Reviewer #3 (Public review):

Summary:

This study investigated the role of AHL15 in regulation of gene expression using AHL15 overexpression lines. Their results do show that more gene are downregulated when AHL15 is upregulated and its binding is not affecting the chromatin accessibility. Further, they investigated AHL15 binds in regions depleted in histone modifications and other epigenetic signatures. Subsequently, they investigated the presence of AHL15 in the gene chromatin loops. They found overlaps with both upregulated and downregulated genes. The methods are appropriately described, but could be improved to include the analysis of self-looping gene boundaries.

Strengths:

Their study clearly showed lack of any specific sequence enrichment in the AHL15 binding sites, other than these being AT-rich, suggesting that AHL proteins do not recognize a specific DNA sequence but are recruited to their AT-rich target sites in another way. The study does suggest significant enrichment of AHL15 binding sites at TSS and TES, and AHL15 sites are depleted of any histone marks. They also identified that AHL15 binding sites overlap with self-looping gene boundaries. The authors have addressed the comments raised in the revised manuscript.

Comments on revised version.

The authors have addressed the comments raised, in the revised manuscript.

Reviewer #3 (Public review):

Summary:

An outstanding question in the field of high frequency oscillations (HFOs) in the context of epilepsy is how these oscillations emerge, considering that they occur at such high frequencies i.e., 250Hz well above the firing ability of single neurons. One hypothesis that has been suggested in the past is that neurons that fire in an out of phase fashion or rather at random intervals may contribute to a spectrum of HFOs ranging from 250-500Hz that observed in epilepsy. However, how possible it is that random action potentials could aggregate to the extent that they could give rise to HFOs in the so-called fast ripple (FRs) frequency range (>200 according to the authors) remains unclear. To test this hypothesis, they used computational modeling to randomly insert action potentials in a signal, and they found that this approach is sufficient to generate FRs. Some of the predictors of whether FRs could occur were neuronal count, firing rate and synchronization. Besides computational modeling, they used different model systems to test whether that would be possible to be observed in neuronal cultures, in epileptic rats (intrahippocampal kainic acid model), and human data. Neuronal cultures treated with picrotoxin did not show evidence that FRs could be generated more than chance aggregation of action potentials. They then asked whether synchronization and firing rate could play a role in the emergence of FRs. They found that changes in neural firing and synchronization, such as those occurring during differences phase of the sleep-wake cycle could affect the number of FRs occurring by chance aggregation, with more FRs seen during periods of wakefulness, a result that they replicated in human data.

The authors largely achieve their proposed aims of demonstrating that random neuronal firing can, in principle, generate FRs. Results from this study could influence current thinking around mechanisms generating FRs in epilepsy. The use of different computational approaches and model systems could offer new analytical methodologies for the study of FRs in the context of brain disease.

Strengths:

(1) The authors used a multi-level approach combining computational modeling with experimental datasets, including neuronal cultures, a rat model of temporal lobe epilepsy and human data.

(2) Identification of key parameters such as neuronal count, firing rate, synchronization and brain state in observed incidence of FRs generated through random aggregation of neural firing.

(3) Cross-species validation increases the likelihood of generalizability of the findings.

Comments on revised version.

The authors have addressed my comments and have no further suggestions or any changes in my assessment.

Reviewer #3 (Public review):

Summary:

In this ambitious study the authors set out to analyse the validity of a number of claims, both minor and major from 400 published articles within the field of Drosophila immunity that were published before 2011. The authors were able to determine initially if claims were supported by comparing to other published literature in the field and if required by experimentally testing 'unchallenged' claims that had not been followed up in subsequent published literature. Using this approach the authors identified a number of claims that had contradictory evidence using new methods or taking into account developments within the field post initial publication. They publish their findings on a publicly available website designed to enable the research community to assess published work within the field with greater clarity.

Strengths:

The work presented is rigorous and methodical, the data presentation is high quality and importantly the data presented support the conclusions. The discussion is balanced and the study is written considerately and respectfully highlighting that the aim of the study is not to assign merit to individual scientists or publications but rather to improve clarity for scientists across the field. The approach carried out by the researchers focuses on testing the validity of the claims made in the original papers rather than testing whether the original experimental methods produced reproducible results. This is an important point since there are many reasons why the original interpretation of data may have understandably led to the claims made. These potential explanations for irreproducible data or conclusions are discussed in detail by the authors for each claim investigated.

The authors have generated an accompanying website which provides a valuable tool for the Drosophila Immunity research community that can be used to fact check key claims and encourages community engagement. This will achieve one important goal of this study - to prevent time loss for scientists who base their research on claims that are irreproducible. The authors rightly point out that it is impossible (and indeed undesirable) to avoid publication of irreproducible results within a field since science is 'an exploratory process where progress is made by constant course correction'. This study is however an important piece of work that will make that course correction more efficient.

Comments on revised version:

I'm impressed with the revisions carried out by the authors. No further comments.

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

Reviewer #3 (Public review):

Yadav et al. revisit the old conundrum of why certain genes encoding inner mitochondrial membrane (IMM) proteins are retained in the mitochondrial genome (mtIMMs; 13 genes in humans) while genes encoding other IMM proteins (nuIMMs) have been relocated to the nuclear genome during evolution. It was noted already in 1986 that nuIMMs have transmembrane helices (TMHs) of lower hydrophobicity than mtIMMs (FEBS Lett 198:1). A number of subsequent studies have shown that some mtIMMs can be converted to nuIMMs by appending a mitochondrial targeting sequence (MTS) and, in most cases, reducing the hydrophobicity of one or more of the TMHs (reviewed in Genes & Dis 7:578). Conversely, increasing the hydrophobicity of a TMH in a nuIMM protein can prevent its import into mitochondria (e.g., PNAS 99:10510). Whether the reduction in TMH hydrophobicity in nuIMMs is mainly driven by the need to bypass cotranslational targeting of the protein to the ER and trafficking along the secretory pathway, or to prevent the protein from being prematurely stuck in the IMM during mitochondrial import, is still, to some extent and open question, and experimental support for both possibilities exists (e.g., FEBS Lett 591:65).

Yadav et al. present an extensive bioinformatics study which nicely confirms the reduced hydrophobicity of nuIMM TMHs compared to mtIMM TMHs (and also compared to TMHs in proteins that traffic along the secretory pathway). The study is based on a lot more sequence data and many more kinds of comparisons than previous publications, and leaves little doubt that the reduction in hydrophobicity in nuIMM TMHs is real.

Based on their bioinformatics analysis, the authors also propose that aliphatic -> Thr substitutions (ATS) are the main driver of hydrophobicity reduction in nuIMMs, TMHs, and that Thr has been enriched also in mtIMMs. TMHs in organisms that depend only on Oxa1-type IMM insertases, possibly in order to reduce the aggregation propensity of these proteins when produced in the mitochondrial matrix. This is a novel hypothesis that has not been proposed in earlier work.

The authors additionally show that a mitochondrially encoded ATP6 protein can be converted into a nuclearly encoded, import-competent protein using ATS to reduce the hydrophobicity of its TMHs and, conversely, that import of a nuclearly encoded ATP6 protein is blocked when Thr residues in its TMHs are mutated to aliphatic residues. Similar findings have been published before; what is new is the use of aliphatic -> Thr substitutions (rather than other aliphatic -> polar substitutions).

Detailed comments:

While previous bioinformatics studies of how gene relocation from the mitochondrial to the nuclear genome impacts protein sequence have focused on TMH hydrophobicity, Yadav et al. expand this to include individual amino acid frequencies. The authors present four types of analyses:

(1) The authors identify E. coli homologs of the 13 genes retained in human mitochondrial DNA, do structure-based sequence alignments of the homologous pairs, and then compare the individual aligned TMH pairs in terms of hydrophobicity and Thr content. The authors also calculate an amino acid substitution matrix from the aligned TMH pairs. These data are presented in Figure 2 (and supplements).

Hydrophobicity is calculated based using the GRAVY algorithm and the Kyte-Doolittle scale, and by calculating the fraction of ALIVF residues. Both nuIMM and mtIMM TMHs are found to be significantly less hydrophobic than the E. coli TMHs (p ≈ 0.002). Thr is reported to be enriched in nuIMM and mtIMM TMHs relative to E. coli TMHs (as are Asn and Pro, while Val and Phe are reduced relative to E. coli TMHs, panel C). Panels F and G show an enrichment of Thr residues in nuIMMs of marginal significance (p = 0.02), and an enrichment in mtIMMs of higher significance (but the statistics are skewed by one mtIMM TMH that apparently contains almost 40% Thr residues). The substitution matrix in panel H indicates that aliphatic I, L, A, and G residues in the E. coli TMHs are often substituted by Thr and Val in the mitochondrial proteins. F does not show this substitution pattern, questioning the use of ALIVF counts in the analysis.

(2) The authors calculate hydrophobicities and amino acid frequencies for all human TMHs found in AlphaFold2 structures and grouped according to their cellular location: ER, Golgi, plasma membrane, nucleus, outer mitochondrial membrane (OMM), nuclearly encoded proteins in the inner mitochondrial membrane (nuIMM), and mitochondrially encoded proteins in the inner mitochondrial membrane (mtIMM). These data are presented in Figure 3 (and supplements).

OMM and nuIMM TMHs are significantly less hydrophobic than the secretory pathway TMHs, whereas mtIMM TMHs seem to have similar hydrophobicities as the secretory ones. nuIMM TMHs may have a slightly increased Thr content compared to secretory pathway proteins, but only one p-value is significant (Golgi vs nuIMM). mtIMM TMHs appear to have a higher Thr content than nuIMM proteins, but the statistics is skewed by a single point at a Thr fraction of 0.25. The Thr enrichment is less pronounced than for the E. coli comparison in Figure 2, Figure 3 Supplement. Fig. 8 shows that TMHs in chloroplast and mitochondrial plant proteins both have reduced hydrophobicity compared to ER-targeted proteins.

(3) Starting from the 66 mitochondrially encoded proteins in Jakobids, the authors identify homologous proteins that have become nuclearly encoded and carry an N-terminal mitochondrial targeting sequence (MTS) in organisms with smaller mitochondrial genomes. As in 1, the authors then do structure-based sequence alignments of the 63 homologous nuIMM and mtIMM TMH pairs found, and compare the individual aligned TMH pairs in terms of hydrophobicity and Thr content. These data are presented in Figure 4 (and supplements).

The reduction in hydrophobicity in nuIMM vs mtIMM TMHs upon relocation to the nucleus is robust and confirms earlier publications (panel C). The increase in Thr content in nuIMM vs mtIMM TMHs is less convincing (p = 0.012, panel G) and is opposite to what is seen in Figure 3C.

(4) By phylogenetic analysis, the authors identify three independent relocations of the ATP6 gene to the nuclear genome, do structure-based sequence alignments of nuIMM and mtIMM pairs representing these three events, and illustrate the reduced hydrophobicity of the TMHs in the nuclearly encoded proteins by hydrophobicity plots. These data are presented in Figure 5 (and supplements).

In panel C, it is not only aliphatic-to-Thr substitutions that reduce the hydrophobicity of the nuATP6 TMHs. How much is the hydrophobicity reduced if the Thr mutations are reversed (i.e., how much does the hydrophobicity reduction depend on ATS specifically and not on other kinds of substitutions)?

In a short experimental section, the authors go on to show that a mitochondrially encoded ATP6 protein can be converted into a nuclearly encoded, import-competent protein using ATS to reduce the hydrophobicity of its TMHs (but functionality is not tested) and, conversely, that import of a nuclearly encoded ATP6 protein is blocked when Thr residues in its TMHs are mutated to aliphatic residues (Figures 6, 7).

Finally, the authors carry out all-atom MD simulations to study the energetics of H-bond formation between Thr side chains and TMH backbone atoms.

In summary, the reduction in TMH hydrophobicity in nuIMMs compared to mtIMMs (and to secretory pathway membrane proteins) is robust and nicely confirms earlier studies. The purported enrichment in Thr content in both nuIMMs and mtIMMs has weaker statistical support in the data: p-values are typically > 0.01 (or may be skewed by an outlier) and appear not to have been corrected for multiple testing in all cases. As is clear from the ATP6 data in Figure 5, aliphatic -> Thr substitutions explain only a fraction of the overall hydrophobicity reduction in nuIMM TMHs, and the role of other hydrophobicity-reducing mutations in gene relocation could be better defined.

Reviewer #3 (Public review):

Summary:

The authors aimed to build a comprehensive understanding of the cell classes that make up the adult zebrafish olfactory bulb (OB) and their connectivity. Using an EM volume covering the three OB layers and several glomeruli, they aim to reconstruct a sufficient sample of all cell classes in the OB. Based on 459 proofread reconstructions, they identify 13 cell classes and a small number of subclasses and offer detailed expert neuroanatomical arguments, largely qualitative, for why these classes are distinct. While they do not have a synapse classification, they further build a manual wiring diagram of connectivity based on these reconstructions, which together offer a strong baseline understanding of the organization of the OB and useful properties to build into a future quantitative assessment of fish neuroanatomy in general.

Strengths:

The paper offers a powerful example of why expert assessment remains a vital tool in neuroanatomy. While quantification is deeply important to scale data, the kind of careful multimodal human investigation of neuronal shape, connectivity, and ultrastructure forms a key basis for discovering and differentiating neuronal cell types. The authors use multi-expert classification to validate qualitative assessments as well, which is a good way to handle cross-individual uncertainty. The descriptions of each type and their relationships are generally quite thorough, and there are enough cells to offer a satisfying baseline categorization. Anyone studying zebrafish OB is likely to get a lot out of this paper.

Weaknesses:

While I am a strong believer in expert neuroanatomical intuition alongside quantitative neuroanatomical approaches to cell typing (especially given the manageable number of cells and cell types), I would have liked a section explaining why this approach was taken as opposed to a more quantitative one from the beginning. I think that this is a case study in where the expert qualitative approaches are useful, especially with fascinating details like the unusual hand-shaped spines. A lot of the strength of the paper comes from this philosophy, in my view, and it would be nice to hear it elucidated by the authors. I would also be curious if the authors came to any conclusions about what might have been possible with a more data-driven approach, although this may be beyond the scope of the work or not terribly interesting.

There were some aspects of the cell typing that I didn't totally understand. Two of the most common issues with cell typing are that (1) it is unclear if cell types are discrete and (2) unusual cells appear that don't have enough peers to classify into an unambiguous type. Did those happen here? For example, it was not obvious to me how it was decided that mitral cells fell into three subclasses: small, medium, and large, and not a continuum from small to large. Similarly, some interneuron classes had subclasses. What anatomical qualities suggested that a split was a subclass-level split vs a class-level split? Were there any cells that were impossible to classify into a group, or was everything actually tidy? How confident are the authors that they have captured the complete diversity of the OB from this sampling?

I generally appreciated the numerous detailed figures and found them generally clear and informative. However, as someone who is not deeply familiar with zebrafish olfactory bulb organization, I would have liked to have at least some global context for each cell class in terms of how typical cells relate to the layers and glomeruli. Figure 18B does a nice job of this with many cells, but it would be clearer to see this along the way with the individual classes (and subclasses) as well.

For the connectivity analysis, I commend the authors for doing as much manual synapse annotation as they did, but the lack of completeness makes it less clear how to interpret the connectivity findings. In particular, how complete is the current assessment? For example, when describing connectivity between pairs of cells, how were the pairs selected? Did they necessarily have contact between the meshes, or innervate the same glomerulus, or were they just two cells of the same type in any location? This is useful to understand how to interpret the connectivity fractions measured.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

Reviewer #3 (Public review):

The study is built on the premise that the SMAC mimetic AZD5582 can reverse HIV latency via the RelB-mediated non-canonical NF-kB pathway activation; however, its molecular regulators remain undefined. The authors use an inducible BioID RelB reporter system in Jurkat N6 cells containing an integrated HIV-1 provirus and identify both the basal and the AZD5582-activated chromatin and transcriptional regulatory network of the non-canonical NF-kB pathway. To further explore the role of the identified regulators, an HIV-CRISPR latency screening approach was used, and based on enrichment or depletion of guide RNAs in the supernatant, genes that inhibited or activated virus reactivation, respectively, were identified. Genes like LRPPRC (RNA-binding protein), KAT7 (acetyltransferase), NSD2 (H3L36 methyltransferase), SIN3A (part of histone deacetylase complex), and others were identified to be essential for HIV reactivation. Nearly half of the identified factors were found to be negative regulators of reactivation, notably CBP/p300 (acetyltransferase), CHD4, USP7, UHRF1, and DNMT1. The roles of KAT7 and p300 were validated through the use of specific inhibitors. To narrow down regulators that are specific for nc NF-kB HIV reactivation vs NF-kB dependent HIV expression, a parallel CRISPR screen was performed with TNF-alpha, which reconfirmed the role of RelB, KAT7, and p300 in AZD5582-mediated HIV reactivation.

Overall, this is a solid and well-designed study that combines complementary approaches to deliver a valuable resource.

The study's strength lies in integrating two high-throughput approaches of RelB proximity labelling and targeted CSIPR screening to identify molecular regulators of the HIV reactivation pathway. A parallel CSIPR screen with TNF-alpha provides key insights into pathway-specific vs shared regulators of latency reversal.

The limitation of the study, as indicated by the authors, is that the assays are based only on HIV output. Additionally, some of the identified interactors may be indirect or context-dependent and may not represent all HIV reservoirs in vivo.

Reviewer #3 (Public review):

Summary:

Drosophila neuroblasts (NBs) serve as a well-established model for studying neural stem cell biology. The intrinsic genetic programs that control their mitotic potential throughout development have been described in remarkable detail, highlighting a series of sequentially expressed transcription factors and RNA-binding proteins that together constitute the temporal patterning system.

However, the mechanisms that limit the number of NB divisions remain largely unknown in a specific subset of NBs known as mushroom body neuroblasts (MB NBs). Unlike other NBs, which terminate proliferation before or shortly after the onset of metamorphosis, MB NBs continue dividing until the end of metamorphosis, ceasing only just before adulthood.<br /> In this study, the authors identify the transcription factor Krüppel (Kr), a member of the conserved Krüppel-like family, as temporally regulated in MB NBs. They demonstrate that Kr knockdown during pupal stages maintains expression of the RNA-binding protein Imp and results in prolonged MB NB proliferation into adulthood. Their data suggest that Kr contributes to the timely silencing of Imp during metamorphosis. The authors further identify Kr-h1, a related transcription factor, as a potential antagonist. While Kr-h1 appears dispensable for the timely termination of MB NBs under normal conditions, its overexpression leads to their continued proliferation and tumor-like expansion in adults.

This work provides the first evidence for a transcription factor-driven temporal regulation mechanism in MB NBs, offering new insight into the control of neural stem cell self-renewal. Given the evolutionary conservation of Krüppel-like factors, this study may have broader implications for the neural stem cell field.

Strengths:

(1)The study possibly identifies a new series of temporal transcription factors that are specific for mushroom body neuroblasts.

(2) The mechanism could be conserved in vertebrates.

Comment on revised version.

The authors have clarified the expression status of Kr in the KrIf-1 mutant, as well as the regulatory interactions among Kr, Kr-h1, and E93. My other concerns have also been satisfactorily addressed. I congratulate the authors on this excellent work and on discovering a novel mechanism that regulates temporal progression and the termination of division in mushroom body neuroblasts.

Reviewer #3 (Public review):

Summary:

S. Keeley & collaborators propose a computational approach to infer time-varying latent variables directly from calcium traces (e.g., obtained with 2p imaging) without the need for deconvolving the traces into spike trains in a preliminary, independent step. Their approach rests on 1 of 3 families of latent models: GPFA, HMM and dynamical systems - which they augment with an observation model that maps latent variables to fluorescence traces. They validate their approach on simulated data as well as a single real dataset, showing that the approach improves latent variable inference and model fitting, compared to more traditional approaches (although not directly compared with the 2-step one; see below). They provide a GitHub repository with code to fit their models (which I have not tested).

Strengths:

The approach is sound and well-motivated. The authors are specialists of latent variable models. The manuscript is succinct, well-written and the figures are clear. I particularly liked the diversity of latent models considered, in particular latent models with continuous (GPFA) vs. discrete (HMM) dynamics, which are useful for characterizing different types of neural computations. The validation on both simulated and real data is convincing.

Weaknesses:

The main weakness point that I see is that the approach is tested only on a single real dataset (odor response dataset). The other model fits are obtained from simulated data. While the results are convincing, it would be useful to see the approach tested on other datasets, for instance datasets with different brain areas, different behavioral conditions, or different calcium indicators. This would help assess the generality of the approach and its robustness to different experimental conditions.

Reviewer #3 (Public review):

Summary:

The authors reanalyze genome assemblies of four indigenous chicken breeds from Yunnan Province together with the red jungle fowl reference (GRCg6a), and search for genes with disrupted protein-coding sequences. They catalog candidate pseudogenes and missing genes to estimate that 7,993 of the ancestral genes are dispensable. They characterize the positional distribution of pseudogenization mutations along coding sequences, their fixation in breeds, their estimated ages, and their pathway enrichments. Most results are replicated in four independent PacBio HiFi-based chicken assemblies. From the biased position of pseudogenization mutations toward CDS ends, their frequent fixation, and the phylogenetic signal in gene-loss patterns, the authors conclude that large-scale loss of function is a major driver of chicken evolution and domestication, consistent with the "less-is-more" hypothesis.

Strengths:

The catalog itself is a substantial resource: the comparison spans multiple closely related genomes, the main patterns are checked in a second, independently assembled set of HiFi genomes, and population resequencing data are used to ask whether the pseudogenization mutations are fixed rather than segregating. The finding that candidate loss-of-function genes are under relaxed purifying selection is well supported. The question of what gene loss contributes to domestication is worth asking, and this is a useful dataset for asking it.

Weaknesses:

The finding that genes carrying disruptive mutations are under relaxed selection is not particularly surprising, and the more interesting claim, that the observed patterns reflect positive selection for gene loss, is less certain in my opinion.

(1) Relaxed purifying selection versus positive selection. The bias of pseudogenization mutations toward the two ends of coding sequences is interpreted as positive or artificial selection, along with elevated dN/dS. But the alternative, that disruptive mutations at gene ends are simply better tolerated, is equally consistent with the results. Several mechanisms would produce this pattern under relaxed constraint without positive selection per se: alternative downstream start sites that rescue 5-prime disruptions; the small fraction of protein truncated by 3-prime disruptions; and the enrichment of disordered regions at protein termini.

(2) The functional status of the pseudogenes is assumed, not demonstrated. Genome-scale work cannot be expected to validate individual genes, but the language of the paper should reflect the candidate status of these calls. Nearly all predicted pseudogenes (~95%) were reported as transcribed in multiple tissues. It is possible for a nonfunctional coding sequence to retain intact regulatory sequences, but the observation deserves more attention in the paper, particularly because transcripts carrying premature termination codons can be the targets of nonsense-mediated decay, which is not discussed. These remain candidate pseudogenes defined by the presence of a putatively large-effect mutation (e.g. premature stop or frameshift).

(3) What "missing" means. For a gene to be scored as completely absent, it could be genuinely deleted, or its allele could be diverged enough that annotation and orthology/mapping no longer detect it. These are different phenomena. Related, since most pseudogenization mutations are reported as fixed or nearly fixed in their populations, the history of alleles matters: it is not clear how the authors established which state is derived, and whether the reference sequence assumed to be functional is in fact the "functional" version.

(4) Limited biological insight into domestication. The main biological interpretation rests on hierarchical clustering of dispensable genes followed by ontology enrichment within clusters (Figure 7a), with narrative connections to breed phenotypes. Only 19.6% of dispensable genes have Gene Ontology assignments, and the phenotype links are speculative. The section on the subspecies origin of the GRCg6a reference is only loosely connected to the loss-of-function story, and the population-genetic analysis supporting it is thin as described.

Reviewer #3 (Public review):

Summary:

This manuscript presents a comprehensive proximity proteomics analysis of Golgi- and endosome-associated CATCHR complexes, including COG, GARP, and EARP, using near-endogenously expressed TurboID-tagged subunits. The study aims to define how these tethering complexes organize distinct trafficking modules involving coiled-coil tethers, Rab-associated proteins, SNAREs, SM proteins, and other trafficking regulators. In addition to validating known associations, the authors identify CCDC186 as a candidate vesicle tether and WWOX as a potential regulator of Golgi homeostasis and glycosylation. The work provides a broad resource for understanding the spatial organization of CATCHR-associated trafficking networks. However, several conclusions require further experimental and statistical support, particularly those interpreting proximity-labeling data as physical interactions or evidence of discrete functional complexes.

Strengths:

The manuscript addresses an important question in membrane trafficking and provides a systematic comparison of the proximity interactomes of COG, GARP, and EARP complexes. The use of near-endogenously expressed TurboID-tagged subunits is a strength, as it may reduce artifacts associated with protein overexpression. The dataset is comprehensive and has the potential to serve as a valuable resource for investigators studying Golgi and endosomal trafficking. The comparative analysis identifies both known trafficking factors and potentially novel regulators, including CCDC186 and WWOX. The functional follow-up experiments add biological relevance to the proteomic findings and extend the study beyond descriptive mapping. Overall, the scope is well defined, and the manuscript proposes an interesting model in which CATCHR complexes act as organizing hubs for vesicle tethering and fusion.

Weaknesses:

A major limitation is that TurboID proximity labeling is repeatedly interpreted as evidence of physical interaction or assembly into discrete complexes, although the method primarily reports spatial proximity. The soluble GFP-TurboID control may not adequately account for enrichment caused by membrane confinement and high local protein concentration in Golgi/endosomal microdomains. Some spatial enrichment analyses do not reach statistical significance, weakening claims of compartment-specific labeling. Several low-fold-change SNARE hits are emphasized despite limited enrichment. Additional quantitative imaging is needed, including colocalization analysis of streptavidin labeling with Golgi/endosomal markers and localization of GFP-TurboID. The WWOX knockdown phenotype requires validation by rescue, independent siRNA, or CRISPR-based approaches. Several supporting data, quantifications, figure corrections, and nomenclature revisions are also needed.

