in Figure 6, should take changes in cellular composition into consideration and normalize to the reduced proportion of apical progenitors.

(6) The authors stated that Notch ligands "accumulate at the cell surface," but all immunostaining appears to be on permeabilized tissue, and no internalization assay is reported. The proposed mechanism is a block in ligand endocytosis. Accumulated Dll1 in permeabilized sections is equally consistent with intracellular retention in stalled endocytic intermediates, which the EM in Figure 3G-H might actually favor.

(7) Inconsistency in the cortical regions examined (Anterior vs posterior, medial vs lateral, etc.). Are the observations and experiments conducted in the same region of the cortex? If so, which region and why?

(8) Statistical tests should be described in sufficient detail in the methods section. The number of samples or replications needs to be reported for all experiments. The "Imaging and Quantification" section also did not provide enough information (the authors only stated that "Cell counts were performed on matched cortical regions across genotypes and developmental stages"). Figure legends report significance (Figs. 6B, 6C, 7D, 7F, 8F, 8L, 8Q, S6) without specifying what was compared or how in the corresponding main text or methods.

(9) The main text attributes the cNICD (Val1744) result to Figure 5A-B, but the Figure 5 legend describes the red channel as "NICD (red; total Notch intracellular domain)," which is the antibody that the main text explicitly distinguishes. Figs. 4 and 5 appear to show overlapping Dll1/NICD data.

Reviewer #2 (Public review):

Summary:

The authors of Saqib et al "Deep phenotyping and multi-omics analyses reveal systems-wide metabolic dysregulation in a refined trisomy mouse model of Down syndrome" were testing the hypothesis that metabolic dysregulation in Ts66Yah Down syndrome model, as compared to euploid mice, would replicate metabolic dysregulation in individuals with DS. The authors then compared the results from Ts66Yah mice in the present study mostly with results from Ts65Dn and Dp1Yey mice, where they had reported metabolic differences in previous studies.

Strengths:

Strengths of the methods and results include multiple phenotypes examining potential metabolic differences, analysis of male and female mice separately with appropriate numbers of mice (power), and the ability to compare the results to other DS mouse models, which is very useful in selecting appropriate DS mouse models to test metabolic and other related phenotypes.

The authors have shown that Ts66Yah mice differ from euploid mice in many of their metabolic parameters. Male and female Ts66Yah mice have differing phenotypes in specific metabolic parameters, but the overall result of glucose intolerance, insulin resistance, altered inflammatory cytokines, and dysregulated serum and liver metabolomes is shared between male and female mice.

Weaknesses:

Weaknesses include how the data are presented. There are some questions about methodology that leave some doubt in the analyses. The comparisons of Ts66Yah to Ts65Dn and Dp1Yey mice are less convincing, mostly due to oversimplifying the genetic constructs of DS mouse models.

Reviewer #2 (Public review):

Summary:

This manuscript extends previous work from Calvin et al. and examines hippocampal representations during approach-avoidance conflict in a robotic predator foraging task. The paradigm itself is very interesting and addresses an important but relatively understudied question in the navigation and foraging literature: how the brain balances risk versus reward during goal-directed behavior. While hippocampal representations of positively valenced goals and future intentions have been extensively studied, much less is known about how these representations evolve during risk-reward tradeoffs involving threat.

The authors use a relatively simple and interpretable decoding approach together with thoughtful behavioral comparisons to ask whether future behavioral outcome can be read out from hippocampal activity before behavior diverges. The most compelling comparison is between mid-track aborts (MTAs) and mid-track continues (MTCs), where the animals initially exhibit very similar pause behavior but ultimately either abort or continue the trajectory. The authors show that decoded location during these pauses differs prior to the overt manifestation of the behavioral decision, suggesting that hippocampal representations may reflect evolving internal evaluation processes during approach-avoidance conflict.

Strengths:

A major strength of the work is the behavioral paradigm itself. This type of risk-reward conflict task is relatively uncommon in the hippocampal navigation literature and provides a rich framework for examining defensive decision-making during naturalistic foraging behavior.

The decoding analyses are also relatively simple and easy to interpret. Rather than relying on highly complex modeling approaches, the authors use straightforward comparisons of decoded spatial representations across behavioral conditions, making the results accessible and conceptually clear.

Another strength is the use of behavioral controls to isolate comparisons between related behaviors. In particular, the comparison between MTAs and MTCs is compelling because the animals exhibit similar pause states before the behavioral outcomes diverge. This provides a useful framework for asking whether hippocampal activity reflects future behavioral outcome before the decision is overtly expressed.

Overall, the study asks an interesting question using a novel paradigm and provides evidence that hippocampal representations during approach-avoidance conflict may reflect future behavioral trajectory.

Weaknesses:

The main weakness is that many of the reported effects are relatively subtle and are not sufficiently controlled for differences in speed, trajectory structure, and other behavioral variables across conditions. While the subtraction plots (green versus purple decoding differences) appear visually striking, the actual effect sizes are fairly small, making it difficult to assess how robust or behaviorally meaningful these differences are.

Relatedly, many of the most interesting questions in this task concern how behavior unfolds dynamically within a trial, yet much of the analysis averages across events and trajectories. As a result, potentially important aspects of the behavior may be obscured.

In particular, the manuscript would benefit from richer characterization of the animals' actual movement trajectories and spatial strategies. Because the analyses rely heavily on linearized position, it is difficult to determine whether animals behave differently in two-dimensional space across conditions. For example, during continued approaches, do animals preferentially hug the wall opposite the robot? Do different behavioral conditions show distinct lateral occupancy or trajectory structure? These types of analyses would make the behavioral interpretation substantially more compelling.

Reviewer #2 (Public review):

This is an interesting study in Drosophila comparing potentially differential requirements for subsets of Kenyon Cells (KCs) and Dopaminergic neurons (DANS) in olfactory dishabituation driven by either a novel odor ("homosensory") or footshock ("heterosensory). The authors measure olfactory aversion to Octanol (OCT) in a T-maze, induce olfactory habituation with 4-minunute prior exposure to OCT, and use either brief yeast odor (YO) or footshock (FS) to achieve dishabituation. The major observation that YO-mediate dishabituation is mediated by reward activated DANs (PAM cluster), while FS mediated dishabituation is mediated by punishment-activated PPL-DANs is generally solid and convincing. Also convincing are experiments showing the involvement of KCs in the pathway for YO and FS induced dishabituation, and the argument that KCs drive DAN activation that causes dishabituation, though not experimentally shown, is more than reasonable. The work is significant because, as the authors take pains to point out, circuits and pathways for dishabituation have been very lightly studied and clear identification of dopaminergic neuron subsets in dishabituation achieved to different means, represents unique and interesting progress.

Comments on revised version.

I am appreciative of the authors' effort to address the various concerns and queries of the reviewers. The revised work, which more clearly positions the work, better specifies the conclusions reached and their relevance to the broader field, is a very interesting and useful addition to the field.

Reviewer #2 (Public review):

Summary:

This manuscript investigates how animals adapt their metabolism to conditions of reduced nutrient availability. Using C. elegans, the authors identify a neuropeptide signaling network that promotes intestinal lipid mobilization when nutrient acquisition is compromised. The study proposes that nutrient stress activates signaling involving NLP-10, NPR-35, FLP-18, and NPR-4, ultimately leading to increased utilization of stored triglycerides.

A major strength of the work is the combination of multiple experimental approaches, including genetic analyses, neuron-specific rescue experiments, starvation paradigms, and lipid measurements. Together, these experiments support the conclusion that the identified neuropeptide pathway contributes to the regulation of energy stores during nutrient stress. The observation that similar signaling components function in both the mutant and starvation contexts strengthens the physiological relevance of the findings.

Strengths:

The revised manuscript is substantially improved and presents a coherent model linking sensory and interneuronal signaling to peripheral metabolic responses. The data convincingly establish functional roles for the identified neuropeptide components in controlling fat storage and mobilization.

Readers should keep in mind that the exact relationships among pathway components are not completely resolved. While the genetic evidence supports placement of NLP-10, NPR-35, FLP-18, and NPR-4 within a common regulatory framework, the data are somewhat less definitive regarding the precise order of signaling events and whether all interactions are direct. The authors appropriately acknowledge some of this complexity, and a clearer distinction between experimentally demonstrated versus inferred connections would further strengthen the presentation.

Overall, this is a carefully executed and biologically interesting study that advances our understanding of neuroendocrine control of metabolism during nutrient limitation.

Weaknesses:

No weaknesses were noted by this reviewer in the revision. My main comment is that whilst I appreciate the authors' efforts to refine the signaling model in response to earlier comments, the pathway architecture remains somewhat complex, particularly regarding the relationships among NLP-10, NPR-35, FLP-18, and NPR-4. I encourage the authors to make one final pass through the text and model figure to clearly distinguish interactions that are directly supported by experimental evidence from those that remain inferred based on genetic relationships. This would help readers accurately interpret the strength of evidence supporting each step of the proposed pathway.

Reviewer #2 (Public review):

The manuscript by Hou et al. tests the hypothesis that SNr neurons' activity is inhibited during specific movements, allowing the selection of specific movements. They do so by performing electrophysiological recordings in SNr, in head-fixed mice trained to perform self-paced reaching or self-paced licking. They first show that SNr neurons present ramping activity hundreds of milliseconds before reaching, with the vast majority of cells presenting positive increases in activity in these rampings. Then they show that the activity of these cells is related to the velocity (and ramping) of movements, independently of the timing to initiate. They verify these findings in spontaneous reaching. To then test the activity of the same cells in self-paced reaching vs. licking, they found that the vast majority of cells present congruent firing between conditions (not opposite firing as expected). Further, they ran controls asking whether dips in activity are related to specific movements, checked if the specific cells that are correlated with lick cycles respond differently in self-paced reaching vs. licking, asked whether there could be differences in activity in reaching to different targets, to finally "opto-tag" SNr- orofacial vs SNr-arm cells and asked whether these differentiate their spiking in reaching vs. licking, concluding that a very minority of SNr neurons distinguished between movements while the vast majority presented congruent modulations among movements.

Strengths:

The study is a tour de force, testing the general assumption of action selection being reflected in SNr spiking.

Weakness:

Even though the authors set up an amazing set of experiments to test the mentioned hypothesis, their final opto-tagging experiment needs to be screened to better support their conclusion.

Reviewer #2 (Public review):

Summary:

The manuscript by Pain et al. explores the functional maturation of key oculomotor populations responsible for gaze stabilization. The work is motivated by our poor understanding of this process in establishing appropriate reflexes during development. The authors focus on two types of abducens motoneurons implicated in the maturation of angular vestibulo-ocular reflexes (VOR) in amphibian tadpoles, namely spontaneous motor units that provide baseline control and silent motor units recruited only during head motion. This is a particularly good choice given the well-defined and evolutionarily conserved functions of these populations and the ease with which they can be tracked during changes in angular VOR as tadpoles develop.

The authors first use semi-intact larval preparations and both step and sinusoidal horizontal stimuli to characterize developmental improvements in angular VOR, which include faster, more robust compensation of head rotation and higher frequency tuning. Next, to explore the central versus peripheral origins of maturational changes, the authors use galvanic vestibular stimulation of the vestibular nerve (which should be independent of canal size) and BrdU-pulse chase to track neurogenesis, which strongly link improvements in VOR to changes in existing circuits, rather than the addition of new ones. To provide a more comprehensive assessment of what these functional changes may entail, they then perform recordings of changes in motor neuron discharge patterns during development specific to horizontal motion. By combining with sophisticated spike sorting analysis, these data reveals improvements in head velocity encoding by silent subtypes, while head positioning by spontaneous subtypes is relatively consistent during development. Finally, electron microscopy analysis of the abducens motor nerve is used to demonstrate that improvements in encoding are accompanied by broad changes in peripheral myelination and innervation.

Together, the data argue that silent abducens motor units show the largest changes in recruitment in response to sinusoidal head rotations, which is accompanied by increases in myelination and signal fidelity. Ultimately, the work suggests that improvements in oculomotor reflexes during development are accomplished by non-uniform changes within defined oculomotor populations, which is novel and interesting.

Strengths:

Overall, the data are very convincing, the analysis is thorough, the findings will be of interest to those in the VOR field, and also set the stage for future investigations aimed at uncovering the mechanistic underpinnings of this cool observation. I only have a few comments to consider for improvement.

Weaknesses:

Major.

(1) A bit more clarity is needed.

For non-specialists, it would be helpful in the introduction (e.g., Line 52) to spell out what the functional differences are to begin between spontaneous and silent subtypes. The contribution to head positioning versus head velocity is raised later on, but bringing it up earlier would be helpful to understand the functional implications of differences up front.

(2) Figure call outs.

These should be double-checked. I found one that did not seem appropriate on line 332. In addition, the reference to Figure 3A and the spike rate are not illustrated.

(3) Spelling and grammar.

It would be helpful to proofread more carefully for typographical errors, particularly in terms of using singular versus plural and the appropriate articles. Most are minor (those that I found listed below), but in one case the lack of clarity was particularly problematic. On Line 518: 'Altogether, past studies and our present work suggest the existence'. Consider revising this sentence; it reads now that motoneurons mature 'conjointly'; however, this does not seem to capture the main point here, which is that they are not maturing at the same time. Do the authors mean staggered or conjointly with the behaviours they control?

(4) Interpretation.

In the discussion, it would be good to be clearer that myelination will not explain the changes in firing pattern, but could explain the behavioural changes. Ideally, this would be linked to the different types, but I appreciate it is not easy to decipher that from EM.

Reviewer #2 (Public review):

Summary:

This study characterizes wing flicking in sexually immature female Drosophila as a transient behavior that produces patterned acoustic signals and suppresses male courtship. The authors show that wing flicking is restricted to a short developmental window, is influenced by multiple components of male courtship and juvenile hormone-dependent maturation, and depends on Dsx+ pC1 neurons. They further show that wing flicking shortens male song bouts and reduces copulation attempts, and propose that it functions as a rejection signal associated with sexual immaturity. Overall, this is an interesting and valuable study that provides new insight into a little-characterized stage of female sexual maturation. The core characterizations are compelling, and some additional clarification and refinement of the mechanistic interpretations would further strengthen the manuscript.

Strengths:

The work expands our understanding of female sexual behavior and highlights sexual maturation as an active transition between distinct behavioral states rather than simply the acquisition of receptivity. The comparison with the post-mating unreceptive state is especially informative. It also brings richness to the understanding by combining behavioral, developmental, and neural analyses and interpretation. The data showing how male behaviors are influenced by female wing flicks are particularly compelling and represent a critical aspect of behavioral characterization that is often overlooked.

Weaknesses:

Event-level analysis of behaviors such as singing, chasing, tapping, licking, or attempted copulation would help clarify the sensory and social context of female wing flicks. The neural conclusion also requires some caution. The split-GAL4 reagent used to assign the phenotype to pC1a labels two pC1 neurons per hemisphere in this study, whereas pC1a is generally described as a single bilateral pair. Given the functional heterogeneity of pC1 subtypes, the current data do not fully exclude a contribution from another pC1 subtype. While the comparison with male agonistic wing signals is informative and interesting, the rejective wing flicks of immature females may be more directly comparable to the previously reported wing flicks and song produced by young and mature males in response to male courtship, which are not mentioned or explored in this study.

Reviewer #2 (Public review):

Summary:

The manuscript presents COB, a computational database for the systematic identification and classification of chloroplast-associated beta-barrel proteins across Viridiplantae. The authors combine evolutionary contact-map features with machine-learning classification, sequence-based searches and structural predictions to generate a large collection of candidate beta-barrel proteins. In addition to established OEP families, they identify two previously uncharacterized groups, BUL16 and BUL22, analyze the evolutionary distribution and structural diversity of beta-barrel proteins, and propose a number of novel hybrid beta-barrel assemblies involving members of the TOC159 family.

Strengths:

A major strength of the study is the establishment of a dedicated and comprehensive resource for chloroplast beta-barrel proteins. These proteins have historically been difficult to identify and predict reliably, and the broad taxonomic coverage of COB should therefore be highly useful for the chloroplast and plant membrane biology communities.

The manuscript also goes beyond database construction by exploring structural and evolutionary properties of the identified proteins. In particular, the analyses of beta-strand number, open versus closed barrel conformations, taxonomic distribution, and the identification of BUL16 and BUL22 generate several interesting hypotheses that may stimulate future experimental work.

The authors are generally appropriately cautious in acknowledging limitations of the structural predictions, particularly with respect to open and hybrid barrel conformations.

Weaknesses:

The principal limitation of the study is that it is entirely computational. This is not necessarily problematic for a database/resource paper, but several of the most interesting biological conclusions remain speculative and are not independently validated.

This is particularly relevant for the proposed hybrid beta-barrel assemblies. While the known TOC75-TOC159 interaction provides a biologically established example, the predicted interactions of TOC159-family proteins with OEP24, OEP80 and TGD4 are more difficult to reconcile with the known functions of these proteins and currently lack experimental support.

A second conceptual issue concerns the newly identified BUL16 and BUL22 groups. The authors explicitly state that these proteins cannot confidently be assigned to the chloroplast outer envelope, yet they are subsequently analyzed together with established OEP families. This makes it difficult to distinguish between evidence for a beta-barrel fold and evidence for chloroplast outer-envelope localization.

Reviewer #2 (Public review):

Strengths

The manuscript claims that standard methods for deep recurrent RL ("meta-RL"; Wang et al., 2017) provide a poor match to behavior data from humans and animals in many situations, including the two-step task and a mouse behavior dataset previously published by these authors. This negative story is solid and convincing.

The manuscript refines this model by introducing an online variant of deep recurrent RL. This is a good idea for many reasons. One compelling one, which the authors could choose to emphasize more, is that neurobiology does not respect a strict separation between "online" periods during which weights do not change and "offline" periods in which they do. Both weights and activities are changing all the time. Another is that the behavior literature provides abundant reason to believe in short-timescale innate reward-seeking mechanisms, which offline batch-based RL does not provide.

The manuscript shows that this online deep recurrent RL can provide a good match to their mouse behavior data. This involves a 2AFC perceptual decision-making task with strong temporal dependencies between the stimuli. In one condition, the stimulus on a particular trial is very likely to be identical to the one on the previous trial and only rarely switches; in another, the stimulus is very likely to differ, and only rarely repeats. Mice find the first condition much easier to learn, perhaps because of an innate win-stay/lose-switch bias which is helpful in the first but harmful in the second.

It also provides evidence that online deep recurrent RL can provide a good match to neural data from the OFC in this task. In the "repeat" condition, their model learns the task largely in weights, which is possible because the weight learning rule is hard-coded to be win-stay/lose-switch. In the "alternate" condition, the model must learn it in activities. Broadly consistent with this, mice in the "alternate" condition, but not the "repeat" condition, show strong task-based representations in the OFC.

Taken together, these results are a meaningful advance in applying the "NeuroAI" research program to the case of trial-by-trial reward learning.

Weaknesses

(1) The key innovation of this manuscript is replacing batch-based recurrent deep RL (which they call "meta RL"), in which weights are updated once per batch via backpropagation through time, with a version of online recurrent deep RL in which weights are updated after every trial.

It is difficult for readers to understand how the version of online deep RL being used works, how it compares to other possibilities, and why it was chosen. The manuscript describes it as truncated backprop through time (TBPTT, equation 9). It is not clear from the equation what the truncation window is. If it is only a single trial, then it is unclear how a network trained in this way manages to do the representation learning necessary to do anything model-based at all.

Various other techniques for real-time recurrent learning exist, many under the heading of "biologically plausible" deep learning. These include techniques involving synaptic eligibility traces (e.g., Murray et al., 2019; Bellec et al., 2020) and possibly others.

The manuscript calls this "Hybrid Deep RL". This name is confusing to me and may be confusing to readers. If this method is a "hybrid" between deep RL and some other thing, it's not clear to me what that other thing might be. It seems rather to be a variant of deep RL. I'd suggest a name that's clearer about that and more specific, perhaps "Online" or "Real-time" Deep RL, or similar. The manuscript also describes it in places as a "two-module" system, which is confusing to readers because it has one module: a single RNN with a single learning rule.

The manuscript would benefit greatly from a more careful discussion of these issues and a positioning of the model within the literature. What is lost when giving up BPTT? (long-timescale representation learning, which is often crucial for RL). What is gained? (learning rules that are local in time and space, and therefore more plausible as mechanistic models of biological learning). Where can we expect this method to fail? (tasks with temporal dependencies that are meaningfully longer than the TBPTT window). What alternatives exist? (eligibility trace methods, etc)

(2) The manuscript claims that their model "reproduced mixed strategies of humans in a two-step task". This portion of the paper is brief and unconvincing.

The key diagnostic plot, the trial-history regression in figure 1e, shows behavior that looks almost entirely model-free. There is little, if any, separation between the lines for "common" and "uncommon" within either of "rewarded" or "unrewarded". The bar plot does show an apparent difference, but these one-trial-back analyses are subject to many confounds.

Human studies involving this task typically involve each participant performing only a single session lasting ~100s of trials. When participants are run for multiple sessions, behavioral strategies change, becoming substantially more "model-based" (Economides et al., 2015). If I understand correctly, the model here is trained for 100 sessions of 100 trials, and the data we are shown comes only from the last 25. A fairer comparison would be to show only the first, or first few, of these sessions.

I imagine that H-DRL is unlikely to show much of a model-based pattern within the first session, as it will require averaging over many trials to learn to sculpt activity dynamics appropriately. By the same token, I am skeptical that H-DRL is plausible as a mechanistic account of the human two-step task - instead, I think it is much more likely that explicit working memory mechanisms are involved (e.g., Otto et al., 2013). H-DRL may be a better model of the two-step task as implemented in animals (Miller et al, 2017; Miranda et al., 2020; Akam et al., 2021).

