Reviewer #1 (Public review):

Summary:

Proteins' misfolding into amyloid fibrils is the hallmark of neurodegenerative disorders. Tau fibrils, in particular, exhibit subtle structural variations that distinguish different pathologies. Understanding the mechanism of amyloid formation requires structural characterization, usually done by NMR or cryo-EM, and insights into fibril packing order and homogeneity remain limited.

Here, the authors exploit DEER echo decays of singly spin-labeled proteins to quantify packing order. While DEER is most used to measure intramolecular distances between two spin labels within a single protein, it also provides access to intermolecular distance distributions through the so-called background decay. This background decay has been theoretically described and can be used to characterize the spatial distribution of spins in terms of local spin concentration and the dimensionality of their arrangement. In the case of singly labeled proteins, the DEER signal contains only this intermolecular information. The authors propose using the extracted dimensionality as a reporter of packing disorder along the fibril axis and demonstrate this approach on the tau protein.

The background decay follows an exponential form with a time constant proportional to alphaD, where D is the dimensionality of the spin distribution and ranges from 1 to 3. For a homogeneous frozen solution of singly spin-labeled proteins, D = 3, and alpha is proportional to pbCL, where pb is the probability of changing the orientation of the spins excited by the DEER pump pulse, and CL is the local spin concentration. In a homogeneous system, CL equals the spin bulk concentration. The parameter pb is instrument-dependent and can be experimentally determined. When 𝐷<3, alpha takes a more complex form (given by Eq. 3), but remains linear C with a pre-factor that depends on 𝑝𝑏 and a defined function of D. For known C and pb, a plot of alpha vs C yields a linear curve, the slope of which can be used to determine D.

This approach was applied to the tau fragment tau187, labeled with a nitroxide spin label at positions 272C, 313C, 322C, and 404C. DEER measurements were performed on mixtures of labeled and unlabeled proteins at different ratios, and D was determined. DEER measurements were performed on mixtures of labeled and unlabeled protein at varying ratios to determine D. Fibril formation was induced by heparin, and the resulting decrease in D was monitored over time, reaching a final value of ~1.5. The authors find that the final dimensionality (D) is reached within 12 minutes and is independent of concentration. Consistent values of D ≈ 1.5 are observed for residues 272C, 313C, and 322C located in the protein core, whereas residue 404C, positioned in the C-terminal "fuzzy" region, yields a higher value of D ≈ 2.

Comparisons across tau variants show that heparin-induced fibrils of longer constructs are mispacked, whereas shorter tau fragments form well-ordered, seeding-competent fibrils with lower conformational variability. Seeded aggregation further improves templating and packing, as indicated by reduced dimensionality. Finally, the authors demonstrate that the local spin density derived from the α parameter can be used to estimate the number of protofilaments.

With the method now established, its application to other amyloid systems may reveal correlations between fibril packing order and disease-related properties.

Strengths:

This study presents an original, conceptually clear method for quantifying fibril packing using a single parameter (dimensionality). The approach is experimentally accessible and straightforward to analyze, making it broadly applicable with standard pulse EPR instrumentation.

Weaknesses:

A discussion about the meaning of D<1 is missing. In addition, the treatment of multi-protofilament fibrils is limited. In particular, it remains unclear how increases in dimensionality arising from multiple protofilaments start to affect D and how it can be distinguished from packing disorder.

Reviewer #2 (Public review):

This manuscript by Tsay et al. reports an EPR (electron paramagnetic resonance) approach based on double electron electron resonance spectroscopy (DEER) to characterize the supramolecular packing of amyloid fibrils. The authors claim that this approach can "deliver an apparent dimensionality of the supramolecular organization of tau fibrils", "assess the amyloid core location and packing order, and track time-resolved formation of aggregation intermediates".

Specifically, the authors used the electron spin echo (ESE) decay to report the arrangement of spin labels in the amyloid fibrils. When the spin labels are arranged in a straight line, a planar surface, or a 3D space, the dimensionality of the ESE decay would be 1, 2, and 3, respectively. To demonstrate their methods, the authors used a singly spin-labeled tau protein, which is involved in several amyloid diseases, including Alzheimer's and other tauopathies. For the truncated 0N4R tau (residues 244-441, named tau187), four labeling sites were studied (272, 313, 322, and 404). Residues 272, 313, and 322 gave a dimensionality of ~1.5, while residue 404 gave a dimensionality of ~2.0. The authors explained that residues 272, 313, and 322 are expected to be part of the amyloid core, while 404 is part of the so-called fuzzy coat. However, the authors then explained that all three amyloid core sites are misaligned because their dimensionality is ~1.5 instead of 1. Using a short tau fragment of 16 amino acids (residues 295-313), the authors show that this peptide formed fibrils with a dimensionality of 0.8. Using the short tau fragment fibrils as seeds, the authors obtained tau187 fibrils with a dimensionality of 1.3. Furthermore, the α parameter (a fitting parameter used to obtain the dimensionality) was used to interpret the protofilament composition.

While this approach has great potential in providing structural insights into amyloid fibrils, there are several critical flaws in experimental design, data analysis, and interpretation in the current version.