Reviewer #3 (Public review):

Summary:

In this work, the authors aim to build on their previous work to further define the mechanism by which GDNF can induce enteric nervous system migration and differentiation in the Holstein mouse model (Hol Tg/Tg) of Hirschsprung's disease (HSCR). They show that GDNF can cause neuronal differentiation from either Schwann cell precursors (SCP) that migrate from the periphery and/or enteric glia (or neural crest precursors located in the intestine). Surprisingly, the rescue effect of GDNF is mediated through signaling with NCAM1 rather than the canonical receptor RET, with blockade of NCAM1 significantly reducing the number of GDNF-treatment induced neurons in the distal colon of Holstein mice. The authors then perform scRNA-seq data of the colonic ENS from Holstein mice +/- GDNF, and pseudotime analysis of this data indicates the SCP downregulate SCP genes and upregulate classical enteric glial genes on their transition to becoming enteric neurons. Following this, the authors employ Dhh-Cre mice (to label SCP), Slc18a2Cre and GFAPCreERT2 (to label enteric glia) and finally Wnt1Cre2 mice (to label neural crest derived cells) to assess the kinetics and end identity of enteric neurons driven by GDNF treatment. Using these animal models, the authors propose a model by which transdifferentiation of SCP gives rise to enteric neurons first, followed by transdifferentiation of enteric glia; the resulting enteric neurons labelled by Dhh, Slc18a2 or GFAP Cres are all biased to a cholinergic lineage, raising a questions about the source of nitrergic neurons. Finally, the authors postulate that at least some nitrergic neurons come from a non-neural crest lineage in GDNF treated Hol Tg/Tg mice as they are not labelled by a Wnt1Cre2 model, while all ENS cells are labelled in three wildtype Wnt1Cre2Tg/+;R26YFP/+ mice.

Strengths and Weaknesses:

This work convincingly shows that NCAM1 is required for GDNF-induced rescue of the Holstein mouse model of HSCR and, along with the NCAM1 staining in human HSCR tissue, highlights the potential of this treatment even in HSCR patients with loss of function RET mutations. The major weakness of this study is in the interpretation of the mouse models; many of these models are not fully penetrant/show mosaicism in other studies (like the DhhCre and Wnt1Cre) or may not only label the specific subpopulation they present (in this case, the GfapCreERT2 and Slc18a2Cre; these genes have been shown in Schwann cells/SCP in other studies, and the expression pattern of these two genes outside of the gut at the relevant developmental timepoints are not shown). While these are unavoidable limitations with our current mouse models, the authors need to clearly acknowledge these limitations in the interpretation of their data (with the DhhCre and Wnt1Cre) or more definitively show the models are only labelling enteric glia (in the case of the Gfap and Slc18a2Cre). In addition, in some figures the authors should add untreated Hol Tg/Tg and ideally WT mice so readers can contextualise the effect of GDNF on Hol Tg/Tg mice. Finally, it appears the statistics throughout the paper are performed on using each ROI taken from n=3-5 animals - this is inappropriate, as images from the same animal are not independent samples. The statistical unit in question for every analysis performed should be 1 animal, thus the statistics should be done on the n=3-5 animals, not the n=3-11 images from 3-5 animals (which leads to an n of 6-55).

Specific comments:

In general I find the author's conclusions are relatively well supported, albeit a little overstated for the evidence presented in the later figures and with the need to correct the statistical analysis. For Figures 1-5, I have only minor comments, as follows.

In Figure 1-2, the Gata4-RFP reporter is used to sort cells for scRNA-seq, and the authors get a large number of non-ENS cells so utilise Elavl4 and Sox10 expression to pull out neurons and glia to analyse. Do other putative neuronal/glial genes pull out the same cells, and can the authors find evidence of non-neural crest derived glia/neurons in this dataset? This would ensure there isn't a bias in this first analysis and strengthen the later claims respectively.

In Figure 3, the interpretation of increased NCAM1 and pFAK would be aided by adding the MFI for the untreated Hol Tg/Tg and ideally WT mice at p10 to assess if this is a normal developmental increase or induced by GDNF treatment - I would ask the authors add at least untreated Hol Tg/Tg at p10 as authors already present this data as images in figure 3D. Similarly, it would be nice to have neuronal counts of untreated Hol Tg/Tg and WT mice presented in Figure 4c to assess if NCAM1 inhibition completely or only partially prevents the effect of GDNF.

In the text accompanying Figure 2 and 5, the authors use a variety of markers of assign SCP or enteric glia identity to the clusters, but some of the enteric glia genes can be expressed by Schwann cells/SCP in other contexts - it is worth adding references to the genes chosen to define SCP and enteric glia so readers can understand why those were chosen and the strength of evidence for each marker. For me, with the exception of Dhh I do not find any of these genes definitive as, at least on the SCP/Schwann cell side, they have been shown to express Gfap, Slc18a2, Sox2 and single cell analysis indicates they also express Cpe. The ability for SCP/Schwann cells to express both Gfap and Slc18a2 is important for the interpretation of the upcoming experiments.

In Figure 6, the authors state in line 213 that "quantitative analysis as a function of time revealed that GFAP+ cells in the distal colon are not all derived from Dhh-expressing SCPs on the first day of treatment"; however, the DhhCre is restricted to specific SCP subsets (Xie, Meng, et al. "Schwann cell precursors contribute to skeletal formation during embryonic development in mice and zebrafish." Proceedings of the National Academy of Sciences 116.30 (2019): 15068-15073) and thus may not be labelling all SCP coming into the intestine. As far as I know there are no models that would allow labelling of all SCP without labelling enteric glia so would require the generation of a new models or techniques that is out of scope for this work - however, the authors should acknowledge the possibility of Dhh negative SCP contributing to the ENS.

Figure 7 and 8 focuses on the contribution of Dhh+ cells, Slc18a2+ cells and cells expressing GFAP between p3-p8 to the ENS of GDNF treated Hol Tg/Tg mice. The authors first show that by p20 the number of neurons per mm2 is equivalent to those at p60, indicating p20 as an appropriate timepoint to assess the relative contribution of each pool of glia to the resulting neurons. The authors show that neurons derived from a DhhCre are detectable first, then Slc18a2, followed finally by GFAP. This data supports the authors claims that neurons first arise from Dhh+ SCP, but as I raised earlier, both Slc18a2 and GFAP can be expressed by schwann cells later in development - the authors in fact show that GFAP is in fact expressed by Dhh+ cells in figure 6, and showed with pseudotime analysis that SCP undergo a transition to Gfap+ Slc18a2+ glial cells before becoming neurons. The data presented here negates that analysis, or at least suggests some Dhh+ cells skip this step, otherwise the kinetics of neuronal differentiation would align across the 3 lines used. Could the authors address this discrepancy? For example, one addition could be to show that SCP/schwann cells on extrinsic gut innervating nerve fibres do not express Gfap or Slc18a2 at the timepoints used. In addition, their scRNA-seq analysis suggests that Gfap & Slc18a2 are expressed in the same cells, so one would expect similar kinetics in both populations - what is the overlap of GFAP staining with Slc18a2-YFP expression at the earlier stages (say p6 and p10?). Alternatively, the inducible nature of the GFAPCreERT2 means some bona fide GFAP expressing cells may be missed if the tamoxifen dose is not saturating, thus underestimating the contribution of this population to the total neuron count. As such, could the authors please show co-staining with GFAP in some induced GFAPCreERT2 samples to assess the effectiveness of this Cre. As with other figures, it would also be nice to add Holstein negative reporters at p20 as a control to assess the level of contribution each one of these cell lineages has in a normally developing animal compared to GDNF treated Hol Tg/Tg mice.

In Figure 9, the authors show that neurons derived from labelled cells in the DhhCre-YFP or Slc18a2Cre-YFP skew strongly cholinergic (81%) compared to nitrergic (13%), but only 52% of the total neurons in GDNF treated Hol Tg/Tg mice are cholinergic, while 42% are NOS1+. The authors say that a similar trend is seen in Slc18a2Cre-YFP mice with a 'slightly higher percentage' reflecting Slc18a2 expression in neurons. However, this 'slightly higher' percentage looks to be about 40%, which is the amount expected in GDNF treated Hol Tg/Tg. I think the authors underplay the level of nitrergic neurons that are labelled in the Slc18a2Cre mice and should consider the possibility that Slc18a2+ glia can give rise to the nitrergic neurons seen in GDNF treated Holstein mice. This data also contrasts with prior reports that indicate SCP tend to become NOS1 expressing in a Ret or Ednrb loss of function HSCR model (Uesaka, Toshihiro, et al. "Enhanced enteric neurogenesis by Schwann cell precursors in mouse models of Hirschsprung disease." Glia 69.11 (2021): 2575-2590), a discrepancy worth discussing.

In Figure 10, the authors assess the contribution of neural crest to the ENS of WT and Hol Tg/Tg mice using a Wnt1Cre2 line, and conclude there is non-neural crest contribution to the ENS of GDNF treated Hol Tg/Tg mice. This is an interesting idea and supported by a few studies the authors mention in the discussion; however, the labelling of neural crest derived cells in Wnt1Cre2 may not be fully penetrant. While this line has a well established leaky phenotype, others have reported it can have patchy recombination in the neural crest too (potentially in a background dependent manner, according to the line information on Jackson laboratories). For example, recent works shows significant mosaicism in the embryonic phase (Gandhi, S., Du, E. J., Pangilinan, E. S., & Harland, R. M. (2024). The Wnt1-Cre2 transgene causes aberrant recombination in non-neural crest cell types. bioRxiv, 2024-11.) This may not be the case in the authors home institution in a FVB background, as they show all HuCD+ and Sox+ cells overlap with the Wnt1Cre2 reporter, but the Hol Tg/Tg mice have aberrant neural crest development - this could interfere Wnt1 expression thus the effectiveness of the Wnt1Cre2, thus leading to non-labelled, but actually neural crest derived cells. It is also possible that those neurons are truly from a non-neural crest background, and the neural crest labelling tools are all flawed in one way or another - as such, I think the authors just need to discuss this alternative possibility in the discussion.

Finally, Figure 11 shows NCAM1 expression in human tissue resections from HSCR patients. This data bolsters the idea that GDNF treatment could be an effective treatment for HSCR. The methods indicate that 3 tissue samples from HSCR patients were obtained, but staining is only shown for one - could all 3 be included, along with secondary only controls? This is important as human tissue resections are often processed many hours after removal, so are prone to higher background. It would also be interesting to include tissue from non-HSCR patients (the controls used most often in other studies are resections from anorectal malformations which are performed at approximately the same age as HSCR resections) to see if NCAM1 expression is influenced by the absence of an ENS in human patients, but I understand these samples are not always possible to obtain. If not possible, the authors could consider analysing publicly available RNA sequencing datasets to assess if RET is solely expressed in mature neuronal populations while NCAM1 is expressed by glial and neuronal populations like in their mice data.

Overall, the authors clearly show that GDNF can drive differentiation of neurons from different glial populations through NCAM1 not RET, and supports continued effort to translate this work to the clinic as a potential therapy for HSCR patients. I am not convinced that the relative contributions of each glial subpopulation can be extrapolated from the mouse models used - this is a problem across the field, and will only be rectified with the development of new models, which is out of scope for this work. This work complements recent work showing NCAM1-GDNF signaling promotes neurogenesis in glia in Ednrb-/- mice (Mueller, Jessica L., et al. "Intramuscular enteric glia persist in Hirschsprung disease and undergo neurogenesis in response to GDNF-NCAM1 signaling." Scientific Reports 15.1 (2025): 33200), and will add to our understanding of neural development.

Reviewer #3 (Public review):

Summary:

The authors were investigating the impact of introducing novel facultative bacterial endosymbionts into the pest aphid, Diuraphis noxia, to explore the possibility of using facultative symbionts as a crop protection tool. They successfully established the vertical transmission of both endosymbionts and performed a series of aphid performance and dispersal experiments together with measurement of aphid feeding on host plant health, growth, and metabolism. While most of the experiments revealed no effect of the endosymbionts, some significant treatment effects were found, showing that Rickettsiella reduced aphid dispersal, and Regiella reduced aphid population growth and feeding damage.

Strengths:

The team worked with two novel facultative symbionts (Rickettsiella viridis and Regiella insecticola) that they were able to successfully establish in D. noxia. The data were collected and analyzed using solid, well-described methodology.

Weaknesses:

While interpretation of the data is reasonable, the few experiments which revealed significant treatment effects rest on relatively small sample sizes.

Measuring symbiont density is difficult. The authors use quantitative PCR to measure the "density" of endosymbionts relative to a host gene. This is a standard approach in the field; however, recent work has shown that endosymbionts like the aphid primary endosymbiont, Buchnera, are variably polyploid [1]; further the aphid cells that house the symbionts are also highly polyploid and variable in their ploidy [2]. It is important to understand that what is being measured when using qPCR is DNA copy number and not quantification of the number of symbiont cells. Alternative approaches to measuring symbiont density include flow cytometry [3], and SymbiQuant [4], a machine vision tool that can quantitatively characterize symbiont populations from DAPI-stained confocal images. These alternate approaches also have their limitations. Currently, there is no perfect approach to measuring symbiont density, which remains an important measure in experiments such as these. Put simply, it is important for a reader to be aware of the limitations of each approach and interpret data accordingly.

[1] Komaki, K., and H. Ishikawa. 2000. Genomic copy number of intracellular bacterial symbionts of aphids varies in response to developmental stage and morph of their host. Insect Biochemistry and Molecular Biology 30:253-258.

[2] Nozaki, T., and S. Shigenobu. 2022. Ploidy dynamics in aphid host cells harboring bacterial symbionts. Scientific Reports 12:9111.

[3] Simonet, P., G. Duport, K. Gaget, M. Weiss-Gayet, S. Colella, G. Febvay, H. Charles, J. Viñuelas, A. Heddi, and F. Calevro. 2016. Direct flow cytometry measurements reveal a fine-tuning of symbiotic cell dynamics according to the host developmental needs in aphid symbiosis. Scientific Reports 6:19967.

[4] James, E. B., X. Pan, O. Schwartz, and A. C. C. Wilson. 2022. SymbiQuant: A machine learning object detection tool for polyploid independent estimates of endosymbiont population size. Frontiers in Microbiology 13:816608.

Impact and Significance:

Food security and production, and pest control are major challenges facing the human population. This work contributes knowledge that will benefit the development of alternate pest control strategies in agriculture.

Reviewer #3 (Public review):

Summary:

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

Strength:

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

Weakness:

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

Reviewer #3 (Public review):

The authors sought to determine when the dorsal hippocampus and mPFC are causally required during a delayed spatial working memory task. Using temporally precise optogenetic silencing in mice performing a delayed cue-guided T-maze task, they tested the effects of transient perturbations during distinct behavioral epochs. Contrary to the common view that these regions are primarily required during the delay period to maintain working memory representations, they report that silencing during the delay had little effect on performance, whereas perturbation during the early phase of central-arm traversal consistently impaired performance. The authors conclude that hippocampal and prefrontal contributions to memory-guided behavior are dynamically engaged during active navigation rather than passive maintenance of information.

The study has several strengths. The behavioral paradigm is carefully designed to dissociate cue, delay, and movement epochs, allowing temporally specific causal manipulations. The systematic comparison of multiple task epochs represents a major strength and provides compelling evidence that the behavioral effects of perturbation are epoch-dependent. The authors also include several important controls, including stimulation during multiple task phases, a longer-delay condition to dissociate task epoch from elapsed time since cue presentation, and analyses of movement trajectories and perseverative behavior that provide additional insight into the nature of the behavioral deficits. Together, these experiments convincingly demonstrate that transient dorsal hippocampal and mPFC perturbations have markedly different behavioral consequences depending on when they occur within a trial.

The evidence is generally solid and supports the primary finding that perturbations during early navigation produce larger impairments than perturbations during the delay period. However, some aspects of the broader interpretation are less well supported. Most notably, the study lacks a non-memory control task, such as a visually guided version of the maze, making it difficult to determine whether the observed deficits specifically reflect disruption of memory-guided behavior or more general impairments in action selection, behavioral flexibility, or movement planning. The observed increase in perseverative responding and delayed commitment to a turn are consistent with either interpretation. In addition, several experimental conditions rely on relatively small numbers of animals, limiting confidence in some negative findings, particularly for the longer-delay and later-run manipulations. Finally, while the Discussion proposes that hippocampal-prefrontal circuits become engaged during the transformation of stored information into action, this mechanistic interpretation remains speculative because no neural recordings accompany the causal manipulations.

Overall, the authors achieve their primary aim of demonstrating that the behavioral consequences of dorsal hippocampal and mPFC silencing depend strongly on task epoch. The data convincingly support the conclusion that these structures are more vulnerable to perturbation during early navigation than during the brief delay period used in this task. The broader conclusion that these findings redefine when hippocampal-prefrontal circuits support working memory should be interpreted more cautiously, as alternative explanations involving action selection or behavioral state remain plausible in the absence of additional control tasks.

The findings are potentially important because they challenge the common assumption that hippocampal and prefrontal contributions to delayed-response tasks are centered on delay-period maintenance. Instead, the work supports the idea that these circuits may be recruited when remembered information is translated into goal-directed behavior. This framework is broadly consistent with recent distributed models of working memory and provides an interesting perspective that may help reconcile previous studies reporting effects during different task phases. The behavioral paradigm and temporally precise perturbation approach should also be useful for future studies aimed at dissecting the dynamic contributions of hippocampal-prefrontal circuits during memory-guided behavior.

Reviewer #3 (Public review):

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

Reviewer #3 (Public review):

Summary:

Analysis of immune receptor repertoires (IRR) needs to take into account the underlying diversity of the repertoires analysed, and the limitations inherent to the technologies used to measure IRRs: limited sampling depth relative to total number of cells and clonotypes, and experimental noise. In this work, Kitanovski and colleagues present ClustIRR. ClustIRR proposes to improve the analysis of immune receptor repertoires, specifically TCRs in the presented applications, by performing two steps: (1) consistent and comparable sequence clustering across repertoires, to account for sparsity of sampling, and (2) estimation of sequence clusters of interest using a Bayesian approach. They showcase ClustIRR performance in 3 scenarios: detection of antigen-specific T cells in a peptide-stimulation, detection of T cells responding to immunotherapy in the context of lung cancer, and analysis of mouse and human T cell repertoires.

Strengths:

(1) The cluster occupancy calculation presents an important conceptual framework which would be of interest and useful to the TCR repertoire field as it smoothly integrates information over a set of experimental conditions or time points. The application to longitudinal TCR sequencing datasets is particularly interesting, and could easily be extended to BCR sequencing datasets. Moreover, the calculation can be performed with any user-defined grouping of TCR clones, which allows for usage of other existing methods as the user wishes.

(2) The results of the human and mouse repertoires provide a very insightful argument for the use of metaclones as opposed to single clones for analysis of repertoires compared to single clones.

Weaknesses:

(1) While ClustIRR provides an interesting framework to analyse TCR sequencing datasets, it is not clear whether ClustIRR can perform more informative sequence clustering than state-of-the-art methods. A comparison of obtained clusters with existing methods would provide a useful benchmark. Moreover, computation time scales quite fast with the number of sequences included. This is a major limitation, as the authors state that a time of 2.5 hours is required for clustering of ~100,000 sequences, a number of clones that can easily be reached when analysing multiple samples together.

(2) The authors claim in the abstract that ClustIRR is integrated with gene expression data. However, in the results presented, the gene expression and TCR sequencing data are analysed separately, and the results are simply correlated. No real integration in the analysis exists for these two data types. The claim should be removed from the abstract. Moreover, the differential gene expression section, while it presents interesting results, lacks clarity and transparency. Presentation of the data in more transparent ways (such as showing violin plots or clustering on the UMAP) would increase the strength of the claims.

(3) The score calculation does not seem to be normalized to take into account the underlying diversities of CDR3a and CDR3b. While still a useful metric for sequence clustering, I worry about the impact of the lack of normalisation on the conclusion that the "alpha chain drives functional convergence through germline bias". The observation that clustering is mostly driven by the J gene is known and expected (https://pmc.ncbi.nlm.nih.gov/articles/PMC5553937/), as the J gene has lower diversity and greater overlap with the definition of CDR3. Because CDR3a is lower diversity generally from CDR3b, it will likely dominate the sequence similarity signal. Thus, it will appear that Ja drives the signal. The authors do try to address this by looking at clusters driven by CDR3b similarity. However, a low number of CDR3b-driven clusters is consistent with the similarity definition. I wonder if instead the appropriate control for this analysis would be to run the same analysis disregarding CDR3a sequence altogether, and quantify whether similar species-specific DCJ are identified when only CDR3b similarity is used? Absence or reduction of species-specific clustering would confirm that the effect is driven by the CDR3a sequence.

Reviewer #3 (Public review):

Summary:

This study presents a comprehensive meta-analysis of choice probability (CP), a classic metric of the relationship between single-neuron responses and an animal's perceptual judgment. The authors compiled data from 59 macaque electrophysiology studies and identified several factors that consistently influence CP magnitude, including neuronal sensitivity, brain area, stimulus duration, and task type. These results provide evidence that helps settle several long-standing debates about how CP should be interpreted.

Strengths:

This work is a rare example of meta-analysis in macaque electrophysiology, focusing on an important and long-debated metric, choice probability (CP), in the study of sensory and decision-making mechanisms. CP has been measured across many studies, but its interpretation remains contentious because it depends on numerous task and recording factors in addition to sensory and decision-making mechanisms. Individual macaque studies also typically include few subjects, and CP effects are generally small, which prevents any single study from drawing strong conclusions about general patterns. The authors identified an ideal use case for meta-analysis and combined fragmented findings from individual primate studies into a coherent picture of which factors matter most for CP and which do not. This work can also serve as a guide for future meta-analyses of macaque electrophysiology data.

Weaknesses:

While the survey and discussion of CP's interpretation are comprehensive, the paper would benefit from a clearer conclusion on why measuring CP remains important and what future directions could make CP more useful for revealing sensory and decision-making mechanisms.

Reviewer #3 (Public review):

Summary:

This study integrates living neuronal circuits with a real-time reinforcement-learning framework to enable closed-loop optimization of directional firing sequences at single-spike resolution. By using the spatiotemporal structure of neuronal firing as feedback, the system adaptively searches for stimulation patterns that increase a predefined propagation reward. The work represents an exciting step toward systematic and adaptive control of living neuronal networks.

Strengths:

The study presents a cutting-edge closed-loop platform that combines neuronal cultures, high-temporal-resolution electrophysiology, and reinforcement learning with millisecond-scale latency. The ability to optimize directional activity propagation at single-spike resolution is particularly novel. More broadly, the work provides a compelling framework for interacting with biological neural networks in an adaptive rather than purely predefined manner.

Weaknesses:

Several aspects of the analysis and interpretation require further clarification. In particular, the definition and computation of directional propagation and reward are not always clear. The generalizability of the optimized stimulation patterns across cultures also remains unclear.

Reviewer #3 (Public review):

In this study Wu & Turrigiano investigate an ethologically relevant form of associative learning (conditioned taste aversion-CTA) and its extinction in the Shank3 KO mouse model of ASD. They also examine the underlying circuits in anterior insular cortex (AIC) simultaneously, using two-photon calcium imaging through GRIN lens. They report that Shank3 KO mice learn CTA slower and suggest that this is mediated by a reduction in tastant-stimulus activity suppression of AIC neurons and reduced signal-to-noise ratio due to increased noise correlations in AIC neurons. Interestingly, once Shank3 KO mice do acquire CTA, they extinguish the aversive memory more rapidly than wild-type. This accelerated extinction is accompanied by a faster loss of neuronal and population-level taste selectivity and coding in the AIC compared to WT mice.

This is an important study that uses in vivo methods to assess circuit dysfunction in a mouse model of ASD, related to sensory perception valence (in this case taste). The study is well executed, the data are of high quality, and the analyses procedures are detailed. Furthermore, the behavioural paradigm is well thought, particularly the approach for assessing extinction through repeated retrieval sessions (T1-T5), which effectively tests discrimination between saccharin and water rather than relying solely on lick counts or total consumption as a measure of extinction. Finally, the statistical tests used are appropriate and justified.

Comments on revised version.

The authors have addressed all comments satisfactorily.

Reviewer #3 (Public review):

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

In their manuscript, Huifang Yan and colleagues perform RNA-seq (CEL-seq) and ATAC-seq experiments to profile the transcriptome and chromatin accessibility of sponge larvae across larval competence, settlement and early postlarval development. Amphimedon, the sponge species that they use, is amenable to lab experiments and can therefore be a convenient model for experimenting with this otherwise difficult to assay ecological parameters and cues. They had previously observed that light conditions (diminished light) at sunset are Sucritical for larvae to enter a pre-settlement stage and prime them for settlement and metamorphosis. In this paper they report that these conditions induce a gain of accessibility in many genes, including transcription factors, and that altering these conditions by providing continuous light at sunset affects this reprogramming event.

Strengths:

The above is a very interesting observation, one that the authors speculate that could have a broader significance and be a theme in many more larvae. I agree with the authors that this is an important finding and I think that the paper will be interesting for the broad readership of eLife. If this is the case, the authors open up a new theme of chromatin regulation, extensively studied in mammalian contexts, but severely understudied in pretty much every other context.

Weaknesses:

I think however that their paper often reports the data in a difficult to follow way, and that other sorts of analyses would have made the results more accessible for the broad readership.

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

Reviewer #3 (Public review):

Summary:

This manuscript presents an engineered 3-step circuit in E. coli that combines toggle-switch-based symmetry breaking with quorum-sensing interactions to generate colony-scale spatial patterns. The work is interesting as a synthetic circuit integration study and as a demonstration of self-organized patterning across physically separated colonies. The authors provided a compelling demonstration of the characterization/tuning of parts to guide the overall system engineering. A notable strength is the demonstration that a single circuit can generate a range of self-organized spatial patterns across separate colonies.

Comments on revised version:

The authors have satisfactorily addressed my previously raised issues.

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

Reviewer #3 (Public review):

Summary:

In their paper, the authors propose using dynamic MEGA-sLASER acquisitions to track the incorporation of ¹³C from uniformly ¹³C-labeled glucose into glutamate(+glutamine) and GABA pools. This approach enables the direct and simultaneous investigation of excitatory and inhibitory neurotransmission and metabolism without requiring a ¹³C radiofrequency coil. While the authors' goals and efforts are commendable, the work has several significant limitations.

Strengths:

Use of excellent hardware (¹H cryoprobe, ultra-high-field MRI scanners); Ambitious objectives.

Weaknesses:

MRS data analysis requires improvement; Small cohort sizes; No demonstration that the proposed acquisition scheme is superior in terms of robustness, accuracy, or performance.

Reviewer #3 (Public review):

Summary:

Determining the ways in which Alzheimer's disease (AD) impacts the neural instantiations of memory is a fundamental aim of neuroscience and likely to be critical to understanding and treating disease progression. Previous work has highlighted how the hippocampal spatial code - typically context-specific - fails to discriminate between different environments in rodent models of AD. Here, Wang et al. leverage new analyses in a rat model of AD to test whether this deficit in 'remapping' reflects an impairment in intrinsic temporal coding. The authors find that the intrinsic temporal code of AD rats does come to effectively discriminate between environments (albeit more weakly than their wild-type counterparts), despite persistent impairments in remapping.

Strengths:

One of the major strengths of this work is its focus on distinguishing between two different types of neural impairments in AD. While previous work has highlighted impairments in remapping, these impairments could be due to: (a) impairments in intrinsic neural computations, or (b) impairments in the way these intrinsic neural computations are anchored to the world. The author's evidence supports the latter, with important implications for the nature of these impairments.