The manuscript claims that human subjects on the two-step task typically use a mixture of both model-based and model-free strategies, and that neurobiology supports a division between the two. Both of these points are controversial, at least: da Silva and Hare (2020) present a compelling argument that humans are primarily model-free, and neural recordings dating back to the very first two-step task paper (Daw et al., 2011) show overlapping signals.

I do think it might be possible to address this with more careful writing and further simulations. But I do not think this is necessary. The rhetorical role of the two-step task in this manuscript seems really just to be illustrating that H-DRL can exhibit different online learning strategies in activities and in weights. This point is made far more convincingly by the analysis of the mouse perceptual decision-making task. I'd recommend simply dropping the two-step task, and its associated claims, from the manuscript.

Reviewer #2 (Public review):

In this manuscript, the authors investigate transcriptional diversity and similarities between mechanosensory hair cells across different species to provide an atlas of hair-cell-enriched genes.

Their analysis encompasses publicly available and a few newly generated single-cell RNA-seq data from human, mouse, chick and zebrafish, in total comprising 43 populations of hair cells from different organs (cochlea, vestibular sense organs, lateral line) and different stages. They first compare the transcriptome of each hair cell population with that of supporting cells from the same data to find hair-cell-enriched genes. To do this, they use a score (AUC) that reflects how well a gene separates both cell populations; this approach identifies about 5000 genes enriched in at least one of 29 mechanosensory populations. Grouping these genes by their enrichment pattern gives 22 clusters of co-regulated genes, many species- or organ-specific and a few shared by most hair cells.

Compared with previously published lists of hair cell genes, they define 884 "pan-HCE" genes enriched in most hair cells across species. This set is enriched for stereocilia and deafness genes. In situ hybridization is presented to support enrichment for 7 of 10 tested genes.

Adding 14 immature populations splits the list into early-and-sustained, transient and late genes. The authors then use these to assess the maturity of hair cells from cochlear organoids, regenerating chicken basilar papilla, reprogrammed fibroblasts, and developing zebrafish, mouse and human tissue. All data are released through a Shiny app and the gEAR portal.

Overall, this is a useful resource for the community, presented in an exploitable format. It is timely; the inclusion of many datasets is very valuable, and the comparison of HCs vs SCs from each organ is a good approach. I have some concerns regarding the clarity in describing the analysis, whether some findings could reflect differences between datasets, and how cell 'maturity' is defined.

Major comments:

(1) The data were generated by different labs, different methods and sequencing platforms. Hierarchical clustering of the data splits cells mainly by species. It is not clear to me that the authors have ruled out that this indeed reflects biology or could be due to technical differences. This needs to be discussed. Is it possible to show that the results hold for data generated with the same platform?

(2) The calculation of enrichment scores needs clarification. In the methods, it says that AUC values below 0.5 were set to 0.5 and then supporting cell-enriched genes were set to 1 − AUC. How do supporting cell-enriched genes like ID2 end up with values like 0.11 (Figure 2C)? Please describe the calculation step by step; how were skipped genes because of low expression handled?

(3) It is not clear how duplicated genes were handled when assigning orthologues; the approach allows one-to-many. How were they combined into a single score? The results say that each human gene was linked separately to each ortholog?

(4) The reference gene list for known hair cell genes seems to overlap with genes reported to be enriched; this seems to be a circular argument. Among the hand-curated list are stereocilia-associated genes, which are then reported to be enriched. Is it possible to test enrichment using gene sets that were not used to choose the threshold?

(5) The authors need to define how they classify cells as mature, maturing, and immature - there are no definitions at all. Provide these and appropriate references. Mouse P7 cells are classified as mature, which they are not. Later data depend on this classification, so it needs to be justified properly.

(6) The authors assess maturity of hair cells in organoids; it seems that this depends on the % of genes in each set (mature, maturing, immature) that pass the cutoff. Has this been compared to a random or control set?

(7) Verification of hair cell enrichment by HCR/RNAscope: This verification is not very convincing for most genes tested. 30% of the genes tested fail, while genes described to be HC-specific are also expressed in surrounding cells. E.g. Figure 5C: both Apba1 and Pcsk5 seem to be expressed in hair cells as well as the surrounding cells; Figure 5D: both genes are not expressed in all hair cells. Figure 5 supplement 2B: the images of the organ of Corti are too low resolution and magnification to identify hair cells reliably. It is not possible to see if these genes are indeed expressed in hair cells. If the authors claim HC enrichment, they need to provide quantification of expression levels e.g. HCs vs SCs and show negative controls.

(8) A new single-cell dataset from human embryos has just become available; van der Valk et al., 1026, Nat. Neuroscience. This dataset fills an important gap in their current analysis, and it would be very good to integrate it.

(9) The figure legends are often a bit short and do not explain the figures well; please review and expand.

Reviewer #2 (Public review):

Summary:

The paper asks whether the classic complementary learning system (CLS) can be instantiated in an LLM.

The authors first show that continual fine-tuning and three regularization remedies (L2, EWC, on-policy distillation) fail to encode new episodes without memory interference. They then propose to use gated LoRA instead: one dedicated low-rank adapter per episode for encoding, with winner-take-all selection among adapters based on embedding similarity for retrieval, and finally replays stored episodes in batches to fine-tune the base weights for consolidation.

The evaluation covers storage capacity, end-to-end recall at up to 1,000 episodes, question answering (via a two-stage "internal RAG" and via direct parametric access), and consolidation, on two datasets: Wikipedia paragraphs and an autobiographical dataset, and two LLMs: Llama-8B and Qwen-8B.

Strengths:

(1) The attempt to instantiate a CLS in an LLM is very interesting, timely, and potentially useful for AI.

(2) The use of LoRA as the episodic encoding mechanism is efficient: they showed that LLMs can store 1,000 episodes with a small amount of parameters (< 0.01% relative to the base model).

(3) The evaluation is careful. The negative results on regularization are informative. An LLM judge is validated against other metrics, with very informative failure examples (e.g., the "Man on the Run" case, where teacher-forced accuracy is 0.91 but every fact is fabricated).

Weaknesses:

I think the main thing is that the two systems, at this point, do not richly interact:

(1) The episodic component is not meaningfully involved in consolidation. In this model, consolidation is batch re-training with replay, not transfer. The consolidated model is trained from the raw passages - the authors state that it "relearns the same episodes into the shared base weights by batch replay of the original passages, rather than transferring parameters from the episodic adapters."

(2) The influence of semantic memory on episodic encoding is not deeply investigated. For example, empirically, schema-consistent information is acquired rapidly (Tse et al., 2007). It would be informative to understand if this model shows a similar effect. Overall, showing that this method works for a pre-trained LLM is a strength, but it would be very exciting to know how that impacts encoding.

(3) This might be out of scope, but I still wonder, eventually, how this kind of system would decide when to use episodic vs semantic memory. This is relevant to the author's points that the low-rank adaptor can be freed after consolidation, which is true, but to do that autonomously, the system needs to know whether a memory is fully consolidated. Overall, I think this would be an interesting future direction.

Reviewer #2 (Public review):

Summary:

The authors set out to assess the in vitro potency of a new POLQ helicase inhibitor, assess its in vitro BRCA1/2 synthetic lethal properties, and confirm how it performs in vivo in PDX and cell line xenografts. The authors also show an anecdotal patient response with a PARP inhibitor + POLQ inhibitor combination.

Strengths:

This is a novel POLQi of the helicase activity. The anecdotal clinical response in a heavily pre-treated patient is interesting.

Weaknesses:

Most of these issues could be addressed by changes to the main text, i.e. by the authors describing limitations/caveats to interpretation, etc.

(1) The clinical anecdote is interesting but underanalyzed. It's difficult, though, to know one way or the other whether the PARPi+ POLQi treatment effect (only partial response) has anything to do with the 53bp1 mutation. There is a causative sign that the 53bp1 mutation could be the cause of PARPi resistance (the increase in 53bp1 mutation allele frequency in PARPi-naïve vs PARPi-resistant tumors), but no similar sign to show that the 53bp1 mutant tumor clone is targeted by POLQi treatment. For example, does 53bp1 mutation allele frequency fall in ascites or ctDNA during the course of treatment, or is the 53bp1 mutation lost in a tumor taken at the end of treatment?

(2) Although the relevant work is cited, the authors should be more explicit in the introduction and state that there is already evidence from 2 papers that Combined loss of BRCA1 and 53bp1 causes enhanced sensitivity to small-molecule POLQ helicase or polymerase inhibitors.

(3) Figure 4. Not exacerbating existing PARPi myelosuppression is great, but the authors have to acknowledge that the myelosuppression is still present when POLQ+ PARPI is used. Single-agent POLQi doesn't give myelotoxicity, but it also does not give strong anti-tumor efficacy. i.e. it would be fair for the authors to acknowledge this.

(4) The increase in reticulocytes when mice are treated with POLQi could be a sign that there is an effect on myeloid lineages, but the animals are responding by increased production of this young myeloid lineage. Alternatively, this reticulocyte increase could drive or be a sign of pathology (polycythaemia vera, etc.). It would be good to see a commentary on this in the Discussion.

(5) Re Figure 4. A therapeutic advance would be to see that, compared to other PARPi combinations such as platinum + PARPi or temozolomide + PARPi, a POLQ + PARPi combination doesn't give the same myelosuppression that these combinations do (by analogy, combined platinum + PARPi gives curative responses in mice but also wipes out the adaptive immune system). The authors need to add data showing the platinum+ PARPi positive control, or at the very least add a limitations of the study section to the manuscript to explain this fairly.

(6) Figure 2 might give the impression that the new POLQi gives a similar, if not greater, BRCA2 synthetic lethal effect than a PARPi (e.g. 2d-f). It would help readers to see the full saruparib and olaparib dose-response curves, not (as per 2 A, B, C) just the single point estimates.

(7) Why was DOTC42510 not profiled for PARPi?

(8) Figure 2f shows error bars on an n=2 dataset - one cannot estimate variance from only an n=2 dataset range.

(9) Is there a statistical analysis on Figure 2G?

(10) Figure 3. Olaparib 25 mg/kg QD is well below the MTD in mice. This makes the olaparib response in HCT116 BRCA2 k/o xenografts look modest when compared to the combinations.

Reviewer #2 (Public review):

Summary:

The authors mapped context-dependent fitness of more than 1,400 clinical K. pneumoniae isolates across 214 antibiotic, chemical, environmental, metal, envelope and nutrient conditions, integrating fitness measurements with whole-genome sequencing to build a genotype-phenotype-fitness framework and a machine learning pipeline for predicting bacterial fitness across environments.

Strengths:

The scale of the dataset is remarkable both in terms of isolates and conditions tested. The central findings that fitness is strongly condition-dependent and that resistance-associated features help under selection are well supported. Resource accessibility (ColonyExplorer portal, code, trained models) is excellent, allowing future integration of different genetic backgrounds and environmental contexts.

Weaknesses:

(1) Normalisation uses a plate-level random effect and plate-median scaling, whereas within-plate (position) effects and their correction are not described, although typically required in such high-density arrays.

(2) Hypermucoid or heavily encapsulated isolates may form smaller but denser colonies. The area-opacity correlation (Figure S3A) does not exclude this, so the virulence-associated costs in selected conditions (Figures 2F-G, S5A) could be partly measurement-driven.

(3) Plasmid effects are attributed causally from observational data. Fitness effects are inferred from short-read replicon calls (Figure 3) and may be confounded by lineage and co-occurring resistance determinants.

(4) The study does not apply linear mixed model GWAS on the continuous fitness phenotype, relying instead on median dichotomization with a non-mixed model that is vulnerable to population structure (especially in a highly clonal species) and could exacerbate apparent separation, inflating association counts.

(5) GWAS-derived features appear to have been selected using all isolates rather than within each training fold. Test-set labels thus influenced which features entered the models, biasing correlation, coverage and interval estimates upward. Although mitigating overfitting, nested hyperparameter tuning and lineage-stratified tests do not remove this bias. Because of this, I do not find the claim that genomic variants predict fitness beyond population structure adequately supported yet.

With the points above addressed, I believe this work would be highly valuable for prioritising genes and variants for functional validation and for generally studying fitness trade-offs in hospital pathogens.

Reviewer #2 (Public review):

Summary:

Large oocytes show prominent waves of cortical contractions. Previous works combining experiments and computational modeling have shown that the waves are driven by gradients of CDK1 kinase activity that trigger excitable Rho activity patterns on the cortex. This present work combines two previously published mathematical models for CDK1 activation and Rho activation, respectively. They show that the models combined can explain diverse shapes of cortical contractions observed in different species and at various stages of development. This shows how the same molecular machinery can generate diverse patterns dependent on the size of the system and the size and position of the cell nucleus.

Strengths:

(1) Carefully done modeling work providing a simple and elegant explanation for a complex cellular behavior.

(2) Very nicely illustrated, simulations can be directly compared to previous experimental observations.

(3) Explains observations made in different model systems, providing a unifying model.

Overall, I find this work important, as it shows that combining models of the CDK1 gradient and Rho activation modules can explain the surface contraction waves observed in oocytes. Strikingly, it elegantly explains the differences seen between different experimental systems. While previously these were considered a 'controversy', modeling shows that the differences are simply a consequence of the difference in the size of the oocytes. In addition, the model makes several intriguing predictions that can be tested in future experiments.

Reviewer #2 (Public review):

Summary:

The authors performed bioinformatic analyses to trace the genomic history of the clinically relevant pT181 plasmid. Specifically, they:

(1) tracked the presence of pT181 across different S. aureus strain backgrounds through time. It was first found in one, later multiple strains, though this may reflect changes in sampling over time.

(2) estimated the mutation rate of the chromosome and plasmid.

(3) estimated the plasmid copy number of pT181, and found that it decreased over time. The latter was supported by two sets of statistical analyses, first showing that the number of single-copy isolates increased over time, and second, that the multicopy isolates demonstrated a lower PCN over time.

(4) reported the different integration sites at which pT181 integrated into the genome.

As a caveat, they mentioned that identical plasmid sequences have variable plasmid copy numbers across different genomes in their dataset.

Strengths:

This is a very solid, well-considered bioinformatic study on publicly available data. I greatly appreciate the thoughtful approach the authors have taken to their subject matter, neither over- nor underselling their results. It is a strength that the authors focussed on a single plasmid in a single bacterial species, as it allowed them to take into account unique knowledge about the biology of this system and really dive deep into the evolution of this specific plasmid. It makes for a compelling case study. At the same time, I think the introduction and discussion can be strengthened to demonstrate what lessons might be drawn from this case study for other plasmids.

Weaknesses:

The finding that the pT181 copy number declined over time is the most interesting claim of the paper to me, and not something that I have seen done before. While the authors have looked at some confounders in this analysis, I think this could be strengthened further in a revision.

For the flow of the storyline, I also think the estimation of mutation rates (starting L181) and integration into the chromosome (starting L255) could be moved to the supplement or a later position in the main text.

Clearly, the use of publicly available data prevents the authors from controlling the growth and sequencing conditions of the isolates. It is striking that they observe a clear signal in spite of this, but I would have loved to see more discussion of the metadata that came with the publicly available sequences and even more use of that metadata to control for confounding.

Comment from the Reviewing Editor:

Reviewer #2's comment about the weakness due to "confounders in this analysis" has been addressed in the revision through additional analysis using metadata to control for confounding.

Reviewer #2 (Public review):

Summary:

The manuscript by Yang and authors presents an interesting set of new microfossils from the early Cambrian of South China. These are interpreted as segmented bilaterians, and so may be the oldest such examples in the fossil record, with implications for our understanding of early animal evolution. Further, the preservation of these fossils as microbial pseudomorphs is a unique pathway to fossilize such important organisms.

Strengths:

The fossils are impressive and arguments for their affinities as segmented bilaterians are convincing. Discussion of somewhat novel preservation is detailed and helpful to understand this taphonomic mode.

Weaknesses:

It needs to be clear what the authors' definition of a "segment" is and how that matches (or not) other interpretations. Arguments by Hannibal and Patel (2013, EvoDevo) convincingly define all segmented organisms based on polarity within repeated units of bilaterally symmetrical animals, but others may disagree. The manuscript would benefit from discussion of this in the introduction, laying out a hypothesis for what we might expect to find as evidence of the earliest segmented animals and then describing these new fossils within that context.

As it currently reads, the definition of a segment is not presented until the discussion and is somewhat ambiguous, seemingly referring to any organism with bilateral symmetry and repeated units. However, modern (e.g. tardigrades) non-segmented animals exhibit these features but are not considered truly segmented. This is critical as various Precambrian fossils (Arborea (Dunn et al., 2019, Palaeontology); Cloudina (Schiffbauer et al., 2020, Nature Communications) Dickinsonia (Gold et al., 2015, Evolution and Development); Parvancorina (Darroch et al., 2017, Biology Letters); Spriggina (Evans et al., 2026, Scientific Reports); Yilingia (Chen et al., 2019, Nature) etc.), have been argued to possess bilateral symmetry ad have bodies composed of repeated units. These older examples should be acknowledged and discussed in detail. Stating that "the identity of these [Precambrian] organisms is controversial" (line 51) is not justification for the main claims of this paper, and it should be clear what distinguishes the new fossils from previously proposed evidence of earlier segmented bilaterians. I believe there is a case to be made in support of the authors' claims, but this context is vital to make that argument convincingly. It is also worth discussing evidence of segmented bilaterians in the early Cambrian trace fossil record, such as Treptichnus pedum (Kesidis et al., 2019, Proceedings B) and Rusophycus (Daley et al., 2018 PNAS), that may predate or be roughly coeval with these fossil remains.

PatientID: None

KindredID: 1

Case: F, 24Y0M, Ethnicity Unknown

DiseaseAssertion: Assumed VHL

FamilyInfo: Third child of healthy unrelated parents; she has two healthy older sisters.

CasePresentingHPOs: HP:0001511; HP:0001999; HP:0000952; HP:0001943; HP:0001508; HP:0000851; HP:0001250; HP:0002650; HP:0001332; HP:0001263; HP:0006880 (Intrauterine growth retardation, Abnormal facial shape, Jaundice, Hypoglycemia, Failure to thrive, Congenital hypothyroidism, Seizure, Scoliosis, Dystonia, Global developmental delay, Cerebellar hemangioblastoma)

CaseHPOFreeText: Normally born after 37 weeks pregnancy. During the prenatal period, she showed intrauterine growth retardation of unknown cause. At birth, dysmorphic features were noted (full list in table 1). After birth, she was treated for physiological jaundice and hypoglycemia. In addition, she has problem for failure to thrive and feeding difficulties associated with poor sucking and regurgitation. Furthermore, she was diagnosed with a congenital hypothyroidism in the neonatal screening. At 26 months old, she developed epilepsy. At 7Y, was considered to have Rett syndrome because of stereotypic hand movements since 2 years old, scoliosis and dystonia. Her psychomotor development was severely delayed: she had no ability to move by herself, visual contact, or social interaction. Brain MRI was performed at 1 year old and 6 years old. Both of these showed no abnormal findings. At 16 years old, follow-up MRI unexpectedly demonstrated a solid tumor with peritumoral edema at right cerebellum; diagnosed a cerebellar hemangioblastoma and confirmed through histopathology.

CaseNotHPOs: HP:0002012 (Abnormality of the abdominal organs)

CaseNotHPOFreeText: Repeat brain MRI, fundoscopy, abdominal echography and CT, blood and urine testing revealed no other tumors associated with VHL.

CasePreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: N/A

Variant: Complete Deletion

LegacyVariant: N/A

CaseProblemVariantFreeText: N/A

ClinVarID: N/A

CAID: N/A

gnomAD: N/A

VariantEvidence: No mutation was detected in MECP2 on chromosome Xq28. At 5 months old, her chromosome analysis of the white blood cells revealed a distal deletion of the short arm of chromosome 3. Genomic DNA from the patient and her parents revealed a de novo 11.0-Mb deletion at 3p26.3-25.3 that included VHL and a paternally inherited 740-kb gain at Xp22.2-22.13, which was predicted to be a benign copy number variation. Unfortunately, since the mutation analysis of VHL was not performed using resected tumor tissues in the present case, “the second hit” could not be confirmed. Thus, the reason remains unclear why a cerebellar hemangioblastoma developed in her.

MutationType: deletion

CivicName: 3p26.3-25.3 11Mb del

MultipleGeneVariants: see VariantEvidence.

Reviewer #2 (Public review):

While the aim of this study is interesting and the methods are advanced, the novel insight and conclusions rather feel limited at present, particularly considering the literature already available on the functional role of p197. There are some notable methodological issues in quantifying the EZF that need to be addressed.

The claims accompanying p197 and its role in EZF tethering to the BB and pBB to the OMM, as well as its effect on mitochondrial membranes, as displayed in Figure 3, should be augmented via additional comparative measurements with tdzoid cell tomograms, given that tomograms were generated for tdzoid cell lines, but are only utilised in a limited manner for Figures S4 and S5. This represents an underutilised opportunity to use this technique to contrast BB, pBB and mitochondrial membrane separation in a comprehensive manner beyond what previous studies could achieve.

The authors need to justify specifically why different methodologies were employed for minimum filament length measurements between NP-40 wild-type cells and non-detergent-treated Zoid cells, preventing these datasets from being broadly comparable.

The reliance on mutant cell lines (primarily zoids) for conclusions needs to be acknowledged to a greater extent, such as when comparing results with previous works (Line 334) and naturally within the limitations mentioned in the discussion.