(1) The authors didn't rigorously establish the central premise of the DEER approach to characterize the supramolecular structure of amyloid fibrils. The parallel in-register β-sheet structure of amyloid fibrils is supposed to give a dimensionality of 1 in the ESE decay analysis. For tau187 fibrils, the authors obtained 1.5. For tau16 fibrils, the authors obtained 0.8. Because the theoretical lower limit of dimensionality is 1, tau16 fibrils do not serve as evidence that this approach can identify a perfectly aligned parallel in-register β-sheets. A 20% deviation from the theoretical value suggests the low accuracy of using ESE decay to report amyloid core structures. The high-resolution structures of tau fibrils have been widely reported using cryo-EM methods; it shouldn't be difficult for the authors to identify a good protein candidate to obtain a dimensionality of 1 to establish their methods. With a good protein candidate, rigorous data analysis should be presented to show how reliable a core site can be distinguished from a supposedly disordered site.

(2) Regarding the claim of probing protofilament composition using the α parameter, the authors should prepare fibrils with defined protofilament composition. A number of amyloid fibril structures have been solved to show different numbers of protofilaments.

(3) Regarding the claim of tracking "time-resolved formation of aggregation intermediates", the authors need to show more than a couple of data points, and the real-time aggregation needs to be accompanied by characterizations with complementary methods such as TEM.

(4) The authors largely ignored progress that has been made on the previous spin labeling studies of amyloid fibrils. A lot of the claims, such as identifying amyloid core, real-time aggregation, and the effects of seeding on structures, have been characterized extensively using continuous-wave EPR. It would be to the benefit of the readers to show what additional values this approach provides over existing methods.

Reviewer #3 (Public review):

In this work, Tsay et al. examine the challenge of inferring the ordering of amyloid fibrils. There is a clear need for such methodology. In their work, they computationally analyze the case of the expected decay in the DEER signal for spins randomly distributed in one, two, and three dimensions and show that (not unexpectedly) the decay is sensitive to dimensionality for a range of spin label concentrations. More intriguingly, they measure the dimensionality of tau amyloid labeled at several positions. Intriguingly, they show uniform (but unexpected) dimensionality when the label is in the fibril core. Through further simulations, they show that this anomalous dimensionality cannot arise from label attraction or repulsion (which can lead to deviations from random positions). Instead, this dimensionality is interpreted (again using compelling simulations) to arise from mis-registering due to changes in packing. Taken together, this paper convincingly shows that the DEER signal can be used to get site-specific information on amyloid dimensionality and can discriminate between regions of fibril core vs the "fuzz coat". Overall, this paper moves forward the methodology and opens up the technique to attractive applications in the areas of amyloid formation. More substantively, the field of DEER has been fixated on the dipolar modulation, and it is only once in a while now that one comes across a paper with a fresh breath of air - this paper certainly is!

Reviewer #1 (Public review):

Summary:

To understand the process of mRNA imprinting, the authors develop a series of unbiased methods to identify and follow proteins that associate with transcripts co-transcriptionally. The methods rely on RNA polymerase II pull-downs or proximity biotinylation to do so, and from these experiments, the authors identify some interesting candidate proteins, including Rpg1 / eIF3a, Ssa1/2, and Spt6. The authors characterize some of these proteins in follow-up experiments and show that Spt6 recruitment depends on Rpb4.

Strengths:

(1) The methods described in this study will be useful for the community beyond their immediate application.

(2) The topic of mRNA imprinting remains an open area in the field, and this paper provides hypothesis-generating datasets that may be of use.

(3) If correct, the idea that eIF3a binds co-transcriptionally would be of interest to the transcription and translation fields.

(4) The data showing the importance of Rpb4 for Spt6 binding are some of the strongest.

Weaknesses:

(1) Two main methods (PROFIT and BioPROFIT) are introduced in this study, both of which make use of a combination of tags, especially on RNA polymerase II subunits, to identify and track proteins that are potentially recruited co-transcriptionally. However, a more thorough characterization is needed to gain a sense of the false negatives and false positives. For instance, there are no direct experiments testing the requirement for transcription for the hits. This is a key experiment.

(2) Alternatives are also not robustly considered. For example, what is the evidence that the proteins remain bound to an RNA through its life cycle, as opposed to rebinding in the cytoplasm? For proteins with known cytoplasmic functions, like Rpg1/eIF3a, this conclusion needs more supporting evidence. This caveat is especially important to consider given the typical or known off-rates of many of these proteins.

(3) Showing direct evidence that biotinylated "target" proteins (like eIF3a) accumulate in the nucleus during short labeling or if nuclear export is blocked is an important control, as is an experiment inhibiting transcription and demonstrating that the signal decreases.

Reviewer #2 (Public review):

Summary:

The authors have provided valuable and solid evidence for the hypothesis, of which Choder is an early advocate, that transcription facilitates the assembly of an mRNA-protein complex that can affect the expression of mRNA (e.g., translation or degradation) in the cytoplasm.

Strengths:

In this work the authors have used two orthogonal approaches: an IP of a Flag labeled Pol II and RNAse digestion to release nascent chain associated proteins followed by mass spectrometry to identify cotranscriptional-associated proteins and then verifying this association with the transcriptional apparatus by proximity labeling technology using biotinylation of a specific sequence (Avi-tag) by the bacterial enzyme, BirA fused to a subunit of Pol II. Many of the proteins identified are thought to be exclusively cytoplasmic, for instance, those important for translation, such as the components of initiation factor EF3. The work represents a significant advance in support of the model where specific mRNAs can assemble proteins needed for their function in the cytoplasm during their transcription.

They also discover that a mutant Pol II subunit, Rbp4, which does not bind certain Avi-tagged proteins, does not facilitate their biotinylation. These results lend credible support to the hypothesis.