Weaknesses:

A handful of weaknesses could be addressed to strengthen this work. Firstly, a number of key measures of place code quality and behavioral quality between groups are omitted. Given that the author's interpretations rely on comparisons between groups and often use decoding analyses for central conclusions, indicating whether the recordings are comparable between groups in terms of behaviors and cell counts would be helpful for the reader. Even better, matching cell counts between groups for decoding analyses could ensure that outcomes are not driven by this potential confound.

Another weakness that is worth addressing involves the pivotal comparison between time-averaged remapping (Figure 4) and intrinsic temporal code discrimination (Figure 3). For temporal code discrimination, the analysis relies on comparisons between pairs of epochs (e.g. from A1B1 to A2B2), while for remapping, the comparison averages together epochs (A1+A2 and B1+B2). As a result, these comparisons are characterizing two slightly different things. Given that this is a key comparison for this paper (cofiring evolves to discriminate contexts while the spatial code does not), I think it would be important to demonstrate that remapping between pairs of epochs also stagnates to more closely mirror the cofiring analysis.

A final weakness is the sharp wave ripple (SWR) ensemble analysis. Here, the authors extract population vectors during SWR (SWR-PVs) during rest in both WT and AD rats. Next, they compare the extent to which SWR-PVs on AVERAGE resemble other SWR-PVs for that epoch. The authors report that this measure increases with experience in AD rats but not wild-type (WT) rats. While the authors interpret this to mean that SWR-PVs come to be more reliable with experience in AD rats, it could alternatively mean that SWR-PVs become less diverse or 'muddier' with experience, while SWR-PVs in WT rats continue to represent diverse trajectories. To make this analysis compelling, a better measure might be something that quantifies the diversity or content of SWR-PVs and something that quantifies similarity between each SWR-PV and the most similar other SWR-PVs.

Reviewer #3 (Public review):

Summary:

This manuscript describes an interesting study (preceded by a pilot study) that combined computational modeling with two tasks to try to tease apart state, trait, and vulnerability-related effects of depression. Specifically, the authors administered a bandit task and a gambling task to healthy controls, currently depressed adults, formerly depressed adults, and adults at familial risk of depression, and then used a suite of computational models to identify group differences in key model parameters. Key results included the detection of lower punishment learning rates (during the bandit task) in the remitted depressed group, which the authors hypothesize may be a compensatory mechanism to counteract the over-reaction to negative feedback that often characterizes depression (and indeed, punishment learning rates were elevated in currently depressed adults). Among non-depressed adults, lower punishment learning rates were associated with more apathy and anhedonia. The remitted depressed group also showed lower lapse rates in the bandit task. In the gambling task, the inverse temperature parameter was elevated in currently depressed adults and in adults at high familial risk for depression; exactly how to interpret this last result seems unclear. This is a revised manuscript, and from what I can see it appears that the authors were responsive to earlier comments.

Strengths (and summary of weaknesses):

I think the study has several noteworthy strengths. Testing four groups is a strength, as there is great interest in teasing apart risk factors of depression vs. "scars" of the illness, and the use of unmedicated individuals removes a common confound. The work is hypothesis-driven, the modeling is sophisticated, and the paper is well-written. Moreover, as the authors note, modeling allowed the authors to identify group differences that were not evident in raw behavioral analyses.

However, I think the manuscript could be further improved because some aspects of the methodology are a bit confusing and/or do not seem optimal. I list these issues in the comments below, but to briefly summarize: (a) I did not understand why the authors estimated separate reward and punishment values for each bandit; (b) the rationale for Bayesian Model Averaging could be strengthened; (c) examining relationships between model parameters and symptom scores only in the control group is suboptimal given the goal to better understand depression; and (d) the group differences in inverse temperature seem like they may depend on potential outliers. I think addressing these concerns would improve the paper and ensure that the study has a strong impact on the field.

Details regarding weaknesses/concerns:

(1) I found a basic aspect of the 4-arm bandit modeling confusing - namely, the use of separate reward and punishment value estimates (see equations 1 and 2 in the supplement). The participants are choosing among the bandits (presumably) based on value estimates for each bandit. Clearly, delivery of rewards and punishments affect those estimates, but it is not clear to me why or how there are separate value terms for rewards and punishments; typically, rewards and punishments influence one overall value estimate per bandit. Can the authors clarify? (I see that similar models were used in references 29 and 30, but additional clarification for readers of the current manuscript would be helpful)

(2) Bayesian Model Averaging (BMA) is new to me and may be new to many readers, and it would be helpful to provide a stronger rationale for the approach. The manuscript argues that model selection amounts to a "winner-takes-all" approach that may introduce bias; maybe, but typically the goal is to figure out which mechanism(s) best explain behavior, and so winner-takes-all is often appropriate. My limited understanding is that BMA is often used when prediction-rather than mechanistic understanding-is the goal. Why is BMA the right choice here?

(3) The authors performed a confirmatory factor analysis on questionnaire data from healthy controls out of concern that extreme scores in the other groups might bias the factor structure; consequently, they can only relate model parameters to their latent factors in the healthy controls. This does not seem optimal. While the negative relationships between punishment learning rate and both anhedonia and apathy in controls are interesting, the controls are not struggling with anhedonia or apathy. It would be valuable to know if similar relationships obtain in the other groups, particularly because this would speak to the study's goal of distinguishing between risk for depression and state/trait aspects of depression.

(4) Figure 5 gives the impression that group differences in inverse temperature may depend on potential outliers in the relative and MDD groups. Is that correct?

(5) The paper notes a group difference in IQ as estimated from the WTAR, and looking at Table 1 it appears that the group difference is driven by a lower WTAR score in the healthy volunteers (HV) from the pilot study. First things first: the score in the HV group is too low to be a standardized WTAR score. Like IQ scores, standardized WTAR scores typically have a mean around 100; scores for the four Study 2 groups look alright, but the Study 1 mean score of 40 is much too low. Can the authors clarify?

Reviewer #3 (Public review):

Summary:

This study aims to characterize the three-dimensional organization of primary cilia in the postnatal mouse growth plate and to determine how ciliary prevalence, length, position, and orientation vary across anatomical regions and stages of chondrocyte differentiation. The authors combine volumetric imaging and automated image analysis with conditional disruption of IFT88, limb immobilization, and bulk transcriptomics. The work provides a valuable anatomical dataset and identifies several interesting spatial patterns, including increased ciliation in the lateral resting zone, longer cilia in hypertrophic chondrocytes, and, most notably, a non-random orientation of ciliary projections despite a much broader distribution of centriole positions.

The descriptive evidence is largely solid, and the imaging dataset should be useful to researchers studying primary cilia, skeletal development, and tissue mechanobiology. However, the evidence is incomplete for several of the central mechanistic conclusions. In particular, the current analyses do not establish that ciliary orientation is uncoupled from basal-body position, and they leave unresolved how ciliary orientation relates to cell orientation. The immobilization experiment also supports a narrower conclusion than the proposed cell-intrinsic orientation program, while the final model linking ciliary angle to multidirectional signal integration remains speculative. Overall, the authors succeed in identifying an interesting and reproducible anatomical pattern, but the mechanism underlying that pattern remains largely open.

Strengths:

A major strength is the scale and anatomical context of the imaging. Quantifying thousands of cilia and tens of thousands of centrioles in three dimensions while preserving information about growth-plate zone and position across the limb is technically demanding. This allows the authors to identify regional differences that would be lost in dissociated cells or bulk tissue measurements.

The study also provides several potentially useful observations. Ciliation is higher in the lateral periphery, particularly in the resting zone, cilia are longer in hypertrophic chondrocytes, and ciliary projections show a reproducible non-random orientation. The latter is the most interesting result of the study and provides a useful foundation for asking how organelle orientation is established within a developing tissue.

I also appreciated that the authors place these observations in several biological contexts rather than stopping at a descriptive atlas. IFT88 disruption alters ciliation and growth-plate cell organization, while immobilization changes growth-plate dimensions, cell morphology and orientation, and the transcriptome even though the measured ciliary properties remain comparatively stable. These perturbations give the anatomical observations useful biological context.

Weaknesses

The main concern is that the claim that ciliary orientation is uncoupled from basal-body position is not directly demonstrated. The manuscript shows that centriole positions are broadly distributed and, separately, that ciliary projections have a preferred tissue-level orientation. Different population-level distributions, however, do not establish independence within individual cells. For example, basal bodies could be broadly distributed while their position determines which of two opposite directions along a common tissue axis the cilium adopts. This possibility is particularly relevant because Centrin-2 labels both centrioles, whereas only one serves as the basal body. The current data therefore support a difference between the population distributions of position and orientation, but not yet the stronger claim that the two are uncoupled.

A related gap is the relationship between cell orientation and ciliary orientation. The manuscript measures both, and cell orientation changes with growth-plate region, IFT88 deletion, and immobilization, while ciliary orientation appears relatively stable. Yet the two measurements are never directly related within the same cells. It is therefore unclear whether cilia adopt a reproducible angle relative to the major axis of their own cell, whether this relationship changes between the center and periphery, or whether changes in cell organization can occur independently of local ciliary alignment. This seems important for interpreting the tissue-level orientation pattern.

The statistical treatment of orientation also deserves caution. These are circular or spherical measurements, yet much of the analysis relies on linear distributions and Kolmogorov-Smirnov tests. This is particularly problematic around the 0{degree sign}/360{degree sign} boundary, where values near 350{degree sign} and 30{degree sign} are geometrically close but appear separated in a linear representation. The manuscript also does not clearly distinguish between a preferred axis, where opposite directions are equivalent, and a preferred polarity, where one direction is favored. In addition, thousands of cilia are nested within a much smaller number of mice, so the apparent statistical power should not be driven primarily by pooled object counts. Given that regional differences are a central theme of the paper, it would also be useful to know more clearly whether the preferred axis or the strength of the orientation bias differs between the middle and lateral growth plate at the animal level. I also could not identify a formal comparison of the centriole or ciliary distributions with an appropriate uniform circular or spherical null. The reported tests mainly compare zones and regions, so the claims that centriole position is non-preferential and ciliary orientation is non-random are not yet statistically established in the form presented.

The IFT88 conditional knockout is not carried through to the principal orientation question. The authors examine cell size, cell-axis organization, ciliation, and cilium length, but do not report whether the remaining cilia retain the preferred orientation or whether centriole positioning changes. Given the central role of this genetic perturbation in the manuscript, this leaves the genetic and orientation arms of the study somewhat disconnected.

Finally, the immobilization experiment and the mechanistic interpretation should be separated more carefully. Only four animals were analyzed in the offloaded and contralateral conditions, and medial and lateral regions were averaged because of the small sample size. The experiment shows that the established ciliary orientation remains relatively stable over a two-week postnatal interval despite clear changes elsewhere in the tissue. It does not exclude a role for mechanical forces earlier in establishing the axis, nor does a nonsignificant difference with four animals demonstrate equivalence. Similarly, the proposed cell-intrinsic orientation program and the final "single-axis blindness" model are interesting hypotheses, but the study does not yet identify what establishes the axis or how the observed ciliary angle would alter sensitivity to a force or biochemical gradient. Those ideas are worth discussing, but they should remain clearly separated from the observations directly supported by the data.

Reviewer #3 (Public review):

This review describes how cryo-EM is moving RNA structural biology from the determination of static structures toward the characterization of conformational ensembles. Eight case studies spanning self-splicing introns, riboswitches, viral RNA elements and synthetic assemblies show how cryo-EM has captured folding intermediates, hinge-mediated domain motions and ligand-dependent switching, and the authors pair these examples with practical guidance on construct design, sample preparation, vitrification and heterogeneity analysis. The argument that conformational heterogeneity is a source of mechanistic information, rather than a limitation, is well supported, and Table 1 will be a useful reference for laboratories entering the field. The manuscript is timely and of broad interest, and I recommend publication after minor revision.

(1) Scaffold-based structure determination (page 14, lines 5-9). The section on chimeric RNAs presents the scaffold strategies as a single group and cites Haack et al. (2025) and Langeberg & Kieft (2023) in one parenthetical, so individual results are not attributed to their sources. The distinction between these studies is substantive. Earlier scaffolds based on the Tetrahymena group I intron (Langeberg & Kieft, 2023) or on RNA origami (Sampedro Vallina et al., Nucleic Acids Res. 51:4613-4624, 2023) resolved the appended RNAs at approximately 4.4-5 Å. Haack et al. (2025) reported the first RNA scaffold to yield a high-resolution structure of the target itself, resolving the ligand-binding pocket of the thiamine pyrophosphate (TPP) riboswitch at 2.5 Å and extending nucleotide-level cryo-EM analysis to small RNAs that had previously been intractable. I ask the authors to attribute each result to its source and to state explicitly that Haack et al. (2025) achieved the first high-resolution structure of a scaffolded target RNA. Because the same study captured the ligand-free TPP riboswitch in an open, Y-shaped conformation, a direct example of the ligand-dependent switching that is central to this review, the TPP riboswitch should also appear in the riboswitch section (page 8, lines 15-36), together with the fluoride riboswitch of Langeberg & Kieft (2023).

(2) Page 6, lines 8-14. Self-splicing introns are described collectively as evolutionary ancestors of the spliceosome that reside in pre-mRNA transcripts. The proposed ancestral relationship to spliceosomal introns and snRNAs applies to group II introns. Group I introns, including the Tetrahymena intron, which interrupts a pre-rRNA, initiate splicing with an exogenous guanosine and are not considered spliceosomal precursors.

Reviewer #3 (Public review):

This paper explored the role of beta rhythms in the context of spatial learning and mPFC-hippocampal dynamics. The authors characterized mPFC and hippocampal beta oscillations, examining how their coordination and their spectral profiles related to learning and prefrontal neuronal firing. Rats performed two tasks, a Y-maze and F-maze, with the F-maze task being more cognitively demanding. Across learning, prefrontal beta oscillation power increased while beta frequency decreased. In contrast, hippocampal beta power and beta frequency decreased. This was particularly for the well-performed and well-learned Y-maze paradigm. The authors identified the timing of beta oscillations, revealing an interesting shift in beta burst timing relative to reward entry as learning progressed. They also discovered an interesting population of prefrontal neurons that were tuned to both prefrontal beta and hippocampal sharp-wave ripple events, revealing a spectrum of SWR-excited and SWR-inhibited neurons that were differentially phase locked to prefrontal beta rhythms.

Reviewer #3 (Public review):

Summary:

The manuscript presents a large common garden experiment across Sweden using solely local germplasm. Additionally, there is a collection of selection experiments that begin investigating the factors shaping fecundity in these populations. This provides an impressive amount of data and analysis investigating the underlying factors involved. Together, this helps support the data showing that fluctuations and interactions are key components determining Arabidopsis fitness and are more broadly applicable across plant and non-plant species.

Strengths:

The field trials are well conducted with extensive effort and sampling. Similarly while the genetic analysis is complex it is well conducted and reflects the complexity of dealing with population structure that may be intricately linked to adaptive structure. This has no real solution and the option of presenting results with and without correction is likely the only appropriate option.

Reviewer #3 (Public review):

Summary:

This study addresses a fundamental but underexplored aspect of homeotic gene function: how regulators of cell identity act during late stages of organ development. The authors take advantage of layer-specific mutants of the MADS-box gene PhDEF in Petunia hybrida to dissect the roles of this floral identity regulator in the epidermis and mesophyll. By combining single-cell RNA sequencing with ChIP-seq analyses in wild-type and mutant chimeric petals, the work demonstrates that, although PhDEF is expressed at comparable levels in both petal layers, it binds to and regulates a substantially larger and more layer-enriched set of genes in the epidermis than in the mesophyll. The identification of both layer-specific and shared PhDEF binding sites supports a model in which pre-existing layer identity modulates the regulatory output of homeotic transcription factors.

Major comments from review at Review Commons:

Dissecting PhDEF binding preferences in the epidermis versus the mesophyll using the star and wico mutants is a clever and powerful approach. However, conclusions involving cell identity should be drawn with caution for two reasons. First, cell identities appear to be altered in the mutants: scRNA-seq data suggest that even cells assigned to the same cluster can be molecularly distinct across genotypes. For example, the transcriptomes of wild-type epidermis and mesophyll are highly similar (Pearson correlation R = 0.94), yet both show lower correlation with epidermal cells from star or wico petals. These results raise questions such as are the cells identified as epidermal cells really strictly epidermal in the mutants? Do you need to take into consideration cell composition of the mutants when you do differential expression analysis? Second, PhDEF is expressed in both epidermal and mesophyll clusters in all genotypes, albeit at different levels and in fewer cells in the mutants. As a result, the ChIP-seq profiles should be interpreted as cell type-enriched rather than cell type-specific. And there are a few things that need clarification:

a) Protoplasting and tissue dissection analyses suggest that mesophyll cells constitute more than 80% of the cells in wild-type petals, whereas the scRNA-seq data indicate a substantially lower proportion. Could this discrepancy reflect technical biases in cell recovery or capture efficiency, or issues related to cell identity assignment during clustering and annotation? Notably, the scRNA-seq data from star and wico petals show mesophyll proportions close to 80%. Is this difference due to an increased abundance of mesophyll cells in the mutants, or could it instead reflect differences in transcriptomic separability? In wild-type petals, the epidermal and mesophyll transcriptomes are highly correlated and express similar numbers of genes, with epidermal cells distinguished mainly by higher expression of a subset of genes. This raises the possibility that mesophyll cells in the wild type occupy a more plastic or less differentiated transcriptional state and may therefore be misclassified as epidermal cells, whereas disruption of regulatory mechanisms in the mutants enhances transcriptional divergence and alters cell clustering outcomes.

b) Cluster 0 appears to show internal heterogeneity, as the expression patterns of KCS3 and LLE2 are largely mutually exclusive. Do these patterns reflect the presence of distinct epidermal cell types within the limb that are currently grouped into a single cluster?

c) Clusters 0 and 7 both exhibit high expression of pigmentation genes, while cluster 7 additionally shows strong enrichment for cell division genes. Are cell cycle genes the primary features distinguishing these two clusters? If cell cycle effects are regressed out, would cluster 7 merge with cluster 0, potentially yielding a more continuous cell state trajectory and helping to resolve the pattern noted in point (b)?

d) Pearson correlation is largely driven by highly expressed genes and may therefore be insensitive to changes in cell identity markers. The conclusions that the less clear separation of mesophyll and epidermal cells in star is due to altered cell identity would be more convincing if supported by independent validation, such as in situ hybridization or reporter analyses, to directly visualize molecular alterations in the relevant cell types when comparing wild-type and mutant tissues.

Significance:

Overall, this work significantly advances our understanding of late homeotic gene function. It establishes compelling evidence for how developmental context constrains transcription factor activity and offers broadly relevant insights for studies of organ patterning. The combination of genetic mosaics with single-cell and chromatin-level analyses represents a powerful and generalizable strategy that will be of interest to both plant developmental biologists and researchers studying transcriptional regulation.

My expertise: single cell genomics, chromatin biology and plant development.

Comments on latest version:

The authors have addressed my concerns and questions. And I agree with your assessment of "valuable" for significance and "solid" for strength of evidence.

Reviewer #3 (Public review):

In this manuscript, Davis and colleagues aimed to identify the molecular sensors and signaling cascade that enable collecting lymphatic vessels to increase their spontaneous contraction frequency in response to intraluminal pressure (pressure-induced chronotropy). They tested whether the process is similar to blood vessel myogenic constriction by relying on cation channels (TRPC6, TRPM4, PKD2, PIEZO1, etc.) or instead require the activation of G-protein-coupled receptors (presumably mechanosensitive GNAQ/GNA11-coupled receptors), using ex vivo pressure myography of mouse popliteal lymphatics, smooth muscle-specific conditional knockouts, quantitative PCR validation, and single-cell RNA sequencing for target prioritization. The authors convincingly demonstrate that pressure-induced chronotropy does not require the cation channels implicated in arterial myogenic tone but is blunted by deletion of GNAQ/GNA11 or IP3 receptor 1, supporting a model of GPCR > IP3 > Ca2+ release > Cl⁻ channel activation > depolarization. The core conclusion is robust. The work redefines lymphatic pacemaking as G-protein-coupled receptor-dependent mechanotransduction, distinct from arterial mechanisms, and provides a genetically validated toolkit that is useful for studying lymphatic function and dysfunction.

Strengths:

(1) The data are of high quality and highly sensitive functional readouts

(2) The systematic genetic targeting is a major strength that overcomes pharmacological artifacts

(3) Careful quantitative analyses of frequency-pressure slopes

Comments on revised version:

The authors have satisfactorily addressed my comments. No additional comments.

Reviewer #3 (Public review):

Summary:

The manuscript by Elley and colleagues describes experiments on the effects of synthetic torpor on ex vivo heart ischemia. The key aspect of the study was the use of viral-vector mediated manipulation of the hypothalamic medial preoptic area (MPA) in rats. They used AAV-CaMKIIa-hM3D(Gq). The authors report that chemogenetic activation of the MPA prior to an ex vivo heart ischemia-reperfusion insult induces cardio protection against infarct size that is independent of prior in vivo hypothermia. Phosphoproteomic analysis of cardiac tissue suggested changes in cell survival and death pathways.

Strengths:

This study has important strengths. The idea is novel. The experimental design is appropriately rationalized and fascinating. The manuscript is written and presented concisely.

Reviewer #3 (Public review):

Summary:

The authors set out to describe how the three basic ingredients of a voluntary motor command, that is excitation, inhibition and neuromodulation, are altered in people with multiple sclerosis. They recorded high-density surface electromyograms from tibialis anterior and soleus during slow triangular contractions in 89 patients and 34 control participants, decomposed the signals into the discharge times of individual motor units, and extracted seven features of the resulting discharge patterns. These features were then combined into three composite scores intended to represent the three ingredients, and the authors asked whether the scores in patients differ from those in controls in their average value, in their spread, and in the shape of their distribution. The working hypothesis was that the motor command is disturbed in different ways in different patients rather than in one consistent direction, and the authors report findings that they consider consistent with that hypothesis.

Strengths:

The dataset is the main strength of this work, and it is quite a considerable one. Studies of motor unit behaviour in neurological populations are usually built on ten or twenty participants, whereas here there are 89 patients spanning the full range of disability together with 34 controls, two muscles per person, and more than 12,000 unique motor units. The clinical characterisation is thorough and includes disease subtype, symptom duration, disability score, walking tests and lesion locations from clinical imaging. The experimental protocol is appropriate and was clearly demanding to deliver in a population of this kind. The treatment of the motor unit data in the statistical models is also more careful than is common in this literature, in particular the recognition that motor unit labels are arbitrary and the correct nesting of units within muscle and within participant. The question itself is well worth asking, and the central observation, that average discharge rates and rate modulation are reduced while a minority of patients sit far above anything seen in controls, is interesting.

Weaknesses:

Three issues limit how far the conclusions can be taken as the paper stands.

First, the framework that gives the paper its title is not the framework that was used. The published approach searches a large database of simulations to find the combination of synaptic inputs that reproduces an observed discharge pattern. What is applied here is a weighted average of standardised discharge features, in which the weights are mutual information values taken from that earlier work and the signs of the weights are supplied by the authors on the basis of the literature. Mutual information measures how much a feature tells you about a parameter. It does not carry the direction of that association, and it does not become a regression coefficient by having a sign attached to it. Since the direction of every conclusion in the paper depends on those assigned signs, the reader has no way of judging how faithfully the composite scores track the physiological quantities they are named after. I do not understand why this was not done with simulations. The authors should expand or clarify this point.

The simulations have known inputs, so the composites could be computed on the simulated discharge patterns and their accuracy reported directly. Two of the features were also modified relative to those simulations, being computed against joint torque rather than against the synaptic drive, and using a normalised version of the hysteresis measure, while the weights derived for the original features were retained. The authors should justify this or specify why this was not done and how it affects the underlying physiology.

A related difficulty is that the three composite scores are not independent of one another. The hysteresis measure contributes to all three of them and several other features contribute to two. Finding abnormality in all three components of the motor command may therefore reflect a single underlying signal expressed three times over. The correlations between the composites are not reported, and without them the reader cannot tell which of these two readings is correct.

Second, the comparison with stroke and with spinal cord injury, which carries much of the novelty of the paper, is asserted rather than demonstrated. A good deal of recent motor unit work in spinal cord injury and in stroke is also omitted, which is an important weakness, and I would encourage the authors to engage with it directly rather than treat those populations as a settled contrast. The abstract and the discussion state that the variability seen here is fundamentally different from the consistency seen in those populations, but no stroke or spinal cord injury data are presented, and no quantitative comparison with published values is offered. This matters because the individual patterns illustrated in the paper, that is, reduced peak discharge rate, compressed rate modulation, a narrowed recruitment range, synchronisation between units, and continued firing after the end of the task, are all well-described features of spastic paresis of other causes. This is commonly observed in people with spinal cord injury as well. One of the three illustrated patients is described as having mild hemiparesis and spasticity. Most of this cohort also carries cervical and thoracic cord lesions in addition to lesions above the spinal cord, so a simpler reading of the spread in these data is a mixture of the mechanisms already known from stroke and from spinal cord injury, present in varying proportions in different patients. That would be a different and considerably less novel conclusion, and it deserves to be considered explicitly. There is a statistical asymmetry in the comparison as well, since the populations described as consistent have been studied in samples of ten to twenty people, and small samples cannot reveal the tails of a distribution.

Third, the central finding of greater variability between patients has plausible alternative explanations that have not been excluded. The value for each participant is the median across the motor units identified in that person, and the number of units varies widely between participants and between the two groups. The precision of a median depends on how many units contribute to it, so precision that differs systematically between groups will inflate the spread of the participant values on its own. In addition, the leg studied was the more affected leg in the patients but the dominant leg in the controls. Choosing the more affected of two limbs is a selection on an extreme value, and it will both shift the patient average and widen the patient distribution for reasons that are purely statistical. Since the Methods state that both legs were recorded, perhaps the authors could consider adding this as an additional control. Finally, the hypothesis is framed in terms of multiple peaks and possible subgroups of patients, yet no test of multimodality is performed, and the density estimates shown do not obviously support it.

The feature carrying by far the largest weight in the neuromodulation score shows no group difference at all, and depending on which version of the hysteresis measure entered the composite, either one or none of its six constituent features differs between groups. The group difference in neuromodulation is also absent in the primary model and emerges only when maximal strength is added as a covariate, a change that the text reports but describes as leaving the results generally unchanged.

Moreover, medication may be assessed more clearly considering the large cohort. Thirty-four of the 89 patients take antispastic drugs, and both tizanidine and baclofen act on the system that the neuromodulation score measures, while others take reuptake inhibitors that act in the opposite direction. A quantitative comparison, with the obvious caveat of confounding by indication, would be needed before the neuromodulation findings can be read as disease-related. The authors should consider this to strengthen the manuscript or justify why this was not done.

Appraisal:

The authors achieve their descriptive aim. They show convincingly that motor unit discharge is altered in multiple sclerosis, that the average change is towards lower discharge rates and reduced rate modulation, and that the patient group is more dispersed than the control group on most measures. I do not think they establish the two claims that give the work its stated significance, that is, that the abnormalities can be attributed specifically to excitatory, inhibitory, and neuromodulatory drive, and that the resulting pattern distinguishes multiple sclerosis from other conditions affecting the same pathways.