The integration of the discussion with relevant literature concerning Cryo-EM and trypanosomatid cytoskeletal studies feels somewhat limited; for example, no mention or comparison with the formative Höög et al. 2012 study for Cryo-EM within trypanosome flagella and basal bodies.

Reviewer #2 (Public review):

Summary:

The article by Ribeiro et al. provides further mechanistic understanding of the abilities of cell-penetrating peptides (CPPs) to cross directly through the membrane. The authors performed a transcriptomic evaluation to conclude that CPPs enter via direct translocation through cells undergoing cell division, particularly in G2/M phase cells. They reasoned that this is due to the expression of KCNH1, a potassium channel, which is hypothesized to fluctuate throughout the cell cycle and peak at G2/M phase. This expression, in turn, hyperpolarizes the cells, enabling the direct translocation of CPPs.

The uptake mechanism of CPPs has long been debated. The authors and other groups have previously mentioned the importance of cell membrane potential and the involvement of specific ion channels in its regulation. The current work further adds a layer of understanding by connecting the occurrence of direct translocation to a specific cell cycle phase, which may be of great relevance when considering the use of CPPs for cells that are constantly dividing, such as cancer cells.

Strengths:

The work took advantage of transcriptomics and the unique use of the PIP-FUCCI system to determine the different uptake of CPPs in different cell cycle stages. All the data are in agreement with each other, especially when observing the membrane potential change through the cell cycle stages. The experiments are well executed and are all complementary to the authors' hypothesis.

Weaknesses:

The article could be further strengthened through:

(1) Implementation of additional confirmatory experiments

(2) Positioning against other recently reported channel proteins

(3) Confirming whether CPPs with different physicochemical properties are also affected by the fluctuation of KCNH1 expression

(4) Determining whether the concept of cell cycle-dependent membrane potential change is applicable not just in model cells, but also in cells that undergo rapid cell cycles, and

(5) Relating CPP direct translocation to known biological phenomena that are physiologically similar.

Reviewer #2 (Public review):

Summary:

The authors ask whether membrane transport could have contributed to the emergence of the molecular configuration characteristic of extant RNA. They examine the permeability of α- and β-furanosides and of different nucleotide phosphate isomers using fatty-acid-based PAMPA systems, supported by partitioning experiments and molecular simulations.<br /> The central finding is that closely related molecular species show different transport behaviour: β-furanosides are preferentially transported, while 5′-phosphorylated nucleotides are less permeable than 2′- and 3′-phosphates. These observations are potentially interesting because membrane transport could influence the composition of an evolving compartment.

Strengths:

(1) The study addresses an interesting and relatively underexplored role for membranes in prebiotic chemical evolution.

(2) The comparison of closely related molecular species provides a potentially useful demonstration that membrane transport can discriminate between different nucleotide configurations. The experimental and computational approaches complement each other.

(3) The analysis of 5′-, 2′-, 3′- and cyclic phosphate species is particularly interesting and could provide a useful framework for thinking about how membrane transport interacts with nucleotide chemistry.

Weaknesses:

(1) The principal concern is that the manuscript moves from differential permeability to evolutionary selection rather too quickly. The experiments demonstrate differences in transport, but do not establish that these differences would produce sufficient intracellular enrichment, persistence or functional consequences to drive selection of the canonical RNA configuration.

(2) The relevance of the PAMPA system to primitive vesicles is also not fully established. Membrane permeability depends strongly on membrane composition and environmental conditions, and the relationship between the membrane compositions used in the principal experiments and plausible primitive vesicles needs to be clearer.

(3) Similarly, the pH-gradient experiments show an effect of pH on transport, but a gradient between two macroscopic PAMPA reservoirs does not demonstrate that primitive fatty-acid vesicles could maintain a comparable gradient.

(4) Finally, some of the key results are presented as changes in relative composition rather than direct measurements of molecular flux. The distinction between compositional enrichment, partitioning and permeability should therefore be clearer. The molecular simulations provide useful support but, given the small calculated free-energy differences and acknowledged limitations in the nucleotide simulations, do not independently establish the mechanism.

Overall, the work contains potentially valuable observations, but I think the conclusions should be framed more cautiously. The most compelling interpretation is that membrane transport could have been one component of a multifactorial chemical-selection process, alongside prebiotic synthesis, stability and nucleotide chemistry, rather than that membrane permeability itself explains the emergence of canonical RNA.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors aim to assess how the functional organisation of the hippocampus is related to the geometry and neurobiological differences of the hippocampus. In particular, the authors focus on the first three eigenvectors of hippocampal-cortical functional connectivity, based on non-linear dimensionality reduction on resting-state functional MRI data. Furthermore, the work aims to describe changes in these functional axes, and their relation to other factors, throughout youth and evaluate whether they are predictive of individual variations in cognition.

Strengths:

A major strength of this study is the attempt to replicate key findings across multiple developmental cohorts.

Weaknesses:

T1w/T2w signal intensity is incorrectly used as synonymous with myelin.

Appraisal of Aims and Conclusions:

The authors partially achieve their aims by illustrating certain age-related changes in hippocampal function; however, the correlative study design is not equipped to examine how these changes are "shaped" by geometry, myelination or gene expression (especially the latter two). Furthermore, conclusions were often overstated based on small effect sizes.

Context and Field Impact:

This work adds to a growing body of literature focused on gradient-based representations of hippocampal topology. By applying these methods across a wide developmental age bracket, it provides a useful reference point for how the hippocampus and wider cortex interact during maturation. To improve utility to the neuroimaging and cognitive neuroscience communities, the nesting of subfields within the eigenvector topology should be addressed too.

Comments on revised version.

I appreciate the authors' revisions and their clear efforts to improve technical transparency throughout the manuscript. I believe the manuscript could have been more strongly improved were the authors more constructive than defensive in their responses, but the manuscript is nevertheless more clear.

Reviewer #2 (Public review):

Summary:

Electron microscopy (EM) offers resolution of neuronal ultrastructure not available through any other technique, but it has a limited "field of view". Also, an EM study's value is limited by the ability to identify neuron types within that field of view. This data set largely overcomes both limitations, first, through the analysis of contiguous blocks of SEM-FIB data spanning almost half a millimeter in extent, serially milled at an exceptionally fine 8 nm to create isotropic 8 nm voxels, and, secondly, through efforts that identified most neurons within the sectioned volume. In overcoming the usual limitations, the authors can offer an overview of the capacity of different classes of neurons to control the size and distribution of their mitochondria - a non-trivial outcome considering the essential role of mitochondria in generating cellular ATP and neurotransmitter precursors. The science community is doubly served by the authors' website "neuPrint" which is accessible to the public, where the data can be visualized and interrogated according to various hypotheses. As it stands, this study fails to reach its otherwise exceptional potential as analysis errors throw a shadow over the findings.

Strengths:

(1) This is the first comprehensive analysis of mitochondrial morphometrics and ultrastructural positioning across a substantial portion of an insect brain. The adult fruit fly brain was previously milled at 8 nm intervals and imaged at 4 nm/pixel (Scheffer et al., 2020), offering an unprecedented data set for mining mitochondrial parameters relevant to neuronal function (this manuscript).

(2) The value of the data set has been amplified through systematic identification of neuronal identities and likely neurotransmitter types. The numbers of mitochondria (>6,000,000), neurons (>20,000), and neuron types (5,555) included in the study are similarly unprecedented.

(3) The language of the manuscript is straightforward and accessible.

Weaknesses:

(1) The data describing mean median (mean of the medians derived from many neuron types) mitochondrial volume (Fig.2B) and mean median length (Fig.2C) are not internally consistent, i.e. an ellipsoid with volume 0.15 cubic microns requires a longest axis of at least ~0.66 microns to remain a valid prolate shape (i.e., for the long axis to exceed the two minor axes); the reported value of 0.55 microns falls well short of this, indicating the two panels are not describing a geometrically coherent object. This inconsistency can be reconciled only by looking at the supplementary data, where the data are reported for each cell type and the relevant column is titled "Median miitochondrion long axis (r0)" (sic). Here it is evident that the parameter being reported on each plot in Figure 2C is in fact the radius of the long axis and not mitochondrion "length" as currently stated.

(2) However, even with the correction referred to above, the mean median mitochondrial diameter, i.e., minor axes, would be 0.5 microns, a robust mitochondrion for otherwise fine central processes, and double the diameters reported from C.elegans motor neurons (Riboul et al., 2024) and interneurons within the optic lobe of the miniature parasitic wasp (Fischer et al., 2017). While it is a dubious exercise to reverse engineer possible ellipsoid body dimensions from the mean of medians of both longest axis and volume, the inconsistency remains when referring to the mean of medians of smaller groups of neurons in the supplementary data.

(3) The estimates of mitochondrial volume density are uncommonly high, i.e., 4%-24%, with a mean median of 10%. These values are 2-3 fold greater than estimates from comparable EM studies, e.g. 4-6% for C.elegans motor neurons (Riboul et al., 2024); 4-6% for Drosophila larval motor neuron terminals (Justs et al., 2022); and 4-8% for lamina-medulla and medial-medulla interneurons in the miniature parasitic wasp (Fischer et al., 2017). Estimates from rat brain are similarly small, not exceeding 8% (e.g., Santuy et al., 2018), leading to concerns that, just like the two-fold error in reporting mitochondrial lengths, a two-fold error has propagated in mitochondrial volume density.

(4) The internal inconsistency in reported mitochondrial axis length can be identified from the manuscript's own numbers alone, using simple arithmetic. By contrast, the plausibility of the resulting mitochondrial minor-axis diameter (~0.5 microns), and of the reported mitochondrial volume density (4-24%, median 10%), can only be assessed by comparison with the wider literature - where comparable EM-based estimates across several species consistently fall in the 0.18-0.24 micron (diameter) and 4-8% (density) range, respectively. Both this literature comparison and the simple arithmetic used to establish the axis-length inconsistency were available to a reviewer. The other parameters reported throughout the manuscript are inscrutable, i.e., not readily checked by arithmetic or by comparison with existing literature. Nevertheless, the errors and implausibilities identified above throw a shadow over every other piece of data.

(5) Putting errors to one side, if the authors wish to report values in an unprecedented range, they need to do so with due diligence and defend the novelty of their data in the context of precedence in the field.

Assessment of whether aims were met:

The authors did not achieve their aims, but those aims remain well within their reach, as processing data sets is generally trivial relative to the physical act of repeating experiments and recapturing ideal experimental conditions.

Discussion of the likely impact:

This manuscript has great bones, and should it receive the attention/rigor it deserves, it is likely to become the go-to reference for mitochondrial morphometrics and positioning across whole central neurons in Drosophila, specifically, and in insects in general.

Several of the findings in this study, should they persist through reanalysis, are likely to be impactful. A particularly interesting finding is that the size of the closest mitochondrion is larger at pre- rather than post-synaptic compartments (line 309; Figure 7). The dogma in neuroscience, mostly stemming from studies on vertebrate neurons and partly supported by the misguided use of fluorescence microscopy to assess mitochondrial size and distribution, is that mitochondria are larger in dendrites than in axons. The ability to assess a compartment as either pre- or post-synaptic in the present study gets to the heart of the matter about pre- vs post-synaptic dependence on mitochondrial function without getting drawn into arguments about more macroscopic cell structures that may or may not be preserved between insects and vertebrates. This finding is worth discussing in the context of the mouse data from Sager et al., 2026, Science, but it is a discussion that requires consideration of the relative volume of the pre- vs post-synaptic compartments, and whether mitochondria have the same opportunity to closely attend postsynaptic compartments.

A second finding, and one that might have received more attention in the discussion, was the observation: "KC presynapses with smaller median distances to mitochondria have larger nearest mitochondria, ... . For KCs and MBONs, the mitochondrial volume is inversely correlated with the distance". There are several avenues here that might have been explored. Firstly, in the context of data processing, and secondly, in the context of bioenergetics, the implication of greater intermittence of input at any particular postsynapse, and less intermittence of output at any particular presynapse. Lastly, implications with regard to the relative reliance on mitochondria for neurotransmitter synthesis between pre- vs post-synaptic compartments, an implication that might be addressed through reference to the particular neurotransmitters of each neuron class and whether that neurotransmitter is directly dependent on neighboring mitochondria for precursors.

Reviewer #2 (Public review):

Summary:

Broadly, the authors use a wide range of lines targeting dopaminergic neurons in different behavioral paradigms to determine their role in behavior.

Strengths:

There is a comprehensive use of lines and assays by a single group, such that internally consistent comparisons can be made.

Weaknesses:

It is not clear to what extent these broad assays will provide concrete answers that enable a "unified hypothesis" of how dopamine functions in behavior.

The manuscript has a feel of being a mix of an original paper, commentary, hypothesis-generating outlook, a focused summary, and a broad review of the field. As someone outside this field, I find that it has its advantages and disadvantages. For example, the final sentence of the Abstract, despite multiple readings, is not obvious to someone outside the field. Plus, due to the large number of references for any one view, it is not clear which work is saying exactly what. For example, which of the fly papers on dopamine clearly say they favor the "common currency" model? Do these references also use this term? Even for a reference that occurs multiple times (Jovanoski and Waddell, 2025), it's not clear what this paper actually proposes.

Also, what does "a reporter for a specific goal" mean (Line 16 of Abstract, and lines 1986-1987 of Discussion)? They provide a nice example of a tethered fly and what happens when it is suddenly released, vs a free-flying fly that has entered a maze--what is the difference here in terms of dopamine function and the fly's "goal" and how does the fly know the difference? When a fly is free flying in a maze, how does it know that it has just been released from a tether, or whether it has entered a maze as it was freely flying outside it?

And for their "unified hypothesis", how does a fly know that it is in "controls its environment" versus "lack of control"?

In sum, I found this paper to be valuable in bringing up the different views of dopamine signaling. However, it was a bit unclear at the end as to what dopamine is actually doing in the fly.

Reviewer #2 (Public review):

Olmstead et al. present ACT-DEPP, a single-nucleus RNA-sequencing atlas of the mouse hippocampus. The atlas profiles transcription after environmental enrichment, after kainate-induced seizure, and at four circadian time points, and all nuclei are registered to the Allen Brain Cell taxonomy. The authors report three main findings. First, seizure and novel experience induce transcriptional programs that differ greatly in magnitude. Second, ZT12 is a transcriptional inflection point shared across cell types. Third, induction of classical immediate early genes (IEGs) is broadly maintained across circadian phases, with only a few genes showing circadian-activity interactions.

The study is ambitious in scope, and the dataset is rigorously collected. Registering the data to the ABC taxonomy makes them easy to reuse and to integrate with other brain-wide resources. In the revision, the authors substantially strengthened the analyses and refined their interpretations. Overall, the descriptive conclusions are well supported.

Locomotor activity was not recorded, so some ZT-dependent effects may partly reflect behavioral state as well as intrinsic clock regulation. The authors clearly acknowledge this in the Limitations section. With relatively few replicates per ZT, small circadian-activity interactions may go undetected. The pathways highlighted in Fig. 6I-J are best viewed as hypothesis-generating. These points are appropriately framed and do not detract from the value of the work. This study will be a useful reference for investigators studying IEG biology, experience-dependent plasticity, and circadian regulation in the brain.

Reviewer #2 (Public review):

Summary:

In this paper, the authors report a quantitative change in the functional connectivity between cortical domains in mice, due to the influence of the psychedelic, DOI. Additionally, they report that specific cell types (and circuits) within the brain may be involved in the changes in the observed functional connectivity. This will have important implications - both for basic neuroscience and with potential translational implications.

Strengths:

This is a fascinating result - especially since the work on psychedelics has predominantly been focused on humans - and therefore a deeper understanding of cell-type-specific responses due to psychedelics has remained poorly studied. Given that psychedelics result in a variety of distinct changes within the circuit - both affecting plasticity and the steady-state dynamics - it is critical to characterize these changes within a system where we can measure changes in individual cell types.

Weaknesses:

The authors attempt to model the types of analyses that have been performed in humans within mice. Perhaps because of their attempt to translate from one system to another, the quantitative justification for each of the specific analyses that have been performed appears unclear within the mouse system. This makes some of their analyses, as currently structured, appear incomplete, and we have detailed the resulting questions in the following comments:

(1) The concept of a "cortical domain" is used in the abstract and is a fundamental component of the paper. We were not familiar with the concept prior to its use in this paper and, given the importance of the concept, it would be helpful to clearly delineate what the authors define as a domain at the outset, so that readers can build their expectations. Further, both AL and PM have been used as acronyms for secondary visual areas (which the authors are very familiar with). This leads to further confusion for the reader. As such, it would be helpful to clearly delineate that these are distinct acronyms for a distinct class of spatial feature within the brain - even within the abstract.

(2) The authors define the AL and PM cortical domains through the use of a threshold on the seed correlation maps. They chose a threshold of 35%, but it's not clear why that threshold was chosen - and what would occur if the authors used a different threshold instead. Would the boundaries change significantly, and would the rest of their results then be affected by this change?

(3) The authors report that the behavioral events that show a maximally distinguishable response in the PM and AL domains are behavioral fidgets. However, the authors do not define either (a) the set of all possible behavioral events which they compared against (and how those behavioral events were assayed), and (b) the quantitative definition of fidgets that was used. This is particularly critical since the authors use the fidget response as a metric of responsiveness in the rest of the paper.

(4) The authors report that the AL and PM domains correlate with the projection patterns of specific neuromodulatory systems. Given the (apparently) arbitrary threshold used in the definition of the AL and PM domains, and the fact that the authors mention that they examined "some of the major neuromodulatory systems", it is unclear how to interpret this result.

(5) The authors claim that the DOI-induced increase in the correlation between the cortical domains is 5-HT2A dependent, and their evidence for this depends on a block of the 5-HT2A receptor via ketanserin, in the presence of DOI. However, the result that they report from ketanserin is a decrease in the correlations below baseline, and not simply a removal of the effect observed in DOI. This suggests that the effect of DOI on the network cannot be simplistically explained through 5-HT2A dependence, and requires some further characterization and / or explanation.

(6) The authors introduce the concept of "amplitude of activity variation". However, they do not define the specific quantification that they are using in the text. Additionally, their justification for the use of the concept is also unclear. Why should the activity of input variation change "on first approximation"? If this is a quantifiable prediction, what is the approximation? Increasing the precision of their introduction of the concept would greatly aid the reader in interpreting the results in Figure 4.

(7) The authors report that EEG responses do not directly reflect the opposing responses observed via widefield calcium imaging. They conclude that "the opposing effects we observe in calcium activity are difficult to relate to psychedelic-induced EEG effects in humans". However, their apparent effects are also not observed in mouse EEG recordings. As such, their claim is unclear - are they reporting that calcium recordings are not reflected in EEG responses, or is there a specific effect present in mice that is absent in humans?

(8) The authors compare the widefield response resulting from stimulation of specific populations of cortical and subcortical neurons prior to and under the influence of DOI. They claim that the resulting changes provide "candidate circuit mechanisms" for the effects observed during DOI. However, the observed changes due to stimulation do not match the specific changes observed during DOI, making it hard to justify their claim. Indeed, one could hypothesize that the observed changes are only due to downstream effects in a secondary population, only indirectly stimulated through the authors' paradigm. As such, clarification of their results and increased precision of their claims would be helpful. As one example, the authors could provide a model for how the specific changes that they observe during stimulation may directly result in the domain-specific changes observed due to DOI.

(9) Following up on question (8), it would also be helpful for the authors to characterize the space of stimulation paradigms within the selected neuronal populations. How specific are the changes that they observe to the specific stimulation paradigm used? Are these stimulation paradigms biologically plausible? And, if not, what precisely do we learn through this stimulation?

Reviewer #2 (Public review):

Summary:

This study investigates how the timing of dopaminergic signals influences associative learning and memory in Drosophila larvae. Using optogenetic activation and inhibition of the dopaminergic neurons DAN-f1 and DAN-g1, the authors examine their roles during different phases of aversive olfactory learning and memory testing. The study reports that manipulating these neurons can alter behavioral performance not only when the manipulation is paired with punishment, but also during non-punished periods and during memory testing. The finding that behavior induced by artificial activation of these neurons can be expressed across different test conditions is also interesting. Overall, the study provides valuable observations concerning the timing-dependent roles of dopaminergic signals in associative learning.

Strengths:

A major strength of the study is the systematic manipulation of the same dopaminergic neurons at different stages of the behavioral paradigm. This provides a useful framework for examining whether dopaminergic signals have functions beyond their conventional role as reinforcement signals during training. The study also combines optogenetic activation and inhibition with behavioral assays and measurements of neuronal responses. Several of the observations are potentially informative for understanding how dopaminergic signals contribute to memory formation and expression in a context- and trimming-dependent manner.

Weaknesses:

The main weakness is that several conclusions are stronger than warranted by the current evidence. The optogenetic control experiments do not fully distinguish the effects of light, genotype, and opsin expression, and some experimental and control genotypes already differ in naïve behavior.

In addition, some conclusions rely on whether individual groups differ from zero rather than on significant differences between experimental groups. This affects key claims concerning loss of memory formation or expression.

The comparison between optogenetic reinforcement and natural punishment is also confounded by a fixed training order, making it difficult to separate reinforcement strength from recency or interference effects.

Finally, the proposed role of baseline dopaminergic activity is not directly supported by physiological measurements in the relevant neurons under the experimental conditions.

Overall, these issues make the evidence for several of the main mechanistic conclusions incomplete.