Weaknesses:

While the proximity labeling provides strong evidence that is consistent with the hypothesis, a proof is still lacking because it is inferred that the enzymatic labeling occurs at the site of transcription (a reasonable assumption). More definitive evidence could be provided by imaging the presence of the cytoplasmic proteins at the transcription site, although this may not be within the expertise of the investigator, so it would require a collaboration.

While not necessarily a significant weakness, it is worth considering that a remote possibility is that the cytoplasmic proteins discovered in this way were not tagged with biotin in the nucleus, but rather in the cytoplasm, where the Pol II-complex, either Flag or BirA tagged, may come in contact with the substrate before it is imported to the nucleus. The authors presumably rule out that the tagging could occur during translation of the Avi-tag on polysomes by inhibiting translation and showing that the tagging of the target protein is not inhibited (the data here is not totally convincing). Whether the Pol II-(BirA or Flag) could react with Avi-tagged proteins, even while briefly in the cytoplasm before nuclear import, is not completely resolved by these experiments since the Avi-tagged proteins could reside in the cytoplasm, not associated with polysomes, but complexed with Pol II subunits. The mutant Rpb does not rule out this possibility since it would not bind its substrate in the cytoplasm. In order to get into the nucleus in the first place, the cytoplasmic proteins would need to be transported there by a complex, possibly involving Pol II subunits, Rpbs. Perhaps the authors could address this possibility in the text.

One confusing issue in the protocol is the efficacy of the biotin-depleted media in which the cells are grown. Biotin is an essential cofactor for many reactions, so there are still endogenous biotin and biotin ligase needed that may add a background level of promiscuous biotinylation of some cytoplasmic proteins, for instance, those containing a universal biotin binding site.

Reviewer #3 (Public review):

Summary:

Various groups over the last several decades have provided many examples of proteins associating with nascent mRNA co-transcriptionally to influence gene expression at subsequent stages, including in the cytoplasm. In this and previously published works, the Choder group has described these events as "mRNA imprinting", which we know as a field that reflects the differential association of proteins with mRNAs in a gene-specific or environmentally induced fashion to regulate gene expression.

In this study, the authors use a proteomics-based approach termed PROFIT to identify factors associated with RNA Pol II in an RNA-dependent manner. The identified interactors have the potential to be part of mRNA-protein complexes (mRNPs) being formed co-transcriptionally with an "mRNA imprinting" function. PROFIT employs a pulldown of RNA Pol II via a tagged Rpb3 subunit, followed by RNase I-mediated elution to isolate proteins associated in an RNA-dependent manner. Proteomics analyses identified known mRNA-associated proteins that have previously been reported as imprinting factors, as well as other proteins involved in gene expression, including factors functioning in the cytoplasm. The authors suggest, based on the RNA-dependence and assumed formation of these interactions with RNA Pol II co-transcriptionally, that these novel hits could be mRNA imprinting factors. Although for most of these factors, it has not been determined whether they associate with RNA-Pol II in the context of transcription with nascent transcripts to contribute to the downstream regulations of these transcripts.

Strengths:

PROFIT successfully identified nuclear factors known to engage mRNA co-transcriptionally. This suggests that the method has the potential to detect imprinting factors. By employing a proximity-labeling technique, termed BioPROFIT, further evidence is provided for some of the novel interactors being in proximity to RNA Pol II. The authors further demonstrate that one of the factors, the eIF3 component Rpg1, exists in two fractions, with a soluble fraction that matures into a ribosome fraction, which is suggestive of Rpg1 traveling along the gene expression pathway with an mRNP to be engaged in translation. In addition, the authors showed that PROFIT detects changes in RNA Pol II associated factors in response to heat shock, consistent with gene expression reprogramming during stress. As such, these methods and proteomics data provide a starting point for a more detailed characterization of mRNP compositions formed in the nucleus and their impact on gene expression at later stages.

Weaknesses:

The authors interpret the interaction data from PROFIT and BioPROFIT under the assumption that this reflects interactions happening co-transcriptionally. There is no discussion of other ways these data may result, or more importantly, controls to prove these assumptions are true. Overall, these assays lack important controls and experimental validations by independent methods to demonstrate that the identified interactions occur co-transcriptionally within the nucleus and do not represent interactions occurring in the cytoplasm or artifacts related to experimental design. For example, the authors focus on Rpg1 as a potential imprinting factor, which would require this protein to shuttle and be localized at transcribing genes. Yet no evidence is presented that Rpg1 enters the nucleus or can be found in association with a transcribed gene, which leaves open the possibility that this interaction is occurring in the cytoplasm or forming post-lysis.