Impact and utility:

The findings and dataset are very novel and, once shared, will be a resource for the field, and I would encourage the authors to release the analysis code and the exact weights alongside it. Moreover, it would have been valuable to see more relationships reported between the clinical measures and motor unit behaviour, in particular disability, walking speed, lesion location and medication.

Reviewer #3 (Public review):

Summary:

In their manuscript "Multiplexed CRISPRi Reveals a Transcriptional Switch Between KLF Activators and Repressors in the Maturing Neocortex", Kirk and colleagues seek to dissect the developmentally regulated pan-neuronal gene programs that control the postnatal maturation of cortical neurons. For this, the authors analyzed newly generated and existing RNA-seq and ATAC-seq of Layer 4 and Layer 6 cortical pyramidal neurons at postnatal day 2 (P2) and day 30 (P30) and identified thousands of shared developmentally regulated genes and genomic (promoter) regions, including genes involved in axon growth (tend to be downregulated) and synaptic function (tend to be upregulated). Motif enrichment analysis of promoters of differentially regulated genes revealed a strong presence of KLF/Sp family binding motifs, pointing to Krüppel-Like Factors (KLFs) as key transcriptional regulators of cortical maturation. Expression profiling showed a developmental switch from activating KLFs (Klf6, Klf7) expressed neonatally to repressive KLFs (Klf9, Klf13) upregulated during maturation. Using an elegant in vivo multiplexed CRISPR interference (CRISPRi) system, the authors achieved efficient, cell-type-specific knockdown of these TFs and showed that Klf9 and Klf13 repress a set of genes that includes cytoskeletal regulators such as Tubb2b, Dpysl3, and Rac3. Conversely, Klf6 and Klf7 promoted the expression of these same genes in the early postnatal period, and their knockdown led to reduced expression of these genes, particularly at P10 when their activating influence is strongest. Since promoters of shared KLF targets were enriched for KLF/Sp motifs but showed little change in chromatin accessibility, the authors propose a model in which distinct KLF family members function either as transcriptional repressors and activators that compete at constitutively accessible promoters and thereby act as a developmental transcriptional switch that coordinates the downregulation of axon growth programs and upregulation of synaptic maturation genes during cortical development.

Strengths:

The study addresses an interesting question and advances our understanding of the transcriptional regulation underlying postnatal cortical development. A major strength of the study lies in the innovative use of in vivo multiplexed CRISPR interference (CRISPRi), which allows for cell-type-specific, combinatorial knockdown of redundant TFs - this an elegant solution to a long-standing challenge in transcription factor research and should be useful also for other neuroscience studies that require local and cell-type-specific gene loss-of-function. Also, the integration of RNA-seq and ATAC-seq across developmental time points provides a robust foundation for identifying direct targets of the KLF family, and the findings are reinforced by cross-species conservation and the identification of targets with clear neurodevelopmental relevance.

Weaknesses:

The major weakness of the study lies in its relatively narrow scope: the study focuses primarily on transcriptional mechanisms and largely lacks functional validation of the neuronal phenotypes that are predicted by the gene expression data (e.g. axonal morphology). Despite these limitations, the paper offers an interesting model for a transcriptional switch during neuronal maturation in the cortex and establishes a powerful methodological framework for dissecting redundant gene networks in vivo.

Reviewer #3 (Public review):

Summary:

This work provides a novel guide for Research Performing Organizations (RPOs) to evaluate researchers in ways that promote responsible research practices. Such a tool could help inform the development of policies that foster these practices within RPOs and potentially influence decisions related to the hiring and promotion of scientific staff. The guide was developed through a modified three-round Delphi process based on a preliminary set of recommendations provided by members of various organizations. Each round included a survey, and items that did not reach a consensus of >80% were reconsidered in the subsequent round. Round 3 consisted of an in-person meeting, during which the final guide was developed based on criteria that reached {greater than or equal to}80% consensus among participants. The guide includes six practices for evaluation (verification efforts, prospective study registration, data/code/materials sharing, reporting transparency, open-access publishing, and disclosure of funding interests) with an additional seventh component. Each practice includes a clearly defined aim and criteria describing what good practice looks like. The complete study methodology, statistical analyses, and supporting data are available through Open Science Framework (OSF).

Strengths:

A major strength of this work is the development of a practical tool that could help foster responsible research practices among candidates and provide promotion and hiring panel members with clearer criteria for assessing such practices. The three-round Delphi process, which included international participants, provides a structured approach to reaching consensus on practices that could contribute positively to the research ecosystem.

Weaknesses:

The recruitment strategy may have introduced selection bias and limited the representation of researchers and other interest holders from diverse geographical and institutional contexts, particularly from the Global South. Although the authors acknowledge that variability across regions and institutions should be considered when applying the guide, greater representation of these contexts would strengthen confidence in the broad applicability and generalizability of the proposed framework.

Reviewer #3 (Public review):

Summary:

Omar Matar and colleagues describe how they successfully closed the life cycle of the tropical tuxedo urchin Mespilia globulus in a small, closed aquarium system. Its capacity to flourish under closed culturing conditions sets this sea urchin apart from most echinoderms and indeed most marine invertebrates, which require high-quality flow-through seawater (i.e. coastal access and a flow-through aquarium system). As M. globulus reaches sexual maturity in captivity in 4-6 months, which is shorter than other sea urchins, it opens the potential for transgenerational studies.

Matar et al. have developed a suite of molecular and genomic techniques and resources, including Cas9-mediated gene knockdown and chromosome-scale genome assemblies for both sexes and colour morphs. These strong aquaculture and genomics platforms suggest that M. globulus can provide insights into aspects of the sea urchin life cycle and history that would be difficult to study in other sea urchin models, which historically have been focused on embryogenesis.

Strengths:

This manuscript announces M. globulus as a novel, genome-enabled echinoderm model that can be cultured in a closed system with a few hundred litres of 23/24oC artificial seawater. This allows for experimental analysis of parts of the sea urchin life cycle that are not particularly tractable in other sea urchins, including the developmental biology of metamorphosis, symbiont interactions and immunity through the life cycle, post-settlement biomineralisation, and sex determination. The capacity to knock out embryonic/larval genes using CRISPR/Cas9 and visualise localised embryonic and larval gene expression using HCR are consistent with claims M. globulus can contribute novel insights into basic and applied (aquaculture) biology of sea urchins and echinoderms.

Weaknesses:

The general weakness of this manuscript is that the authors do not explain aspects of their study in enough detail (e.g. a single figure - Figure 2 - presents results of analysis of normal development, HCR and Cas9 knockdowns). This manuscript provides limited methodological detail about the culture system and the analysis of gene expression. Here are a few suggestions on how to improve the manuscript:

First, the authors should provide a thorough description of the methods used to cultivate and maintain M. globulus. This should include further details about the closed aquarium system; a schematic of the system would be insightful. Basic details about husbandry are needed, including (i) stocking densities of adults, embryos/larvae, postlarvae/juveniles; (ii) frequencies of level and water changes/top-ups; (iii) feeding regime at all phases of the life cycle (amount/sea urchin, post-feeding cleaning, etc.). These and other details are essential for the uptake of this model system. This documentation would provide the foundation for future improvements, which include shortening the time to acquire larval competence and sexual maturity, and improving and standardising larval settlement.

Second, the description of the procurement and analysis of mRNA is brief, unreferenced and reads as protocols used for an established model species (e.g. what is PFA in this case - the concentration of paraformaldehyde and the buffer can vary markedly between organisms and life stages). Even the RNA extraction protocols can vary between and within species. For instance, highly pigmented tissues tend to be more difficult to procure useable RNA. The HCR analysis, which is also scantily described, is restricted to embryonic and larval stages. Given the emphasis on the capacity of the M. globulus system to analyse all phases of the life cycle, it would be good to know if HCR can be performed on settled postlarvae, juveniles and adult tissues.

Third, the authors should consider dividing Figure 2, which consists of confocal images of normal development, HCR results and CRISPR/Cas9 knockout results, into three separate figures that explore these studies separately. A figure on normal development could, for instance, include documentation of metamorphosis, with a suite of images of postlarval stages. A figure documenting HCR could be expanded to include more stages, higher magnification images and other genes. A figure on the Cas9 knockdown of a PKS gene can provide details on the normal expression of this gene using HCR and possibly qRT-PCR.

Fourth, there should be consideration of providing more characterisation about the protein-coding genes comprising the chromosomal region (Chr. 4) that has marked differences between sexes. This could go beyond Supplementary Table 4 and Supplementary Figure 5B, and include analysis of expression in adult tissues (this would be enhanced by having matching male and female tissues), and KEGG pathways and GO enrichments.

Overall, this is an exciting development in the study of echinoids and echinoderms. This manuscript can be improved by providing the reader with more details. Given the exciting prospect of being able to study M. globulus settlement and metamorphosis in detail, the authors may consider providing more information about this part of the life cycle.

Reviewer #3 (Public review):

Summary:

This manuscript asks whether biogeographic patterns are similarly predictable across temporal scales in two sympatric clingfishes (Gouania). By integrating oceanographic dispersal simulations, population genomics, mtDNA, and demographic inference, the authors find broadly concordant contemporary connectivity between species but less consistent deeper phylogeographic and demographic histories. The study addresses an interesting question, although several aspects of the genomic analyses and their interpretation need further attention.

Strengths:

The manuscript has a strong comparative design and an interesting biological system. Comparing sympatric species provides a useful framework for asking how consistently shared environmental processes translate into similar evolutionary patterns. The integration of oceanographic, population-genomic, phylogeographic, and demographic evidence across temporal scales is a particular strength. The most convincing result is that contemporary connectivity appears more predictable across species than deeper phylogeographic and demographic history.

Weaknesses:

The main new dataset is whole-genome resequencing of 85 individuals (46 G. orientalis, 39 G. hofrichteri) from five localities on Crete and Kythira. These reads were mapped to the chromosome-level Gouania willdenowi assembly (fGouWil2; publicly labeled as G. willdenowi but referred to throughout as the G. adriatica reference) produced by the Vertebrate Genomes Project (Rhie et al. 2021). No new reference genome was generated here, which limits the genomic contribution. Ideally, each focal species would have its own reference.

Mapping both species to a third congener also creates an important reference-bias issue. Figure 6a places G. orientalis closer to the reference lineage than G. hofrichteri, so the more distant species is expected to map less well and yield fewer callable sites. That is the direction observed: G. hofrichteri yields nearly half as many called variants (3.17 M vs 5.82 M). This could reflect real diversity, differential mapping, or both. Per-species mapping rates, missingness, and callable-site counts should therefore be reported, and outlier comparisons repeated using sites callable in both species.

Coverage is also low and uneven: 69 individuals were targeted at 5X and 16 at 15X. This is probably adequate for broad population structure, but more problematic for heterozygosity- and frequency-based statistics, as hard calls at ~5X can bias heterozygosity, nucleotide diversity, FIS, and nearly fixed variants. The single 15X G. orientalis from NNE-Mades, which has unusually high diversity and affects FST, illustrates the issue. Key results should therefore be checked using genotype likelihoods, common-depth downsampling, or the higher-coverage subset. Table S1 should also report realized depth and missingness and clarify the discrepancy with the Methods, which state that two individuals per population were sequenced at 15X. Agreement among PCA, admixture, and FST is not independent reassurance because all use the same hard-called genotype matrix (see Lou et al. 2021; Helmkampf et al. 2025).

Much of the remaining dataset comes from previous studies or public resources: the COI sequences, four of five MSMC2 genomes, the VGP genome, and Copernicus currents. Thus, the main new contribution is the Crete-Kythira population-genomic dataset. The strongest result is that contemporary connectivity is broadly concordant between species, whereas deeper phylogeographic and demographic histories are less predictable. The geographic mismatch among datasets, however, limits direct comparisons across scales.

The least secure result is parallel genomic divergence, which is emphasized in the title. The null model for parallel divergence is problematic. The enrichment test treats 29,039 windows as independent even though 60-kb windows overlap by 30 kb. Adjacent windows are therefore not independent, and both species also share the same reference architecture. The significance of 8 shared windows among 397 and 220 outliers may consequently be inflated. This should be tested with a null model that preserves genomic autocorrelation, such as non-overlapping blocks or permutations of larger genomic segments. Otherwise, the parallelism claim should be softened, particularly in the title.

The gene-level analysis raises the same concern and also contains several inconsistencies. The background is 23,789 genes in the Methods, but 21,274 in Table S3; the Methods specify {plus minus}30 kb around window midpoints whereas Table S6 appears to use 120 kb; and the nearly fixed-variant analysis uses 45 and 8 genes in the Methods versus 52 and 14 in the Results/Table S3. These numbers need to be reconciled. I would also avoid placing much weight on a single shared tuba gene without an appropriate null model.

The MSMC2 analysis uses one diploid genome per species but is interpreted as species-level demographic history. Because these genomes come from different localities and the manuscript itself shows substantial within-species structure, species and locality are confounded. These results should be framed as exploratory histories of the sampled genomes/populations unless additional individuals are analyzed.

The link between oceanography and genomic connectivity is also mainly visual. Because this is a central aim, it should be tested quantitatively, for example by comparing a Lagrangian connectivity matrix with genetic distance while accounting for geographic distance. Similarly, the correlation between pairwise FST values across species should use a matrix-based permutation test rather than a standard Spearman p-value based on 10 non-independent comparisons. The Methods explicitly state that correlations between oceanography and population structure are tested, but no such analysis is reported.

A few additional points need attention. The NNE-Mades G. orientalis is excluded from some analyses but retained in others despite strongly affecting results. The particle-drift model assumes passive 2D advection and omits larval behavior and vertical movement, which should be stated. Mitochondrial-nuclear discordance could also reflect stochasticity, introgression, or sex-biased dispersal. Finally, several inconsistencies need correction, including duplicate Figure S6/missing S7, four versus five populations in the Figure 2 caption, the ENA accession range, and several typos.

Reviewer #3 (Public review):

Summary:

The work by Abdelnaby et al. investigated the different Orai isoforms, and also especially Orai1 alpha and beta for NFAT 1 and NFAT 4 translocation, but also its impact in genetically modified T-cells. This is a very well-performed work that manages to monitor NFAT nuclear/cytosol ratio even in a time-dependent manner. Overall, this study has been very well performed, is clearly written and discusses the literature very accurately.

Strengths:

For monitoring NFAT translocation, the authors have managed to observe this pattern even in a time-dependent manner. The authors may like to describe how this analysis was done with more details, as I believe some software assistance was needed to distinguish the nuclear to cytosolic region for a huge number of cells. Clearly, this analysis provides a very clear picture of the time course of translocation and allows for a precise statistical analysis.

Remarkably, NFAT1 and NFAT4 translocation was not substantially different for Orai1 alpha- and Orai1 beta-mediated Ca2+ signals. This has also been observed previously by Zhang et al. for NFAT1 from the same laboratory. Clearly, this is in contrast to the work of reference 38 (Kar et al.). All these publications have been performed with a huge amount of data, and I believe that clearly stating this difference in results from previous work is an additional important aspect of this manuscript.

In line with the similar NFAT translocation efficiency of Orai1 alpha- or beta-containing cells, the authors carefully evaluated differentially expressed genes in CD4+ T cells. They did not find a substantial difference in these cells that were transduced with Orai1 alpha or beta - clear orthogonal evidence for a conserved function of Orai1 alpha and beta for transcriptional activation.

Weaknesses:

This reviewer identified no obvious weaknesses in this work.

Reviewer #3 (Public review):

Summary:

This manuscript investigates strategies to overcome resistance to PARP inhibitors (olaparib) in a mouse model of BRCA1-related triple-negative breast cancer. They test whether combining olaparib with a PI3K inhibitor (alpelisib) or with an immune-stimulating agent (Poly(I:C)) can improve outcomes in both drug-sensitive and drug-resistant tumors, and find that both combinations extend survival in sensitive tumors, while only the PI3K combination partially overcomes established resistance. To understand the biological basis for these differences, the authors apply a previously developed hydrogel-based assay that measures seven microRNAs directly within tumor tissue sections, preserving their spatial location. They then build a set of computational tools-based on a topic-modeling method called latent Dirichlet allocation (LDA), principal component analysis (PCA), and an image-similarity measure called SSIM-to interpret these spatial microRNA patterns and relate them to tumor drug sensitivity, treatment type, and the surrounding immune cell environment.

Strengths:

(1) The in vivo survival experiments are well powered and rigorously analyzed, with appropriate statistical testing (log-rank tests) clearly supporting the central efficacy claims: both combination therapies benefit sensitive tumors, and PI3K inhibition (but not Poly(I:C)) partially rescues resistant tumors.

(2) The spatial microRNA measurement technology, while previously published, is thoughtfully applied here to an earlier treatment timepoint (10 days) than prior work, which is a reasonable and useful extension aimed at capturing biology before advanced tumor changes complicate interpretation.

(3) The core finding that a microRNA topic dominated by miR-21 associates with drug sensitivity, while a topic dominated by let-7a associates with resistance, is supported by a formal statistical test (a Mann-Whitney U test comparing principal component scores between sensitive and resistant tumors), giving reasonable confidence in this specific result.

(4) The idea of layering an immune-infiltration similarity map (SSIM) onto the microRNA topic maps is a creative and potentially broadly useful way to connect molecular spatial patterns with tissue architecture, and could be adapted to other spatial biomarker studies beyond this one.

Weaknesses:

(1) Several of the paper's central spatial and immune-colocalization findings are supported only by visual inspection of small numbers of samples (commonly three tumors per treatment group) rather than by formal statistical comparison. This applies to the treatment-type separation shown in Figure 3B.iii and to the immune-microRNA co-localization patterns shown in Figure 4B.ii, both of which are described narratively without accompanying statistical tests.

(2) The claim that Poly(I:C) fails to alter the immune composition of resistant tumors, unlike PI3K inhibition, is stated qualitatively ("little difference") without a quantitative comparison, which weakens confidence in this specific interpretation.

(3) All spatial analyses derive from a single mouse model at a single treatment timepoint, with no independent tumor cohort used to test whether the identified microRNA topics and their treatment/sensitivity associations generalize. This is an appropriate scope for an initial proof-of-concept study but means the biomarker potential of the approach remains unproven outside this specific dataset.

Overall, the authors achieve their stated aims, and the evidence broadly supports the paper's central conclusions distinguishing drug-sensitive from drug-resistant tumors. The additional analyses linking treatment regimen and immune infiltration to spatial microRNA patterns are plausible and consistent with the survival data, though they rely on small sample sizes and, in places, qualitative rather than statistical comparisons, and would benefit from further validation. The analytical framework itself - particularly the SSIM-based integration of molecular and cellular spatial data - is a useful and creative contribution with potential utility for other groups working on spatial biomarker discovery.

Reviewer #3 (Public review):

Summary:

This study investigates how the activity of hypothalamic paraventricular oxytocin (PVNOT) neurons relates to physiological states in female mice, with a particular focus on behavioral states and thermogenic sympathetic activity. To address this question, the authors combined automated video-based behavioral classification with calcium imaging of PVNOT neuron activity. Sympathetic thermogenesis was inferred from surface temperature changes measured by infrared thermography, and the authors have made their custom analysis scripts available. The authors report that strong, pulsatile activation of PVNOT neurons was "occasionally" observed immediately before transitions from resting to active states. This observation suggests that PVNOT neuronal activity may facilitate the transition from rest to activity. This phenomenon was observed in both pair-housed and individually housed animals. Taken together, these findings raise the possibility that the oxytocinergic system contributes to naturalistic behavior transitions even in the absence of social interactions. However, concerns regarding the selectivity of GCaMP expression in oxytocin-expressing neurons call into question the validity of the recorded PVNOT neuronal activity. The revised manuscript improves the presentation and interpretation of the data. Nevertheless, because the authors have not provided additional experiments or analyses addressing the major methodological concerns, the evidence supporting the central conclusions remains essentially unchanged.

Strengths:

The oxytocinergic neural system is believed to subserve a wide range of physiological functions. Elucidating these roles requires monitoring PVNOT neuronal activity under diverse behavioral contexts, as well as manipulating this activity to establish causal relationships. In this study, the authors present a technically sound experimental framework that integrates behavioral tracking in both individually and group-housed mice with the monitoring and manipulation of PVNOT neuron activity. This setup represents a valuable methodological resource for researchers investigating the physiological functions of oxytocin.

Weaknesses:

(1) Immunohistochemical validation of selective GCaMP expression in oxytocin-expressing neurons showed that only 24-51% of GCaMP-positive neurons expressed oxytocin. As an alternative approach, the authors argue that the similarity between calcium dynamics recorded in virgin and lactating animals supports the identity of the recorded neurons as oxytocin neurons. While this physiological comparison is interesting, it does not constitute direct evidence for cell-type specificity of GCaMP expression. The revised manuscript now acknowledges that in situ hybridization targeting oxytocin mRNA would provide a more reliable validation, but such validation has not been performed. Therefore, uncertainty regarding the identity of the recorded neurons remains, limiting confidence in the interpretation of the calcium imaging data.

(2) Although the authors' interpretation is generally consistent with the data presented, their main conclusions rely heavily on observational findings. Moreover, optogenetic stimulation of PVNOT neurons failed to robustly recapitulate behavioral state transitions (Figs. 6D and S5B). Further interventional experiments remain necessary to rigorously test the authors' interpretation and establish a causal relationship between PVNOT activity and rest-to-active transitions. In particular, loss-of-function approaches targeting the PVNOT system, such as OXTR antagonism, inhibitory optogenetics, or cell-type-specific ablation, remain essential to determine whether perturbation of this system alters behavioral state transitions. Although the authors expanded the Discussion to acknowledge this limitation, the revised manuscript provides no additional experimental evidence addressing it.

Comments on revised version.

I appreciate the authors' efforts to clarify the manuscript and to discuss the limitations more explicitly. Nevertheless, because my major concerns have been addressed primarily through revised interpretation rather than new evidence, my overall assessment of the scientific support for the principal conclusions remains unchanged.

Reviewer #3 (Public review):

Summary:

This manuscript addresses a fundamental question in cognitive neuroscience: which event should serve as the temporal reference for neural processing during natural vision? While fixation onset has traditionally been treated as the analogue of stimulus onset in free-viewing experiments, the authors convincingly demonstrate that this assumption is incomplete.

The study utilizes a remarkable natural-viewing dataset consisting of simultaneous MEG and eye-tracking recordings collected during the exploration of thousands of natural scenes. The authors compare several candidate eye-movement events and evaluate which event best explains the timing of the early M100 response. Across several complementary analyses, saccade-related events consistently outperform fixation onset, with peak saccade curvature emerging as the event that best predicts neural response timing.

Strengths:

A particular strength of the work is that the conclusions do not rely on a single analytical approach. Instead, multiple independent analyses converge on the same interpretation, increasing confidence that the observed timing relationships are robust rather than analysis-specific. The comparison between natural-viewing responses and classical stimulus-onset responses is especially compelling and highlights qualitative differences in their spatiotemporal organization. Of particular conceptual importance, the findings support the broader perspective that perception is intrinsically linked to action and internally generated sensorimotor processes. This aligns well with growing evidence that oculomotor action and active sampling play central roles in perception. The work contributes to an important ongoing shift in how natural vision should be studied experimentally and interpreted theoretically.

Weaknesses:

I identified no major weaknesses in the study. The main limitation is the relatively small number of participants, despite the exceptionally rich dataset. Future work in larger cohorts and across complementary electrophysiological recording modalities will help establish the generalizability of the reported temporal relationships.

Reviewer #3 (Public review):

Summary:

This work aims to understand the mechanisms that platelets use to interact with and compact fibrin fibers during clot formation. This is an important process during wound healing and recent work has demonstrated that platelets play a critical role in generating the force required to drive accumulation of fibrin. The authors argue that current models are insufficient to account for the observed reduction in clot volume and propose that platelets actively 'wind-up' these fibers by undergoing myosin-dependent rotation. While interesting, the experiments performed by the authors do not directly test this mechanism and further evidence is required to support their claims.

Weaknesses:

(1) The motivation to switch from the system used in Figure 1 and 2 to the '2D fiber-retraction assay' is not clear. While the authors state that this system has 'reduced complexity' the differences between these assays appears to disrupt the 'cage-like' organization of fibrin around platelets shown in Figure 1 and 2 (compare images in Figure 2 with those in Figure 4). An in-depth comparison of two methods is needed to support the conclusions from the 2D system. Furthermore, the change in plasma volume (Figure 2 vs Figure 7) should also be tested - the authors state that this increases fibrin fiber formation, but this is not quantified or demonstrated in the figures. Notably, this appears to change the morphology of the fibrin fibers shown (comparing Figure 2 and Figure 7).

(2) It is unclear how the classification of platelets as 'fiber-winding' versus 'fiber compaction' differs in Figure 2. The criteria used for these classifications should be stated. Further, it seems premature to characterize fibers as wound without having established this earlier in the manuscript.

(3) Is the 'gearwheel' different from the 'cage' of fibrin fibers? They appear similar, but it is difficult to distinguish between these with only qualitative descriptions of these phenotypes.

(4) The quantification of platelet extensions in Figure 9 is confusing. While the those in 9A are clear, those in 9B are not. For instance, what is the difference between #7 and #8 in the middle panel of 9B? It does not seem like #8 is labeling an extension.

(5) It is unclear what the modeling accomplishes as there is no comparison between the results of these simulations and their experiments.

(6) The data presented in Figure 12 provides the most direct support for their mechanism, but falls short of directly testing their claims. These experiments should be repeated to include blebbistatin to test the contribution of myosin and include quantitative rather than qualitative comparisons of these experiments.

Comments on revised version:

The manuscript is substantially improved in clarity and organization. The authors have adequately addressed most of my concerns regarding presentation and interpretation through revisions to the text and figures. However, my primary mechanistic concerns remain unresolved. Although the proposed model is now presented more cautiously, the revised manuscript still does not directly test the central mechanism, and the conclusions therefore remain insufficiently supported.

Reviewer #3 (Public review):

Summary:

Normandin et al. explore the coding of stimuli predicting an aversive event in the prelimbic cortex. Stimuli could either be explicitly paired, explicitly unpaired, or novel but with an inferred association with the aversive event (generalization). Long-term tracking of GCaMP positive neurons allowed them to examine how coding evolves out to a month following training. In general, they found two types of ensemble codes. One was ensembles coding for each stimulus independently, but with enhanced responding to the one eliciting a freezing response. The other was ensembles that responded to all stimuli in proportion to their similarity to the stimulus paired with the aversive event, either increasing or decreasing their activation with the degree of freezing elicited by a stimulus. Importantly, this second set of ensembles was more stable across days, potentially providing a memory trace.

Strengths:

(1) The authors track ensembles in prelimbic cortex over long time scales, providing valuable information on the consolidation of neural codes.