Reviewer #2 (Public review):

Summary:

This is an important, timely and extremely well-conducted study, providing 3D EM data of the highest quality for identification of modulatory axons in the neocortex. The field of connectomics, in which ever larger 3D EM volumes are being acquired, has so far focused on glutamatergic and GABAergic synaptic circuits in the cerebral cortex. These obviously constitute the major fraction of synapses and are known to form high-specificity networks. However, it has been clear that modulatory systems originating in subcortical structures are an important additional defining property of neocortical circuits. Without a clear identification of these specific axon types, their identification in 3D EM data has so far not been possible at scale.

Strength:

The present study addresses this issue and resolves the problem of identifying modulatory axons in 3D EM data of the cerebral cortex. Given the fast pace of development in the field of connectomics, such high-quality information is extremely relevant and of substantial impact for the field. The study is conducted beautifully, and the figures are very clear.

Slight weakness:

For the quantitative analyses, it would be highly appreciated to plot the raw data distributions as scatter plots. This is particularly important because, for many readers, these data will serve as a benchmark for classifying and identifying such synapses. Thus, any parameter distributions that are (close to) disjunct will be extremely informative. Along those lines, even at the level of the already extracted parameters, it would be very interesting to see whether the authors can already train a simple classifier that would allow the identification of cholinergic and serotonergic synapses or axons in the low-dimensional parameter space that has already been sampled.

Reviewer #2 (Public review):

Summary:

The manuscript presents a comprehensive and useful evaluation of the association between extinction risk and niche limits. If these results were clearly articulated, then the manuscript would make a valuable contribution to the literature. However, I struggled to understand the manuscript, as the terms and methodology are not clearly defined. I realize that methods do not belong in the main text, but as written, it is often very difficult to understand what was calculated.

To give an example. In the abstract, the main result is: "Species with colder upper thermal (niche) limits are more likely to be classified as threatened across taxa." In the 1st paragraph of the discussion, we read: "We show that... species with narrower and colder thermal niches face greater threats to extinction...". These are three different variables: colder upper thermal limits, narrower thermal niches and colder thermal niches. I really did not see where these were all defined in the manuscript, why so many different variables are considered and why the results are not consistent. I think this is likely a case of insufficient care in term definition and usage, but I found it rather difficult to follow the manuscript as a result. Clearer short explanations of terms (including a sense of how they were calculated/what they actually mean) and consistent use of terms would make the manuscript much more understandable.

Next, there are extra bits of information - for example, much of a paragraph in the introduction devoted to dispersal, many references to land-use (though I did not see that land-use was considered in this manuscript), and overly long discussion paragraphs that are highly speculative (or at least lack citations).

Finally, while I am a big proponent of identifying macro-ecological patterns, I would appreciate a critical discussion of what assumptions are made (i.e., everything about a microclimate; no consideration of extremes, etc.) and what the approach may be missing. This is not to invalidate the work, but to give the reader greater confidence that such broad-scale analyses are informative and are not glossing over too much species-specific ecology or finer spatial or temporal scale heterogeneity.

In summary, I think the paper has great potential but would benefit from a very conscious and careful revision.

Reviewer #2 (Public review):

Summary:

In a series of experiments, the authors addressed the effect of the number of CS1-CS2 pairings, their order, and the time between their pairings and the second phase (CS2-US) in a sensory-preconditioning procedure. They hypothesized that:

(1) A higher number of CS1-CS2 pairings would favor the integration of CS1-CS2 and CS2-US associations via chaining at Test of CS1, as the CS2 develops a stronger prediction of the absence of CS1 in Phase 1, thus preventing the formation of a CS1-US association in Phase 2 and a chaining mechanism at Test of CS1 (CS1-->CS2-->US). In contrast, when the order is reversed (CS2-->CS1), a higher number of pairings should not change the mechanism of integration, as CS2 becomes a predictor of the presence of CS1. This would preserve the retrieval of CS1 and the formation of a CS1-US association in Phase 2 (mediated learning), while weakening the CS1-->CS2 link required for chaining at Test.

(2) A longer interval between the two phases of sensory-preconditioning would disrupt the retrieval of the order of the CS1-->CS2 association. Consequently, CS2 is no longer a good predictor of the absence of CS1, and the latter gets associated with the US in Phase 2, thus favoring a chaining mechanism at Test of CS1.

(3) A reminder of the CS1-CS2 order after a long interval between the two phases of sensory-preconditioning should restore the retrieval of the order of the CS1-->CS2 association, thus reversing the effect of time, and favoring a chaining strategy at Test of CS1.

To distinguish the two mechanisms, the authors blocked NMDA receptors in the PRh with DAPV before Stage 2. If integration occurs through a mediated CS1-US association formed in Stage 2, DAPV should reduce freezing to CS1 at test; if it occurs through chaining at test, DAPV should have no effect.

Strengths:

The strategies developed by the authors to address this issue are elegant and well controlled. While the sensory-preconditioning procedure has been extensively investigated, the mechanisms underlying the integration of separately acquired associations remain unclear. This study determines - in a convincing manner - the conditions under which a conditioned response is expressed via a chaining or mediated learning mechanism.

Weaknesses:

The role of NMDA antagonism is somewhat ambiguous throughout the manuscript. Does it prevent retrieval or acquisition or both?

(The Introduction (lines 98 to 101) refers to retrieval, while the Results (lines 128 to 132) and Discussion (lines 329 to 331) refer to acquisition.

Reviewer #3 (Public review):

Summary:

The primary objective of this study was to establish a practical and functional framework for propagation of stable transgenic cell lines of Blastocystis, a common animal gut microeukaryote. Although the work focused on Blastocystis ST7-B, a subtype with relatively low prevalence in humans, this choice is justified by its association with more frequent negative health effects. Beyond their relevance to the medical field, the methodological advances described here have the potential to also expand cell biology studies of this anaerobic organism, including its unusual mitochondria and redox metabolism.

Strengths:

Prior to this work, genetic tools for Blastocystis were very limited, relying on a single strong promoter-terminator combination. The authors successfully expanded the available promoter set across a range of expression strengths by testing two dozen variants in luciferase-based assays. Critically, they developed an integrated workflow from a modular transgenic construct design to an expanded inventory of molecular components (promoters, reporters), optimized DNA delivery, stepwise antibiotic resistance-mediated clonal selection and propagation, and to reporter validation. The evaluation of several anaerobiosis-compatible labeling strategies for live (and fixed) cell optical imaging will be particularly useful, with the SNAP-tag system appearing especially promising for Blastocystis.

Reviewer #2 (Public review):

Summary:

The authors aim to determine whether mitochondrial genotype influence telomere length. By generating cybrids harboring different mitochondrial backgrounds, the authors seek to establish a mechanistic link between mitochondrial status and telomere biology.

Strengths:

A major strength of the study is the use of cybrid technology, which provides a great approach to investigate the role of mitochondrial DNA independently of the nuclear genome. The authors also employ multiple complementary assays to assess telomere-related phenotypes associated with mitochondrial dysfunction. Together, these experiments generate an interesting dataset that will be of value to researchers interested in the intersection between mitochondrial biology, genome stability, aging and development. These results also build on previous work supporting roles for ROS/mitochondria in driving telomere shortening.

Overall, this study provides interesting evidence linking mitochondrial background to telomere biology. The cybrid models represent a useful resource for the field and the work raises important questions regarding mitochondria-telomere communication.

Comments on revised version.

In my view, authors have answered satisfactorily my concerns.

Reviewer #2 (Public review):

Summary:

The authors investigate whether inhibition of the WNK-SPAK/OSR1 pathway using the allosteric inhibitor WNK463 improves neuronal chloride homeostasis and suppresses epileptiform activity in organotypic hippocampal slice cultures. Using SuperClomeleon imaging combined with extracellular field recordings, they demonstrate that WNK463 accelerates recovery of intracellular chloride following chloride loading, reduces interictal chloride accumulation, and progressively suppresses recurrent ictal-like discharges. Pharmacological inhibition and siRNA-mediated knockdown of NKCC1 and KCC2 are then used to investigate the contribution of these transporters to the anti-ictal effects of WNK463.

Strengths:

The study addresses an important question in the field of chloride homeostasis and epilepsy and combines complementary experimental approaches. In particular, the distinction between baseline chloride measured in the presence of TTX and activity-dependent interictal chloride accumulation provides a useful conceptual framework for interpreting previous studies of WNK-SPAK inhibition. The imaging, electrophysiological, and pharmacological data are internally consistent and support the conclusion that WNK463 alters chloride dynamics and substantially suppresses ictal-like activity in this model.

Weaknesses:

The revised manuscript addresses several concerns raised in my initial review. The authors have broadened the discussion of KCC2 dysfunction and added further context on therapeutic targeting, as well as a useful discussion of the limitations of the organotypic hippocampal slice model.

My main remaining concern is the mechanistic interpretation of the data. The authors have appropriately softened several statements and now acknowledge that membrane potential and the GABA reversal potential were not directly measured. However, strong mechanistic conclusions remain in the revised manuscript, including statements that the anti-ictal effects of WNK463 are "primarily mediated" by reduced NKCC1 and enhanced KCC2 transport activity and are "mainly due to" NKCC1 inhibition and KCC2 activation. These conclusions go beyond what is directly demonstrated, as WNK463-induced regulation of NKCC1 and KCC2 was not directly measured in this preparation. Lee et al. (2022) provides important support for this mechanism, demonstrating reduced KCC2-T1007 phosphorylation and enhanced KCC2-dependent chloride extrusion following WNK463 treatment. However, these findings were obtained using different experimental preparations, including cultured cortical neurons and an acute 4-AP seizure model. The current experiments therefore support a contribution of NKCC1 and KCC2, but do not directly establish coordinated regulation of these transporters as the primary mechanism underlying seizure suppression. This is also relevant to the simultaneous NKCC1/KCC2 inhibition experiments, where the overall anti-ictal effect of WNK463 is reduced but not completely abolished.

The statistical reporting has also been improved, but the experimental unit remains unclear for analyses involving multiple cells recorded from individual slices. The authors clarify the use of repeated-measures analyses, but it remains unclear whether individual cells or slices were treated as independent observations and how multiple cells from the same slice were accounted for statistically.

Reviewer #2 (Public review):

In this manuscript, the authors investigate how the oncogenic fusion protein NUP98-KDM5A alters gene expression in leukemia, using a combination of cellular experiments with model and patient cell lines, as well as in vitro studies. Upon transfection of U2OS cells with mEGFP-tagged NUP98-KDM5A, the authors show that the fusion proteins form sub-micrometer puncta, whereas KDM5A alone does not. These foci are also observed at expected native expression levels (using OpenCell data). The tag has an effect here, as switching to an mCherry tag raises the apparent saturation concentration for phase separation. Finally, the authors show via super-resolution imaging that the foci correlate with H3K4me3 distribution.

In vitro, the fusion protein forms amorphous, gel-like condensates at double-digit nanomolar concentrations. Truncation analysis identifies PHD3 of KDM5A as required for maximal phase separation, consistent with the ability of the protein to bind H3K4me3 peptides. Addition of polynucleosomes increases the amount of fusion protein partitioning into the condensate in an H3K4me3-binding-dependent manner. Condensates are gel-like with slow internal dynamics in vitro; in cells, however, the dynamics depend on the position of the EGFP tag (no corresponding experiments with mCherry are shown). Reconstitution with H3K4me3- and H3K4me0-modified arrays shows colocalization with both wild-type NUP98-KDM5A and the binding mutant. Here, H3K4me3 arrays recruit ~20% more protein and yield gel-like structures in a manner dependent on the PTM and on the PHD finger.

In cells, the fusion protein colocalizes with H3K4me3-marked loci, including the HOX clusters, as confirmed by FISH. Finally, re-analysis of published expression datasets from patient cells shows that genes are predominantly upregulated and that the upregulated genes are H3K4me3-marked.

This is a well-executed mechanistic study. The data convincingly establish that NUP98-KDM5A forms sub-micrometer foci at realistic expression levels, that these foci correlate with H3K4me3-marked sites, that the PHD3-H3K4me3 interaction mediates chromatin binding while the NUP98 moiety drives phase separation in vitro, that foci in cells overlap genes heavily decorated with H3K4me3, and that H3K4me3-marked genes are those found to be upregulated in patient datasets. These are important mechanistic findings and of interest to the community.

Reviewer #2 (Public review):

Summary:

The authors set out to examine how psilocybin acutely alters learning in rats in a reward-based probabilistic choice task. Additionally, they asked how psilocybin acutely alters encoding of decision variables in the prefrontal cortex (PFC).

Strengths:

The behavioral paradigm is robust. Thorough and rigorous computational analyses suggest that psilocybin may differentially modulate the impact of rewarded and unrewarded outcomes and enhance exploration under uncertainty. Single-unit neural activity in PFC suggests a potential mechanism for these effects, with psilocybin inducing opposing effects on encoding of rewarded and unrewarded actions. These findings are potentially exciting and important.

Weaknesses:

The major weakness of this work is the lack of a saline-treated control. All conclusions rest on analyses that are limited to within-animal comparisons between a baseline session and a later session in which rats were injected with psilocybin prior to session start. There is no control for the injection stress or passage of time. The manuscript mentions that 5 of 8 mice were also tested in a session following a saline injection, but because data were lost for some animals, this was not pursued. Figure S1G reports very limited analysis of this data to suggest that there is no effect. At a minimum, this must be analyzed in full (behavioral and neural data) and included in the manuscript to support the central claim that the effects reported are indeed attributable to psilocybin.

Beyond this, it would be of interest to examine if the effect of psilocybin is restricted to the acute session or if any effects persist.

Reviewer #2 (Public review):

The authors track structural role representations during stimulus processing, which is relevant to study how prior experience shapes new learning. They employ a sequence learning task based on graph factorization and record brain activity using MEG to investigate whether graph factors are reused during learning and inference in a new graph. MEG data were recorded during passive stimulus presentation, while behavior is assessed in a subset of probe trials testing either experienced or inferred successions in the graph. The authors report neural similarity among stimuli conditional on their role in subprocesses, and a correlation between evidence for role representations and performance in experience probes, but not in inference probes.

The interpretation of "knowledge transfer" in the title and abstract is, however, constrained by the absence of a corresponding relationship with performance in the inference probes, which provide the behavioral measure of transfer. While the authors discuss why role representations might relate more closely to experience than inference probes in this task, this distinction makes the link between the neural findings and transfer weaker than the current framing suggests. (E.g., "was positively related to behavioral success in subprocess knowledge transfer" in the abstract could be misleading)

More broadly, the framing of the results as a mechanism for structure transfer or structural scaffolding appears stronger than warranted by the evidence. The findings indicate representations consistent with reusable roles, but do not establish that these representations functionally facilitate learning and transfer. The claims should align with this distinction between evidence for representational reuse and structural scaffolding.

Also, apart from abstract roles, simpler categorical grouping of different stimuli belonging to the same subprocess could produce increased similarity.

Relatedly, although the MEG-behavior (experience probes) correlation is significant for Component 6 but not Component 4, the difference between the two correlations is not significant. Interpreting the correlation as selective to the structurally relevant graph is thus difficult to statistically substantiate.

Overall, the study provides a well-designed paradigm for investigating compositional representations during structural generalization at high temporal resolution. However, the findings do not provide sufficient evidence to frame them as a mechanism for structure transfer.

Reviewer #2 (Public review):

The revised manuscript has improved substantially. The authors have addressed the main concerns raised during the previous round by introducing an IVT-based calibration framework, evaluating background calls across probability thresholds, examining basecalling mismatches, and replacing "stoichiometry" with the more appropriate term "predicted modification proportion." The expanded discussion of non-DRACH predictions is also useful and appropriately balanced. Overall, the study now provides a valuable practical framework for Dorado-based RNA modification analysis.

Reviewer #2 (Public review):

Ding et al. examine the role of TIE1 in cardiac chamber morphogenesis using genetic mouse models targeting Tie1, Tek, or both, and analyzing endocardial cell-mediated chamber formation across multiple embryonic developmental and postnatal stages, supported by analysis of published single cell datasets and new bulk RNA seq analyses of murine cardiac tissue. The authors find that Tie1 and Tek expression is higher in atrial than ventricular endocardial cells. Notably, endothelial TIE1 is required for atrial trabeculation at E12.5, but is less critical in ventricular trabeculation. TIE1 also acts synergistically with TIE2 during atrial trabeculation. While Tie1 deficiency alone does not cause defects at E10.5, combined heterozygous deletion of Tek disrupts both atrial and ventricular development at E10.5. This synergy is further supported by analyses at later embryonic stages and in postnatal hearts.

Strengths:

The study is well designed, clearly written, and supported by high-quality figures. The performed experiments demonstrate a previously unrecognized role for TIE1 in cardiac development and identify synergistic control of cardiac morphogenesis by TIE1 and TIE2. This synergy is consistent with the previously identified roles of TIE1 and TIE2 in venous development and with TIE1 involvement in angiopoietin-dependent postnatal vascular and lymphatic remodeling. Together, these findings support a role for TIE1 as a contributor to ANGPT1-TIE2 signaling during heart development.

Weaknesses:

The manuscript does not include direct mechanistic studies; however, RNA seq analysis of atria and ventricles showed reduced expression of Tek, Dll1, and Notch1 upon Tie1 deficiency in developing hearts. Although no direct mechanistic experiments are performed, potential mechanisms based on previously published work such as TIE1-TIE2 heterodimer formation, effects on endothelial junctions, migration, survival and venous development are discussed in the context of TIE1-TIE2 synergy.

Reviewer #2 (Public review):

Summary:

This manuscript examined the impact of sulcal morphology on reading development. A very specific feature on the ventral surface of the brain was identified, namely the presence of an interruption in the posterior portion of the left occipitotemporal sulcus (pOTS). Compared to children with a continuous pOTS, children with an interruption at age 5 years had better reading ability at age 8. This was a large effect measured in 43 children. Surprisingly, this morphological feature was a better predictor of reading ability than measures of pre-literacy cognitive skills, such as phonological awareness. The effect was tested and reproduced across several different measures of reading ability. The authors hypothesised that the mechanism underlying this benefit related to greater local connectivity, which confers a computational advantage. This was demonstrated using analysis of diffusion-weighted imaging data in 29 of the children obtained at age 8.

Strengths:

The novelty of the manuscript is threefold: (i) the measure was made in children who were pre-literate (previous work was in older children and adults); (ii) longitudinal brain imaging and behavioural data were analysed; and (iii) diffusion data were analysed to test a hypothesis about the underlying mechanism.

The manuscript is exceptionally well written. The methods are detailed and easily reproduced. The approach is thoughtful and meticulous. All possible alternatives appear to have been considered. Where possible, further analyses have been done to address these alternatives. For example, the testing of the specificity of the sulcal interruption to left pOTS was an important addition. None predicted reading skills.

Reviewer #2 (Public review):

Summary:

This manuscript challenges the long-standing assumption that Yoda1 and Yoda2 are PIEZO1-selective activators. Using patch-clamp electrophysiology and calcium imaging in HEK293TΔPZ1 cells overexpressing PIEZO2, the authors demonstrate that Yoda1 potentiates PIEZO2 stretch-activated currents to a similar extent as PIEZO1 and slows PIEZO2 poking-current inactivation (albeit with lower efficacy). They further show that the more potent analog Yoda2 affects PIEZO2 at nanomolar concentrations and use mutagenesis and molecular dynamics simulations to propose that Yoda2's benzoic acid group forms a transient salt bridge with R1724 in the putative Yoda binding pocket, explaining its enhanced potency.

Strengths:

The authors are established Piezo/biophysics experts; the study is highly important, technically competent, and carries significant implications for the reinterpretation of prior work that used Yoda compounds as PIEZO1-selective probes.

The core finding that Yoda1 modulates PIEZO2 stretch currents is convincing and important.

Reviewer #2 (Public review):

Summary:

This is a very interesting paper bringing new and important information about the poorly understood rhodopsin 7 photoreceptive molecule. The very ancient origin of the gene is revealed in addition to data supporting a signaling pathway that is different from the one known for the canonical rhodopsins. Precise expression data, particularly in the optic lobe of the fly, as well as clear behavioral phenotypes in responses to light changes, make this study a strong contribution to the understanding of the still-debated function of rhodopsin 7.

Specific comments

(1) Title and abstract: Contribution of Rh7 to circadian clock regulation

a) It is not that clear to me what rhodopsin does in terms of circadian regulation (even though its function might be circadianly regulated). The clear role in the light/dark distribution of activity might not be circadian per se, but mostly light/dark- driven and there is no evidence here for a role in the entrainment of the clock.

b) The authors should cite Lazopulo, which nicely shows that Rh7 has an important role in peripheral neurons to allow flies to escape from blue light (see below).

(2) Figure 2 C

The finding showing that Galphaz but not Galphaq can trigger signaling from light-excited Rh7 is a very intriguing finding to better understand Rh7 function. Since Galphaz is related to Gi/o, it would be interesting to test those, for example, by expressing RNAi with Rh7-gal4 and testing the Light-dark or light-off response behavior.

(3) Figures 3-4

The change in the locomotor activity distribution between light and dark in LD conditions provides a nice assay for Rh7 function. Since Lazopulo et al. (2019) have shown that wild-type but not Rh7 mutants do escape from blue light, it would be important to compare and discuss these LD behavior data with the Lazopulo results. Precisely, is this nighttime preference linked to blue light?

The expression data are really nice and show that Rh7 is mostly a non-retinal photoreceptor. However, the paper would be strongly reinforced by correlating this with the LD behavior. The LD phenotype should be tested in flies with Rh7 expression rescued under Rh7gal4 control (as done for the startle response). This is important to show whether the expression pattern is likely responsible for the described Rh7 function in LD. If L5 and or M11 drivers are available, they should be used to rescue Rh7? Since expression in some clock neurons is shown, the rescue experiment should also be done with a clock neuron driver.