To the possibility of in vitro interactions, in the PROFIT assay, yeast collected from a 3L culture is cryo-ground and resuspended in 7 mL of lysis buffer. This ratio of cell material to buffer will create a highly concentrated cell lysate that is subsequently used over ~6.5 hours, which is the time for centrifugation, DNase I digestion, and immunoprecipitation. These conditions have a very high probability of promoting new interactions between RNA, RNA Poll II, other proteins, and/or RNA Pol II-associated nascent RNA complexes in vitro. Notably, the PROFIT assay detects many highly expressed proteins but does not identify many of the factors known to be loaded into nuclear mRNPs (e.g., Yra1, THO complex, Sub2, or Nab2). The BioPROFIT assay is used to try to address this issue, but biotinylation may occur post-lysis because the desalting process to remove biotin is performed just before the immunoprecipitation, providing ~2 hours for the reaction to happen in vitro. In addition, even if the biotinylation occurs in cells, nothing about this assay indicates this is occurring in the context of transcribing RNA Pol II or nascent transcripts. To address this major issue, the authors should add a mixing control to show that the detected interactions between RNA Pol II and the identified factors are produced in cells, not in the cell lysate. Specifically, mixing cell grindates from two independent yeast strains (e.g., RPB3-FLAG strain mixed with a TIF4631-HA strain) with the lysate used in the PROFIT assay with western blotting. In this case, if the interaction is detected, the interaction is produced in the cell lysate. To verify PROFIT hits associated with transcribing RNA Pol II and nascent transcripts, BIOPROFIT should be performed in cells treated with a transcription inhibitor (e.g., thiolutin) or mutants blocking transcription by Pol II. These types of verifications should be performed for the multiple novel hits reported in the manuscript.

Another in vitro issue must also be addressed. In the PROFIT assay, elution of RNA-associated factors from the immunoprecipitated material is performed by RNase I digestion, but the reaction time is very long (3 hours) at room temperature. During such a long incubation time and at higher temperature (i.e., above 4 Celsius), it is possible that non-RNA-mediated interactors dissociate from the beads and/or protein binding partners. This possibility is made more problematic by the fact that the authors define interactors using fold change over an Rpb3 no tag sample, where the sample does not contain isolated RNA Pol II complexes and their associated protein-binding partners. As such, even a small amount of non-RNA-mediated RNA Poll II interactors that elute would appear significantly enriched. For this point, a comparison of +/- RNase I elution in the Rpb3-FLAG pulldown sample should be performed using PROFIT.

Other points to address:

(1) The cartoon in Figure 1A should be corrected to present the PROFIT experiment as described in the text. Specifically, in the cartoon, UV is shown to be applied to cells, but this is done with cell grindate.

(2) The cartoon in Figure 2A should be corrected. In the cartoon, it shows the biotin ligase biotinylating proximal proteins during DNase digestion as well as on the Sepharose beads, but in theory, the majority of the biotinylation reaction occurs in cells. In addition, the cartoon depicts biotinylation of proximal proteins, but the system described uses wild-type BirA to specifically biotinylate an Avi-tag. To perform non-specific labeling of proximal proteins, BirA* would need to be used. Finally, the cartoon indicates mass spectrometry analysis of labeled proteins, but this is not done in the manuscript.

(3) In the text, the sentence "However, no bio-Spt6-Avi was released from the complexes containing Pol II mutants (Fig. 5C)" appears to have two errors. "Pol II mutants" should likely be "rpb4 mutant" and "Fig. 5C" is probably "Fig. 6C".

(4) In the Figure 6 legend, the sentence "The bulk Spt6 was detected by anti-HIS Abs that bound to (HIS)x6, which was placed upstream of the FLAG" suggests that "FLAG" should be "Avi-tag." Please correct it if necessary and accurately describe it in the strain list.

(5) On page 18, Npl3 is listed and discussed, but never mentioned anywhere prior in the paper. For example, the paragraph states "...our observation that it binds nascent RNA in an Rpb4-dependent manner...", but Npl3 is not listed in the supplemental Table 4, which lists PROFIT hits affected by rpb4∆. If Npl3 is to be discussed, the associated data needs to be properly presented.

Reviewer #1 (Public review):

Summary:

In this study, Zhu et al. address spider silk spidroin evolution using long-read transcriptomics across 12 spider species. The study provides a novel evolutionary framework for spidroin diversification, proposing the existence of two ancient ancestral templates, i.e., AS and GS, and tracing how these templates diversified into major spidroin classes observed in radiated spiders. The manuscript further focused on the evolutionary history of multiple known spidroin proteins, with some previous hypotheses being revised.

Strengths:

A major challenge in silk biology, the highly repetitive content, was well addressed in this study by full-length transcriptome sequencing. Also, the authors performed very detailed analyses on sequence features across a wide range of species. I therefore think the study is supported by sound levels of sampling, technology, and analysis.

Weaknesses:

The manuscript presents a lot of detail regarding various sequence features and derived claims, but these features are sometimes not friendly to an audience not working with spider silks. Also, the current figures are not very helpful for understanding those described patterns. I found many colorful, trivial elements in almost every figure, but how their organization supported the corresponding statement was often unclear to me. I recommend that the authors further improve the figure design, including presenting a schematic evolutionary history for those spider silk proteins.

Reviewer #2 (Public review):

Summary:

This paper utilizes long-read transcriptomics across 12 representative spider species to propose a new evolutionary framework for spider silk proteins (spidroins). By identifying ancestral templates in the most basal spider lineages, the authors trace how simple genetic materials diversified into the high-performance fibers used by modern spiders.

Strengths:

(1) The authors utilized PacBio ISO-Seq (long-read transcriptomics), which is essential for resolving the massive, highly repetitive sequences of spidroin genes that often cause gaps in traditional short-read assemblies.

(2) The researchers sampled 12 species representing the major nodes of spider evolution, including the basal Mesothelae, the Mygalomorphae (tarantulas), and the highly diverse Araneomorphae.

(3) The study successfully identified two distinct primordial spidroins in basal spiders: the AS-type (alanine-serine-rich) and the GS-type (glycine-serine-rich) proteins.