(2) Neural coding of generalization is examined, which is under examined in the field.

Comments on revised version.

The authors have convincingly and thoroughly addressed my concerns. I have no further issues regarding this study.

Reviewer #3 (Public review):

Summary

This study aims to contest recent findings that prenatal exposure to natural sounds and anthropogenic noise before hatching affects development and fitness in an altricial songbird. To this aim, it attempts to estimate hearing capacities of zebra finch nestlings. It first uses responses to clicks in nestlings and adults to estimate differences in hearing thresholds but uses experimental parameters that systematically lower nestling responses. It then measures responses to tones but restrict nestling data to a protocol (long tones) that failed in adults; response to tones with a correct protocol (short tones) is repeated in adults only. Thirdly, it includes data on loud airborne sound making eggs vibrate, even though this has no relevance to embryonic vibration perception by direct contact with the incubating parent or in incubators. Lastly, it includes a lengthy discussion on how these results, even though inaccurate or incorrect, would show that zebra finch nestlings and embryos are "functionally deaf". It fails to note that even if the experiment had been performed correctly and still failed to detect an auditory response in 2 day hatchlings - which is unlikely given the above and findings in other songbirds - this would not somehow eliminate the developmental effects of prenatal sounds that have been empirically demonstrated.

Strength:

The study is not performed adequately to bring reliable answers, but it addresses the important topic of hearing development in altricial songbirds and the long-held (but untested) assumption that altricial avian embryos cannot hear. More broadly, there is a need to reassess avian auditory perception - and the methodological approaches to measure it - given the accumulating evidence that some bird species respond to high frequency biological sounds beyond their known hearing range.

Weaknesses:

The study presents many experimental flaws that specifically compromise response detection in immature animals in the first experiment, and data from the remaining two experiments are invalid. The revision did not fix any of these issues.

i) Response to clicks, Fig 1: Unlike what the revision claims, the deviations from validated protocols (too rapid stimulation, too few measurements, low temperature, reliance on clicks only) do lead to a potentially large underestimation of nestling hearing sensitivity. Calculation and new data trying to show that these deviations would not matter are wrong or insufficient. Even without strict conventions on ABR methodology, it is striking that every single experimental choice made here i) greatly differs from other avian studies and ii) reduces - rather than maximises - response detection, specifically for low amplitude signals (as in nestlings or near thresholds).

ii) Response to frequency tones, Fig 2: The experiment on frequency sensitivity (long tone burst) failed in adults (positive control) with 0 to only about half of the adults responding (Fig S1), and sensitivity underestimated by 30dB (Fig2). Measures with a failed positive control are invalid, even if the overall pattern of the few data points obtained in nestlings vaguely resemble (but does not match) expectations. That the experiment was repeated in adults with a correct protocol (short tone pips) - but not in nestlings - is highly misleading.

iii) Attempts to validate the protocols above by comparing to published adult values are meaningless (response A.3.1; Fig 5). The one experiment being compared (short tones) was only performed in adults in this study. This cannot validate results obtained by different methodologies in nestlings, for responses to clicks or long tones.

iv) Vibrations: No previous results on the effect of zebra finch heat calls on development rely on the hypothesis or assumption that airborne sound from heat calls makes eggs vibrate. This idea is solely attributable to the authors of the preprint, and is not biologically or experimentally realistic. All speculations from this experiment (Fig3) on vibration perception by embryos or heat call effects are meaningless.

v) Writing and presentation: The text throughout is highly misinformative for non-specialists or any reader not carefully inspecting figures (including in supplementary material) and methods. The confusion is greatly aggravated in the (6-page long) discussion which i) fails to recognise and account for the study shortcomings, and instead ii) greatly overstates the results and what they mean, and iii) misrepresents current knowledge by excluding highly relevant studies showing evidence of early sound perception in embryos and hatchlings, and introducing many errors in the presentation of published papers. This creates an illusion of a strong mismatch between heat-calls and zebra finch sensory capacities, for which the study actually does not provide any evidence for, and which is extremely unlikely to exist.

vi) The revision did not address any of the major flaws of the study outlined above (see detailed assessment below). In particular,<br /> ** For point i):<br /> - estimations for the effect of having done too few (400) measurements are wrong - the effect on hearing thresholds cannot be calculated, but would be much greater than 4dB with the expected 37% noise reduction with the standard 1000 sweeps;

- the new data provided does not measure the impact of high stimulus rate, and measures on adults largely underestimate effects on nestling response;

- body temperature, now provided in the revision, is at the lowest extreme for the species, which may increase hearing thresholds;

- tones do elicit a stronger and earlier response than clicks. Whether this is related to stimulus duration does not change the fact that clicks underestimate hearing thresholds, and delay hearing onset by several days.<br /> ** For points ii) and iv):<br /> No new data or any valid explanation was provided in the revision. It is still the case that nestling frequency responses were obtained with an experiment where the positive control failed; and the vibrating egg experiment is irrelevant to vibration perception in embryos and any observed effects of heat calls on development.<br /> ** The revision greatly lengthens the speculations about heat call perception, based on inaccurate or totally incorrect data.

Conclusion and impact:<br /> i) Overall, the study fails to provide any reliable estimate of zebra finch nestling hearing capacities: it underestimates hearing onsets and thresholds (clicks) and gives no information on nestling frequency sensitivity. It is extremely likely that a better designed ABR experiment, or a more sensitive methodology (e.g. electrophysiology), would have detected a response in 2 day-old nestlings, as in other songbirds. The conclusion of "deafness" in hatchlings (or embryos, which were not tested) is clearly unsupported.<br /> If zebra finch hatchlings were clearly deaf, a well-designed study would have shown this a lot more convincingly.

ii) The data on adults in not new (3 prior studies) - although this study generally underestimates zebra finch high frequency sensitivity. The presentation in relation to heat-calls is flawed: true values of heat-call frequency range and sound levels (>6kHz at 45dB) fall within the adult hearing range.

iii) Without any new evidence, this study does not progress our understanding of heat-call and noise impact on development. Exactly the same issue remains: that heat-call and noise effects on development contradict the general VIEW on hearing ontogeny in altricial birds. But we have learnt nothing from this study on hearing ontogeny or frequency sensitivity. The study provides no reliable neuroscience data to advance the debate.

iv) The largest section of the preprint is a highly speculative discussion, based on erroneous data and wrong interpretations, as well as a misrepresentation of what is known. That these issues would not be recognised is - in my view - of serious concern.

Detailed assessment:

The summary by the authors of my major concerns (R3.A0) is incorrect and misreport many of my statements, without giving any meaningful answers. I will not engage in such discussion.

My actual three major concerns do remain:

(1) Results on Fig 1 overestimate hearing onset and thresholds: All experimental parameters chosen to test responses to clicks are known to lower detection. From their cumulative impact, there is absolutely no doubt that the data in Fig 1 underestimate hearing capacities, and disproportionately so in nestlings compared to adults. Therefore, no quantitative estimate of nestling hearing threshold, absolute or relative (i.e. the estimated "54dB difference"), can be taken from this study. The age of hearing onset based on clicks (4 day old) is also wrong.

(2) All results on Fig 2 are false: 40 to 100% of adults (positive control) failed to respond within their normal hearing range (Fig S1). Data on nestling frequency sensitivity using this methodology (Fig 2) are clearly invalid.

(3) All results on Fig 3 are irrelevant: they assume zebra finch parents are hoovering in front of their nest while heat-calling rather than incubating their eggs, which is nonsense. Any speculation on the role of bone-conduction or vibrotactile stimulation for embryonic heat-call detection is simply unfounded.

The authors' responses to these comments are largely mistaken (see below), and do not change the 3 facts stated above. Overall, the data produced are unreliable, and so are the interpretations and conclusions.<br /> The conclusions that i) heat-calls fall outside of adult hearing range and ii) young nestlings (and embryos) are deaf, are both incorrect. The study provides no actual estimate of nestling hearing and how far off their hearing range heat-calls fall.

(1) Responses to clicks underestimate hearing abilities, specifically in nestlings.

(i) DISPROPORTIONATE EFFECT OF PROTOCOL ON NESTLINGS: As reported in other avian studies (e.g. Brittan Powell et al 2004), because of their immature neural system, nestlings show low amplitude waveforms compared to adults. This occurs even with sound much louder than their hearing threshold. For example here, 8 day old nestlings show waveforms of only low amplitude at 95dB, even though they respond to sound 40dB softer, at 55dB (Fig 1H).

This characteristic of nestling waveforms means that:<br /> - nestling responses are harder to detect,<br /> - nestling responses are more easily attenuated below the detection criteria (>2 S/N),<br /> - a low amplitude response in nestlings at a given sound level does not predict that softer sounds will not be perceived by the animal.

Any deviation in protocol that attenuates waveform amplitude will therefore disproportionately affect nestling thresholds (and artificially lead to the "54dB" estimate).<br /> Even without universal standards (resp R3.A1.1), knowing this, the protocol should be adjusted to maximise response detection. This study does exactly the opposite.

(ii) INSUFFICIENT MEASUREMENTS: using only 400 sweeps, rather than the typical 1000 sweeps, reduces response detection, especially in nestlings.

- The claim that using 1000 sweeps would only decrease the hearing threshold by 4dB (response A3.2; ms L530) is false:<br /> - Based on the square root relationship mentioned by the authors, using 1000 averages instead of 400 would improve the signal-to-noise ratio by 37%, which would allow detecting many small amplitude waves which are currently hidden in the abnormally high noise.<br /> - Lowering the noise floor level by 4dB does not mean that the hearing threshold would only decrease by 4dB. The improvement in hearing threshold would be much greater, especially in nestlings.

(iii) UNSUITABLY HIGH STIMULATION RATE: the new data on pairs of clicks greatly underestimate the attenuation by high stimulation rates, and so do adult measurements compared to nestlings'.<br /> - the click rate used in this study (25 clicks per second) is 5 to 25 times faster than most previous studies in young birds (e.g. Saunders et al 1973, 1974: 1 stim/sec or less; Katayama 1985: 3.3 clicks/sec; Brittan-Powell et al 2004: 4 stim/sec), and 6 times faster than zebra finch heat-calls.<br /> - responses to pairs of clicks (new data in Fig S3; L 543-552, response A3.3) does not measure the response dampening caused by high stimulation rates. Response attenuation (adaptation) after a single click is much weaker than after a train of 400 consecutive clicks (or even just 30). This paired-click measure ignores the cumulative attenuation observed with many consecutive stimuli. Paired-click paradigm is used to measure immediate refractoriness for other purposes (e.g. temporal resolution, diagnosis tool) but does not replicate high stimulation rates.<br /> It is unclear why the authors chose to use this weak approximation rather than simply replicating the measurements at the same slow rate as in other avian studies. This would have given a straightforward answer, comparable to previous studies that have demonstrated the detrimental impact of fast rate on response strength by directly comparing responses to different stimulation rates (e.g. Saunders et al 1973; Brittan-Powell et al 2004).<br /> - Adults are less sensitive to high stimulation rates than immature individuals (e.g. Saunders et al 1973; Khayutin 1985; Brittan-Powell et al 2004 and 6 references cited therein). Effects of fast rate in adults (new data in Fig S3) therefore largely underestimate effects on nestlings (not measured), and the high stimulation rate used in this study increases the relative difference between nestlings and adults.<br /> - Given the 2 points above, it is incorrect to conclude from this new data that the high stimulation rate used had "no effect on ABR amplitude or thresholds" (responses A3.3 L551). Instead, given that 40ms (i.e. interval for 25 clicks/ sec) is at the limit of what adults can handle after a single click, this new data does confirm that the stimulation rate is indeed too high and underestimates hearing in nestlings (and adults to a lesser extent).

(iv) LOW BODY TEMPERATURE can reduce ABR response.<br /> - The average body temperature (39.5C) now provided in the revised manuscript (response A2) is at the lowest extreme of the range for zebra finches. The normal average body temperature for zebra finches is 41C at low ambient temperature, rising to 43-44C at high ambient temperatures (when heat-calls would be produced). This value of 41C is consistent across many studies in wild and domestic zebra finches (e.g. Wojciechowski et al 2020 [avr 41C at 23C]; Udino and Mariette 2022 [avg 41, min=40C at 32C]; Bech and Midtgard 1981 [avr ~41C]; Pessato et al 2022 avg=41C at 27C]). The only study reporting a body temperature average as low as 39C (Cooper et al 2020) was indeed under hypothermic conditions, obtained in adult zebra finches under extreme fasting conditions (17hrs of food deprivation), at ambient temperature below thermoneutrality, during the night (i.e. during "nocturnal hypothermia", when bird body temperature is normally lower).<br /> Therefore 39.5C does corresponds to hypothermic conditions for most individuals. While this body temperature remains considerably higher than that used experimentally to supress hearing response, using a below-normal body temperature, added to other factors, may lower wave amplitude and therefore increase hearing threshold estimates.

(v) CLICKS UNDERESTIMATE HEARING ONSETS<br /> - My statement that, compared to tones, clicks elicit a smaller response, at a later age, is correct (Saunders et al 1973; Brittan-Powell et al 2004).<br /> - It does not matter whether this is due to differences in stimulation duration (response R3.A2.1), since clicks are always much shorter than tones. What matters is that hearing onset based on responses to clicks has been found to underestimate the earliest age at which an auditory response can be obtained by several days (Saunders et al 1973).<br /> - Without any valid nestling data for tones (see below), this study, based on clicks only, does not provide the true age of hearing onset.<br /> - The suggestion of using 170dB clicks (response R3.A2.1) is very odd. The correct approach would have been to use a correct protocol for tones in nestlings, rather than solely correcting it for adults (see below).

(vi) CONFUSING INTERPRETATION OF WAVE AMPLITUDE AND LATENCY<br /> - the text implies throughout that nestlings lacking an adult-like amplitude and latency is a sign of poor hearing (e.g. L122 "ABR wave I gradually reaches maturity 25 days after hatching").<br /> - It should be clarified that this is a characteristic of immature systems but does mean these nestlings do not hear. For example, this characteristic persists even in 10d old nestlings which have similar hearing thresholds to adults.

(2). Results on nestling frequency sensitivity are invalid. Using long (25ms) tones with subdermal electrodes is incorrect, and not used by anyone other than the authors. The failure to repeat the experiment with a correct protocol (5ms tones) in nestlings is misleading:

(i) When 60% of 4-day old chicks respond to clicks (Fig 1), they are described as "functionally deaf" (L78, L261). By contrast, when 0% to 60% of adults respond to tones in the core of their hearing range (Fig S1 for data shown in Fig 2), this is merely presented as a methodological limitation (text added L186-197), and the authors still proceed to presenting results on nestlings with a method that failed in adults (positive control).

(ii) When the positive control fails, the experiment is failed. This principle applies in Neuroscience as in any scientific field.

(iii) That the experiment repeated in adults with the standard and correct short tone protocol (5ms) gave normal results, is not a validation. Instead, it proves the point that 25ms is unsuitable (regardless of whether that is due to rising time, L399). This correction does NOTHING to fix results in nestlings, which were ONLY done with the unsuitable 25ms tones.

(iv) The mixture of adult data with 5 and 25ms tones, instead, creates confusion, because differences in protocols between nestlings and adults are blurred in the text. The abnormally small proportion of adults responding (0 to 60%) with 25ms tones is only shown in supplementary material, and attenuated in the main text (L186: "about half").

(v) The claim (response R3.A1.2) that long 25ms tones is commonly used with the ABR methodology used here is false:<br /> ** ALL (but 1) avian studies using tones of 20ms or longer (cited by myself or by the authors in their reply R3.A1.2) used a different ABR methodology, with electrodes implanted through the skull. The only exception, using 25ms tones with subdermal electrodes (as here), is a study by the authors themselves.<br /> ** All other avian studies with subdermal electrodes used short tones, of 5ms or less (e.g. Brittan-Powell et al 2002, 2004; Henry & Lucas 2008), as in the corrected adult experiment.<br /> ** My initial comment already specified this difference in electrode placement.<br /> ** The results here (failing in adults) unquestionably show that the method of long tones with subdermal electrodes does not work, and the literature shows that no one else uses it.

(vi) When the aim of the study is to contest effects of high frequency sounds (L39-45), it is odd to choose a protocol specifically directed at testing responses to very low frequencies (responses A3.1, R3A.0; L398-403) and that compromises all results.

(vii) That the shape of the few data points obtained in nestlings with long tones would broadly resemble expectations (responses A3.1, R3A.0) is not a validation: See ii.<br /> - The shape is not even correct:<br /> ** Why aren't 4 and 6 day old nestlings responding to any frequency when they were responding to clicks (in spite of poor detection conditions for clicks)?<br /> *** Why are they not responding, when 2 day old flycatchers respond to tones from 1 to 4kHz at 45dB, and 0.5 to 5kHz at 60dB (Aleksandrov and Dmitrieva 1992, Korneeva et al 2006)?

(viii) That only relative measures between nestlings and adults matter (resp R3.A4; L404) is incorrect. Measures that are qualitatively (see vii) and quantitatively (see i) wrong cannot be compared.<br /> If these data with long tones were indeed acceptable, why repeat the experiment in adults with a correct protocol, even before this flaw was pointed to during the review?

(ix) The unusually low number of measurements (400 instead of 1000; L530) will have reduced detectability of low amplitude responses, in nestlings and near thresholds, here as in the click experiment (see above). The claim that this would only increase threshold by 4dB (responses A3.2, L530) is wrong (see above).

(x) There is no explanation as to why only half of the 6-day old nestlings (n=5-6) were tested with tones. Would having 10 in this group shown a response at 6-day old?

(xi) Even the correct 5ms tone protocol in adults overestimated thresholds at higher frequencies (>3kHz) by up to 30dB compared to other published estimates (Fig 5). If inter-population differences among domestic zebra finches (L 393; Fig 5 legend; resp R3.A4) were enough to cause 30dB differences, results on domestic zebra finches here should not be extrapolated to those on wild-derived Australian zebra finches documenting developmental effects of heat calls.

The authors give no other elements than the above in their responses that could demonstrate the validity of their nestling frequency data.

Based on other studies, we can expect zebra finch hearing to develop sensitivity to high frequencies after that to middle frequencies. But pretending that this study provides any evidence towards this is wrong. There is no valid data.

(3) The experiment using loud sounds to make eggs vibrate is biologically and experimentally meaningless. The claim that these measurements would rule out vibration perception in embryos is totally unfounded. The preprint is creating the illusion of having ruled-out a mechanism that they have not tested.

(i) zebra finches are not hovering in front of their nest when heat-calling. They are in physical contact with the eggs while incubating, with vibrations expected to travel directly through solids from adults to eggs, without attenuation.

(ii) the claim that previous studies on heat-call developmental impact rely on the assumption of airborne sound making egg vibrate (response A3.5, R3.A8) is incorrect. This is conceptually wrong and there is no indication of this in any paper.

(iii) vibration perception in embryos in birds and other taxa (e.g. amphibian, reptiles and insects) rely on direct contact with the source, not on loud airborne sounds shaking eggs. Beyond any consideration on heat-calls, claiming that this experiment could give any indication of vibration perception in embryos is nonsense.

The authors give no information in their responses that could demonstrate the validity of this experiment.

(4) Interpretation and discussion

All other songbird studies, individually and collectively, show much greater hearing sensitivity in nestlings that what this study is trying to suggest (e.g Khayutin 1985; Korneeva et al 2006; Aleksandrov and Dmitrieva 1992; Rivera et al 2018; Platzen and Magrath 2004; Haff & Magrath 2012). The only way the authors can reach their conclusion is by:<br /> - presenting data that greatly underestimate hearing sensitivity in zebra finches (see sections 1 and 2) and overstating them, and;<br /> - misrepresenting current knowledge by excluding relevant papers and being unclear about what the literature shows.<br /> This study tries to impose the idea that zebra finch do not detect any sound before day 4-6 post-hatch, and have extremely rudimental hearing until day 8-10 post-hatch. By contrast, other studies show very young hatchlings respond to sound 1 to 3 days after hatching (first age tested) and show quite sophisticated, and totally functional, responses to relevant sounds at 5 days old.

This is not to say that hearing does not continue to improve post-hatch in birds, or that sensitivity to mid frequencies post-hatch would not precede that to high frequencies. But statements throughout this study are so exaggerated and/or wrong that nothing can be learnt. It is undeniable that this study fails to provide the useful and balanced assessment needed to establish were true heat-calls actually sit relative to zebra finch adult, nestling and embryonic hearing range.

It is literally impossible to correct every wrong statement in the discussion and responses to reviewers. I focus here on some examples related the claims of "nestling deafness" or of heat-calls being outside of adult zebra finch hearing range, as well as inaccuracies leading to an apparent match with current literature.

(i) MISALIGNMENT WITH OTHER STUDIES AND MISREPORTING

*** Neurological evidence

- Highly relevant evidence on response to sound in zebra finch embryos (Rivera et al<br /> 2018) is totally excluded.<br /> Excluding this study on the basis that it used a different methodology that does not directly quantifying auditory sensitivity (resp R3.A6a) is a poor justification. Evidence, even indirect or imperfect, should be brought to the attention of the readers.

- This applies to many other studies cited in my first review and arbitrarily excluded here. If one wants to conclude on "deafness", all evidence, even indirect, should be considered. Excluding non-ABR studies means the conclusion cannot be extended beyond flat ABR traces.

- Other studies show much greater sensitivity that what the text describes:

* Flycatcher hatchlings at 2-3d post hatch (first age tested), respond across a wide range of frequencies (0.3 to 5kHz), at low to moderate sound levels (45-65dB)<br /> (Aleksandrov and Dmitrieva 1992, Korneeva et al 2006).

* Stating that these studies in flycatchers "likely yield lower thresholds" (L364) is an astonishing understatement. Thresholds in Aleksandrov and Dmitrieva (1992) at 2-3 day old were 35 dB lower than those here at 4 day old with clicks, and 60 to 80dB lower than those with tones of 1-2kHz at 8 day old.

* Claims that "sensitivity improves rapidly postnatally, with ~40 dB threshold decreases (L365)" is also misreporting these studies' findings. Thresholds decreased by 25dB consistently at 9 out of 11 frequencies tested from 0.3 to 8kHz (Aleksandrov and Dmitrieva, 1992). A difference of 40dB was only found at 5-6kHz (Aleksandrov and Dmitrieva, 1992). Likewise, improvement also varied from 25 to 40dB in Korneeva 2006. These do not average to "~40 dB".

* Even birds developing 4 times slower than songbirds (budgerigars) show a response at 5 day old. My statement that Brittan-Powell 2004 shows an auditory response at 5d old is correct. Re-response R3.A2.2: Fig 1B shows one example for ONE individual. All other figures based on multiple individuals shows at least some individuals responded at 5-6 days old at frequencies less than 4Khz (Fig 1C, 4 and 5).

- Many inaccuracies on avian hearing remain uncorrected in the revision.<br /> * e.g. L 329: extrapolating high frequency hearing (>6khZ) from precocial species is incorrect because even adult chicken and ducks are not sensitive to high frequencies.<br /> The authors imply elsewhere that species of songbirds cannot be compared (resp A4, R3.A6), but make extrapolations from species that are far more remote, phylogenetically, developmentally and ecologically than other songbirds.

*** Behavioural evidence

- contradicts this study findings:

* When correctly cited and described, the literature, does not support the authors' statement that nestling behavioural response "typically emerges between ~5-10 days post-hatch" (L358). It emerges earlier, at an unknown age, including potentially from hatch (present at 1.5day) for innate responses (see below).

* Results in other songbirds are not consistent with this study finding that a "response to loud click stimuli is first detectable at 4-8 days post hatch" (L230). Instead they show nestling hearing capacities described here are abnormally poor.

* Therefore, the conclusion that "The timeline of behavioural studies closely matches the onset and maturation of ABR responses observed here in zebra finches" (L360) is wrong.

- shows a very early response (1-2day post hatch), with no known onset:

* songbird behavioural response to sound, with parental alarm call suppressing begging, has been demonstrated in nestlings as young as 1.5 or 3 days old (Khayutin 1985, Korneeva et al 2006, Aleksandrov and Dmitrieva 1992). None of these results are mentioned in the revision when discussing behavioural evidence (L357-360).

* Instead, they exclusively mention studies that started testing nestlings at 5-6 days old, or much later (e.g. 17 day old: Suzuki 2011; 14 day old: Barati & McDonald 2017).

* ALL of these studies (cited or not) demonstrated a significant response of nestlings to calls on the first age tested. NONE tested the onset of this response.

* the one study looking at progression across 3 ages, at 5, 8 and 11 day old shows parental alarm calls suppress nestling calling at day 5 as much as later on, with no effect of age (Platzen and Magrath 2004). The authors failed to acknowledge this in their response (resp A4) or revision (L359).

* the claim that Haff & Magrath (2012) showed nestlings did not respond at 5-6 day old but did at 10-11 days (resp A4) is wrong. At 5 day old, they responded to their own species alarm calls, as well as to another similar sounding species and to the sound of predators themselves (Table2 in Haff & Magrath 2012; as in Platzen and Magrath 2004).

- learning, not just hearing, improves nestling response with age:

* Haff & Magrath (2012) showed that by 10-11 day old, nestlings had learnt to also respond to heterospecifics, demonstrating that learning improves nestling response with age.

* the intensity of the response to low frequency sound improved more with age than that to high frequency calls. If improvement were related to hearing limitations, the opposite would be expected (Haff & Magrath (2012).

- nestlings discriminate complex calls, including at high frequency

* By 5-6 day old, nestlings can already discriminate several different sounds indicative of danger, among the complex natural acoustic background (Haff & Magrath 2012).

* nestling do not respond indiscriminately to any calls, but only to relevant sounds that specifically present a threat to them (Haff and Magrath 2012; Magrath et al 2006).

* the idea that nestlings only distinguish "low frequency broadband cues" (L362) is inaccurate (resp R3.A6). Nestlings respond to scrubwren chip calls and fairywren alarm calls that are narrowband calls with the fundamental at 8 and 10 kHz respectively (Platzen and Magrath 2004; Haff and Magrath 2012).

* These studies indeed "do not imply mature auditory sensitivity" (L362), they show that auditory maturity is not needed to show a perfectly functional response to biologically meaningful sounds in a natural context.

- Overall, every other songbird species tested shows greater hearing sensitivity than that proclaimed here for zebra finches. It is very unlikely that zebra finches would be such an outlier.

(ii) INCORRECT CONCLUSION ON DEAFNESS

Deafness of young zebra finch nestlings cannot be demonstrated because:

- the study has no valid response to tones in nestlings to establish the age of hearing onset.

- responses to clicks are greatly underestimated (see 1). Had correct, more sensitive, protocol parameters been used, it is very likely that:<br /> * Most 2 day old nestlings would have responded to clicks, instead of 60% of 4 day old nestlings.<br /> * Thresholds would be lower, especially in nestlings.<br /> The >54dB difference between nestlings and adults based on an assumed 95dB threshold in 2d old nestlings is wrong.