In the same line, can the LD phenotype (or startle response phenotype of Figure 4) be restored by expressing Rh7 under ppk control, as shown for the blue light avoidance phenotype by Lazopulo et al?

Finally, the Rh7 "darkfly" rescued flies should be tested in LD.

Reviewer #2 (Public review):

The authors aimed to understand how the transcription factor Erbb4 contributes local circuit dynamics and affects performance. This is an interesting question because Erbb4 is largely localized to PV cells in the olfactory bulb (OB) and upregulated following exposure to an olfactory discrimination task.

Strengths:

They have now validated their local, PV-specific knockout strategy, which more directly tests their hypothesis.

The inclusion of optogenetically evoked presynaptic responses from PV cells onto M/T cells also strengthened the conclusion that this synapse was altered.

The authors also now included slice recordings from PV cells, which helps to form a more comprehensive picture of the circuit.

Weaknesses:

(1) The data used to support their spike sorting approach is now included but is difficult to evaluate. In Figure 4 - supp 1B, they show what appears to be mean + sem of concatenated waveforms from one tetrode, but this is not explained anywhere. Moreover, the timescale seems to be off by an order of magnitude. It also seems that the spike widths in panel E are bimodally distributed, with many of the half-widths being more consistent with fast-spiking cells. There is a high density of PV cells in the same layer where they record M/T cells, so is it not possible that some of the units were PV cells (note: this was suggested in the previous review as a potential exciting direction to further validate their findings)? It is still unclear how the authors confidently record from only M/T cells when there is no mention of how other units (e.g., fast-spiking cells) are excluded. The methods state that only M/T cells can be recorded from this layer, which is an oversimplification and appears at odds with the apparent diversity in waveforms and responses.

(2) Upon review, the authors acknowledge that the PV-Erbb4+/+ and PV-Erbb4-/- mice were recorded in separate batches where the control mice were exposed to unwanted artefacts related to a solenoid valve. While we don't know how much that change influenced the results, even with careful analysis, it does draw attention to a major weakness in study design where controls and experimental mice are not run simultaneously. The presence of an artefact from the solenoid valve (perhaps a noise?) immediately before odor presentation for controls, but not experimental mice, should be considered by the reader when it comes to evaluating the odor-evoked responses.

Overall, I feel the manuscript is much improved and more solidly demonstrates that Erbb4 expression in PV cells plays a major role in regulating local OB circuit dynamics and influences odor processing. The remaining weaknesses surrounding the in vivo single unit recordings from PV-Erbb4+/+ versus PV-Erbb4-/- mice reduce my confidence in the validity of the results, but the general agreement of the in vitro work is more re-assuring.

Reviewer #2 (Public review):

Summary:

This study by Suzuki et al. reports an interesting stereo-selective role of D-serine in regulating one-carbon metabolism during neurodevelopment to adapt the functional transition, probably through the competition with mitochondrial transport of L-serine. The authors provide a multi-layered set of evidence, including metabolomics, enzyme assays, mitochondrial transport competition and functional assays in immature/neural progenitor cells, to build up a conceptual integration of D-serine as both a neurotransmitter and a metabolic regulator in central neural system, which raises a broad potential interest to the neuroscience and metabolism communities.

Strengths:

This work provides a conceptual advance that D-serine is not only serves as a traditional neurotransmitter in central neural system but also critically contributes to metabolic regulation of neural cells. The authors performed solid metabolomic assays to validate the suppressive effect of D-serine on one-carbon metabolic pathway, providing some evidence that D-serine competitively inhibits mitochondrial serine transport, but not directly impairs SHMT2 enzymatic activity. All these data indicate a critical role of D-serine synthesis during neural maturation and suggest a potential translational strategy for targeting serine metabolism in neural tumors.

Comments on previous version.

My previous concerns have been appropriately addressed or discussed in this revised version of manuscript. I have to say that, at this stage, I have no further questions.

Reviewer #2 (Public review):

Summary:

The authors study cardiac deceleration during threat responses in Drosophila. Particularly, it focuses on identifying the neuronal control of this deceleration. Using behavioral and cardiac tracking and analysis, genetics and calcium imaging, they identify two pairs of dopaminergic neurons involved in cardiac deceleration during air puff responses.

Strengths:

The study is overall well done and the paper clearly written. Particularly the work on identifying the two pairs of dopaminergic neurons involved in cardiac deceleration using a series of drivers and generating new ones is rigorous and extensive. Finally, the authors manipulate heartbeat to investigate how it influences threat responses. Although the evidence that heartbeat deceleration-induced increase in locomotion remains partial, the study reveals important insight in the relationship between locomotion, cardiac activity and neuromodulatory system during threat responses.

Reviewer #2 (Public Review):

The authors develop an experimental framework to precisely control temperature in an arena and make quantitative measurements of thermal avoidance behavior in Drosophila. They find that wild-type animals show robust heat avoidance behavior and suggest that antennal ablation reduces this heat avoidance. They also make a novel claim that antennal inputs do more than simple heat detection but play a role in navigation strategies like centrophobism and are needed for decision-making at thermal boundaries. The fundamental two-choice behavior paradigm or the use of peltier modules for temperature control have been employed previously in the field. The strength of this work lies in detailed behavioral measurements. However, the study falls short due to a lack of adequate experimental controls. The authors then model this behavior as a classical Braitenberg vehicle of sensory-motor transformation. Overall, the paper has some interesting insights into in-depth analysis of Drosophila navigation in thermal gradients, but their evidence only partially supports their claims.

Reviewer #3 (Public review):

Summary:

Ma et al. use human-chimpanzee tetraploid cells to examine species differences in DNA methylation. They identify differentially methylated regions under cis or trans regulation. Cis-DMRs are enriched near SNVs that disrupt or create CpGs, providing a plausible mechanism for cis changes in methylation. They also seek to identify transcription factors that might affect methylation in trans, as well as gene sets with evidence for consistent changes in methylation and expression between humans and chimpanzees, suggesting that they may underlie lineage-specific traits.

Strengths:

The authors have generated a new dataset across multiple different cell types examining differences in DNA methylation between humans and chimpanzees using human diploid cells, chimpanzee diploid cells, and human-chimpanzee tetraploid cells. Using this dataset, they identify that cis-DMRs are enriched near SNVs that disrupt or create CpGs compared to trans-DMRs and identify transcription factors as candidate trans-acting factors. Both identified SNVs and transcription factors are good candidates for future experimentation. The authors also find that cis-DMRs are more highly correlated with cis-expressed genes than trans-DMRs with trans-expressed genes, providing evidence that methylation and expression are linked genome-wide. Further, they apply a clever sign test approach to identify gene pathways with evidence for lineage-specific selection.

Comments on revised version.

They authors have addressed my concerns from their initial manuscript. In particular, the authors have noted the limitations of their study as appropriate, including the use of permissive FDR cut-offs.

Reviewer #2 (Public review):

Summary:

The revised manuscript by Nohara et al. presents a novel 3D assay that allows for stratification of epithelia, active EMT through small pores, and active MET. They show that 3um pores allow for keratinocytes to sample the pore through filopodia and up regulate EMT genes to transverse the pores to the other side of the membrane where EMT genes are down regulated as the cells re-establish stratified epithelia. The TGFbeta pathway and actin polymerization promotes movement of cells into the pores and Piezo1 and KRT6 actively block this movement. This work provides a novel 3D assay that is likely to become a benchmark to analyze these processes using a more complex system than other current culture-specific EMT and MET assays.

Strengths:

The strengths of the manuscript include the foundational analysis of the pathways involved in establishing the 3-compartment epithelium. The authors have incorporated live imaging, drug studies, KO analysis, and RNA-sequencing to show the relevant pathways involved. The authors were responsive to the original round of reviews and present a much-improved study with greater details on molecular markers, quantification, and clarity.

Reviewer #2 (Public review):

This study from de Boer, Lamme, Verdwaald and Schafer describes the de novo AI-guided design of miniproteins that target the chemokine CCL25, with the aim to modulate the activation and signalling of the chemokine receptors CCR9 and ACKR4. The study focuses on characterising four miniproteins that all bind CCL25 with good affinity. Three designs appear to prevent CCL25 binding to both CCR9 and ACKR4, with increasing concentrations of miniproteins resulting in decreased arrestin (both receptors) and mini G protein recruitment (CCR9), less inhibition of forskolin-stimulated cAMP (CCR9), and decreased GRK3 recruitment and receptor internalisation (CCR9). One miniprotein, VUP25111, changes the properties of CCL25 rather than preventing ligand/receptor interactions, resulting in greater selectivity for CCR9 over ACKR4 and a G protein-biased signalling profile (maintenance of mini G protein recruitment, GRK3 recruitment, inhibition of cAMP and receptor internalisation, but loss of arrestin recruitment). VUP25111 also maintained chemotactic migration in MOLT-4 T lymphoblast cells, whereas this response was lost in the presence of the other three miniproteins.

Overall, this is a very interesting application of AI-designed de novo miniproteins to modulate GPCR responses by directly binding the ligand rather than the receptor. This is a conceptually very intriguing approach that could, in principle, be extended to other GPCR systems beyond the chemokine family. The authors deploy an impressive array of assays spanning multiple signalling endpoints, providing a thorough picture of how each miniprotein influences receptor activation and downstream signalling. The presentation of concentration-response relationships for CCL25 alone and in the presence of each miniprotein is particularly informative, and the figures are very well constructed throughout. The inclusion of clear cartoons illustrating the basis of each assay is a nice touch that will help readers from outside the immediate field follow the logic of each experiment.

Reviewer #2 (Public review):

Summary:

In this manuscript, Hossaim and colleagues investigate the role of the ITK kinase in modulating inflammation in a pristane-induced lung hemorrhage model. Using a germline ITK KO mouse, they report that loss of ITK skews the T cell compartment toward a memory-like state, expanding Tregs, and conferring protection against alveolar hemorrhage, inflammatory monocyte recruitment, proteinuria, and systemic cytokine elevation. They further show that transfer of ITK-deficient Tregs into wild-type hosts with established disease attenuates injury and shifts the cytokine balance toward resolution, and that ITK-deficient Tregs carry a transcriptional signature enriched for OXPHOS, mTORC1, MYC, and cell-cycle programs. While these observations are interesting for the development of potential immunotherapies, there are several issues with the methodological approach that support the authors' claims, tempering my enthusiasm for this manuscript.

Strengths:

(1) The clinical motivation and potential targeted therapies are relevant.

(2) The murine phenotype seems robust.

Comments on revised version.

The authors have addressed my comments appropriately.

GroupID: 63

DiseaseAssertion: VHL

GroupSize: 3

GroupNumberMales: N/A

GroupNumberFemales: N/A

GroupEthnicity: Japanese

GroupAgeRange: N/A

GroupInfo: 3 individuals from a single family (ID 63).

GroupPresentingHPOs: HP:0001732; HP:0005584; HP:0010797 (Abnormality of the pancreas, Renal cell carcinoma, Hemangioblastoma)

GroupHPOFreeText: CNS hemangioblastoma (2/3), renal cell carcinoma (3/3) and pancreatic cyst or tumor (unspecified) (1/3).

GroupNotHPOs: HP:0009711 (Retinal capillary hemangioma)

GroupNotHPOFreeText: N/A

GroupPreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: N/A

Variant: p.S168Tfs*2 (c.503_513del)

LegacyVariant: c.716_726del (c.716del11bp)

CaseProblemVariantFreeText: N/A

ClinVarID: N/A

CAID: CA2573131812, https://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA2573131812

gnomAD: N/A

VariantEvidence: N/A

MutationType: deletion; minus_2_frameshift_variant

CivicName: S168Tfs*2 (c.503_513del)

MultipleGeneVariants: N/A

PMID: 16409463

Notes: several variants, currently curating c.823T>C (p.Cys275Arg) and family 11 proband is compound het with p.Pro1337Leu. Nucleotide position are not provided and the family is asserted as type B but no GPIb, RIPA or VWF platelet binding assay data are reported.

Case# RQMR10_V-2

Demographics: Female, 20 yrs at report,

CasePresentingHPOs HP:0000729, HP:0001763, HP:0001761, HP:0001956, HP:0000577

CaseNotHPOs

CasePreviousTesting

CaseMethod1

CaseMethod2

CaseGenotypingMethod

Variant NM_017890.4:c.6879delT(p.Phe2293Leufs*24)

ClinVar

CAID CA4824093

gnomAD 0.00003250 (South Asian)

VariantEvidence The proband is homozygous for the variant. This variant introduces a frameshift, resulting in premature protein truncation. The variant was found to cosegregate with disease in the family and was not found in 293 healthy ethnicity-matched controls. It is reported in a heterozygous state at a frequency of 0.00003250 in South Asians in gnomAD.

CaseAddInfo

CasePMIDs

Reviewer #2 (Public review):

Summary:

The authors seek to understand the hierarchical temporal representation of social communicative actions in the posterior superior temporal sulcus and other regions of the lateral pathway. They apply an analysis framework in which they predict fMRI responses from EEG responses to the same stimuli. (The EEG stimuli were shorter versions of the same clips shown as fMRI stimuli.) They examine which temporal segments of EEG data are most predictive of responses in which part of the brain. They find that early (< 100 ms post-stimulus) EEG activity is predictive of responses in early visual cortex, and that later (~ 150 ms post-stimulus) EEG activity is predictive of responses in the lateral visual stream. They then model the fMRI responses predicted by the EEG signals - which they term the "EEG-relevant" portion of the fMRI signal - as a function of various social and visual factors. They conclude that social interactions elicit responses in aSTS-SI later than low-level visual features elicit responses in early visual cortex, and they provide some evidence that mid-level social primitives elicit responses in mid-level areas somewhere in between.

Strengths:

I find the question and the framework interesting. The extraction of "EEG-relevant" fMRI signal as a target for visual and social feature-based encoding models has potential, though the approach in this instance suffers from low signal-to-noise. The evidence that the anterior portion of the superior temporal sulcus responds to communicative actions is clear and supports prior work in this regard.

Weaknesses:

I think there are several important points that the authors need to address to strengthen their case, and I think that many points could use more explicit description, explanation, and discussion.

Major concerns:

First, the basis of the EEG predictions of fMRI activity is not clear. The paradigm relies on predicting fMRI responses at each voxel from a weighted combination of EEG responses measured anywhere over the scalp. However, the authors never show any kind of weight map to visualize which electrodes on the scalp were weighted to predict responses in each fMRI voxel or ROI. I see that the use of PCA to orthogonalize the EEG responses creates problems with localizing the predictive signal in EEG to particular electrodes. Is the PCA step necessary? In any case, some form of analysis to estimate where in the brain the predictive EEG signal is coming from seems important. It's fairly clear that the best signal quality in the EEG recordings is in the early visual cortex. Thus, it could be the case that electrodes over EVC provide the main basis for predictions across the entire brain, with different temporal phases of EVC activity associated with activity in different regions in the fMRI data. It could also be the case (tho less likely in my opinion) that activity in regions away from EVC on the scalp provides the basis for predicting more anterior brain regions in the fMRI data. These two possibilities each suggest a substantially different interpretation of the present results: in the former case, there may be some sort of feedback loop between early visual areas and lateral areas; in the latter case, the hierarchical interpretation the authors have laid out seems more plausible. It seems important to draw a distinction between these possibilities.

In any case, the authors should say more in the discussion about the sources of EEG signals and fMRI signals and the relationship between the two.

On a similar note: the authors show that their EEG data was reliable based on a split-half analysis of the data. However, the authors only show average split-half correlation across electrodes and subjects. It stands to reason that some electrodes were much more reliable than others. The authors should show a map of electrode reliability across the scalp. One relevant question is: are there reliable electrode locations throughout the visual cortex? Or are the EEG signals only, or by far most reliable, in EVC? (How reliable is the EEG signal in regions that are at least somewhat near to the location of the lateral regions that is the principal object of investigation here?)

The use of predicted fMRI signals (predicted by EEG) as a target for image & social feature encoding models is only valid to the extent that the EEG signals accurately predict the fMRI signals. The prediction accuracy going from EEG to fMRI is not very high - a correlation of less than 0.2 in most regions besides EVC and the anterior extent of the activity along the ventral temporal lobe. In the best-predicted regions, the activity only reaches a correlation of 0.3. The majority of the signal (> 90% of the variance) is unmodeled in this analysis. Thus, I would hesitate to characterize the evidence for activation by particular stimulus features at particular times in this analysis as "strong". The authors themselves are somewhat ambivalent on this point, as well. While they write in the conclusion: "Finally, we find strong evidence that social interactions are computed via hierarchical computation of low- and mid-level social visual features in the lateral visual pathway." However, This statement conflicts somewhat with other statements the authors make in the discussion section: "Together, these results suggest that lateral visual regions may not be organized in a strict feedforward hierarchy as has previously been suggested" Later in the discussion there is more ambivalence about the hierarchical nature of the representation: "Our findings suggest that the processing of social action features from posterior to anterior brain regions does not adhere to a strict feedforward hierarchy." The authors should present a more consistent interpretation of their findings.

Finally, the low performance of most of the mid-level models in mid-level regions is a concern; it points to poor signal overall. The common threads in the models that do predict responses above chance seem to be space (spatial expanse and agent distance) and salience (to the extent that arousal may constitute salience). This raises a question: Were the social model features correlated with either of the low-level model features?

I think that the overriding issue here is poor signal quality in the EEG data. The estimate of repeatability from the split-half analysis is not high, even on average for the whole brain; it is likely to be very poor for areas outside EVC. I think this matters. An important null hypothesis to consider is that there may be a few fairly low-level, or at least simple and straightforward, elements of the response that are reflected in the noisy EEG signal. Chief among these is likely to be that the most salient or high-contrast retinotopic regions in the image, which both AlexNet and motion energy models would likely pick up on, are likely to elicit responses in EVC. This is likely why EVC is by far the best-predicted region, both based on the raw EEG data and in the two-step regression analysis. There could be elements of spatial priority at all levels of the visual system. This could be what the authors are observing: there is some relatively small (in terms of variance explained) component of the signal that is common to many regions, and which emerges somewhat later in later regions due to conduction delays.

I think an important exception to this signal problem is the finding of communicative actions eliciting responses in aSTS-SI. That seems very robust and clear.

Medium comments:

There should be a section in the methods on how the ROIs were defined. (There is a brief description in the results section, but this is not sufficient). The ROI outlines should also be displayed somewhere in the paper (not just circles showing the general region as in Figure 1). Figure 3 would be a nice place for them, as would Figure S3.

It's also worth noting in the methods and discussion that the use of template ROIs for hMT in particular is potentially sub-optimal. hMT lies in anatomically variable locations across subjects. One reason for the seemingly weak signal in hMT may be that the voxels being averaged do not fall within the true hMT for each subject.

In the results (section 3.2.1), the authors write, "We scored the regression by predicting the EEG-predicted fMRI activity (estimated fMRItest) to the held-out test videos and then correlating the actual fMRI response (fMRItest) in the test set to this prediction at each time point (estimated fMRItest)." Unless I am missing something, I think the authors can safely say that they predicted responses to the withheld stimuli without specifying the intended prediction target (whether it is the fMRI data or the estimated fMRI data), because the prediction is simply based on weights, labeled or estimated image features, and stimuli. So, the prediction might as well be a direct prediction of the fMRI test data. It seems to me that mentioning the estimated fMRI test in this sentence only muddies the waters.

In the two-stage regression analysis, were all the visual and social features fit to the EEG-predicted fMRI data in a single regression? Was this regression regularized? How many parameters total were fit? (This is clear for the AlexNet and motion energy models, but not clear for the other features - were they all single-feature models?)

Reviewer #2 (Public review):

Summary:

Drosophila haematopoiesis has been shown to be governed by a number of signalling pathways such as JAK/STAT and Dpp. This important study shows a role for nutrient sensing and autophagy in determining blood cell differentiation. The authors show that General control non-derepressible 5 (Gcn5), a histone acetyltransferase affects blood cell differentiation. Gcn5 also negatively regulates autophagy through its effector TFEB which directly regulates autophagy genes. The authors also show that mTORC1 modulates Gcn5 levels and through it TFEB activity thus acting as a fine-tuning mechanism which maintains optimal levels of autophagy.

Strengths:

The main strength of the work lies in the interesting finding that cellular metabolic processes such as autophagy has a direct role in blood cell differentiation and has the potential to be of interest to those working on vertebrate haematopoiesis as well. The report has generated intriguing data, using promoters specific for sub sections of the lymph gland, that different cellular subsets of the lymph gland contribute differently towards haematopoiesis, but this is not followed up in detail and the final conclusions are derived from a combination of whole lymph gland perturbations as well as those from specific promoters.

Weakness:

(1) Gc5 seems to be expressed throughout the lymph gland but modulating it in the subsections do not have the same result. It is very striking that the knockdown of Gcn5 in the prohemocyte population does not have an effect on differentiation whereas overexpression does. And the modulations of Gcn5 in PSC also has variable effects across hemocyte subpopulations which is not explored in the manuscript. Interestingly, also the domain deletion constructs show differential effect on blood cell differentiation when altered solely in the prohemocytes which is not explained. While Gcn5 can be seen in all sections of the lymph gland in the first figure, under the HHLT-Gal4 and Hml-Gal4, Gcn5 looks cytoplasmic and almost completely excluded from the nucleus strikingly unlike Gcn5 expression under the Collier-Gal4 and Dome-Gal4. The rest of the experiments in the manuscript are done with multiple promoters, with autophagy flux measured by modulating Gcn5 with a pan hemocyte promoter, but the mTORC1-Gcn5 axis is explored using chemical modulators which affect the whole of the lymph gland (Fig7) or using two pro-hemocyte promoters (Fig8).