Weaknesses:

(1) The GS-Type "Base Gene" Paradox

The paper proposes that the GS-type gene (Liphistius sp._5400) in Liphistius (the most ancient spider lineage) is the prototype for all modern dragline silk. However, the data presented significantly undermines this conclusion.

Every functional spider silk protein requires N-terminal and C-terminal domains to control fiber assembly. The authors admit that neither the N- nor the C-terminal of this GS-type protein shows homology to any known spidroins. Because it lacks these domains, the authors explicitly state that it "may not assemble into typical silk fibers". The authors are identifying this as a "base gene" solely because it contains poly-GS motifs. Their logic is that because GS motifs are found in modern silk and other silk-producing insects, this must be the ancestor.

In the same spider, the AS-type gene (Liphistius sp._6705) does have recognizable C-terminal sequences and motifs similar to modern eggcase silk. This proves that "real" spidroins existed in Liphistius, making the claim that the non-homologous GS-type is a "spidroin ancestor" look like a misidentification of a general repetitive protein.

(2) Overstated Classification of FLAG in RTA Spiders

The authors identified a transcript in the RTA spider Heteropoda davidbowie (H.dav_6495) and labeled it a "Flag-like spidroin". This label is based on the repetitive internal motifs, which contain "GPGGX" and "GPG"-the classic building blocks of flagelliform capture silk. However, both the N- and C-termini of this gene are highly homologous to ampullate spidroins (MaSp), not typical Flag proteins. By calling it a "Flag-like spidroin" rather than a "MaSp with GPG motifs," the authors are forcing an evolutionary narrative. It is equally possible that this is simply a divergent Major Ampullate spidroin that evolved capture-like motifs, rather than a capture silk gene that "moved" into the ampullate gland.

The authors explicitly state, "Its origin could not be traced through sequence analysis". This admission directly contradicts the confidence with which they propose a "revised evolutionary trajectory".

Appraisal and Impact

This study provides a high-resolution map of spider silk evolution by utilizing long-read transcriptomics to bridge the gap between basal and derived lineages. By identifying the earliest known genetic templates for silk, the paper offers a significant leap forward in understanding how complex biological materials originate, though it raises critical questions about the functional definition of a "spidroin".

Reviewer #3 (Public review):

Summary:

In this study, Zhu et al. use long-read transcriptomes, with correction using short-read RNA-seq, from 12 spider species that span the major evolutionary lineages to investigate the diversification of spider silk proteins (spidroins). Here, they identify 60 spidroin sequences and propose that two highly divergent sequences found in the basal Liphistius sp., where one is an alanine-serine-rich (AS-type), and one is a glycine-serine-rich (GS-type), represent ancestral templates from which all major spidroin families diversified. Using separate phylogenetic analyses for N-terminal domains, C-terminal domains, and repetitive domains, the authors argue that the AS-type lineage remained relatively conserved and gave rise to tubuliform spidroins (TuSp) used in eggcase silk, while the GS-type lineage evolved into minor ampullate spidroins (MiSp) and may have provided the substrate for major ampullate spidroins (MaSp). In addition, they describe a specific flagelliform-like (flag) transcript in a basal clade spider, with MaSp-like terminal domains, and propose that Flag was co-opted into ampullate silk glands before being progressively lost in more derived retrolateral tibial apophysis (RTA) lineages.

Strengths:

The taxon sampling is a strength of this study, covering representative species at key nodes across spider evolution, from the earliest-diverging Mesothelae through Mygalomorphae and into the most derived Araneomorphae lineages, which enables the authors to make comparative inferences about ancestral states. Also, the use of long-read sequencing is well-suited to the problem since spidroin genes contain highly repetitive coding sequences that would be very hard to resolve by short-read assembly alone. Thus, retrieving 30 full-length sequences in this context is notable, and the assembly quality appears reasonable for transcriptomic resources, with BUSCO completeness values reported between 85% and 93% across species.

The decision to analyse N-terminal, C-terminal, and repetitive domains in separate phylogenetic trees is methodologically sound and yields a biologically interesting result: terminal domains show greater diversification in basal lineages than repetitive regions, suggesting that specialisation of silk gland microenvironments preceded compositional innovation in the repetitive sequences.

Weaknesses:

While the paper has strengths in providing a useful comparative resource and generating interesting hypotheses, several of the central evolutionary conclusions are not directly supported by the current data. There are three main elements that require further attention:

(1) The GS-type Liphistius sequence (Liphistius sp._5400) is central to the manuscript's model for the origin of GA-rich ampullate spidroins, but the authors describe it as a spidroin-like transcript whose N- and C-terminal regions lack homology to known spidroins and may not support typical silk-fiber assembly. Since its terminal domains are excluded from the phylogenetic analyses, the proposed scenario, GS-type to MiSp to MaSp, rests largely on repeat-region similarity. Supplementary materials provided in this study further indicate no predicted signal peptide, although this feature alone is not unique among the annotated silk proteins. The manuscript should therefore either provide a stronger justification for treating Liphistius sp._5400 as an ancestral spidroin or more consistently frame it as a spidroin-like, repeat-based intermediate. The distinction between repeat-region clustering and full functional homology should also be made explicit.

(2) The whole-body transcriptome approach is an important sampling limitation that is acknowledged here, where the authors note that they were unable to recover complete spidroin repertoires for each species. Because the newly generated data are not silk-gland-specific, the absence of a transcript in a given species should be interpreted with caution and not equated directly with gene absence. This is particularly relevant to the manuscript's proposed loss of Flag during RTA evolution. In the focal taxa, the inference combines one positive transcript in H. davidbowie with non-detection in H. diardi, while broader support comes from limited synteny-based absence in a small number of external genomes. Therefore, while the Flag-loss scenario could be plausible, it remains suggestive rather than conclusive without more targeted silk-gland sampling or broader genomic validation.