- based on data on other songbird nestlings, a difference of 25dB would be more realistic (at frequencies of 1-2 kHz, comparable to clicks, Aleksandrov and Dmitrieva 1992, Korneeva et al 2006). This greatly contrasts with the >54dB estimate here. While the authors qualify their estimate as "conservative" (legend Fig4, L293), it is actually greatly overestimated.

- Even assuming the data in this study were correct, the interpretation is erroneous. Concluding "deafness of young nestlings" is incorrect when 60% of 4 day old nestlings respond to clicks at 80dB. This is especially wrong given that the ABR method systematically overestimates thresholds by 20-40dB.

- That ABR is used in humans to diagnose deafness (L263) does not make ABR the most suitable method for birds, when evidence shows that other methods (e.g. electrophysiology or behaviour) are more accurate. Hearing screening in humans can only use non-invasive methods, and has additional criteria than accuracy (price, ease, etc).

- The discussion fails to acknowledge the implications of the limitations of the study (detailed above). For example, talking in broad terms of differences in protocols (L392-395), does not tell readers that this study, because of the parameters chosen, led to an underestimation of zebra finch hearing capacities.<br /> The discussion instead greatly overstates what the study shows (e.g. L229-232, 261, 277-278, 288, 332, 340, etc).

(iii) INCORRECT CONCLUSION THAT HEAT-CALL FALL OUTSIDE THE ADULT HEARING RANGE.

- heat-calls fall outside the adult hearing range solely because of the authors' decision to:<br /> * restrict heat-call frequency range to the authors' own estimation (7-10kHz) on 4 birds instead of using published values that caused developmental impact (e.g. Katsis et al: 6-10kHz), or were produced in vitro (5.9kHz; Anttonnen et al 2025);

* lowering heat call sound level (34dB at 10 cm) to measurements on 4 isolated birds under unknown temperature conditions for an unknown amount of time, when the same paper gives values of 43.4 dB at 1 m (range: 30.7-53.2) in standard in vitro conditions.

* As soon as EITHER of these two values is corrected, the statement that heat calls are outside zebra finch adult hearing range is false.

* In addition, assuming constant heat-call sound level across contexts is unreasonable. That zebra finch can produce inspiratory syllable very similar to heat calls and during inspiration at 65dB during song (Goller & Dalley 2001) argues against the assumption that heat-calls are always soft.

- Other species also show perception, not just production (resp R3.A10a), of calls above their known hearing range (10-20kHz; e.g. Duque et al 2020). The zebra finch is not the only case in birds of mismatch between signals perceived and known hearing range.

Reviewer #3 (Public review):

Summary:

The authors report and characterize the formation of aggregate microstructures by the motile filamentous cyanobacterium Fluctiforma draycotensis, which exhibits gliding motility accompanied by rotation along the long axis while excreting EPS. In experiments with motile F. draycotensis cultures, they observed the formation of granular structures composed of cyanobacteria and other material (iron, polystyrene beads, etc.), with macrostructures on the scale of 1mm within 24 hours. The structures were motile at speeds comparable to that of the cyanobacteria filaments, resulting in their growth through coalescence over time. Notably, such macrostructures were absent in nonmotile F. draycotensis, pointing to the role of filament motility in their formation. Through experiments examining the micro-scale dynamics, inert material such as small polystyrene beads was found to be transported by the gliding, buckling, and plectoneme dynamics of the filaments, pointing to the underlying mechanism by which particles are collected into larger-scale microgranule structures.

To interrogate the properties that drive the cyanobacteria filament buckling, plectoneme formation, and entanglement, the authors develop a mechanical model for filaments as nearly inextensible, slender bodies with resistance to twisting and bending under active gliding forces and torques and responding to fluid flows and surface adhesion. They derive expressions for the thresholds for buckling and twisting instabilities, which are additionally demonstrated and interrogated through simulation via the Immersed Boundary Method. Most importantly, bending and plectoneme formation only occur with sufficiently long filaments, and the threshold is shorter for bending than for plectoneme formation. Experimental observations with wild-type filaments agree with the model-predicted thresholds. The authors perform additional experiments with shorter filaments below both thresholds, including the filamentous bacterium Pseudanabaena, which fail to collect particles (though can in principle form macrostructures).

Strengths:

This work appears to be novel (notably, the discovery and characterization of the particle collection behavior of a filamentous cyanobacterium) and has interesting implications for both naturally observed cyanobacterial macrostructures as well as the controllable parameters in engineering them. The experimental and modeling work is well motivated, contributing to the broader understanding of macrostructure formation and material aggregation through active filament dynamics (not exclusive to cyanobacteria), as well as the underlying physical properties governing important filamentous cyanobacterium dynamics. As such, I would expect the results of this paper to be of broad interest to both biophysicists and microbiologists. Generally, the manuscript is well written with clear, compelling figures that illustrate the important conclusions of this study.

Weaknesses:

In the section on "Shorter gliding filaments cannot collect particles nor form granule macrostructures", the filamentous cyanobacteria considered *all* fall below the predicted thresholds for bending and twisting. The "long" F. draycotensis are 60 microns in length, notably less than the 120 and 320 micron thresholds derived in the previous section as well as the lengths of filaments considered in Figure 3D, yet these "long" 60 micron filaments form macrostructures. How can this be understood in the context of the model predictions? Is the nature of the macrostructures in Figure 4B, the microscale parameters, or the collection of particles somehow different than those with filaments an order of magnitude longer in earlier parts of the paper? The paper would be stronger if these sorts of questions were addressed in the text and/or with supplementary figures.

Reviewer #3 (Public review):

Most models of reinforcement learning in the brain treat the question of how the external world is represented as an afterthought. In "Error driven representation learning in the mesolimbic system", the authors begin by calling attention to this limitation of previous work, then proceed to show that neural activity in part of the ventral striatum evolves in a manner consistent with dopaminergic RPE-driven representation learning. Overall, the question is interesting, the modeling is well done, and the claims are bold. While I am not convinced that olfactory tubercle outputs _mainly_ reflect state features acquired through error-driven learning (see main points below), I am now more willing to believe that they might. I am confident that this work will spark discussion.

Strengths:

The latent weight trajectory inference approach is a nice application of Kalman filtering, the model validation and comparison steps are well executed and thorough, the discussion is nicely written and includes an appropriate caveat about the weight transport problem, and the general idea of striatal output being value-like but also having state-like aspects that evolve over time is thought-provoking.

Weaknesses:

In my view, the main limitation of this work is that the authors do not clearly rule out (1) non-representation learning and (2) non-error-driven learning. This fits with the narrow research question stated at the end of the introduction (L71-72), which is confirmatory in nature and does not claim to exclude alternatives, but other aspects of the framing are less consistent.

Main points of criticism

(1) Representation vs. value learning

The first two pages of the manuscript gave me the impression that the authors wish to draw a clear distinction between representation and value learning, and that they would squarely position this paper as a study of representation learning. The substance of the work does not seem consistent with this positioning, a mismatch that could be addressed either by incorporating new analysis and discussion or by changing the framing.

Examples of emphasis on representation:

- Abstract L14-17 defines value and representation learning and states why they are different.

- First three paragraphs of introduction explain the power of learned representations.

- Results L103-108 attribute state representations to OTu and value to downstream regions.

For this level of emphasis, it would be good to see a convincing argument that OTu MSN activity is better understood as a state representation than as the output of a value function.

Options for establishing OTu as state and not value:

- Explicitly claim in the introduction that previous work has established this, and explain why evidence of stimulus valence being encoded in this area (citations on L68-70) does not favour the value interpretation. Discussing how OTu differs from other parts of ventral striatum that are canonically seen as value coding would also help.

- Directly compare the extent of state vs. value encoding in the present data. The $w=1$ control is a good step in this direction, but its connection to value coding is mentioned only in passing.

(2) Error driven learning vs. other types of learning

Similar to my previous point, the authors seem to claim that the representational changes they study are specifically error driven. While the authors include a good number of controls and ablations, it was not obvious to me that any of them correspond to a form of non-error driven learning that could plausibly generate useful representations. Adding Hebbian learning or a sparsifying learning rule would strengthen this aspect of the work.

Reviewer #3 (Public review):

Summary:

In this study, the authors show that exposure of cancer cells to DSBs induced enzymatically or by ionizing radiation (IR) results in large-scale genomic amplifications (DIGA: DSB-induced genomic amplifications) that are detectable by FACS. This phenomenon was observed in diverse cancer cell lines and was shown to be limited by p53 in paired cell lines. In principle, DIGA could result from re-initiation of DNA synthesis within the same cell cycle, mitotic segregation errors, or repair-associated DNA synthesis. The authors conduct experiments to distinguish between these possibilities and conclude that DIGA is the result of extensive RAD51-dependent DNA synthesis.

Strengths:

The observation of genome amplification in response to DSBs specifically in cancer cells is striking, particularly at high IR doses or by AsiSI endonuclease induction. At 9 Gy, almost 50% of cells have a >4N DNA content. The correlation between high levels of DIGA and reduced clonogenic survival of melanoma cells suggests that DIGA contributes to IR-induced cytotoxicity. The authors convincingly show that DIGA occurs in a single cell cycle and is not due to chromosome mis-segregation at mitosis. Because DIGA is partially dependent on resection nucleases, POLD3, POLD4, RAD52 and RAD51, the authors conclude that DIGA results from repair synthesis by a BIR-like process.

There are significant weaknesses in the study:

(1) It is unclear to this reviewer how BIR-like synthesis could increase genomic DNA copy number by 50% or more, as indicated by the FACS plots. In the case of AsiSI, there are ~150 sites that are efficiently cleaved in U2OS cells. Each of these sites would need to prime extensive synthesis by an inefficient migrating D-loop mechanism. Studies of BIR-like synthesis at DSBs in U2OS cells have shown that repair tracts are fairly short, around 3-10 kb in length. Thus, it is hard to rationalize how BIR at a few hundred DSBs could initiate such large changes in genome size.

(2) The BrdU-FACS plot shown in Figure S4 shows that most of the cells have an 8N content 48 and 72 h after AsiSI induction and do not have much BrdU incorporated. This finding would suggest that genomic DNA doubling is mostly independent of de novo synthesis. Also, there does not seem to be a continuum from 4N up to 8N in these plots as one might expect from variable length DNA synthesis tracts.

(3) The requirement for XRCC4, XLF and LIG4 to promote DIGA is puzzling if synthesis occurs by a BIR-like mechanism. Although there is evidence that DNA synthesis at DSBs can be initiated by homology-directed strand invasion and terminated by NHEJ, this mechanism would limit the extent of synthesis to a few kb at each site. One might have expected an increase in DIGA in the absence of core NHEJ factors because of the increased number of DSBs available for BIR.

(4) Although the authors rule out re-replication as a contributor to IR-induced DIGA, the FACS profiles of MLN4924 and 9 Gy treatment look remarkably similar, and both would be inhibited by aphidicolin treatment. A previous study showed that DSBs induce endoreduplication in Arabidopsis (Adachi et al. PNAS 2011).

Reviewer #3 (Public review):

Summary:

The authors aimed at a comprehensive phenotypic characterization of the roles of all Rab proteins expressed in PN neurons in developing Drosophila olfactory system. Important data are shown for a number of these Rabs with small/no phenotypes (in the Supplements) as well as the main endosomal Rabs, Rab5, 7, and 11 in the main figures.

Strengths:

The mosaic analysis is a great strength allowing visualization of small clones or single neuron morphologies. This also allows some assessment of cell autonomy of the observed phenotypes. The impact of the work lies in the comprehensiveness of the experiments. The rescue experiments are a strength. The added developmental data strengthen the impact of the paper.

Weaknesses:

The main weakness is that the experiments do not address the mechanisms that are affected by the loss of these Rab proteins, especially in terms of the most significant cargos. The insights thus do not extend far beyond what is already known from other work in many systems.

Reviewer #3 (Public review):

Summary:

Cells and tissues are viscoelastic materials. However, metabolic processes that underly survival, growth and migration render the cell as an active matter at non-equilibrium. These two facts contribute to the difficulty of probing mechanical properties especially with sub-cellular resolution. However, the concept that the mechanical phenotype can be indicative of normal physiology necessitates approaches of defining the cellular phenotype. Here, Muenker et al evokes a powerful argument for mapping intracellular mechanics using optical tweezer- active microrheology. They present a suite of parameters towards a definition of a mechanical fingerprint. This is a compelling idea. There are some concerns as detailed below

Strengths:

These are technically challenging experiments and the authors provide systematic approaches to probe a system at non-equilibrium.

Weaknesses:

The importance of the mechanical fingerprint is diluted due to some missing controls needed for biological relevance. As it reads, sinusoidal waves are applied sequentially from 1- 1024Hz.<br /> Please clarify if amplitude is the same for each frequency, also how many frequencies are used?

On this point, due to perturbations due to alterations in pre-stress, are the orders of frequencies randomized?

How many beads are probed in a given cell.

Is the graph in 1 c G', G" per cell or average of many cells?

Figure 1e is quite nice, however is there an equivalent performed in a non-linear ECM such as collagen for comparison, in a similar vein can the equivalent be calculated for cells with/without treatment with low doses of cycloheximide to reduce protein synthesis? Yes, cytoskeletal elements are important for cell mechanics, but cytoplasm crowding is often an overlooked factor.

The biggest issue is the interpretation of the different factors as each of these cells have different energetic needs.<br /> The comparison between cancer cells with different aggressiveness, immune and epithelial cells.<br /> For example, some types of cancer cells will be dominated by glycolysis vs oxphos, which will influence both the cytoplasmic and nuclear mechanics?

It would be useful to carefully assess factors not restricted to<br /> a) Cytoskeleton<br /> b) Protein synthesis<br /> c) Metabolic state

For similar lines and/ or cells where there are lineages that are either more metastatic in cancer, normal counterpart or drug resistant in an effort to link the fingerprint to a biological output. Specifically, is migration, proliferation, survival correlated with the measurements.

The reviewer is sensitive to the technical difficulties of the experiments. However, the interpretation and importance of the mechanical fingerprinting requires additional work as mentioned above.

Reviewer #3 (Public Review):

Summary:

In this work, Styer et al. explore host selection as a means for recruiting microbes that may aid their host under stressful conditions, in this case under drought stress, as an alternative to target-SynCom design. They do so by subjecting rice plants to several generations of soil transplantation, and by using the most successful rice plants as donors for the next generation. By using several NGS approaches and very thorough bioinformatics analysis, the authors identify potential microbial taxa and the associated functions enriched in the conditions of interest.

Strengths:

In general, I think this approach was very much needed in the field as an alternative to SynComs, which are still not readily usable in croplands. This work sets the grounds for future similar approaches, using different stresses and different host plants.

In this work, the experimental setup is well thought-through and well-replicated. In addition, an exhaustive set of preliminary experiments was performed before deciding on the final panel of soils to use and scoring methodology. The figures are clear and well-explained.

Reviewer #3 (Public review):

Summary:

The study by Dwulet et al. explores how the development of spontaneous neural activity in primary sensory cortices influences the co-alignment of multiple sensory modalities in higher-order brain areas (HOAs). To address this question, they focus on connectivity between the primary visual (V1) and somatosensory (S1) cortices and an associative cortical area (RL) in mice. The authors combine experimental (wide-field and two-photon calcium imaging) and computational approaches to show that spontaneous activity matures at a different pace across these brain regions. Their data indicate that S1 develops more rapidly than V1, which is possibly beneficial for RL's integration of visual and somatosensory inputs through correlated spontaneous activity. Using a computational model, they demonstrate that a moderate correlation between V1 and S1 activity can optimally guide the formation of bimodal neurons in RL, which are crucial for maximizing the decodability of multisensory stimuli. This finding highlights the role of correlated spontaneous activity in primary sensory cortices in establishing co-aligned topographic multimodal sensory representations in downstream circuits.

Strengths:

The manuscript is well written and it provides strong enough evidence to support the main claim of the authors. The insights on the role of correlated activity on instructing co-aligned multisensory maps in HOAs are not trivial and are an important advancement for the field.

Weaknesses:

In the opinion of this reviewer, the study has no major weaknesses. A drawback of the work is that none of the predictions of the computational modeling have been corroborated through mechanistic experimental manipulations of early brain activity.

Comments on revised version:

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

Reviewer #3 (Public review):

Summary:

The authors sought to define how GATA6-dependent programming of resident peritoneal macrophages regulates lipid metabolism and, in turn, eosinophil survival. By integrating a myeloid-restricted GATA6-deficiency model with cellular phenotyping, lipidomic analyses, and measurements of lipid mediators, the study attempts to connect macrophage transcriptional identity to sphingolipid and cysteinyl leukotriene pathways that may shape eosinophil persistence. The work also appears intended to provide a mechanistic bridge between prior observations from this group and others regarding GATA6-positive macrophages, lipid metabolism, and eosinophil homeostasis.

Strengths:

(1) The study addresses an important and understudied question: how tissue-resident macrophage identity controls the local lipid environment and thereby influences eosinophil survival.

(2) The use of a genetically defined GATA6-deficiency model provides a biologically relevant framework for testing the contribution of macrophage programming.

(3) The lipidomic data broaden the analysis beyond a single mediator and identify coordinated changes in sphingolipids and glycerophospholipids that may generate useful hypotheses for the field.

(4) The finding that GATA6 deficiency promotes eosinophil survival is clear, potentially important, and consistent with prior work cited by the authors.

(5) The study is performed by a knowledgeable team and brings together macrophage biology, eosinophil biology, and lipid metabolism in a way that is likely to interest several research communities.

Weaknesses:

(1) The central mechanistic chain-GATA6 deficiency leading to altered sphingolipid abundance, altered LTE4 production, and consequently increased eosinophil survival-is not fully demonstrated. The data support associations among these features, but the causal order remains uncertain.

(2) The cited literature linking sphingolipid and cysteinyl leukotriene biosynthesis does not substitute for direct testing in this model. Perturbation or rescue experiments targeting sphingolipid synthesis and cysteinyl leukotriene production would be needed to establish necessity and directionality.

(3) The broader lipidomic changes complicate the emphasis on sphingolipids. Because glycerophospholipids are also increased, the phenotype may reflect more extensive membrane-lipid remodeling, altered phospholipase activity, changes in the Lands cycle, or shifts in free fatty-acid availability.

(4) The manuscript would benefit from a clearer distinction between observations made directly in GATA6-deficient peritoneal macrophages and mechanistic inferences extrapolated from prior studies.

(5) The physiological and pathological relevance is not yet sufficiently established. It remains unclear whether enhanced eosinophil survival translates into altered eosinophil accumulation, activation, or tissue injury during inflammatory disease in the peritoneal cavity or lung.

Reviewer #3 (Public review):

In this work, Brown and colleagues report that the photosensor protein LITE-1 of the nematode C. elegans may also a chemosensor that can be activated by high concentrations of the compound diacetly. LITE-1 was described as a putative ion channel of the gustatory receptor family, which is mainly constituted by insect odorant receptors. These form tetrameric ion channels that can be activated by odorant. Specificity is achieved by forming heteromeric channels from three copies of the odorant receptor co-receptor (ORCO) and another subunit that resembles ORCO in the pore-forming C-terminus, but brings in a binding site for the respective odorant. LITE-1 has a very similar structure, according to Alphafold3 predictions, and also carries a binding pocket. In LITE-1, this was proposed to be occupied by a light-absorbing molecule that activates the channel when a photon is absorbed. Alternatively, compounds generated by absorption of high-energy photons may be formed in vivo and bound by the LITE-1 binding pocket. Koh et al. now demonstrate that another, non-light activated compound, diacetyl, at high concentrations, can activate cells expressing LITE-1. Such (chemosensory) cells are also responsible for the avoidance of high concentrations of diacetyl. For this aspect, the protein seems to act in neurons, which are not necessarily the same cells in which LITE-1 is evoking the photophobic response. LITE-1 activation in excitable cells, i.e muscles, causes strong body contraction and paralysis, and the authors show that this is also the case when diacetyl is present. This action is surprisingly rapid, i.e. within 10 seconds after adding diacetyl, raising the question of how the compound can enter the body so quickly. The authors further present molecular docking studies showing that diacetyl could occupy the binding pocket of LITE-1. Last, they show that another compound chemically resembling diacetyl, i.e. 2,3-pentanedione, can also induce avoidance in a LITE-1 dependent manner, though not as potently.

The data are intriguing and the demonstration of LITE-1 being a diacetyl chemosensor is interesting. Following the first submission and review, the authors addressed most of the questions that this reviewer had and improved the paper significantly. It will add to the further understanding of the still-mysterious multimodal sensory ion channel LITE-1.

The authors identified mutants lacking diacetyl responses. In their chemotaxis assay (Fig. 1A, B), they show that lite-1 mutants do not avoid high concentrations of diacetyl. However, the animals actually show attraction, as the chemotaxis index was positive. If the lite-1 animals were insensitive, they should be indifferent and the chemotaxis index should be close to zero. The authors now showed that other neurons contribute to the avoidance response that are not themselves bona fide chemosensory neurons, as the avoidance behavior remained in a tax-4 mutant, that is lacking most sensory neuron responses. The authors further showed that diacetyl responses of ADL can be rescued by expressing LITE-1 specifically in this neuron in a lite-1 mutant background, thus demonstrating that LITE-1 acts cell-autonomously in ADL to affect avoidance behavior.

The effect of diacetyl on muscle cells (Fig. 3C) is pretty rapid. As shown in the initial submission, already during 1 minute after application the animals are almost maximally contracted. The authors now provide a time course with data points every 10 seconds. This shows that contraction is maximal already after 10 seconds of exposure (provided the zero time point is the one where diacetyl is added). This is remarkable, as the compound would have to either pass the worm cuticle, or enter through the gut and diffuse through the body to reach the muscle cells. It would be of interest to compare this to time courses of other pharmacological agents that need to enter the worm's body for their action. As a comparison, often sodium azide is used to paralyze worms for imaging purposes. This molecule is even smaller than diacetyl, but azide action typically requires more time for maximal effects. Could there be active transport mechanisms involved? Maybe diacetyl can pass through some transporters for related molecules into / through intestinal cells quickly, to then reach the body fluid and muscle cells.

One alternative explanation could be that other mechanisms may be at play. E.g. diacetyl may be immediately sensed by ciliated chemosensory neurons that might release a signaling molecule that leads to activation of LITE-1 in muscles, or that sensitizes it somehow, responding to light used for filming animals. The authors addressed these concerns by repeating their assay in a lite-1 mutant background. The authors had tested unc-13 mutants to rule out indirect effects on the neurons recorded. Likewise, eliminating neuropeptide signaling via unc-31 mutants would have been informative, as neuropeptide signaling plays a role in LITE-1-mediated light avoidance behavior (PMID 40238937, 39489735).

Molecular docking studies are now described in more detail. The authors also provide a structural model of the diacetyl-docked LITE-1. In this model, only one of the four putative binding sites carries diacetyl. Maybe the authors could test how structure is affected if all four sites contain diacetyl? Mutations of LITE-1 that lack aminoacids shown to be contacted by diacetyl have been described (C300, R222). It would have been insightful to test if such mutants are still activated by diacetyl. This would also verify that there is not an unknown breakdown product of diacetyl that could affect LITE-1 function through oxidative pathways, as diacetyl has been implicated as a photosensitizer in the past.

Reviewer #3 (Public review):

Summary:

The authors aim to compare proposal models of perceptual decision making using a joint modeling approach, where they fit models to both behavioral outcomes as well as CPP. Most notably, they compare a standard evidence accumulation model with models that track the evidence without integrating it over time (extrema detection). The authors report that the joint CPP-behavioral data do not discriminate between two of their proposals.

Strengths:

This is an interesting finding that reinforces the idea that what we believe to see based on aggregation over trials may not be what happens on every single trial. The models are creative and the simulations are convincing, relating the models to multiple neural markers of decision formation. These include the CPP but also mu/beta power spectra.

Initial weaknesses:

Contrary to the original draft, the current version now clarifies the goals of the study as well as the role of internal vs external noise.

The authors clarify their stance on the optimality of extrema detection in the Public Review, where they make some good points. Specifically, they argue that models that have studied optimal decision making in the past have considered a quite narrow definition of optimality, for example, ignoring energetic costs.

The authors clarified the fixed value of the scaling parameter (which apparently was already mentioned in the initial draft, which I had missed). My comment about the discrepancy between the bounded integration model and the EZ diffusion model can thus be ignored. However, I do think that the similarity of the bounded integration model and the EZ diffusion model could be acknowledged in the manuscript somewhere.

I still think the choice of modeling the grand average accuracy and EEG signal may distort the results. As Figure 1 reveals, there is in fact substantial variation in the change in accuracy across conditions. I can imagine that one model may be preferred over another based on the aggregate data, while the other model is preferred for some participants. On the group level, this may lead to different conclusions, at least quantitatively (e.g., in terms of G2), but potentially even qualitatively. This is especially true for the neurally-informed models because of the covariation between behavioral and neural data.

Reviewer #3 (Public review):

Summary:

This study found that ADF serotonergic neurons have a significant role in extending lifespan mediated by HIF-1, as well as serotonin receptor SER-7 in the GABAergic RIS interneurons. The author focuses on the sufficiency and necessity of components from the central nervous system and how they contribute to aging upon hypoxia.

Previous work from the lab has identified that the stabilization of HIF-1 in neurons is sufficient to extend lifespan through the serotonin receptor, SER-7, which subsequently activates fmo-2 in the intestine and leads to lifespan extension. Building on this, the author sought to determine which serotonergic neurons are involved and found that serotonin signaling in ADF neurons is required for lifespan extension mediated by HIF-1.

The author next tested which subset of neurons requires Ser-7 expression to rescue hypoxic response. They found that ser-7 expression in multiple neurons is sufficient to induce fmo-2, with the top candidate being the RIS neuron. Ablation of the RIS neuron did not extend lifespan, suggesting that ser-7 expression in the RIS neuron is required for lifespan extension, positioning it as a key component in the longevity signaling pathway.

The author also investigated neurotransmitters and found that GABA and tyramine are important components in this circuit. They showed that the tyramine receptor called tyra-3 is required for vhl-1-mediated longevity. Given that tyra-3 is expressed in oxygen- and carbon dioxide-sensing neurons, the author demonstrated that these sensing neurons work downstream of serotonin signaling. Lastly, the author screened neuropeptide/receptor binding pairs and identified NLP-17 as playing a role in hypoxia-mediated longevity.

Originality and Significance:

This research is significant in that it uncovers components that are sufficient and necessary for lifespan extension via the hypoxic response. It provides comprehensive data supporting longevity induced by HIF-1-mediated hypoxic response, in conjunction with fmo-2, a longevity gene, as demonstrated in previous work from the lab. Moreover, it provides a number of new transgenic worm tools for C. elegans and aging communities.

Conclusions:

This study provides insights into how hypoxic response regulates aging in a cell non-autonomous manner, outlining a potential circuit involving neurons, neurotransmitters, and neuropeptides.

Reviewer #3 (Public review):

Summary:

Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.