(2) The knockdown of Gcn5 seems to affect the gland size (A compared to B and C). Since mTORC1 is a central regulator of cell size, it is possible that some of the effects seen in these knockdowns are potentially through mTORC1 affecting size suggesting that the signalling axis between mTORC1 and Gcn5 might not be a one-way axis as suggested in Figure 9. Also, this would mean that in experiments where absolute cell counts of crystal cells or niche cells are used to assess blood cell differentiation, further analysis to consider total cell numbers in the lymph gland would strengthen the manuscript.

(3) A genetic manipulation of mTORC1 specifically in the pro hemocytes would strengthen the role of mTORC1 in the pathway rather than the chemical modulation which affects the whole of the lymph gland.

Comments on the revised manuscript:

Overall, the revisions make the narrative more coherent and pertinent to the results presented. The authors have also added new data and the analysis supports their conclusions.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

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

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

Reviewer #3 (Public review):

The manuscript investigates the coupling of saccadic eye-movements (S) with directed tail flips (T), and the remarkable discovery is that tail flips that are preceded by a conjugate sacced (S-T) can be credibly classified as specific "volitional" turns that are distinguished from the standard tail movements that seem to be more of a spontaneous and "impulsive" nature.

They show that 'turning intent', as indicated by a small increase in S, is elevated by aversive odors, while 'gliding intent', as indicated by a decrease in S and an increase in undulation cycles, is elevated by appetitive odors.

This is a very important finding, which is backed up by a thorough behavioral analysis, and the identification of neural populations in the pallium and sub-pallium that clearly distinguish between these kinds of turns is very promising. Here they identify neuronal populations that are preferentially active during - and predictive of - coupled (S-T) versus isolated (T) tail flips.

Especially the fact that S-T turns (but not T turns) can be predicted already by pre-event, ramping, pallial activity is intriguing.

The authors then go on and demonstrate that the frequency of (S-T) turns is modulated in fish exposed to appetitive or aversive odors.<br /> Specifically, they quantify the aversiveness and appetitive-ness of several odors in a free swimming assay, they select a couple of these odors based on their valence and they demonstrate that these odors induce moderate modulation in the frequency of eye saccades (S) and tail flips (T) and (S-T) turns.

The study is rigorous and thorough and the findings are informative and novel.

In important controls they confirm that brain wide imaging can distinguish between appetitive and aversive context, and they show that pallial activation by aversive odors is consistent with neural activity in the rhombencephalon that correlates with turning activity, whereas sub-pallial activation by appetitive odors correlates with rhombencephalic activity related to gliding.

It's all very good.

Comments on revised version.

The authors have addressed all my concerns.

Reviewer #2 (Public review):

Summary:

This study presents a fundamental new finding - the identification of a sensory-neuron itch receptor, MRGPRX4, as an unexpected melanoma oncogene through a mechanism of lineage-inappropriate expression rather than mutation. The evidence supporting the core observation (tumor-specific upregulation, restriction to invasive transcriptional states, and sufficiency to drive fully penetrant metastatic melanoma in vivo) is compelling, drawing on convergent human genomic datasets and a well-controlled genetic mouse model. However, several of the mechanistic and translational claims - particularly regarding causal drivers of invasion, the immunosuppressive tumor microenvironment, and in vivo pharmacological efficacy - rely on correlative evidence.

Strengths:

The authors propose that MRGPRX4, normally restricted to a subset of peripheral sensory neurons, is aberrantly re-expressed in melanoma rather than through mutational mechanisms, and that this re-expression is sufficient to drive tumorigenesis through basal, ligand-independent GPCR signaling. This is a genuinely novel model for oncogenesis, and the manuscript deploys an impressive range of approaches - bulk and single-cell transcriptomics, spatial transcriptomics, proteomics, phosphoproteomics, and pharmacology - to support it.

The claim that MRGPRX4 is selectively upregulated in melanoma and confined to neural-crest-like/invasive transcriptional states is well supported, with consistent results across multiple independent human scRNA-seq datasets. The claim that ectopic MRGPRX4 is sufficient to drive melanoma is convincingly demonstrated by the fully penetrant, metastatic phenotype in the Tyr-CreER;MRGPRX4-LSL model, with appropriate specificity controls showing that MRGPRX1, MRGPRX2, and MRGPRX3 do not phenocopy this effect.

The claim that MRGPRX4 signals through basal, ligand-independent activity is reasonably well supported by bile-acid quantification showing endogenous ligand concentrations well below the EC50 required for activation.

Overall assessment:

The manuscript's central, most novel claim - that lineage-inappropriate expression of a sensory GPCR is sufficient to drive melanoma - is compellingly supported. The secondary mechanistic and translational claims built around this finding are convincing and consistent with the broader literature but are currently supported by correlative rather than causal or functional evidence.

Reviewer #2 (Public review):

Summary:

In this manuscript Zhu, Emanuelli and colleagues describe a novel pharmacological activator of the Integrated Stress Response kinase GCN2. The work is conclusive and biochemically solid. This work significantly adds to the pharmacological arsenal targeting the ISR and in particular GCN2.

Strengths:

Strong biochemistry, novel molecular activator of GCN2 (GCN1 independent).

Weaknesses:

Rationale for the screen not exploited in the results (e.g. pathogenic GCN2 mutants), lots of cell-based read-outs not endogenous.

Comments on revised version.

The authors did a great job at addressing my initial critique on their manuscript and consequently I have no further comment.

Reviewer #2 (Public review):

Summary:

This manuscript, by Kang and colleagues, examines the activity of the Gasdermin (GSDM) family of proteins in epidermal skin cells called keratinocytes. Gasdermins are a relatively recently discovered family of pore-forming proteins that are generally studied for their role in inflammasome activation and pyroptosis. The role and functions of GSDMs in keratinocytes are not fully understood, but programmed cell death is important to keratinocytes as part of the cornification process. Keratinocyte pyroptosis associated with GSDMD and GSDME has been linked to the pathogenesis of psoriasis, and GSDMA-mediated pyroptosis in keratinocytes is responsive to Staphylococcus aureus infection. These data suggest that defining the role of GSDM in keratinocytes could provide valuable insight into the treatment of skin pathologies, particularly those involving inflammation.

Strengths:

In this study, the authors use two human keratinocyte cell lines to focus primarily on the biology of GSDM in keratinocytes, particularly A, B and C, both during homeostatic conditions and in response to some pathogenic stimuli. The data indicate that the full-length GSDMA and B proteins are colocalizing not to the plasma membrane, but to the cytoskeletal, cytoplasmic or membrane fractions, in direct contrast to a number of other studies. Further, there are data showing the GSDM are not cleaved by the same proteases as in other studies, and that GSDMA is not involved control of Vaccinia virus skin infection. The controls for cleavage done in HeLa cells with a number of inhibitors were particularly strong, and the overall finding is interesting and potentially important, providing novel insight in the biology of GSDM proteins in keratinocytes. The second half of the paper focuses on treatment with calyculin A, a protein phosphatase inhibitor that the authors report recapitulates acantholysis, i.e. the loss of cell-cell junctions between keratinocytes. Calyculin A altered the localization of GSDM but increased cell death and keratin release irrespective of GSDM activity, suggesting that acantholytic cell death does not involve GSDM. Overall, these data are an interesting study showing differences in GSDM biology in keratinocytes and indicating that GSDM are not involved in a number of pathogenic processes associated with skin diseases.

Weaknesses:

Unfortunately, the data supporting these findings are not particularly strong and are somewhat confusing, and the study is not well organized. One of the major weaknesses is that most work was done in two human keratinocyte cell lines, and subcellular localization subcellular localization results were not consistent between the two lines. This is not hugely surprising, as cell lines even from the same cell type are very different, and the authors note that there may be some variability associated with the model used. However, the inconsistency between the two lines, alongside the lack of comparison to primary cells or patient data, makes it very difficult to determine which, if any, result accurately recapitulates the biology in human cells. The authors do suggest that the N-TERT line is more similar to primary cells but rely on a single study and no other research or data to support this. Several other experiments compare the keratinocyte lines to a colorectal adenocarcinoma cell line, showing the keratinocytes are also different from these cells, but it is not clear that they support the information about GSDM in either keratinocyte line other than showing that keratinocytes are distinct from colorectal cells.

There are several specific concerns regarding the data, particularly the western blotting. In a number of the blots, there are bands which are not mentioned. For example, in Figure 3A, "GSDMA and GSDMB translocated from the cytoskeleton equally to both the cytoplasmic and membrane fractions upon calyculin A treatment". But in the western blot shown, GSDMA moves from being entirely in the cytoskeleton to having lighter bands in the cytoskeleton and chromatin-bound regions and heavier bands in the membrane and cytoplasm. GSDMB shows heavier bands in the membrane and cytoplasm after Calyculin A, but the heaviest band remains in the chromatin-bound region. In addition, the western blots have no associated N or pooled data graph with statistics, so it is not clear how many studies it represents or if there were significant changes. A lack of statistical analysis was also seen in the Yoyo-1, membrane ballooning and cell death analyses in 3A, 3C, 4B and 4C, with the membrane ballooning finding being entirely qualitative and not using any image analysis - comparison of P2A or cell area could resolve this. The methods do note that unpaired T-tests and 2-way ANOVAs were performed in some places, so the results of these should be clearly shown on the graphs. Beyond this, all of the localization studies rely on a single modality, western blotting. This finding should be supported by orthogonal approaches, ideally immunofluorescence. Finally, the presence of GAPDH in the supernatant (5A) raises an important issue regarding nonspecific intracellular release. If the GAPDH is present in the sup that is representative of cell damage and does not correlate with the absence of other intracellular markers - such as those found in lysates - in the supernatant.

The final weakness of the paper was that it is not really clear what the study is trying to say, and it was difficult to follow the logic and data, with several figures being out of order. There are a number of statements that feel overinterpreted, such as stating that clathrin-mediated endocytosis is required for calyculin A mediated acantholysis. While it is clear that Calyculin A recapitulates features similar to acantholysis, it is a broad perturbation of the cell, and it is not clear that it is the same process. Further, the pharmacological evidence is not strong enough to make such a clear claim about the role of clathrin-mediated endocytosis. Similarly, the statement that "GSDMs can induce pyroptosis not only in their cleaved forms but also as full-length proteins through specific post-translational modifications" is much too strong for these data. The terminology surrounding "pyroptosis" should be used more carefully, as yoyo-1 uptake and membrane ballooning demonstrate membrane permeability and morphological changes but are not sufficient for cell death.

Reviewer #3 (Public review):

Summary:

The present work was aimed at investigating the specific contributions of thalamic nuclei to associative threat learning and extinction. Using fMRI, it examined activation patterns across pulvinar divisions, the lateral geniculate nucleus (LGN), and the mediodorsal thalamus (MD) during threat acquisition, extinction, and recall. It goals was to uncover whether distinct thalamic systems support different modes of learning-automatic survival mechanisms versus more deliberate processes-and to propose a hierarchical pulvinar model of fear conditioning. The manuscript also tried to refine current neuroanatomical models of threat learning and memory, highlighting the role of thalamic nuclei in it.

Strengths:

(1) Valuable theoretical elaboration and modeling regarding the differential role of pulvinar subdivisions on feedforward (inferior, lateral) and higher-order integration (anterior), and their functional interplay with other relevant subcortical and cortical structures in associative threat and extinction learning.

(2) Large sample sizes and multipronged analytical approaches were used for hypothesis testing.

(3) Exhaustive literature review in the field of associative threat, as well as regarding the role of thalamic nuclei and other brain structures in it.

Comments on revised version:

My three main concerns remain insufficiently addressed or only partially addressed in the study limitations. First, using 3 mm isotropic voxels limits anatomical and functional specificity when investigating small thalamic nuclei. Higher-resolution fMRI protocols and probabilistic thalamic atlases that allow subject-specific segmentation have been available for several years and could have been used to improve anatomical precision. Second, although analyses in normalized space are widely accepted for group-level analyses, normalization can distort the size and boundaries of small thalamic nuclei. Analyses in native space, using subject-specific segmentation, would help address this limitation, particularly given the relatively low functional resolution. Finally, standard preprocessing procedures and the inclusion of motion parameters as covariates do not fully exclude residual motion-related effects or potential differences in motion across experimental conditions. The authors do not report additional motion quality-control procedures, such as excluding participants with excessive motion or substantial translational or rotational drift. Taken together, these concerns prevent me from upgrading my assessment.

Reviewer #2 (Public review):

Summary:

This work by Hayford, Baleami, Stauffer et al. addresses the standard view of drug-tolerant persister (DTP) cells in cancer. The current model treats each DTP as a uniform, single-cell phenotype that survives therapy. Building on the authors' earlier "idling" DTP concept (Paudel et al. 2018), this study instead provides evidence that DTPs are heterogeneous populations of multiple co-existing sub-states whose individual division/death rates balance out to produce near-zero net population growth.

Using BRAF-mutant melanoma (SKMEL5) cells treated with the BRAF inhibitor PLX4720, the authors combine single-cell RNA-seq, bulk RNA-seq, ATAC-seq, DNA lineage barcoding (ClonMapper), calcium-flux assays, and ferroptosis drug-response assays to show:

(1) Idling DTP populations show fewer, but still multiple, distinct transcriptomic sub-states compared to untreated cells (Figure 1).

(2) They arise from nearly all pre-existing cell lineages rather than from selective expansion of a resistant clone (Figure 2).

(3) Transcriptomic and epigenomic data converge on altered ion-channel/calcium-handling genes; functional calcium-flux assays confirm reduced store-operated calcium entry (SOCE) in these cells (Figure 3).

(4) These cells are around 3-fold more sensitive to the ferroptosis inducer RSL3 (GPX4 inhibitor), an effect reversible by the ferroptosis inhibitor ferrostatin-1 (Figure 4).

The authors propose targeted landscaping, in which drug treatment reshapes a cell population's phenotypic landscape rather than selecting a single resistant subtype, implying that eradicating DTPs will require sequential interventions that progressively narrow the landscape, rather than targeting a presumed uniform persister state.

Strengths:

(1) scRNA-seq, bulk RNA-seq, ATAC-seq, lineage barcoding, calcium-flux assays, and drug-response assays all point toward the same conclusions, strengthening confidence in the findings.

(2) The ClonMapper approach directly tests (and rules out) clonal selection as the origin of DTPs - a key alternative explanation the authors address rigorously.

(3) The calcium-flux assay and RSL3/ferrostatin-1 ferroptosis experiments provide direct functional confirmation of transcriptomic/epigenomic predictions, moving beyond correlative sequencing data.

(4) The "bulk" vs. "single-cell-state" and the "targeted landscaping" analogy is useful for framing future treatment strategies.

(5) Discussion explicitly flags that the data are not time-resolved single-cell trajectories, and that the ferroptosis vulnerability's exact molecular driver is not identified.

Weaknesses:

(1) All experiments use one melanoma cell line (SKMEL5 and its subclones), so generalizability to other BRAF-mutant melanomas or other cancer types/drugs is unproven.

(2) As the authors acknowledge, the study lacks time-lapse/lineage-resolved tracking of individual cells transitioning between sub-states.

(3) Much of the ion-channel and metabolic pathway evidence relies on gene set enrichment, which is suggestive but not itself mechanistic; the calcium-flux assay has the main functional role.

(4) The paper demonstrates increased RSL3 sensitivity and rescue by ferrostatin-1, but doesn't identify the specific molecular driver linking ion-channel remodeling to ferroptosis susceptibility.

Reviewer #2 (Public review):

Liu, He, et al. record intracranial EEG from the hippocampus and lateral temporal lobe in thirteen neurosurgical patients performing a delayed match-to-sample visual short-term memory task. They ask whether hippocampal sharp-wave ripples, well established in long-term memory consolidation, also contribute to the active maintenance of visual representations. The authors report that hippocampal ripple rates ramp up across the 7-second maintenance period, that hippocampal ripples co-occur with lateral temporal ripples, and that these coupled events coincide with above-chance category decoding in the lateral temporal lobe. These findings are interpreted within the dynamic coding framework of working memory. In this revision, the authors have been very responsive and have added ripple validation, aperiodic and high-frequency activity controls, direct interaction tests, and peak-lag analyses, which have strengthened the manuscript considerably. The study now provides solid evidence that hippocampal high-frequency activity ramps up during maintenance in a manner that scales with short-term memory success. Some of the more specific interpretations remain open: whether the effect is ripple-specific, whether it is specific to short-term memory, and whether coupled ripples link the hippocampal ramp to cortical reactivation. The work will be of considerable interest to researchers studying working memory and human ripples and opens promising directions for future studies with parametric manipulations and broader cortical coverage.

Reviewer #2 (Public review):

Summary:

This work highlights a new approach to build an array of highly stable peptide MHC-I using temperature exchange, which can be used to identify antigen specific CD8+ T cells.

Strengths:

In this work, the authors have proposed an alternative method to reload the peptide MHC-I molecule. Their temperature-exchange approach is distinct from current reloadable peptide MHC technologies involving photolabile peptide, empty MHC-I (Nat Commun 11, 1314 (2020). https://doi.org/10.1038/s41467-020-14862-4), tapasin/TAPBPR chaperone-assisted (eLife 7:e40126.), enzyme exchangeable (https://pubmed.ncbi.nlm.nih.gov/31201791/) and small alcohol (Curr Res Immunol. 2022 Aug 18;3:167-174. doi: 10.1016/j.crimmu.2022.08.002) approaches

Reviewer #2 (Public review):

The revised manuscript has improved substantially. The authors have addressed the main concerns raised during the previous round by introducing an IVT-based calibration framework, evaluating background calls across probability thresholds, examining basecalling mismatches, and replacing "stoichiometry" with the more appropriate term "predicted modification proportion." The expanded discussion of non-DRACH predictions is also useful and appropriately balanced. Overall, the study now provides a valuable practical framework for Dorado-based RNA modification analysis.

I have only a few remaining comments, all of which should be addressable through clarification and minor textual revisions.

(1) The comparison between NHDF and HD10.6 cells does not appear to include independent biological replicates. The observed differences in predicted m6A/A proportions should therefore be presented as differences between the two datasets rather than as general cell-type-specific effects. The interpretation is already cautious in the Discussion, but similar wording should be used in the Abstract, Results heading, and title if applicable.

(2) The direction of the comparison in Figure 3D should be clarified. The percentages represent the fraction of GLORI sites that is also detected by Dorado. Thus, the analysis shows that Dorado increasingly recovers high-proportion GLORI sites. It does not directly demonstrate that high-proportion Dorado predictions are increasingly validated by GLORI. The text should reflect this distinction, and the figure should clearly identify which circle represents each method.

(3) The ratio of calls detected in IVT relative to biological samples is a useful empirical measure of background, but it is not formally a false-positive rate because the true status and testability of every site are not known. A term such as "IVT-to-sample call ratio" or "empirical background-call ratio" would be more precise and would not alter the main conclusion regarding the value of stringent filtering.

(4) The description of the change-point framework still contains the placeholder "at least X-fold more signal." The actual enrichment threshold should be provided. The authors should also ensure that a log2 ratio threshold of 1.5 is not described as 1.5-fold enrichment, since these values are not equivalent.

These are relatively minor issues. The authors have responded constructively to the previous reviews, and I do not consider further experimental work necessary.

Reviewer #2 (Public review):

The manuscript is extremely clear and well written, the experimental evidence is strong and the methods are clearly described, so I do not have major issues to raise. A few minor issues are listed below. Some of these were presented in my first review but were not addressed by the authors.

(2) In Section 3.3 the authors say that direct paths connecting Class I and Class IV behave similarly to indirect paths, despite having lower scores according to the RBM. How generic is this? Does it also happen for other classes? This might be an important point to address as direct paths are easier to sample.

(3) The path shown in Fig.4 goes through a region of non-functionality around sequences 18-19. It seems that the sample path is basically exploring the functional regions for Class I and Class II/III separately trying to approach the other class, but then it can't really do the switch.

By contrast the path going from Class I to Class IV seems able to perform the functional switch in a single step (20-21) without losing too much of the function.

Perhaps the authors could better comment on this? Is this a limitation of the sampling method, or a fundamental biological fact?

(4) On page 12 it is stated that temperature was chosen to 1/3 to maximize the score. This is important and should be mentioned earlier (I didn't notice it until that point).

(5) On page 13 it is stated that "However, the scores of the ancestral sequences along the phylogenetic pathways assigned by the RBM are significantly lower than the ones of the RBM-designed sequences. This result is expected as ASR reconstruction does not take into account epistasis, differently from RBM, and we expect ASR sequences to generally be of lesser quality."

I was very surprised by this result. My own experience with ASR shows that on the contrary, sequences found by ASR (via maximum likelihood) tend to have high scores in the (R)BM and tend to be more stable than extant sequences. I attribute this to the fact that ASR typically finds a "consensus" sequence that maximizes the contribution to the score coming from the fields (the profile), which is typically dominant over the epistatic signal, resulting in a bigger score. Maybe the authors did not use maximum likelihood in the ASR? Some clarification might be useful here.