(3) The Flag co-option model is interesting, but as presented now, it is based on limited evidence: a single Flag-like transcript in H. davidbowie, the absence of detection in H. diardi, restricted synteny comparisons, and terminal-domain similarity to ampullate spidroins. The manuscript does not present proteomic evidence that this Flag-like protein is incorporated into ampullate silk fibers, nor does it show a series of pseudogenized or truncated Flag loci across derived RTA lineages. This is a plausible and interesting scenario, but it should be framed more consistently as a testable hypothesis rather than as an established evolutionary pathway.

For this current scoping review, we have adopted the preprint review service.

Reviewer: María Sol Ruiz, Paul Hassan Ilegbusi, CLARA AMAKA NKPOIKANKE AKPAN, Rameshwari Prasad, Prasakthi Venkatesan, Shailee Rasania, Shawn Asadian, Randa Salah Gomaa Mahmoud, Raimi Morufu Olalekan (BSc. M.Sc. Ph.D. MNES, REHO, LEHO, FAIWMES)), J Moonga, Vasco Medeiros, Toba Isaac Olatoye, & Philip Hotor

Date: February 9, 2026

Title: PREreview of "AI Perspectives on the Present and Future of Antidepressant Pharmaceutical Treatment Based on Anti-inflammatory Strategies: A Scoping Review of Randomised Controlled Clinical Trials"

Zenodo

DOI: https://doi.org/10.5281/zenodo.18559746

URL: https://prereview.org/reviews/18559746

Version: 1.1755.0 User Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36 URL: https://www.medrxiv.org/content/10.1101/2024.12.31.24319839v3 Fingerprint: N/A Account: thoseformylovedpeople (acct:thoseformylovedpeople@hypothes.is) Date: Mon Apr 20 2026 04:19:40 GMT+0800

Reviewer #1 (Public review):

Summary:

This foundational study builds on prior work from this group to reveal the complexities underlying ligand-dependent RXRγ-Nur77 heterodimer formation, offering a compelling re-evaluation of their earlier conclusions. The Authors examine how a library of RXR ligands influences the biophysical, structural, and functional properties of Nur77. They find that although the Nur77-RXRγ heterodimer shares notable functional similarities with the Nurr1-RXRα complex, it also exhibits unique features - notably, both dimer dissociation and classical agonist-driven activities. This work advances our understanding of the nuanced behaviors of nuclear receptor heterodimers, which have important implications for health and disease.

Strengths:

(1) Builds on previous work by providing a comprehensive analysis that examines whether Nur77-RXRγ heterodimer formation parallels that of the Nurr1-RXRα complex.

(2) Systematic evaluation of a library of RXR ligands provides a broad survey of functional outputs.

(3) Careful reanalysis of previous work sheds new light on how NR4A heterodimers function.

Weaknesses:

(1) Some conclusions appear overstated or are not well substantiated by the work presented. It's unclear how the data support a non-classical mode of agonism, for example, based on the data shown.

(2) Some assays have relatively few replicates, with only two in some cases.

Comments on revisions:

I'm satisfied with the revised version.

Reviewer #2 (Public review):

Summary:

This study explores the mechanisms by which binding of the nuclear receptor RXRg regulates its heterodimeric partner Nur77. Previously, this group made the interesting discovery that ligand-dependent activation of RXRg bound to a related partner, Nurr1, does not occur through a classical pharmacological mechanism but through agonist-dependent dissociation of the complex through disruption of their ligand binding domain (LBD) interactions. Here, they revisit this paradigm with Nur77. In contrast to Nurr1, the authors do not have the reagents to clearly support a role for LBD dissociation. Following from the model of partial ligand-dependent dissociation of the LBD heterodimer, the experimental data (NMR, ITC, SEC) are interesting and quite complex.

Strengths:

The authors do a rigorous job of describing the data and providing possible interpretations and caveats. Revisiting the analysis of Nurr1, they identify the crucial role that selective Nurr1-RXRg agonists played in supporting the LBD dissociation model; without analogous compounds for the Nur77-RXRg complex, it is difficult to invoke this mechanism. Interestingly, treatment with the Nurr1-RXRg selective agonist HX600 suggests it can induce some LBD dissociation. Therefore, there may be some similarities between regulation of Nurr1 and Nur77 by RXRg.

Weaknesses:

Despite evidence supporting a partial role for RXRg LBD dissociation as a mechanism to activate Nur77, other data demonstrate that a fundamentally different regulatory mechanism likely exists in the Nur77-RXRg complex that involves the RXRg disordered NTD. The decision to describe further study of this as outside the scope of this work is unfortunate, as it closed off an avenue that could have provided fruitful data informing the apparently distinct regulatory mechanisms of the Nur77-RXRg complex. Given the uncertainty in the importance of the partial roles of the pharmacological mechanism, LBD dissociation, and the RXRg NTD, this study may have limited impact on the field.

Comments on revisions:

I'm satisfied with the revision.

Reviewer #1 (Public review):

Summary:

In this study, the authors evaluated inter-areal interactions in different types of neuronal recordings, timescales, and species". The method consists of computing the variance explained by a linear decoder that attempts to predict individual neural responses (firing rates) in one area based on neural responses in another area.