Strengths:

(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.

(2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given trial or test session can differ from canonical palatability.

(3) The animal's preferences for various tastes are reflected in the neurophysiological recordings, despite that behavior and physiology sessions are separated by several weeks, with an intervening surgery, across multiple delivery methods (active licking vs passive delivery), and across homeostatic states (thirsty vs sated).

(4) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time.<br /> The findings are novel and impactful, and the results are relatively complete.

Weaknesses:

(1) The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.

(2) It is not clear how the analyses in Figure 3 emerge from the behavioral patterns across Figures 2A2 and 2B2. Along these lines, citric acid responses for R1 and R2 and saccharine responses for R7 and R8 are not shown in Figures 2A2 and 2B2, thus, we cannot determine if they change from the first to final BAT sessions. Salt, even at low concentrations, may not be palatable when rats are in a water-restricted state. It would be interesting to see Figure 3's analysis exclusively for sweet or for bitter tastes.

(3) A potential reason why a single taste experience session causes taste palatability responses in GC neurons to revert to a reflection of canonical palatability ranks, as is seemingly shown in Figure 6, is not discussed.

Reviewer #3 (Public review):

In this manuscript, the authors strived to develop a highly efficient drug survival assay for in vitro cultured human malaria parasites P. falciparum. This was done by generating a transgenic P. falciparum line using a creLox strategy that allows detection of (presumably) viable parasites by a β-lactamase assay. To estimate the Limit of quantification of the recombined P. falciparum NF54i-lacZ, the authors ultimately designed a protocol in which viable parasites are detected by the luminescence of β-D-galactoside generated by β-lactamase within the transgenic parasites. For this, the parasite must be incubated with rapamycin for 120 hours to induce CreLox recombinase, which places β-lactamase under an active promoter. Using this assay, termed MULTI-i2, the author shows interactions between two antimalarial drug pairs that were previously demonstrated by another assay. In the case of pyronaridine and piperaquine pair, the NULT-i2 assay generated some additional insights compared to the previous assay, presumably by virtue of including more concentration datapoints. In conclusion, the authors argue that the MULTI-i2 assay is much less resource-intensive and time-consuming and can be applied on a large scale at a much lower cost and with the highest efficiency.

Overall, the data generated in this manuscript are clear and well represented, and I am convinced that MULTI-i2 provides yet another of many drug assays for malaria parasites and could be put to good use. However, I struggle to fully appreciate the merit of his study, as the manuscript reads more like a technical document than a scientific study.

I particularly lack an understanding of the strengths and weaknesses/limitations of the MULTI-i2 methodology and, thus, its applicability. I also do not fully appreciate the need for such an elaborate luminescence-based experimental setup. It would be good if some of these issues were addressed.

Specifically:

(1) The whole assay is based on detecting parasites by luminescence after 120 hr (5 days) after drug exposure. During that time, presumably the parasites that survived the drug pressure regrow to a detectable level and, at the same time, perform efficacious CreLox-based recombination to produce β-D-galactoside for detection. Is this necessary? How superior is this detection method to other methods, such as Fluorescence-assisted Cell Sorting (FACS) e.t.c.? Moreover, the 5-day growth-CreLox-β-D-galactoside production could introduce a series of confounding effects. In my view, more studies (beyond comparisons with a single existing method) would be useful for understanding this entire process.

(2) Related to that above, how would MULTI-i2 perform in case of drugs that do not necessarily kill all parasites, such as artemisinin? In the case of artemisinin, it is becoming evident that at least a small fraction of the parasite revives after treatment via a temporary dormancy state. This has, in fact, also been shown for other drugs such as mefloquine, pyrimethamine, etc. Would such a situation produce a range of false readings? In general, in its current state, it is hard to see what the limitations of this method are, which makes it hard to decide whether to use it for a particular application.

(3) Given the stated cost and labor efficiency of MULTI-i2, it is disappointing to see only two applications for two drug pairs: atovaquone/proguanil and piperquine/pyronaridine, for both of which their interactions were already known. The manuscript would benefit greatly if the authors demonstrated more drug interactions and identified (and ultimately validated) new ones. This would certainly make MULT-i2 method more attractive. In particular, it would be nice to see if one could use MULTI-i2 for studies of triple combinations as enthusiastically suggested.

(4) Throughout the manuscript, the authors claim that MULTI-i2 is considerably less expensive and can be done much faster than previous methods. In my view, this is not exactly a scientific argument. The cost of an assay depends heavily on the cost of reagents and labor, which are subject to market price fluctuations. The efficiency and time consumption can very much depend on laboratory organization etc. Unless the author could specifically demonstrate where and how these assays are cheaper and faster, I suggest not discussing this.

Comments on revisions:

I have no more comments.

Reviewer #3 (Public review):

Summary:

The authors are trying to provide the molecular basis for the emerging role of VDAC2 in mitochondrial apoptosis. They use multiple approaches from biochemistry, cell biology, and structural biology.

Strengths:

Isolating the VDAC2-BAX complex and providing the molecular basis of this interaction in mitochondrial apoptosis is pretty innovative and significant.

The authors have tried to validate their results using multiple approaches, which corroborates the quality of the study.

Weaknesses:

The scientific data and its presentation could be improved.

Case#: Patient 9, female, Mexican, symptoms onset 6 yrs. ago, Mexico City

DiseaseAssertion: IRD

FamilyInfo: parents are non-sanguineous and asymptomatic, they also denied any history related to ocular diseases. Information disclosed that the mother had one stillbirth and three miscarriages, but denied any related diseases/health issues to this child.

CasePresentingHPOs: HP:00305, HP:00080, HP:0000493, HP:0025586, HP:0030329, HP:0012713

CaseHPOFreeText: Proband presented with light sensitivity as well as adaptation difficulties when going from dark-to-light. Right eye was 20/200 and left eye was 20/160 from the visual acuity test. Macular bull's eye appearance. Subnormal rod and cone responses. Peripapillary sparing retina.

CaseNotHPOs: HP:0007737, HP:0000750, HP:0000510

CaseNotHPOFreeText: No afferent pupillary defect. No anomalies in anterior segment.

Genotyping Method: QIAamp DNA Blood Kit was used to extract gDNA and quantification/purity of the sample was found using a NanoDrop 2000 spectrophotometer. 293 genes were sequenced. gDNA was sequenced via Illumina technology. Following, certain sequences were additionally analyzed against a reference genome in order to identify changes and interpret.

PreviouslyPublished: n/a

Variant: NM_000350.3(ABCA4):c.4926C>G (p.Ser1642Arg), NM_000350.3(ABCA4):c.5044_5058del (p.Val1682_Val1686del)

ClinVar: 99332, 99340

CAID: n/a

SupplementalData: Phenotype data in results section as well as figures 1, 2, and 3 showing phenotypic testing results.

Reviewer #3 (Public review):

Summary:

In their manuscript, the authors assess how TRPML1 influences lysosome trafficking and function in astrocytes using a neuron-astrocyte coculture system. Specifically, it was found that activation of TRPML1 reduces LEL motility in astrocyte branches while TRPML1 increases it, and a model was proposed in which TRPML1 coordinates lysosomal positioning along astrocyte branches, enabling LELs to locally regulate actin-membrane linkers that influence PAP (peripheral astrocyte processes) structure and plasticity. The authors primarily use pharmacology to buttress their findings.

Strengths:

TRPML1 is currently under investigation as a potential therapeutic target for lysosomal storage disorders and neurodegenerative diseases such as AD and PD. The manuscript is hence timely and important as the crosstalk between glia cells and neurons is likewise increasingly recognized as disease-relevant.

Weaknesses:

Unfortunately, experiments performed to investigate the role of TRPML1 are based purely on pharmacological tools. No bona fide KO data are available. At least for some experiments, this should be done. MLIV iPSC lines are available.

If no KO controls are being provided, at least the authors shall use ML1-SA1 (EVP-169) as an agonist instead of ML-SA1, because ML-SA1 activates all three TRPML channels, which would be a major problem for this study. Likewise, the ML-SI3 antagonist also has effects on other TRPMLs. EDME is a blocker with higher specificity for TRPML1.

When using pharmacological tools, the original papers relating to these tools may be cited. For example:

- For ML-SA1 https://www.nature.com/articles/ncomms1735 and for MK6-83 https://www.nature.com/articles/ncomms5681

- ML-SI3 as a blocker for TRPML1 seems problematic: https://pubmed.ncbi.nlm.nih.gov/33187805/ ; an alternative may be: https://www.nature.com/articles/s41598-021-87817-4

No reference is made to other important regulators of lysosomal cation homeostasis such as TPC1 or TPC2, two other Ca2+/Na+ permeable cation channels, or other TRPML channels. The authors should provide some data supporting or excluding a role of these channels.

Reviewer #3 (Public review):

Summary:

This manuscript describes a web-based tool that allows researchers to compare large numbers of representative ("plausible") conformations of proteins. It also includes energetic analysis from multiple widely used structure-prediction methods.

Strengths:

This tool will likely be useful for students who want to learn more about the ensemble properties of proteins. The resource is well organized and it represents a large amount of computing resources.

Weaknesses:

It is not entirely clear how the database may be utilized by other groups to advance research. It could be helpful if the authors add a short section that provides example use cases that illustrate how this database can support new strategies for studying protein dynamics.

Reviewer #3 (Public review):

Summary:

The authors were investigating the impact of introducing novel facultative bacterial endosymbionts into the pest aphid, Diuraphis noxia, to explore the possibility of using facultative symbionts as a crop protection tool. They successfully established the vertical transmission of both endosymbionts and performed a series of aphid performance and dispersal experiments together with measurement of aphid feeding on host plant health, growth, and metabolism. While most of the experiments revealed no effect of the endosymbionts, some significant treatment effects were found, showing that Rickettsiella reduced aphid dispersal, and Regiella reduced aphid population growth and feeding damage.

Strengths:

The team worked with two novel facultative symbionts (Rickettsiella viridis and Regiella insecticola) that they were able to successfully establish in D. noxia. The data were collected and analyzed using solid, well-described methodology.

Weaknesses:

While interpretation of the data is reasonable, the few experiments which revealed significant treatment effects rest on relatively small sample sizes.

Measuring symbiont density is difficult. The authors use quantitative PCR to measure the "density" of endosymbionts relative to a host gene. This is a standard approach in the field; however, recent work has shown that endosymbionts like the aphid primary endosymbiont, Buchnera, are variably polyploid [1]; further the aphid cells that house the symbionts are also highly polyploid and variable in their ploidy [2]. It is important to understand that what is being measured when using qPCR is DNA copy number and not quantification of the number of symbiont cells. Alternative approaches to measuring symbiont density include flow cytometry [3], and SymbiQuant [4], a machine vision tool that can quantitatively characterize symbiont populations from DAPI-stained confocal images. These alternate approaches also have their limitations. Currently, there is no perfect approach to measuring symbiont density, which remains an important measure in experiments such as these. Put simply, it is important for a reader to be aware of the limitations of each approach and interpret data accordingly.

[1] Komaki, K., and H. Ishikawa. 2000. Genomic copy number of intracellular bacterial symbionts of aphids varies in response to developmental stage and morph of their host. Insect Biochemistry and Molecular Biology 30:253-258.<br /> [2] Nozaki, T., and S. Shigenobu. 2022. Ploidy dynamics in aphid host cells harboring bacterial symbionts. Scientific Reports 12:9111.<br /> [3] Simonet, P., G. Duport, K. Gaget, M. Weiss-Gayet, S. Colella, G. Febvay, H. Charles, J. Viñuelas, A. Heddi, and F. Calevro. 2016. Direct flow cytometry measurements reveal a fine-tuning of symbiotic cell dynamics according to the host developmental needs in aphid symbiosis. Scientific Reports 6:19967.<br /> [4] James, E. B., X. Pan, O. Schwartz, and A. C. C. Wilson. 2022. SymbiQuant: A machine learning object detection tool for polyploid independent estimates of endosymbiont population size. Frontiers in Microbiology 13:816608.

Impact and Significance:

Food security and production, and pest control are major challenges facing the human population. This work contributes knowledge that will benefit the development of alternate pest control strategies in agriculture.

Reviewer #3 (Public review):

Summary:

Araveti et al. demonstrate that Type I Interferon (IFN-I) and Interferon-gamma (IFN-γ) play opposing roles in regulating lipid peroxidation and host resistance during Mycobacterium tuberculosis (Mtb) infection. While both the two pathways drive inflammation, they differ fundamentally in cell protection. IFN-I signaling triggers a destructive, self-amplifying cycle catalysing the generation of reactive oxygen species (ROS) and lipid peroxidation, which ultimately impairs the host's ability to clear Mtb. Conversely, the authors demonstrate that IFN-γ couples antimicrobial activation with cytoprotection. It primes macrophages to fight the mycobacteria while simultaneously shielding them from oxidative stress. It achieves this by sequestering iron, which successfully prevents ROS from converting into damaging lipid peroxidation products.

Strengths:

Ultimately, this study highlights a critical biological distinction: IFN-γ safely balances inflammatory activation with cellular defense, whereas IFN-I promotes uncontrolled pathological damage. This divergent coupling of inflammation and cytoprotection carries major consequences for disease progression and host survival.

Weaknesses:

This study demonstrates all the findings in specific mouse strains. How these translate in human macrophages is not well characterised, thus raising the issue of its overall impact in tuberculosis disease.

Reviewer #3 (Public review):

Summary:

This is an excellent piece of work which sheds greater light on the responses of cattle to both experimental and natural infection in cattle with Mycobacterium bovis infection, using data from different experimental and field groups.

Strengths:

The work is based on robust analysis of a range of highly relevant experimental and field sample sets, using transcriptomic approaches. It provides insight into pathogenesis and disease responses, as well as some evidence regarding potential future diagnostic advances.

Weaknesses:

I have some simple, but important comments on how the work is discussed. (Consequently, most comments focus on the discussion section). In particular, I suggest that the authors have confused or conflated the great progress that they have made in improving the understanding of the responses to M. bovis infection with an improved ability to practically improve the diagnosis of the infection in the field. Minor differences in specificity and sensitivity - and predictive values - of tests or assays being used can have profound effects in different prevalence settings on the farm, and I don't feel that this understanding is adequately reflected in the discussion in particular. In this respect, the authors really should, in my view, focus not on the outstanding results that come in or from their model fitting approaches, to focus on their model testing results in relation to extrapolated meaning / external validity. The text uses words like 'robust' and 'highly accurate' which are meaningless in the context of test interpretation in the field.

I get their enthusiasm, based on really interesting findings in relation to disease progression and immune and inflammatory responses, but these indistinct claims rather devalue the quality of the rest of their work in my view.

One great challenge in work of this nature, using natural cases from farms, is that there is no gold standard for identifying the cases that current diagnostic approaches miss and which they hope their new approaches can help with. This is not discussed or mentioned in their enthusiasm for what they have achieved. Their field datasets are based on the current, insensitively detected cases. It misses the 'occult' cases that are present but undiagnosed.

Reviewer #3 (Public review):

Summary:

In this submission Wang and colleagues jointly examine the association between depression and anxiety symptoms and individuals' affective reactivity to reward prediction errors in Ruttledge et al.'s gambling paradigm. Taking a bifactor approach to anxiety and depression in several non-clinical (and one clinical sample), the authors find that anxiety-specific symptoms relate to over-reactivity of mood to reward prediction errors (RPEs) as well as heightened mood variability , while depression-specific symptoms relate to blunted mood sensitivity to RPEs. These depression-, but not-anxiety specific relationships replicated in patient samples.

Strengths:

I was impressed that the data-driven, transdiagnostic approach employed by the authors uncovered specific relationships between anxiety and depression-specific factors and RPE reactivity in a well characterized task and computational model, especially in a non-clinical sample. This sheds new light on how these affective processes may be perturbed-and importantly, in different ways-by anxiety and depression symptoms. Likewise, the replication of the depression-specific finding (RPE hypo-reactivity) in a clinical sample was nice to see.

Weaknesses:

While the anxiety- and depression-specific factors had differential effects on mood variability (Fig 2A-D) and RPE reactivity (Fig 2E-G) in all samples, such that the correlations between the two factors and these mood parameters were significantly different, the anxiety factor was not consistently (significantly) associated with either mood-related parameter across samples. However, the authors resolve anxiety-specific predictive effects when they collapse across datasets. While it is intuitive that achieving a larger effective sample size would afford the power necessary to detect such individual differences, this struck me as a major caveat for this set of results.

The associations the authors observe between the 'common factor' of depression and anxiety and risk-aptitudes tendencies-presumably the alpha (exponent) parameter in a prospect theory-type subjective value model. But where is this analysis explained? (i.e. how was this model formulated and how were risk attitude parameters estimated?) And what is the interpretation of this finding-is there precedent for looking at risk attitudes in this task? And why would these predictive effects only be observed in relation to the common, but not unique factors of anxiety and depression?

Comments on revised version.

I believe the weaknesses identified in the previous round of review have been adequately addressed by the authors, and my suggestions concerning clarity of presentation have by and large been implemented by the authors.

Reviewer #3 (Public review):

Summary:

In the revised manuscript, the authors provide additional details and evidence regarding the robustness and utility of their method.

Strengths:

(1) The authors provide a very precise automatic identification of marmosets in their home cage, to levels comparable to animal health professional.

(2) This method is robust across lightning, camera angles etc but importantly is able to identify marmosets in naturalistic conditions, which can be of tremendous value to neuroscientists and to ecological or behavioral studies.

(3) Easy to use and implement, requiring minimal settings. Phone videos can even be used.

Weaknesses:

While the manuscript improved tremendously from the previous version, given the nature of the paper, it is still a strenuous read.

Comments on revised version.

The authors did a good job of addressing my previous concerns and I don't have more comments.

Reviewer #3 (Public review):

Summary:

Interest was especially focused on how foreknowledge of the orientation of either the target or the distractor could be used to resolve the competition between these stimuli and properly report the target color. The target or distractor was pre-cued by a solid or dashed orientation cue. They were displayed with slightly different presentation frequencies, which allowed for examining their sensory processing with steady-state visual evoked responses (SSVERs). Furthermore, orientation decoding was performed, which revealed that orientation cues selectively affected processing after stimulus onset related to the dominant but not the non-dominant eye. EEG analyses additionally focused on parietal alpha activity and frontal theta, both during the anticipatory phase and the stimulus-processing phase. Cueing the distractor vs. the target induced increased right parietal alpha power during the anticipatory phase, but this did not result in direct inhibition of distractor features. During the stimulus-processing phase, cueing the distractor resulted in increased theta activity. Finally, a generalized linear model was employed wherein trial-by-trial behavior (precision in target color report) was predicted by target and distractor SSVERs, type of pre-cued stimulus (target/distractor), preparatory parietal alpha power, stimulus processing-related frontal theta power, eye dominance, and all their interactions. Performance in the case of reduced sensory processing of the target (based on SSVER) showed more deviations when sensory processing of the distractor was high, but no such effect was observed when sensory processing of the target was high. The latter effects were modulated by eye dominance and cue. Increased theta reduced distractor sensory processing but not target sensory processing. Increased alpha was only beneficial when sensory evidence for both target and distractor was low. Results were interpreted as favoring sensory gating before stimulus onset, reflected by increased parietal alpha (i.e., pro-active control), while theta activity especially seemed relevant to suppress distractor activity (i.e., reactive control) thereby favoring target-related performance.

Strengths:

The authors convincingly show that EEG can provide crucial information about how the human brain deals with the conflict between a target and distractor in a binocular rivalry paradigm with pre-cues signaling either the target or the distractor orientation. An important aspect of the study is the focus on precision of target color report, in combination with the possibility to assess SSVERs to the target and distractor. The strength of this study may actually also be its weakness; the question is whether the presented ideas on proactive and reactive mechanisms can be generalized to paradigms that do not employ binocular rivalry. Separation of target and distractor processing by selectively presenting them to the left/right eye increases the conflict when the target is presented at the non-dominant eye, but what happens in the absence of binocular rivalry concerning the target-distractor conflict?

Weaknesses:

An important aspect of the study relates to the cue manipulation. In many studies, cues are often informative but not mandatory. Couldn't one argue that in this study task performance crucially depends on cue processing, as without the cue, it becomes difficult to tell apart the target from the distractor. It could be argued that participants are able to do this based on the slight difference in flickering frequency, but I doubt whether this is possible at all. However, if this were the case, then they might use this as an alternative cue and ignore the orientation cue. What do participants experience while performing this task? As the cue can be considered to be mandatory, the question may be raised what strategy the participants actually employed. If the target was cued, they simply may have prepared for this orienting and could ignore the distractor. However, if the distractor was cued, they could use two strategies: search for the stimulus without the cued orientation, or first detect the distractor, and then orient towards the other stimulus. The ideas and results on parietal alpha and frontal theta in combination with the other findings are certainly very interesting, but recently, it has also been argued that frontal theta may be more related to action control (e.g., see Panek et al., https://doi.org/10.1093/cercor/bhaf276) and also pro-active control (Cooper et al., 2017). So, it might be that increased theta reflects suppression of the response related to the distractor, which feeds back on its sensory processing. This raises the question whether there is possibly also some evidence on functional connectivity between frontal and posterior regions that varies depending on the precise condition. Are the results also shining a new light on the relation between attentional orienting and eye dominance (e.g., see Schintu et al., 2020)?

https://www.thingiverse.com/thing:7402637/files

Reviewer #3 (Public review):

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

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

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

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

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

Concerns & comments:

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

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

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

Reviewer #3 (Public review):

Summary:

An outstanding question in the field of high frequency oscillations (HFOs) in the context of epilepsy is how these oscillations emerge, considering that they occur at such high frequencies i.e., 250Hz well above the firing ability of single neurons. One hypothesis that has been suggested in the past is that neurons that fire in an out of phase fashion or rather at random intervals may contribute to a spectrum of HFOs ranging from 250-500Hz that observed in epilepsy. However, how possible it is that random action potentials could aggregate to the extent that they could give rise to HFOs in the so-called fast ripple (FRs) frequency range (>200 according to the authors) remains unclear. To test this hypothesis, they used computational modeling to randomly insert action potentials in a signal, and they found that this approach is sufficient to generate FRs. Some of the predictors of whether FRs could occur were neuronal count, firing rate and synchronization. Besides computational modeling, they used different model systems to test whether that would be possible to be observed in neuronal cultures, in epileptic rats (intrahippocampal kainic acid model), and human data. Neuronal cultures treated with picrotoxin did not show evidence that FRs could be generated more than chance aggregation of action potentials. They then asked whether synchronization and firing rate could play a role in the emergence of FRs. They found that changes in neural firing and synchronization, such as those occurring during differences phase of the sleep-wake cycle could affect the number of FRs occurring by chance aggregation, with more FRs seen during periods of wakefulness, a result that they replicated in human data.

The authors largely achieve their proposed aims of demonstrating that random neuronal firing can, in principle, generate FRs. Results from this study could influence current thinking around mechanisms generating FRs in epilepsy. The use of different computational approaches and model systems could offer new analytical methodologies for the study of FRs in the context of brain disease.

Strengths:

(1) The authors used a multi-level approach combining computational modeling with experimental datasets, including neuronal cultures, a rat model of temporal lobe epilepsy and human data.

(2) Identification of key parameters such as neuronal count, firing rate, synchronization and brain state in observed incidence of FRs generated through random aggregation of neural firing.

(3) Cross-species validation increases the likelihood of generalizability of the findings.

Minor weakness:

(1)The analyses conducted in human data lack direct comparison with sleep data due to no available data, but would encourage future investigations directly comparing HFOs during wakefulness and nocturnal sleep.

Comments on revised version.

The authors have addressed my comments and I have no further suggestions.

Reviewer #3 (Public review):

This revised manuscript by Fontana et al. aims to study how animals respond to fearful stimuli, with a specific focus on brain regions involved in predicting animals that passively freeze or those that actively evade the threat. I continue to be enthusiastic about the study. The study addresses an important question regarding individual variation in fear-related behavior and links these behavioral phenotypes to whole-brain activity patterns in adult zebrafish. The combination of a contextual fear conditioning paradigm, strain/sex comparisons, behavioral clustering, and AZBA-based c-Fos mapping makes this a valuable contribution to the field, not just in answering the question posed by the authors, but also in formulating a framework for using adult zebrafish for whole brain analysis of complex behaviors. Overall, I find the authors have responded to my concerns:

(1) I still think that separating memory acquisition and consolidation is an interesting question, and further use of the framework will need to eventually solve that; however, I also appreciate that this may be beyond the scope of the current study, and I appreciate the authors acknowledging this in the manuscript.

(2) Regarding Figure 3, I also agree that this is difficult to present differently, and I appreciate the authors adding text to the body to clarify things. My one request is that the sentence (lines 214-215) that reads: "This increase in evasion in the vehicle group likely represents a response to the water disturbance that occurs when solution is added to the tank." Be changed to: "This increase in evasion in the vehicle group may represent a response to the water disturbance that occurs when solution is added to the tank." While it is entirely possible, there are no concrete data to support that this is "likely."

(3) I appreciate the clarification regarding the PLS-derived contrasts in Figure 6A and in the body.

Overall, this is a really interesting paper that will have a wide-ranging impact. All of my concerns have been addressed.

Reviewer #3 (Public review):

Summary:

In this useful work, the authors characterize the auxin-based gene expression system (AGES) as a tool for studying ageing. They found that this system can be applied to ageing studies. In addition, they identified important drawbacks of the methods, including effects of insertion sites and sex on induction of the system, and that some auxin doses may have inadvertent effects on body mass and physiology. Overall, the study extends the AGES system for use in fly ageing studies and highlights some caveats. While the findings are solid pointers, more extensive characterization is needed to benchmark the extent of the caveats identified.

Strengths:

The study provides the first longitudinal evaluation of the AGES system's induction efficiency across the entire Drosophila lifespan. The authors also highlighted a number of caveats of the AGES system in ageing animals. These are all important points to be considered when using this system, and findings should be interpreted keeping these caveats in mind.

Weaknesses:

There were inconsistencies with auxin dosages between the figures.

(1) The authors used a higher dose of auxin (20mM) compared to the original AGES paper (McClure, 2022) in Fig 3. The auxin dose-dependent effects are not linear for TAG and protein levels, highlighting that the genotype-dependent effects may be highly variable and may yield quite different results in other studies.

(2) The results in Figure 4 showing the lack of induction in the brain are quite interesting; however, only 5mM auxin is tested. Characterizing dose-dependence for the variability of the induction in tissue types would be useful. At the very least, recapitulating prior results at 10mM should be done.

(3) Food intake and hydration status were not measured alongside body mass, TAG, and protein endpoints. The changes seen could be an effect of decreased feeding or fluid balance rather than metabolic reprogramming.