Reviewer #2 (Public review):

Summary:

The revised manuscript by Nohara et al. presents a novel 3D assay that allows for stratification of epithelia, active EMT through small pores, and active MET. They show that 3um pores allow for keratinocytes to sample the pore through filopodia and up regulate EMT genes to transverse the pores to the other side of the membrane where EMT genes are down regulated as the cells re-establish stratified epithelia. The TGFbeta pathway and actin polymerization promotes movement of cells into the pores and Piezo1 and KRT6 actively block this movement. This work provides a novel 3D assay that is likely to become a benchmark to analyze these processes using a more complex system than other current culture-specific EMT and MET assays.

Strengths:

The strengths of the manuscript include the foundational analysis of the pathways involved in establishing the 3-compartment epithelium. The authors have incorporated live imaging, drug studies, KO analysis, and RNA-sequencing to show the relevant pathways involved. The authors were responsive to the original round of reviews and present a much-improved study with greater details on molecular markers, quantification, and clarity.

Reviewer #2 (Public review):

Enhancer elements are regulatory DNA sequences that are capable of driving specific expression patterns. As these elements are generally short and context-independent, enhancers can be used in expression vectors (e.g., packaged in an adeno-associated virus, AAV) to limit expression to target cell populations. This approach was identified as a major strategy for cell-type-specific manipulation in the brain and has been pursued by both standard research studies as well as large-scale efforts led by the BRAIN Initiative. This manuscript describes a major effort to generate enhancer-AAVs targeting astrocytes and oligodendrocytes orchestrated by a large research team led by the Allen Institute for Brain Science. This manuscript parallels other recent publications describing sets of enhancer-AAVs, following rigorous, similar methods with relatively broad testing and application.

To identify and screen candidate enhancers, the scientists prioritized candidates via analysis of single-nucleus accessibility and methylation datasets (i.e., snATAC-seq) and tested them in mice. The scientists prioritized candidate enhancers that exhibited specificity of accessibility in the target cell type. Following selection, the scientists cloned the candidate sequences into AAV vectors with a minimal promoter and reporter gene, packaged the virus, delivered it to the mouse brain, and screened for activity based on reporter expression. Candidates that passed initial screening were further characterized via imaging and sorting, followed by single-cell RNA-seq. This process had around a 50% success rate and yielded 25 astrocyte and 21 oligodendrocyte enhancer-AAVs with the targeted cell-type-specific expression patterns.

The scientists went on to test for subtype-specific activity patterns, finding wide diversity in astrocyte activities across sub-populations and conversely, homogenous oligodendrocyte activation. They optimized a few of these via concatenating the enhancer core sequence to increase expression levels of the reporter gene and showed strong specificity and completeness of cell targeting for a set of these enhancer-AAVs. Following characterization and validation, they then deployed these enhancer-AAVs in a number of demonstration applications to show the utility for basic and translational science. All the constructs developed here are available for public use via Addgene, ensuring that these new tools can be used by other researchers.

There really are no obvious weaknesses in the work presented here, from the generation of the enhancer-AAVs to use in sophisticated validation studies. The enhancer-AAV testing is rigorous and provides critical information necessary for other scientists to select and use these constructs. The applications demonstrate the power of enhancer-AAV approaches. The toolbox presented here may not enable specific targeting of all relevant cellular subtypes or activity states for astrocytes and oligodendrocytes, and future work will be needed to fully understand the activity of the enhancers, identity of the target cell types, and context-dependent utility of these constructs. However, the set of enhancer-AAVs developed here should be transformative for researchers working on accessing and manipulating these cell types and have a major impact on the field.

Reviewer #2 (Public review):

Summary:

This study aims to assess the drivers of the interspecific distribution of transposable elements (TEs), using genomes from the Sauropsids clade. In particular, the authors test the mutational hazard hypothesis (MHH), which stipulates that slightly deleterious TEs should preferentially accumulate in populations with smaller population sizes, where purifying selection is weaker.

Strengths:

The manuscript is clearly written, and the underlying concepts (MHH, Simpson's paradox) are well introduced and discussed. In that respect, the manuscript is highly didactic and an enjoyable read. The authors employ state-of-the-art phylogeny-aware statistical procedures, including structural equation modeling to infer causal relationships. The results are of high interest: the authors report that correlations between TE frequencies and proxies of population size (life-history traits - such as body mass, and molecular traits - such as the dN/dS ratio), which appear at large evolutionary scales, disappear when within-clade relationships are examined. The authors discuss these results and propose that at shorter time scales, clade- and TE family-specific bursts dominate the dynamics of TE dynamics.

Weaknesses:

While I generally agree with the authors' interpretation and find these results of great interest, I have several concerns about the underlying statistical analyses and think that some possible alternative explanations of a methodological nature should be checked to confirm the results.

(1) Three types of statistical analyses are employed: Pearson correlation coefficient, phylogenetic generalised least-squares (PGLS), and structural equation modelling (SEM). Both the Pearson correlation test and the PGLS assume normality (binormality in the case of Pearson's correlation) and linearity (and this is very likely the case for SEM as well, although it depends on the exact method employed). Deviation from these hypotheses can create erroneous P-value computations (in both directions). I could not find any assessment of how well the data fit these hypotheses in the manuscript; differences in significance could reflect differences in how the data fulfil these assumptions across time scales. Figure 2a suggests at least a deviation from normality, be it because of the Aves clade alone. A non-parametric correlation (such as Kendall's tau) should be used instead of Pearson's correlation, as it assumes neither normality nor linearity. The PGLS analysis is trickier in that respect: the authors should check the normality and independence of the model residuals (and likely homoscedasticity too, unless the underlying model allows for lineage-specific variance). An alternative would be to transform the variables using phylogenetic independent contrasts (PIC, which still assumes a Brownian model - as does the PGLS) and use a non-parametric correlation on the resulting PICs. I don't have expertise in SEM, but a quick search suggests that non-parametric (or at least non-linear) SEM implementations are available.

(2) The positive correlation (l208, l299) with generation time should be further commented on, as it has the opposite direction as expected. Following the proposed rationale, species with shorter generation times should have more TEs, shouldn't they? Unless "generation time" here actually refers to the number of generations per year, instead of the duration of a generation, as the text suggests?

Reviewer #2 (Public review):

Summary:

Negreira et al. present an application of a novel single-cell genomics approach to investigate the genetic heterogeneity of Leishmania parasites. Leishmania, while also representing a major global disease with hundreds of thousands of cases annually, serves as a model to test the rigor of the sequencing strategy. Its complex karyotypic nature necessitates a method that is capable of resolving natural variation to better understand genome dynamics. Importantly, an earlier single-cell genomics platform (10x Chromium) is no longer available, and new methods need to be evaluated to fill in this gap.

The study was designed to evaluate whether a capsule-based cell capture method combined with primary template-directed amplification (PTA) could maintain levels of genomic heterogeneity represented in an equal mixture of two Leishmania strains. This was a high bar, given the relatively small protozoan genome and prior studies that showed limitations of single-cell genomics, especially for gene-level copy number changes. Overall, the study found that semi-permeable capsules (SPC) are an effective way to isolate high-quality single cells. Additionally, short reads from amplified genomes effectively maintained the relative levels of variation in the two strains on the chromosome, gene copy, and individual base level. Thus, this method will be useful to evaluate adaptive strategies of Leishmania. Many researchers will also refer to these studies to set up SPC collection and PTA methods for their organism of choice.

Strengths:

(1) The use of SPC and PTA in a non-bacterial organism is novel. The study displays the utility of these methods to isolate and amplify single genomes to a level that can be sequenced, despite being a motile organism with a GC-rich genome.

(2) The authors clearly outlined their optimization strategy and provided numerous quality-control metrics that inspire confidence in the success of achieving even chromosomal coverage relative to ploidy.

(3) The use of two distinct Leishmania strains with known clonal status provided strong evidence that PTA-based amplification could reflect genome differences and displayed the utility of the method for studies of rare genotypes.

(4) Evaluating the SPCs pre- and post-amplification with microscopy is a practical and robust way of determining the success of SPC formation and PTA.

(5) The authors show that the PTA-based approach easily resolved major genotypic ploidy in agreement with a prior 10x Chromium-based study. The new method had improved resolution of drug resistance genotypes in the form of both copy-number variations and single-nucleotide polymorphisms.

(6) In general, the authors are very thorough in describing the methods, including those used to optimize PTA lysis and amplification steps (fresh vs frozen cells, naked DNA vs sorted cells, etc). This demonstrates a depth of knowledge about the procedure and leaves few unanswered questions.

(7) The custom, multifaceted, computational assessment of coverage evenness is a major strength of the study and demonstrates that the authors acknowledge potential computational factors that could impact the analysis.

Reviewer #2 (Public review):

Summary:

In this manuscript, Hossaim and colleagues investigate the role of the ITK kinase in modulating inflammation in a pristane-induced lung hemorrhage model. Using a germline ITK KO mouse, they report that loss of ITK skews the T cell compartment toward a memory-like state, expanding Tregs, and conferring protection against alveolar hemorrhage, inflammatory monocyte recruitment, proteinuria, and systemic cytokine elevation. They further show that transfer of ITK-deficient Tregs into wild-type hosts with established disease attenuates injury and shifts the cytokine balance toward resolution, and that ITK-deficient Tregs carry a transcriptional signature enriched for OXPHOS, mTORC1, MYC, and cell-cycle programs. While these observations are interesting for the development of potential immunotherapies, there are several issues with the methodological approach that support the authors' claims, tempering my enthusiasm for this manuscript.

Strengths:

(1) The clinical motivation and potential targeted therapies are relevant.

(2) The murine phenotype seems robust.

Comments on revised version.

The authors have addressed my comments appropriately.

PMID: 41513982

Gene: RNU4-2

HGNC: 10193

Disease: inherited retinal dystrophy

MONDO: 0019118

InheritancePattern: autosomal dominant

Prevalence: The prevelance of retinitis pigmentosa in general is between 1 in 750 and 1 in 9,000 individuals (PMID: 37108642). The proportion of those cases caused by RNU4-2 variants is not well known.

Penetrance: Penetrance appears to be incomplete, with family pedigrees including apparently unaffected carriers.

GroupID: 1

DiseaseAssertion: VHL

Group size: 1

GroupNumberMales: N/A

GroupNumberFemales: N/A

GroupEthnicity: N/A

GroupAgeRange: N/A

Group Info: Retrospective analysis of a longitudinal cohort of 21 VHL germline mutation carriers and retinal haemangioblastomas. All patients were categorized in two genotypic categories: missense mutations and truncating mutations. 

GroupPresentingHPOs: HP:0009711 (retinal capillary hemangioma)

GroupHPOFreeText: The patient presented with retinal capillary hemangioma.

GroupNotHPOs: N/A

GroupNotHPOFreeText: N/A

GroupPreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: Table S1

Variant: Exon 1-2 deletion

CaseProblemVariantFreeText: N/A

LegacyVariant: N/A

ClinVarID: N/A

CAID: N/A

gnomAD: N/A

VariantEvidence: Chr3(GRCh37): g.10182301_10188764del; c.-1231_463+444del6464

MutationType: exon_loss_deletion

CivicName: Exon 1-2 deletion

MultipleGeneVariants: N/A

Reviewer #2 (Public review):

The authors tackle the question of whether conceptual absence is neurally encoded in the same way as perceptual absence. On one hand, the neural bases of perceptual absence have been largely investigated, as exemplified by the study of neural correlates of perception of aware vs. unaware stimuli, and on the other hand, the overlapping neural encoding of symbolic ('0') and non-symbolic (number of dots) formats of numerical absence has been previously established (Barnett & Fleming, 2024). However, the direct comparison of neural representations between numerical and perceptual absences remained to be investigated.

This article fills this gap by designing a Magneto-EncephaloGraphy (MEG) study using multi-voxel pattern analysis (MVPA) and temporal generalization to probe the similarity of neural patterns across the representation of perceptual absence (lack of stimuli), symbolic ('0'), and non-symbolic (number of dots) formats of numerical absence. They confirmed previously obtained evidence for shared neural representation across both formats of numerical absence. They report evidence for an absence of shared representation between both formats of numerical absence on one hand and perceptual absence on the other hand, while controlling for the confounding effect of low-level visual features. Their results overall support the conclusion that conceptual and perceptual absence are neurally encoded in a distinct way and speak in favour of a boundary between the representation of the concepts and the percept of absence.

Major strengths:

(1) Behavioral and neuroimaging results convincingly demonstrate that neural encoding of symbolic and non-symbolic absences is shared and situated on a graded, abstract neural number line, replicating previous results, notably from the authors themselves (Barnett & Fleming, 2024).

(2) They show that neural encoding of perceptual and numerical absence do not generalise across each other, while controlling for spurious confounds due to visual stimuli that are commonly shared in the cases of non-symbolic numerical absence and perceptual absences.

(3) They adequately use Bayesian analysis to distinguish absence of evidence vs. evidence of absence to support their claim.

(4) The interpretation of numerical absence as a representation of the concept of "nothingness" is adequate, although it might be further discussed by distinguishing the concept of "zero" on a number line from the concept of nothingness and that of an empty set (Nieder, 2016).

(5) The discussion about development and metacognition paves an interesting road for further investigation on the acquisition of the concept of zero, especially in light of debates on the progressive development of metacognitive abilities in children (Goupil & Kouider, 2019).

(6) Data and code are published with open-source access, allowing the community to further investigate the points as major weaknesses evoked below, if desired.

Comments on revised version.

I appreciated the authors' responses as they provided important additions to their discussion and supplementary information.

Reviewer #2 (Public review):

The authors document that GM-CSF prevents the loss of ILC3 populations and inhibits pro-inflammatory cytokine production during gut inflammation. They combine a preclinical model of gut inflammation in zebrafish with spatial transcriptomic analysis of samples from Crohn's disease patients. The data show that GM-CSF ameliorates gut inflammation by (1) curtailing the differentiation of disease-associated ILC1 and (2) by "boosting" the tissue repair function of ILC3.

The topic of the manuscript is interesting and highly relevant to the question of how intestinal inflammation is initiated or perpetuated. However, there are some limitations. Specifically, it remains unclear through which distinct molecular programs macrophages maintain "beneficial" ILC3 and how they prevent the differentiation or expansion of disease-associated ILC1. Based on previous data, these processes may be interconnected as ILC3 show remarkable plasticity and can convert into ILC1-like cells ("ex-ILC3").

Reviewer #2 (Public review):

Summary:

This work presents a valuable resource by generating a comprehensive bulk RNA sequencing catalogue of gene expression in the mouse duodenum and ileum during the first postnatal month. The central findings of this work are based on analysis of this dataset. Specifically, the authors characterized molecular shifts that occur as the intestine matures from an immature to an adult-like state, investigating both temporal changes and regional differences between the proximal and distal small intestine. They also sought to validate key findings through complementary methods and to provide comparative context with human intestinal samples derived from premature infants. This study will provide a solid reference dataset for the community of researchers studying postnatal gastrointestinal development and diseases that arise during these stages. However, the study lacks in functional validation of the interpretations.

Strengths:

(1) The inclusion of numerous time points (day 0 through 4 weeks) and comparative analyses throughout the first postnatal month.

(2) Validation of key interpretations of RNA-seq data by other methods

(3) Linking mouse postnatal development to human premature infant development, enhancing its clinical relevance. The inclusion of human intestinal biopsy and organoid data for comparison further strengthens this link.

(4) The investigation covers a wide array of developmental gene categories with known significance, including epithelial differentiation markers (e.g., Vil1, Muc2, Lyz1), intestinal stem cell markers (e.g., Lgr5, Olfm4, Ascl2), mesenchymal markers (e.g., Pdgfra, Vim), Wnt signaling components (e.g., Wnt3, Wnt5a, Ctnnb1), and various immune genes (e.g., defensins, T cell, B cell, ILC, macrophage markers).

Weaknesses:

(1) The primary limitation is that there is no functional validation. The study primarily focuses on interpretation of RNA expression. This is a common limitation of transcriptomic "atlas" studies, but the functional and mechanistic relevance of these interpretations remains to be determined.

(2) The data are derived from bulk RNA-Seq of full-thickness intestinal tissue. While this approach helps capture rare cell types and both epithelial and mesenchymal components simultaneously, it does not provide cell-type specific gene expression profiles, which might obscure important nuances. Future investigations using single-cell sequencing would be a logical follow-up.

(3) The day 4 samples were omitted due to quality issues, which might have led to missing some dynamic changes, especially given that some ISC genes show dynamic changes around day 6.

Reviewer #2 (Public review):

Summary:

This is a technically sophisticated effort to visualize the nanoscale organization of native surface AMPA receptors in adult mouse brain. The authors combine chemical labeling of endogenous AMPARs with 3D, 2-color super-resolution microscopy. They use this to compare hippocampal and cortical AMPAR organization and to characterize changes in a P301S mutant mouse tauopathy model. The authors report a greater density of surface AMPAR localizations in hippocampal CA1 than in two examined cortical regions, as well as a smaller fraction of AMPARs organized in synaptic clusters. They report that PS19 tauopathy is associated with reduced hippocampal AMPAR localization density, a relative depletion of the population classified as extrasynaptic, and reduced organization of receptors within synaptic nanodomains.

Strengths:

The methodological contribution is substantial. The authors developed a valuable set of methods for examining endogenous surface AMPAR organization in brain tissue. The imaging methodology could be useful across many questions in synaptic neuroscience.

Weaknesses:

While the biological observations are interesting, several aspects of the neuroscience interpretation require clarification, and some of the biological conclusions should be weakened unless the measurements can better justify them. I provide several comments intended to strengthen this interesting manuscript.

(1) The authors measure fluorescent localization counts but often interpret these as AMPAR numbers. I believe that individual fluorophores can generate multiple localizations and that labeling efficiency can vary, challenging their frequent interpretation as AMPAR numbers. Importantly, AMPARs are tetrameric complexes with potentially different subunit compositions, so one AMPAR could be observed as 0-4 fluorescent localizations. The uncertainty is particularly important for comparisons of AMPAR abundance per cluster and nanodomain. The authors should consistently distinguish localization density from receptor number and clarify which conclusions depend on assumptions about labeling and the assumptions of photophysical equivalence between experimental conditions. The paper would be more rigorous and the claims more legitimate if the authors were disciplined throughout about using empirical descriptors instead of interpretations.

(2) The distinction between synaptic and extrasynaptic receptors is central to the manuscript. The authors identify synaptic receptors either through density-based clustering or proximity to PSD-95 clusters. However, neither measure directly establishes anatomical synaptic localization. This is particularly consequential because only ~1/3 of morphologically identified YFP-positive spines contain PSD-95 clusters that meet the detection criteria. It seems to me that AMPARs associated with weakly-labeled or undetected synapses could then be erroneously classified as extrasynaptic. PSD-95 detection should be assessed across regions and genotypes, and a sensitivity analysis undertaken to ensure that the conclusions are robust to the choices of clustering and distance thresholds.

(3) The authors suggest that the larger extrasynaptic AMPAR population in the hippocampus provides a reserve for rapid synaptic recruitment and that its reduction in tauopathy constrains plasticity. This is plausible, but receptor abundance does not establish receptor mobility or availability for synaptic capture. The importance of AMPAR surface diffusion for hippocampal LTP and contextual learning has been demonstrated (see Penn et al., Nature, 2017), but the current study does not measure diffusion or receptor exchange. The authors should temper their interpretation and explain the caveats.

(4) The authors infer that there has been a preferential extrasynaptic AMPAR loss from the reduced total AMPAR localization density and the increased synaptic fraction they measured. Because both synaptic and extrasynaptic populations could decrease even when the synaptic fraction increases, they should calculate the synaptic and extrasynaptic localization densities separately for each mouse. This would clarify the magnitude and selectivity of the changes that they report and help to reconcile the potentially contradictory increased synaptic fraction with reduced AMPAR localization density within synaptic clusters and nanodomains.

(5) The authors adjust DBSCAN parameters for PS19 hippocampus in response to reduced overall localization density. Because the resulting clustering measurements are themselves used to establish genotype differences, it is important to demonstrate that the conclusions are robust to parameter selection. Analyses using identical parameter sets across conditions, together with simulations in which WT localization patterns are subsampled to match PS19 localization density, would help determine whether the reported changes reflect biological reorganization rather than density-dependent detection effects. Similar validation is important for the nanodomain analysis. I found that, in general, the authors should have made a greater effort to ensure that their measurements are robust to the parameter choices.

(6) CA1 comparisons with motor/somatosensory cortex require more precise anatomical characterization. What cortical layers and hippocampal dendritic compartments are being compared? These are not homogeneous structures in almost any regard. AMPAR or, more empirically, fluorescence localization density per imaging field is not necessarily equivalent to AMPAR density per dendritic membrane area or per synapse. Differences in neuropil composition, spine density, and synapse density could contribute to the observed differences when there is, in fact, no AMPAR density per dendritic membrane area. Proper anatomical normalization or a stricter comparison of equivalent neuronal compartments would strengthen the interpretation. I believe that the authors are aware of the issue, as they did an analysis along a single dendrite, but this has to be done more thoroughly for them to justify the conclusions they assert.

(7) The main comparisons involve three mice per group, and the authors correctly analyze mouse-level averages. Nonetheless, the small number of biological replicates limits the strength of the conclusions about regional specificity and disease effects. The test statistics and degrees of freedom, not just the p-values, should be presented. In addition, effect sizes and individual mouse values should be presented. The absence of a statistically significant cortical effect does not establish that cortex is unaffected. There could be genotype-by-region interactions, which the authors should check.