The authors apply the method to previously published calcium imaging data from layer 4 and layers 2/3 of 4 mice, and simultaneously recorded Utah array spiking data from areas V1 and V4 of 3 monkeys. They report distributions over "variance explained" numbers for several combinations: from mouse V1 L4 to mouse V1 L2/3, from L2/3 to L4, from monkey V1 to monkey V4, and from V4 to V1. For their monkey data, they also report the corresponding results for different temporal shifts. Overall, they find the expected results: responses in each of the two neural populations are predictive of responses in the other, more so when the stimulus is not controlled than when it is, and with sometimes different results for different stimulus classes (e.g., gratings vs. natural images).

Strengths:

(1) use of existing data

(2) addresses an interesting question

Weaknesses:

The data and analysis results are presented in a way that invites direct comparison between mouse L4<->L2/3 variance explained numbers, and monkey V1<->V4 variance explained numbers. This comparison is highly problematic and can't be taken at face value as the authors themselves clearly acknowledge in the Discussion and reply to the reviews. The datasets simply differ in too many aspects. If the goal of the authors is not to compare, then the analyses should be presented separately, allowing for a more detailed analysis of each (also see below).

Understanding which patterns in the data are robust and which are idiosyncratic to individual animals/recordings is complicated by the fact that some figures appear to show a single mouse and some averages over all four mice with no indication over whether the results are consistent across mice. For the monkey results, all figures in the main text appear to only show a single monkey, with the other two monkey results in the SI. Again, it is not clearly presented and discussed which aspects of the results are robust, and which differ between monkeys.

Furthermore, there are literally dozens of statistical comparisons between various conditions and metrics in the main figures without them being sufficiently organized around robust new insights, that will likely replicate, and that can inform our understanding of the underlying processes, or constrain computational models.

Reviewer #2 (Public review):

Summary:

In this work the authors investigated the extent of shared variability in cortical population activity in the visual cortex in mice and macaques under conditions of spontaneous activity and visual stimulation. They argue that by studying the average response to repeated presentations of sensory stimuli, investigators are discounting the contribution of variable population responses that can have significant impact at the single trial level. They hypothesized that, because these fluctuations are to some degree shared across cortical populations depending on the sources of these fluctuations and the relative connectivity between cortical populations within a network, one should be able to predict the response in one cortical population given the response of another cortical population on a single trial, and the degree of predictability should vary with factors such as retinotopic overlap, visual stimulation, and the directionality of canonical cortical circuits.

To test this, the authors analyzed previously collected and publicly available datasets and data recorded themselves. These include calcium imaging of the primary visual cortex in mice and electrophysiology recordings in V1 and V4 of macaques under different conditions of visual stimulation. The strength of this data is that it includes simultaneous recordings of hundreds of neurons across cortical layers or areas and under different conditions of sensory stimulation and behavioral state. However, the weaknesses of calcium dynamics (which has lower temporal resolution and misses some non-linear dynamics in cortical activity) and multi-unit envelope activity and LFPs (which reflects fluctuations in population activity rather than the variance in individual unit spike trains), underestimates the variability of individual neurons which may vary widely in their participation in shared sources of variance.

From their analysis, they found that there was significant predictability of activity between layer II/III and layer IV responses in mice and V1 and V4 activity in macaques, although the specific degree of predictability varied somewhat with the condition of the comparison and with differences in the quality of recordings between the datasets. The authors deployed a variety of analytic controls and explored a variety of comparisons that are both appropriate and convincing that there is a significant degree of predictability in population responses at the single trial level consistent with their hypothesis. This demonstrates that a significant fraction of cortical responses to stimuli are not due solely to the feedforward response to sensory input, and if we are to understand the computations that take place in cortex, we must also understand how sensory responses interact with other sources of activity in cortical networks. Overall, this work highlights that, beyond the traditionally studied average evoked responses considered in systems neuroscience, there is a significant contribution of shared variability in cortical populations that may contextualize sensory representations depending on a host of factors that may be independent from the sensory signals being studied.

Strengths:

This work considers a variety of conditions that may influence the relative predictability between cortical populations, including receptive field overlap, latency that may reflect feed-forward or feedback delays, and stimulus type and sensory condition. Their analytic approach is well designed and statistically rigorous. They acknowledge the limitations of the data and do not over-interpret their findings.

Weaknesses:

The different recording modalities between species and scales (within vs. across cortical areas) limit the interpretability of the inter-species comparisons, and while this is not the stated goal of the authors, the juxtaposition of these two datasets invites comparison.

Reviewer #3 (Public review):

Neural activity in visual cortex has primarily been studied in terms of responses to external visual stimuli. While the variability of neural inputs to a visual area are known to also influence visual responses, the contribution of this stimulus independent component to overall visual responses has not been well characterized.

In this study, the authors analyze datasets from both mice (a previous V1 Ca++ imaging study) and monkeys (data from a previous study and new large-scale electrophysiological recordings from V1-V4). Using regression models, they examine the predictability of neural activity between Layer 4 and Layer 2/3 in mice and between V1 and V4 in monkeys. Their main finding is that significant predictions are possible even in the absence of visual input, highlighting the influence of stimulus independent downstream activity on neural responses. These findings can inform future modeling work of neural responses in visual cortex to account for such non-visual influences.