Reviewer #3 (Public review):

In the current work, Turrero Garcia et al. investigate the molecular, electrophysiological, and behavioral outcomes of conditionally knocking out cells from a unique developmentally-defined subpopulation in the lateral septum (LS). The authors focused on targeting cells from the Nkx2.1 developmental lineage that also expressed the transcriptional regulator Prdm16, which is uniquely upregulated in LS postmitotic neurons. They observed that this mutant line (Nkx2.1Cre;Prdm16fl/fl;Ai14; cKO) resulted in complete ablation of neurons from the Nkx2.1-lineage exclusively in the LS and not in the medial septum, making it an ideal model to study a lineage-defined subpopulation in the LS. They performed single-nucleus RNA-sequencing and uncovered four neuronal subtypes missing in the cKO. Furthermore, the authors validated this expression loss in the subtype expressing Crhr2 and observed a reduction of UNC-3 inputs (a neuropeptide with high affinity for Crhr2), highlighting additional disruptions in circuit connectivity. Loss of Prdm16 in Nkx2.1-lineage cells only resulted in mild electrophysiological changes in the LS. Finally, the authors performed a battery of behavioral assays to study anxiety-like behaviors and threat avoidance and observed increases in exploratory behaviors in some but not all assays. It should be noted that the cKO line also results in 30% loss of cortical interneurons, which could be contributing to the behavioral phenotype and not be exclusively due to LS loss. Overall, this manuscript takes a novel perspective by providing unique insights into how embryonic origin gives rise to mature molecular identity and distinct behaviors in adults. Therefore, it elegantly links developmental origin to mature molecular identity and function in the LS, an important question understudied in the field.

The conclusions of the work are overall supported by the data and limitations discussed, but some of the findings, in particular the histological and behavioral results, need to be extended.

Strengths:

(1) Utilizing developmental origin as a marker for mature neuronal identity and function is a valuable approach which remains under-utilized in the field and serves to provide a deeper understanding of how circuits are shaped to allow for appropriate behavioral responses.

(2) The authors perform a comprehensive analysis of the cKO mutant to determine the role of the developmentally defined Prdm16 in Nkx2.1-lineage cells at the molecular, cellular, electrophysiological, and behavioral level.

(3) The sn-RNAseq dataset in the LS of WT and cKO mice will be valuable to the neuroscience community.

(4) The authors perform an extensive array of behavioral paradigms investigating the balance between threat avoidance and exploratory behavior, performing all experiments in both male and female mice to determine whether the same developmental origin can lead to sex-specific differences.

Weaknesses:

(1) It remains unknown whether the reduction of UCN3 inputs to the LS is due to loss of the Nkx2.1-lineage in the LS itself or due to reductions in the number of UCN3 cells that provide innervation to the LS in the cKO (Figure 3). The authors speculate and include anecdotal observations that the perifornical region of the hypothalamus (PeFAH) provides inputs to the LS and could be the region driving the differences in UCN3 inputs in cKO. The authors should expand on this histological data and directly test whether the UCN3 inputs are indeed originating from PeFAH and whether the loss of Prdm16 in the Nkx2.1 lineage leads to a reduction in cell numbers in these inputs. These would disentangle the authors' claims on whether it is due to loss of Prdm16 in the Nkx2.1 lineage cells in the LS or whether it is due to loss of Nkx2.1 lineage neurons in upstream regions.

(2) The authors claim that there is an increase in exploratory drive in cKO mice, even though the dark-light test showed increases in time spent in the dark side for cKO mice in comparison to controls (Figure 5C). They discuss that this could be due to the mice being placed first in the light compartment of the chamber during the light hours, so they spend more time exploring the dark compartment of the chamber instead, which would be considered 'novel'. If this were the case, to make the results more solid, authors should place cKO mice in the dark compartment of the chamber during the dark hours and then record time spent in both chambers. If there was indeed an increase in exploratory drive in new environments, the authors should see increases in time spent in the light compartment.

(3) The result that cKO mice spend more time than controls exploring the inlet with TMT is of interest (Figure 5E, F). It needs to be highlighted that this is primarily the case in male mice and there is a trend in females. To confirm that the increases in exploration time of the inlet are not due to overall increases in general arousal, locomotion (e.g., velocity; pixels/frame) should be assessed in cKO vs controls.

(4) Statistical analysis correcting for repeated testing should be performed when running multiple t-tests in the same dataset, such as when analyzing histological results in Figure 1 and Figure 6 to increase confidence in the presented results.

(5) An important consideration that the authors address in the discussion is that the loss of Prdm16 in Nkx2.1-lineage cells is, for the most part, restricted to the LS, but other regions such as the cortex also show decreases in this population. Therefore, to strengthen the authors' conclusions that Nkx2.1-lineage neurons in the LS are indeed directly responsible for balancing threat avoidance and exploratory drive, targeted manipulation experiments, or at least additional c-Fos experiments assessing activity of Nkx2.1-derived cells in the LS will need to be performed in the future.

Reviewer #3 (Public review):

Summary:

Using a combination of Miniscope imaging and optogenetic manipulation, Amadei et al. reveal how oxytocin receptor neurons in the prefrontal cortex of mice control pyramidal subpopulations and socio-sexual behavior. This work was planned and executed carefully and provides a novel and important angle to study the oxytocin system in the cortex. According to their results, oxytocin receptor neurons help discriminate between sexual and non-sexual stimuli, most likely by controlling different pyramidal subpopulations that are either most active during trials that include a sexual stimulus or that include non-sexual stimuli. I highly appreciate this article; however, I have one major concern related to the modeling part.

Strengths:

(1) Well-designed experiments.

(2) Rigorous analysis.

(3) Generates a new avenue to study socio-sexual decision making and creates a hypothesis about the connectivity of oxytocin-sensitive circuits.

Weaknesses:

(1) Major

In their last figure (Figure 4), the authors generated a computational model that, according to the authors, reveals a potential network mechanism in which oxytocin receptor (OXTR) neurons are connected to both pyramidal populations with certain connectivity rules. Although the model seems to reproduce the experimental results, some assumptions of the model seem to be poorly supported. If I understood correctly, the authors simply assumed that the strength of the connections between OXTR neurons and MALE neurons is the same as the strength of the connections between OXTR neurons and OTHER neurons. The authors neither discuss literature supporting such connectivity nor provide experimental evidence for this. I also could not find information about the magnitude of the synaptic weights to each of these populations. I guess these parameters are critical for the outcome of the simulation, and it may be worth exploring the outcome of simulating the different combinations of connectivity and synaptic weights between OXTR neurons and pyramids, as well as the degree of recurrent connectivity within the pyramidal subpopulations. Further, the authors should at least discuss in depth how inhibitory OXTR neuronal subtypes (they have different properties that could potentially be implemented in the modeling) may match their computational model best. If the current model remains the most promising, the authors should clearly discuss which experimental trajectory should be taken next to actually provide proof for its correctness (e.g. whether and how it would be possible to determine the predicted connectivity experimentally).

(2) Minor

The authors state regarding counterbalancing in Figure 1 and Figure S4C: "The social presentation order (male or female first), as well as the locations of the social and milk options (left or right relative to start arm), were fixed over sessions within a given subject, but varied over subjects (Figure S4C)". In Figure S4C, it looks as if there are fewer animals in which the male was always presented first than animals in which the female was always presented first (~ 16 vs. 20). While the difference is not very big, it may be influential. To fully exclude a sequence effect, I would suggest adding male-first animals until both groups are the same size.

Reviewer #3 (Public review):

Kittur et al. ask whether human spatial memory is organized around topological relations (meaning coarse structural properties such as T-junctions, crosses, and holes) rather than around Euclidean properties such as angle and length. Across four experiments, adults and children studied letter-like figures and reproduced them from memory by drawing. The authors report two complementary patterns: metric features are systematically distorted, with angles pulled toward 90 degrees and line-length ratios compressed toward an average, while topologically critical features are comparatively well preserved. The central test contrasts T-junctions with L-junctions, which are visually similar but topologically distinct, since an L-junction reduces to a straight line, whereas a T-junction does not. A serial reproduction experiment amplifies both patterns across chains of participants, and a fourth experiment extends the findings to children aged five to eight.

Strengths:

The question is a good one and sits at a productive intersection of topics. It bears on debates about the representational format of cognitive maps, on proposals about the primitives of visual perception, and on a classic developmental claim from Piaget and Inhelder that has rarely been tested directly.

The drawing paradigm is well chosen and offers something that the group's earlier forced-choice work could not. Because participants produce an open-ended response, distortion of metric detail and preservation of structure can be observed within a single response, and the relationship between them can be examined directly. The serial reproduction experiment is a particularly effective use of this affordance. The choice of the T-junction versus L-junction contrast as the primary test is well-motivated, since it holds the number of junctions constant and varies only topological relevance. It is also worth noting for readers that the central claim of a representational privilege for topologically distinct features was previously established by this group using forced-choice paradigms in both adults and children. That a similar conclusion emerges from free generation is a genuine strength, since the two methods have very different sources of error.

The work is carefully executed. Sample sizes, dependent variables, and analyses were preregistered; stimuli were purpose-built for each question, including the deliberate exclusion of 90-degree angles so that no reference angle was available; drawings were double-coded; and the full set of raw drawings is being released publicly.

Weaknesses:

Drawing is treated as a transparent window onto representation, and motor limitations are not considered. Drawing is a motor act, drawing skill varies widely across individuals, and the manuscript does not discuss motor limitations at any point. As the study is designed, representational imprecision cannot be separated from difficulty of precise reproduction. The clearest way to resolve it might be asking adults to copy the figures exactly while the stimulus remains visible. If the biases persist under direct copying, then some portion of the effect is production rather than memory. Because the topological findings have already been demonstrated in keypress-only paradigms, this concern affects the metric distortion results most heavily, which are the novel contribution of the present paper.

Three distinct claims are treated as one, and the data speak mainly to the weakest of them. The paper moves between a claim about mnemonic robustness (topological features survive degradation better than metric features), one about representational architecture (topology is a base layer with metric detail superimposed on top), and a claim about priority (topology is encoded prior to metric detail). The experiments show evidence for robustness, which is a claim about what is lost first. Robustness does not entail architecture: an encoder with a single layer, whose loss happens to spare structure, produces the same pattern with no layered format and no claim about encoding order. Earlier work does address format, because false "same" judgments to topologically matched but metrically different items show that topology plays a role in what the system treats as equivalent. Preservation counting measures robustness, rather than equivalence.

Some alternative hypotheses to consider/address: (a) A capacity-limited memory that reconstructs from a prior produces the metric distortions with no commitment to topology. A literal absence of angle encoding, which the authors invoke, predicts noisy and unconstrained recall rather than recall pulled toward a particular value. The observed pattern reflects a structured prior. (b) The result that does discriminate might be confounded with local salience. A memory that adds uniform noise to all parts of a figure does not predict that T-junctions are preserved better than L-junctions; however, a three-way branch point is plausibly more locally distinctive than a corner, so a salience-weighted-but-topology-free account predicts the same ordering. (c) Motor simplification also predicts the same ordering, since omitting an L-junction converts a bend into a straight line, which is easier to draw, whereas omitting a T-junction requires dropping a stroke.

The better a feature works as a topological marker, the less variance it produces and the harder it is to test, so the method is best powered where the theoretical signal is weakest. Holes are the textbook case of a topological invariant and are reported to disappear from drawings less than one percent of the time, but they are excluded from formal analysis because they are at ceiling and have no matched comparison feature. It would be useful to see bidirectional rates for holes (both how often a hole disappears and how often participants spuriously close an open figure into a loop).

In Experiment 4, the conclusion of developmental stability rests on a nonsignificant effect of age, which is failure to detect a change rather than evidence of stability. An equivalence test or an estimate of the precision of the null is better support for developmental stability. Motor skill is confounded with age throughout. So this is an experiment that shows that the effect generalizes to childhood, but cannot adjudicate a developmental question.

In the serial reproduction experiment, chains were intermixed so that each participant contributed one drawing to each of ten chains. The final drawings are therefore linked through shared intermediate participants, and an individual with an idiosyncratic drawing style influences ten chains at once, so the reported degrees of freedom are somewhat generous. The analysis also focuses on the final drawings and sets aside the nine hundred intermediate ones, which are the data that would show where in a chain metric detail collapses and whether structure ever breaks.

To formalize the topology is to strengthen the argument: each figure is a one-dimensional complex (its underlying graph), treated intrinsically and up to homeomorphism. The homeomorphism type is what remains after suppressing all degree-2 vertices. Under this definition, every feature in this paper's taxonomy becomes one kind of object, namely a homeomorphism invariant of the graph: number of components, first Betti number, and the degree sequence of three or greater with its adjacency structure. Relatedly, the term "metric" needs to be unpacked. The 90-degree bias concerns angle, whereas the 4:2:1 result concerns length ratios, which are affine rather than strictly metric. The stronger statement available is that distortion appears at every level above topology in the transformation hierarchy while preservation occurs at the topological level, which connects directly to Chen's (2005) invariance hierarchy that is already cited.

Appraisal and impact:

The authors aimed to show that topological structure is preferentially retained in memory while metric detail is lost, and in the sense of relative preservation they succeed. The dissociation is real, replicates across two stimulus sets, amplifies under serial reproduction, and appears in young children. What the data do not establish is the stronger architectural claim that topology is a base representational layer, nor that the metric distortions specifically implicate topology rather than general properties of reconstructive memory. Separating these claims would communicate the well-supported result better. Conceptually, the work strengthens a growing case that coarse relational structure deserves a place alongside Euclidean properties in accounts of spatial representation. Practically, the public release of the full set of adult and child drawings, including excluded ones, is a resource that will support analyses well beyond those reported in this paper, and the serial reproduction design is a method that others will want to borrow.

Reviewer #3 (Public review):

Summary:

Normandin et al. explore the coding of stimuli predicting an aversive event in the prelimbic cortex. Stimuli could either be explicitly paired, explicitly unpaired, or novel but with an inferred association with the aversive event (generalization). Long-term tracking of GCaMP-positive neurons allowed them to examine how coding evolves out to a month following training. In general, they found two types of ensemble codes. One was ensembles coding for each stimulus independently, but with enhanced responding to the one eliciting a freezing response. The other was ensembles that responded to all stimuli in proportion to their similarity to the stimulus paired with the aversive event, either increasing or decreasing their activation with the degree of freezing elicited by a stimulus. Importantly, this second set of ensembles was more stable across days, potentially providing a memory trace.

Strengths:

(1) The authors track ensembles in prelimbic cortex over long time scales, providing valuable information on the consolidation of neural codes.

(2) Neural coding of generalization is examined, which is under-examined in the field.

Weaknesses:

(1) Difficult to determine if responses treated as encoding stimulus valence are driven instead by the behavior that the stimulus elicits, freezing.

(2) The study implies that the identified ensembles are causally related to valence memory, but no experimental interventions are performed to justify this.

Reviewer #3 (Public Review):

In this revised manuscript, White et al. aimed to understand the wound-induced syncytia formation behavior in wound repair of Drosophila melanogaster pupal notum. For this purpose, the authors characterized two different types of adherens junctions' outcomes during syncytia formation around the wound region - border breakdown versus apical shrinking which appear to happen in different time points and for different time durations. The authors characterized cell-cell fusion events using cytoplasmic, junctional and nuclear markers. They determined that about half of the cells within 70 um radii from the wound undergo cell-cell fusion. They studied wound induction on the border between control epithelia and pnr domain suggesting that Atg1 is required for post-wound syncytia formation and wound closure. They showed that during wound closure syncytia gradually invade the wound leading edge mostly by radial fusion events. The data suggests that intercalation of cells from the leading edge slows down the wound closure process. They propose that cell fluidity of syncytial cells plays a role in wound closure speed. Finally, the authors showed that actin is concentrated to the front edge of syncytia located in the wound leading edge. The authors described some aspects of syncytia formation during wound closure using different approaches.

Reviewer #3 (Public review):

Summary

Across species, dopamine release serves seemingly diverse functions, such as reinforcing memories and regulating locomotion and flight. However, whether distinct dopaminergic neurons (DANs) are allocated to each function is unclear. In this study, Toshima et al. have used the numerically simple organization of the Drosophila larval brain to answer this question. They use optogenetic activation to systematically stimulate a small set of DANs, individually and collectively, and study the effect on diverse functions such as memory formation, retrieval, and locomotion. The reproducibility of optogenetic activation is a strength of this approach. At the same time, this is a caveat, as optogenetic activation may not recapitulate natural modes of activation and may lead to outcomes not observed under natural conditions. They find that singly or collectively, DL1 DANs can induce punishment and/or safety memory formation and retrieval. DANs can even gate the expression of memory. Finally, the same DANs also modulate locomotion in the larvae. The authors speculate that dopaminergic neurons in other species may also share such overlapping functions. Their findings are nicely summarised in Figure 9.

Strengths

The study systematically activates neurons in the DL1 cluster. Individual and collective stimulation of the Dl1 DANs has been conducted to assess the induction and gating of aversive punishment memory, safety memory, and acute locomotion.

Specific adult Drosophila DANs are known to induce dual behaviors and functions. The same MP1/y1pedc DANs are recognized for gating appetitive memory expression and representing aversive teaching signals downstream of sensory stimuli such as electric shocks, bitter tastes, and heat. Neurons in the PPL1 cluster regulate adult flight and food-seeking behavior. The authors deserve credit for conducting an organized examination of dopaminergic neuronal functions in larvae, thereby making their findings more comparable and facilitating the proposal of a holistic model.

They have provided substantial evidence for their findings and have frequently presented replicated behavioral datasets. They have been transparent about the results that were difficult to explain. Additionally, they have provided an impressive body of supporting data to strengthen their main findings.

Weaknesses

As mentioned above, optogenetic activation may not recreate natural neuronal activation in response to external stimuli. This could have led to outcomes that will not occur under other natural circumstances.

Comments on revised version.

I appreciate the author's responses, and I do not have any comments or suggestions at this point.

Reviewer #3 (Public review):

Summary:

This manuscript examines functional plasticity in auditory cortices of people born deaf (or early deaf). Deaf participants (N=13, all native BSL signers) and hearing controls (N=18) performed a delay-match-to-sample working memory task in visual and somatosensory modalities, attending either to frequency (temporal task) or spatial pattern (spatial task). Using fMRI and univariate and representational similarity analysis (RSA), the authors test what type of information is represented in auditory areas of hearing controls and deaf participants. Different types of tasks and stimulus features are examined, including low-level sensory features (vibration/movement frequency and spatial position of stimuli on screen/hand) as well as task features (attending to space vs. frequency) and stimulus modality (somatosensory vs. visual).

There are a number of interesting findings. In early auditory cortices (right Heschl's gyrus), only Deaf participants show above baseline responses and only in the somatosensory task. In secondary auditory/multisensory STS, only deaf participants show above-rest responses to both visual and somatosensory stimuli. In RSA analysis, d/Deaf, but not hearing participants, show sensitivity to the frequency of somatosensory vibration when finger position is held constant (SFRm model). RSA in auditory cortices finds sensitivity to sensory modality (visual vs. somatosensory) information in both hearing and deaf participants, with an enhanced effect in the d/Deaf group. A similar d/Deafness enhancement effect was observed for task (frequency vs. spatial) but only within modality, not across, suggesting a less abstract representation.

Strengths:

The paper has a number of strengths. Running tasks in multiple modalities in the same study is a technical challenge and adds valuable information, since, as it turns out, early auditory areas of deaf people are sensitive to somatosensory but not visual frequency. Moreover, it makes it possible to look for modality coding.

The RSA analysis finds sensitivity to task and modality features not detectable with univariate analyses.

Overall, the findings of differences across modalities (visual and somatosensory) in auditory cortices are very interesting. Having parallel tasks in the two modalities makes it all the more important that only somatosensory stimuli activate HG. The discussion of this finding and ideas about alternative possible paths of somatosensory and visual information to the auditory cortices in d/Deaf individuals is also interesting.

The authors conclude that there is both evidence for shared and different function across hearing and deaf groups, and this makes sense. It's refreshing that the authors acknowledge that the simplicity dichotomy of preservation vs. change present in the literature and presented in the introduction turns out not to explain the findings.

Weaknesses:

The participant sample is not very large, but this is a difficult-to-recruit population, and it is within an acceptable range since the authors have taken care to run a robust study design and collect a large amount of data from each person.

Some weakness of the paper includes incomplete presentation of the results and conclusions that do not follow from the data.

The results are presented in a way that makes it hard to track what is significant and how the auditory ROIs are different or similar to the control regions. It is also difficult to connect the written text results with the figures. In some cases, the figures look like there is no significant effect, but the results report that there is one.

It is not clear that three-way interactions were tested for (e.g., group, by task, by modality). This complicates the interpretation of significant two-way interactions, e.g., task by group. For example, in STG a task by group interaction is reported, but the plot suggests that this is driven primarily by the somatosensory modality.

The paper suggests that frequency/temporal information is coded in auditory areas of d/Deaf participants but not location/spatial information. This is stated in the Results and in the Discussion as a major point. But this is not quite true in the somatosensory case and not true in the visual case at all, as far as I can tell. In the somatosensory case, since frequency is only coded in a finger-dependent manner, location is in fact coded in this regard. This pattern differs from what is observed in the somatosensory cortex, where frequency is coded in a finger-dependent and independent manner as well as location as such. In the visual case, it seems like temporal, i.e., frequency information, is not coded at all in the auditory ROIs. Although it is also puzzling that visual frequency is not coded in the visual 'control' ROI. An incomplete presentation of results motivates some conclusions that are not warranted, e.g., coding in the auditory cortex reflects a preservation of its function - i.e., frequency but not location coding.

A second related issue is that some dimensions (e.g., visual frequency, modality-independent task) show no neural response anywhere in the brain, including in the canonical visual, somatosensory, and amodal networks. For these dimensions, the current experiment does not offer a good test. This is fine but should be clearly stated in the results and the Discussion so there is no confusion about which hypotheses are really tested. Right now, the results say things like "the control ROI shows the expected pattern", but in some cases it's more complicated and failures to observe effects constrain what can ultimately be expected in auditory areas. This is okay, but needs to be made clear in the results and discussion, and auditory results need to be interpreted in this context.

Relatedly, the paper has no whole cortex searchlight analyses, and it is not clear why. If nothing comes out in the small sample of n=13, that is okay, but at least the collapsed hearing and d/Deaf sample data should be shown for each dimension. This will give the reader a sense of what is to be expected and contextualize the results.

Some claims are made which are not supported by the data: "However, it is likely that the representation of somatosensory frequency in deaf individuals relies on the same mechanisms used to represent auditory temporal frequency in hearing individuals." Likewise, the paper goes on to say, 'the underlying computations might be the same'. True, they might be, but they also might be different. No evidence is presented to support one or the other hypothesis, so both should be stated, and it should be stated that these cannot be distinguished based on the presented data.

Conclusions-wise, the paper sometimes makes sweeping claims that go well beyond what the evidence supports and fails to provide caveats. The first paragraph of the Discussion states: "Overall, these findings suggest that crossmodal plasticity relies on representational and functional configurations that are present across individuals and modulated by sensory experience." Such a sweeping conclusion about cross-modal plasticity in general is not supported or refuted by the present data. There is no evidence that 'representations' or 'functional configurations' are the same across groups. What are functional configurations? What representations are shared? Second, it is far too general to make claims about 'cross-modal plasticity' based on one study with one population.

Reviewer #3 (Public review):

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

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

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

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

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

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

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

There are a few statistical issues:

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

This paper addresses an important question (relationship between DN and dATN, and the role of retinotopic coding) and uses a set of novel analyses.

Strengths:

Important question, novel analytical approaches (pRF-informed functional connectivity analysis).

Weaknesses:

Some of the analyses are not described with sufficient clarity, especially the final analysis related to Fig. 3.

Comments on revised version.

Related to my previous comment 3), the removal of the labels "bottom-up" and "top-down" in the final analysis is a big improvement. However, I still don't fully understand how the 10 most aligned pRFs and the 10 most anti-matched pRFs are selected. The methods section on this has some ambiguity: "the 10 with the smallest Euclidean distance in RF center (x,y)". Does this mean that these are the pRFs closest to fovea? If not, what is the Euclidean distance referring to? Likewise, I don't understand how the anti-matched voxels are selected. This makes the interpretation of Fig 3 difficult.

My previous comment about baseline activation was to compare the matched voxels with randomly selected voxels, instead of with anti-matched voxels. The authors responded that there was a technical difficulty with this.

Reviewer #3 (Public review):

Summary:

Forbes et al. present a new approach for identifying cis-regulatory elements in large genomes. Using Parhyale hawaiensis, a crustacean with a large genome (~3.6 Gb, comparable in size to the human genome), the authors show that current methods for identifying cis-regulatory elements, effective in smaller genomes, are markedly inefficient in organisms with large genomes. To address this limitation, they combine bulk ATAC-seq and single-cell (sc) ATAC-seq to identify chromatin regions that are either ubiquitously accessible or specifically accessible in particular cell types. They further integrate comparative genomics across multiple Parhyale species (P. hawaiensis, P. aquilina, and P. darvishi), selected at appropriate phylogenetic distances (20-95 million years divergence), to pinpoint conserved open chromatin regions likely under functional constraint.

Using this strategy, the authors predict a set of ubiquitous and cell-type-specific cis-regulatory elements. Importantly, they validate these predictions using rigorous transgenic reporter assays, convincingly demonstrating that their approach can successfully identify functional regulatory elements where previous methods had failed.

Strengths:

The approach introduced by Forbes et al. is conceptually straightforward, efficient, and readily transferable to other organisms. The validation experiments show not only that a substantial proportion of the predicted elements are functional, but also that the method is capable of identifying both ubiquitous and cell-type-specific regulatory elements. Given that the identification of regulatory regions remains a major bottleneck in understanding the molecular mechanisms underlying processes of development and regeneration, this work has the potential to make a significant impact in developmental and regeneration biology, particularly for studies involving non-model organisms with large genomes.

An additional strength is the demonstration that only the genome of the focal species requires high-quality sequencing and assembly. In contrast, species used solely for comparative analysis can be sequenced at low coverage without assembly, substantially reducing costs and increasing the accessibility of the approach.

Weaknesses:

While the method is effective in identifying regulatory elements that are active ubiquitously or in differentiated cell types, it failed in detecting elements associated with developmentally regulated genes. This may be due to trivial reasons, such as very low level of expression of the selected genes. However, as acknowledged by the authors, it may also indicate inherent challenges in identifying regulatory elements associated with developmentally dynamic gene regulation, compared to those associated with genes expressed in differentiated cell types.

A second limitation, also acknowledged by the authors, is the absence of chromatin conformation capture data, which would help link distal regulatory elements to their target genes. This limitation may be particularly relevant for developmentally regulated genes, where long-range regulatory interactions may be critical.

Addressing these limitations will be an important direction for future work. Nonetheless, the approach as presented in this manuscript represents a key contribution that sets the stage for further methodological advances in the identification of cis-regulatory elements in large genomes.

Comments on revised version.

I am fully satisfied with the current version of the manuscript.

Reviewer #3 (Public review):

Summary:

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

Major comment:

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

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

Significance:

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

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

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

Comments on the previous version:

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