(8) The authors describe that 6-mo PS19 mice precede neurodegeneration and they assert that the observed AMPAR-associated changes relate to the onset of synaptic dysfunction. However, Yoshiyama et al., Neuron, 2007 demonstrate hippocampal synapse loss and impaired synaptic function at three months in PS19 mice, before detecting fibrillary tau. The present observations likely occur after synaptic pathology in thePS19 mice. The authors should distinguish structural correlates of established dysfunction from the mechanisms that may have initiated that dysfunction. More importantly, the assertion that neuronal or synaptic anatomy is preserved in the PS19 mice should be supported by appropriate measurements.

(9) The reduction of the AMPAR nanodomain organization is potentially important. However, the authors do not measure presynaptic release sites or trans-synaptic alignment. To be rigorous, the data establish altered postsynaptic receptor organization but do not demonstrate disruption of trans-synaptic nanocolumns or establish the functional consequences for quantal transmission. If the authors do not provide evidence for these claims they should not make them and make it clear these are (reasonable) speculations for which they did not collect the data.

Reviewer #2 (Public review):

Summary:

The manuscript investigates whether transglutaminase 2 (TG2) released from intestinal epithelial cells (IECs) can trigger anti-TG2 autoantibody production by activating TG2-specific B cells that recognize the TG2-gluten complex, with assistance from gluten-specific T cells.

Strengths:

The main study is well designed and mechanistically important, providing direct evidence for a longstanding hypothesis in celiac disease pathogenesis. The mouse model, which uses an inducible TG2 fusion to a deamidated gluten peptide sequence (TG2-DGP fusion) in IECs, is elegant and directly tests a mechanism that could previously only be inferred indirectly. The Sollid group previously showed TG2 is present in the gut lumen (Meling et al., 2024) and can facilitate B-T cell collaboration in vitro (Iversen et al., 2020). This paper closes the loop by showing IEC-restricted TG2-DGP expression drives autoantibody formation in vivo. The experiments include appropriate controls, including the use of a Tgm2-/- B cell donor to avoid confounding by TG2 shed during cell isolation. The paper also shows that B cell expansion occurs in Peyer's patches (PP), whereas class-switching involves mesenteric lymph nodes (MLN).

Weaknesses:

Some suggestions for revision:

(1) Physiological relevance of antigen dose and kinetics. The dox-induced pulse of TG2-DGP expression is a bolus-like event, whereas endogenous IEC shedding and TG2 release presumably occur continuously at low levels. The author should discuss more how the kinetics and magnitude of the induced signal compare with estimated physiological TG2 derived from IEC turnover, and whether the strong immunogenicity observed here could overstate the potency of the natural process.

(2) The translational value of the model may hinge on epitope relevance. Celiac anti-TG2 antibodies preferentially target a small number of conformational epitopes on open TG2, and this restriction is tied to how TG2 is captured and presented. A central claim of the paper is that IEC-derived TG2 is the relevant source for autoantibody elicitation, rather than extracellular matrix-associated TG2 or other pools. If IEC-derived TG2 only activates non-human-relevant epitopes, that would undermine the claim that this is the mechanism responsible for the actual patient autoantibody repertoire. Whether IEC-derived TG2-DGP would recruit a broader range of TG2 epitopes seen in human disease, or only the subset compatible with this conformational epitope, is worth examining.

Reviewer #2 (Public review):

Summary:

This study examines how genes involved in cellular recycling (autophagy) influence lifespan under different experimental conditions. The findings help clarify why previous studies have reported conflicting results about whether blocking autophagy shortens or extends lifespan. The work will be of interest to researchers studying aging and cellular stress responses, particularly those using model organisms.

Strengths:

The findings are valuable, as they help resolve inconsistencies within a specific subfield of aging research. The evidence presented is solid, as the data broadly support the primary claims of the study. In addition, the discussion is thorough and thoughtfully integrates the findings within the broader context of the field.

Reviewer #2 (Public review):

Summary:

In this MEG study, the authors assess the oscillatory dynamics of object naming in 27 participants. The authors observed two distinct theta-coupled networks: a ventral occipito-temporal network linked to object recognition and a medial fronto-temporal network linked to lexico-semantic retrieval. Moreover, when contrasting object naming to a scrambled image control task, they observed increased theta-gamma phase amplitude coupling in the LIFG and fusiform gyrus. The authors conclude that theta-coupling ensures the binding of recognition and retrieval processes in object naming, while the theta-gamma phase amplitude coupling ensures local computations within those networks.

Strengths:

Addressing the question 'which oscillatory dynamics govern speech planning' is interesting and timely, since not much empirical evidence on this topic is currently available. Whole-brain MEG assessing phase and phase-amplitude coupling for object naming are not commonly attempted, and the study boosts a lot of power. Furthermore, the writing is concise and clear, making the paper easy to follow and pleasant to read.

Weaknesses:

My overall assessment is that the claims and conclusions of this study are stronger than the evidence supports, and this is because of several design weaknesses which I will detail below. Moreover, this study gives too one-sided a perspective on the current state-of-the-art in the neurophysiology of speech planning/object naming, and the presented conclusions can solely be understood from that narrow theoretical perspective.

(1) One of my bigger concerns is the lack of any language-related manipulations that allows to independently isolate the processing components the authors discuss. Object naming (speech planning) is a complex, multifaceted mental operation where different representations and processing levels can overlap in time and space (e.g., Indefrey, 2011; Hickok, 2012; Dell et al., 2013; Munding et al., 2016; Strijkers & Costa, 2016; Nozari & Pinet, 2020; De Zubicaray, 2023; Pickering & Strijkers, 2025). Without attempting to isolate those specific representations and/or processes and solely looking at object naming in general, the only manner to interpret the data is by relying on a priori defined and often one-to-one mappings of space and function in light of a specific theoretical perspective. This is indeed what the authors do in the current study, by linking certain brain regions they observe (e.g., more occipital for object recognition and more fronto-temporal for lexico-semantics) to the function assigned in dual-route models (note also that the authors rely on the dual-route models of comprehension, not production per se; see below). While doing so is of course legitimate, it does strongly reduce the impact of the results since they do not concern a direct empirical demonstration of the authors' conclusions, but an indirect inference based on a single theoretical framework. To give a few examples of how this weakens the conclusions: How do the authors know the fronto-temporal network is lexico-semantic and not phonological, as many other models would pose (e.g., Indefrey & Level, 2004; 2011; Hickok, 2012; 2014; De Zubicaray, 2023)? Similarly, theta-coupling in production is not uniquely related to lexico-semantics, but could also be linked to the syllabic rate (speech rate; e.g., Giraud & Poeppel, 2012; Meyer, 2018), or to working memory in production (e.g., Piai & Zheng, 2019). The occipito-temporal network may be involved in object recognition, but it may also reflect (embodied) word semantics, in particular for imageable objects as in the current study (e.g., Strijkers & Costa, 2016; Pulvermuller, 2018). In my opinion, given the lack of independent manipulations, these (and other) alternative conclusions are equally plausible, and the authors' interpretations of the data pattern are tentative at best; this is a substantial limitation in my view.

(2) The authors may object that they do have a contrast, namely the control condition. However, the control condition and the object naming condition differ in so many crucial aspects that it does not serve as a reliable control to address the above issues with interpretational strength. The control condition solely deals with low-level visual input, but not with conceptual and language-related processing, nor with motor preparation and articulation (since repeatedly saying 'no' is a flawed control for the more complex and variable naming responses required in object naming).

(3) Moreover, the timings between the control and object naming conditions are confounded: Naming and control RTs differ by 270 ms. The 1 s pre-response window therefore spans 0.08 to 1.08 s post-stimulus in naming, but −0.19 to +0.81 s in control. Hence, part of the contrast may rather reflect 'when' something was measured rather than 'what' processing differences it is supposed to reflect.

(4) As far as I can tell, the authors did not assess potential pre-articulation motor confounds in the data via ICA and/or recorded EMG activity (e.g., Vos et al., 2010; Porcaro et al., 2015; Strijkers et al., 2017; Abbasi et al., 2021). This may be relevant since motor preparatory activity is known to emerge well before actual articulation (e.g., Ries et al., 2013), and may (pre-)track articulatory speech rate in the theta range (e.g., Abbasi et al., 2023).

(5) Why are items not modelled as random intercepts/slopes? With 200 different stimuli, item variability seems relevant to include in the statistical models.

(6) I found that the authors gave a very restricted and not up-to-date literature overview on the neural basis of object naming. They mainly consider a dual route architecture. I understand that the authors don't wish (nor have to) give a complete overview of the different neurobiological models of object naming. However, the dual route model they cite is the speech processing and comprehension model (e.g., Hickok & Poeppel, 2004; 2007), which has no clear link to object naming, and they don't mention the production versions of a dual route architecture (e.g., Ueno et al., 2011; Hickok, 2012). Moreover, as mentioned also in comment 1, several other critical models (and more relevant for object naming specifically) are neglected: classical sequential object naming models (e.g., Indefrey & Levelt, 2004; Indefrey, 2011; Roelofs, 2014), interactive models (e.g., Dell et al., 2013; Dell & Chang, 2014), or more recent parallel models (e.g., Strijkers & Costa, 2016; Pickering & Costa, 2025) (for recent reviews see: e.g., Nozari & Pinet, 2020; Kerr et al., 2023; de Zubicaray, 2023). If the authors prefer to solely focus on a single class of models, that's fine (though a model that is more tightly linked to object naming would have been more relevant), but that limited scope should at the very least be acknowledged.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate luminal architectural reorganization during embryo implantation. Using 3D histological reconstruction, single-cell RNA sequencing, spatial transcriptomics, and genetic mouse models, the authors the uterine luminal epithelium undergoes rapid morphogenetic remodeling during the implantation. They further propose that a stress-responsive p38α signaling program coordinates epithelial remodeling and epithelial-stromal communication to establish an implantation-competent uterine lumen.

Strengths:

This study clearly described the changes of luminal architecture during implantation. Moreover, they also used integration of multiple advanced techniques, including 3D tissue reconstruction, single-cell transcriptomics, spatial transcriptomics, and genetic mouse models, which together provide a detailed description of the molecular characters of the uterine architecture during implantation.

Weaknesses:

The principal knockout model uses Pgr-Cre, which deletes p38α in both epithelial and stromal compartments. Importantly, they also added epithelial-specific knockout (Ltf-Cre) demonstrates that epithelial deletion alone produces only mild abnormalities and does not recapitulate the severe implantation phenotype observed in the uterine knockout. These data suggest that the critical function of p38α likely resides in stromal cells or in epithelial-stromal interactions. Consequently, the manuscript's title and central conclusion currently a little bit overstate epithelial regulation.

The data of this study are now support the main conclusions.

Impact of the work on the field:

This work will likely become an important resource for researchers studying embryo implantation because it provides a detailed atlas of peri-implantation uterine morphology integrated with spatial and single-cell transcriptomic datasets. The imaging methodology and quantitative description of luminal remodeling are likely to be widely useful.

Reviewer #2 (Public review):

Summary:

Rezaei et al. determined the cryo-electron microscopy structure of human ABCG5/8 in complex with ergosterol, revealing a conserved sterol-binding site at the transmembrane domain (TMD) interface, signifying the role of a specific key residue, Y432, in the interaction. ATPase assays showed the effective stimulation of ATPase activity of ABCG5/8 in the presence of ergosterol than cholesterol and CHS. The role of Y432 in the interaction with the transporter and stimulation of its ATPase activity was further supported by a mutagenesis study.

Strengths:

The study has been supported well with the structural and biochemical data combined with molecular dynamics simulations. The methods are very well detailed, which is helpful for the reader.

Weaknesses:

A few more experiments along similar lines (structures and mutagenesis and ATPase experiments) would have strengthened the molecular mechanism of sterol transport. Also, the physiological relevance of this sterol-ABCG5/8 structure has not been elaborated or connected via any experiments.

Reviewer #2 (Public review):

The authors developed a reinforcement learning framework to identify stimulation patterns that produce a predefined activity pattern in neuronal networks cultured on a microelectrode array. The main contribution of this study lies in the development and validation of the experimental framework, rather than in providing new insight into neuronal response properties or the biological mechanisms underlying network activity.

A major strength of the work is that the framework is implemented using open-source software. Electrical stimulation through microelectrode arrays is already widely used, and the proposed approach is therefore likely to be of interest to researchers seeking to automate the exploration and optimization of stimulation parameters. The authors also provide the software, hardware configuration, and experimental data, which substantially improves transparency and should facilitate reproduction and further development of the system by other laboratories.

The experiments provide a useful proof of concept showing that the framework can search for stimulation patterns associated with the predefined task in the tested neuronal network. The evidence is solid for demonstrating the feasibility of the approach within this specific experimental configuration. In particular, the closed-loop integration of stimulation, recording, evaluation of the neuronal response, and subsequent selection of stimulation patterns is clearly implemented and experimentally tested.

An important limitation is that the framework was evaluated using a relatively small and highly structured network. Four stimulation electrodes were positioned around a single network, and stimulation was delivered to microchannels in which axons were concentrated. This configuration is well suited to the initial demonstration, but it remains uncertain whether the same approach will perform similarly in conventional monolayer dissociated cultures, larger networks, or systems with different numbers and spatial arrangements of electrodes. Additional validation across a broader range of network structures and experimental configurations would therefore be needed to establish the general applicability of the framework.

Overall, this study presents a valuable and reproducible methodological framework for the closed-loop optimization of electrical stimulation in cultured neuronal networks. Its likely impact lies primarily in providing an accessible experimental and computational platform that can be adapted for studies requiring systematic exploration of stimulation patterns, although the extent to which the current findings generalize beyond the tested network configuration remains to be determined.

Reviewer #2 (Public review):

Summary:

The authors build on previous work demonstrating that the microtubule-associated protein CKAP2 functions at the mitotic spindle to regulate microtubule dynamics for proper chromosome segregation. Here they show that CKAP2 and its paralog CKAP2L also function at the cilium to regulate cilium formation and Hedgehog signaling.

Strengths:

A major strength of the manuscript is the use of GFP-CKAP2 knockin cells and the examination of single and double CKAP2 and CKAP2L knockout cells. The authors use live imaging of the knockin cells to show that CKAP2 localizes to the primary cilium of RPE1 cells after assembly.

The authors use the knockout cells to show that loss of either CKAP2 or CKAP2L results in longer cilia and a dampened Hedgehog response, whereas loss of both CKAP2 and CKAP2L results in shorter cilia and an even more dampened Hedgehog response.

Weaknesses:

The data are compelling and support the conclusions. However, the findings would be significantly strengthened by using additional markers of the cilium, not just acetylated tubulin.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors report on how a prion protein originating in yeast inside the gut interacts with a host insect's physiology to influence organismal traits important to fitness. Other recent studies have strongly found that interactions between gut yeasts and their Drosophila hosts are important to feeding behaviour, growth performance, and cold tolerance. They even point to the gut-brain axis. The authors extended from this work, combining screening approaches, genome-wide association studies, and forward genetics to determine that octopamine and serotonin signalling are important to these effects.

Strengths:

The data represent a valuable addition to the literature. There is a growing appreciation that host-microbiome interactions are important to animal performance and fitness, and this work provides strong evidence for a causal role of a prion protein in such a relationship. The high-throughput screening approaches and sequencing were, to the best of my knowledge, completed correctly. The idea that prion proteins could influence host physiology in beneficial ways is compelling, and the authors have done a great job of walking the reader through their reasoning and the multiple lines of evidence that support their reasoning.

Weaknesses:

While this is an interesting topic that is worthy of investigation, and identifying a causal role for a prion protein in host effects is a significant advance, other aspects of the novelty of the work are strongly overstated. No clear justification is provided for why cold tolerance was a particular trait of interest in the present study. Yet, a recent publication in the Journal of Experimental Biology clearly demonstrated a role of gut yeasts in Drosophila cold tolerance and is not cited or otherwise acknowledged [see: Jiménez-Padilla and Sinclair. Gut yeasts accelerate chill coma recovery in Drosophila melanogaster. J Exp Biol (2026) 229 (9): jeb251533]. I trust this is an oversight and will be corrected. Integrating this knowledge into the narrative of the paper would strongly improve the reasoning for this study.

The authors rely on a review of chill coma mechanisms from 2011 as their primary source of information for interpreting their results in relation to insect cold tolerance. A great deal of work has been done by several research groups on the physiology of cold tolerance in chill-susceptible insects in the past 15 years, much of which has relied on Drosophila as a model system. I think this is important and could inform the conclusions being reached and future work that extends from this work. For example, it is now well established that the renal system is centrally important to insect cold tolerance, including chill coma recovery phenotypes, but this is not being acknowledged or integrated into the narrative here and should be. Many of the mechanisms of interest highlighted in the present work are also directly relevant to other ionoregulatory epithelia beyond nervous tissue, and global knockdowns using the UAS/Gal4 approach will impact all tissues in which these genes are expressed. Figure 2D shows how these genes are enriched in the CNS, but also the midgut and Malpighian tubules, two ionoregulatory organs of import to cold tolerance.

The method the authors used to assess cold tolerance is not typically preferred by researchers who commonly study these phenotypes. Holding flies continuously at 4-6{degree sign}C and assessing whether or not they remain active is a reasonable approach, and I am confident that the differences observed among groups are real. However, chill coma onset and recovery are thought to be mediated by a complex combination of temperature effects on nervous, muscle, and renal function, and this assay method may conflate chill coma resistance with the capacity for chill coma recovery. This means variation in this assay could arise from differences in initial CTmin, plasticity in CTmin induced by RCH before chill coma is induced, or plasticity in neurumuscular/renal function that allows for recovery during the assay. I say this in part because Drosophila are documented to enter chill coma and then recover while the assay is ongoing, particularly around temperatures close to the CTmin [see: MacMillan et al., The capacity to maintain ion and water homeostasis underlies interspecific variation in Drosophila cold tolerance. Sci. Rep. 5:18607]. Therefore, flies active after 2 hours may never have entered chill coma, or may have entered and then recovered. Serotonin and octopamine signalling may be critical to one or both of these possibilities, and maybe in different ways, especially because these hormones play critically important roles in both renal and nervous function.

Infectious prions are known to cross gut epithelia and the Drosophila gut is known to take up large organic molecules via endocytosis and exocytosis, so I am not entirely convinced that the effects observed necessarily result through the gut-brain axis. Future studies could be focused on observing whether these prion proteins remain in the gut, or act directly on other tissues via the hemolymph.

The CAFÉ assay used in the study is a tried and true way to measure feeding behaviour in Drosophila. The data in Figure 1H look like a single measurement per individual, unless they have been modified for presentation. From the text provided, it is not clear to me how the authors accounted for changes in feeding behaviour at different times of day. Feeding behaviour is regulated by the circadian rhythm in digestive tissues. If activity is enhanced at night, as in the present study, are MRPL10+ flies also feeding during the night more than the WT flies?

Reviewer #2 (Public review):

Summary:

The author uses multinomial logistic regression to assign fitness values to SARS-CoV-2 and Influenza H3N2 clades. The mean population fitness, referred to as the cumulative fitness flux, is tracked throughout evolution to describe the rate of adaptation. The author shows a clear decrease in the rate of adaptation for SARS-CoV-2 after mid 2022, coinciding with the emergence of the Omicron clades; the rate of adaptation of H3N2 is much slower, but inferred to be constant in time. With this analysis, the author confirms Fisher's fundamental theorem of natural selection. Using a fine-grained clade definition scheme, the author attributes changes in fitness to substitutions in different genomic regions, highlighting the importance of the spike and, in particular, the RBD region of spike. A fitness model based on a multinomial regression is shown to perform better than EvEscape and protein-language-model semanticity to predict changes in fitness.

Strengths:

The author provides an interactive reading experience on the github.io hosted website, which has additional interactivity with figures and offers simple manipulation of what is shown. The methods are well explained and are easy to follow. The conclusions of the study- the decrease in the rate of adaptation of SARS-CoV-2, mutations in Spike S1 as the main driver of evolution, and the Spike S1 mutational count as predictor of fitness change - are clear to follow.

Weaknesses:

(1) The main conclusions that (i) the rate of adaptation of SARS-CoV-2 has decreased after Jun 2022 and is lower than in Influenza H3N2 and (ii) that mutations in Spike S1 are the main drivers of evolution are not novel.

(2) At the core, the multinomial logistic regression assumes a time-independent fitness value for each viral clade, while the clade fitness is not constant and depends on the population immunity, which will change over time. As a result, the fitness increase can be wrongly interpreted: The author observes a doubling of the mean fitness every 6 months from Jan 2021 to Jun 2022, and every 2.4 years from July 2022 to Dec 2025. We would then expect that the mean fitness in Dec 2025, \bar{f}_t, has increased more than 2 to the power 4, as compared to the mean fitness in Jan 2021, \bar{f}_0 (\bar{f}_t > 2^{4} \bar{f}_0). Is the mean viral fitness in Dec 2025 more than 16 times higher than in Jan 2021? Fitness differences between viral populations at different times cannot be meaningfully compared because the fitness landscape is time-dependent. The increase in fitness, without accounting for the change in population immunity, is in part an artifact of the multinomial regression.

(3) The multinomial regression treats the fitness landscape on which the virus evolves as static. However the fitness landscape is actually time-dependent, as population immunity changes. For a static fitness landscape, the cumulative fitness flux is the mean population fitness. However, for a dynamic fitness landscape, this is no longer the case: the (mean population) fitness changes both because of adaptation, and because changes of changes in the fitness landscape, i.e., an increase in population immunity will decrease fitness non-uniformly, also affecting individual selection coefficients s_i(t). This leads to cases where the cumulative fitness flux between t_0 and t_n can be positive, but the fitness remains the same. This study neglects the time-dependence of the fitness landscape and therefore incorrectly calculate the fitness flux, leading to an overestimation of the rate of adaptation.