The authors perform a thorough analysis comparing regression-based predictions for a wide variety of combinations of stimulus conditions and directions of influence. While many of the predictability pattens are largely in line with expectations (eg., downstream layers/areas predicting upstream activity), it is valuable to have these relationships quantified as the authors have done here. Predictability also depended on stimulus type, but these dependencies were not consistent across animals, making it difficult to draw general conclusions. Finally, they show robust predictions even during spontaneous activity which are only partially accounted for by available behavioral metrics. Together, these analyses provide a valuable quantification of stimulus-independent components of visual cortical activity and their potential role in shaping sensory responses.

Reviewer #1 (Public review):

Summary:

This study presents a map of neurons responding to aversive stimuli in zebrafish and suggests that the regions containing these neurons are homologous to mammalian brain areas involved in aversive processing. Specifically, this study found that neurons in a part of the pallium, the homolog of the amygdala, responded vigorously to strongly noxious and fully looming stimuli, but not to the milder cues. In contrast, neurons in another part of the pallium responded to all of these stimuli. The findings provide valuable insights into the neural mechanisms underlying negative-valence computation in zebrafish.

Strengths:

This study performed whole-brain functional imaging using two-photon light-sheet microscopy and identified the activity of individual neurons in awake zebrafish. This technique is highly valuable and will be broadly applicable to future studies aimed at elucidating the neural mechanisms underlying zebrafish behavior at single-neuron resolution.

Weaknesses:

Although this study reports neuronal responses to aversive stimuli, it did not directly assess how aversive these stimuli were for zebrafish. In general, studies of this kind quantify the aversiveness of test stimuli by measuring behavioral indices such as avoidance or escape responses. The present study states that "neurons responded vigorously to strongly noxious and fully looming stimuli, but not to milder cues." However, the authors did not provide behavioral evidence demonstrating that the stimuli were indeed aversive or that the so-called milder cues were perceived as less aversive by the animals. Without a behavioral measure of aversiveness, it is difficult to determine whether the reported neural responses reflect negative-valence processing, rather than general sensory salience or stimulus intensity.

Reviewer #2 (Public review):

Summary:

The authors aim to map neurons encoding negative valence at the whole-brain scale in larval zebrafish. Using two-photon light-sheet imaging combined with various aversive stimuli, they visualize and quantify stimulus-evoked neural responses, identify the anatomical locations of responsive neurons, and explore the possibility of genetically accessing Rl neurons that respond preferentially to strongly noxious stimuli.

Strengths:

The major strength of this study lies in its use of two-photon light-sheet imaging, which provides a system-level characterization of neuronal response to aversive stimuli. The authors systematically compare multiple classes of aversive stimuli (heat, electric shock, looming, etc.), showing that strongly threatening stimuli converge on a compact neuronal population in the Rl, supporting the robustness of the finding. Finally, the identification of Tiam2a expression in these neurons provides a potential genetic handle for future functional studies.

Weaknesses:

The main weakness of the study is the lack of causal evidence supporting the functional role of the identified neurons. Without optogenetic, chemogenetic, or ablation experiments, it is difficult to determine whether these neurons are required for or sufficient to encode negative valence. In addition, the study does not include positive-valence or neutral stimuli controls, making it difficult to distinguish whether the observed neural responses reflect valence per se or more general downstream response such as motor output. Finally, the lack of behavioral readouts limits the ability to directly link the identified neural populations to defensive behaviors.

Reviewer #3 (Public review):

Overview and Strengths:

Accurate evaluation of threat levels allows animals to determine whether to escape. The precise mechanism underlying threat evaluation remains unclear. Smith et al. identified a cluster of neurons in the zebrafish rostrolateral dorsal pallium (Rl) that respond differentially to varying levels of negative-valence stimuli.

This work leverages the small size and optical transparency of the larval zebrafish, using two-photon selective plane illumination microscopy to assay the response of pallial neurons to various negative-valence stimuli. Interestingly, unlike the ventromedial pallium and habenula, which responded to all stimuli tested, neurons in the Rl were activated by a selection of stimuli representing relatively higher levels of threats. By leveraging a zebrafish brain atlas, the authors identified a transgenic line labeling a tiam2a+ cluster of neurons that appears to be the activated population in the Rl. Together, these results demonstrate a subpopulation of pallial neurons that likely categorizes the strength of negative valence in larval zebrafish.

The primary conclusions of this work are well supported by the data. The identification of a neuronal cluster that may underlie the categorization of threat-associated sensory stimuli is significant. Furthermore, this study generates a high-quality functional imaging dataset using cutting-edge microscopy, setting the foundation for understanding the neuronal encoding of emotions in zebrafish.

Results from this work set the stage to answer further exciting questions: How do tiam2a+ Rl neurons modulate the activity of the hindbrain escape circuit? What is the functional role of the Rl population inhibited by threat stimuli? Computationally, how does Rl integrate sensory signals and classify threat levels? How does the activity of Rl change in the context of habituation and conditioning? Future work may use more nuanced stimuli and combine new genetic tools, behavioral recording, and circuit-level analysis to systematically reveal how emotions modulate defensive behaviors.

Weaknesses:

The impact of this work could be further enhanced by incorporating more sophisticated data analysis and by more clearly anchoring the findings within the known framework of zebrafish defensive behavior.

(1) The authors performed statistical analyses across six ROIs per experiment in Figures 1E/J, 3E/J, and 6B/D/F. This increases the probability of Type I errors. Applying multiple comparison corrections would mitigate this concern. Given that most stimuli (except for the "IR heating") are non-directional, the authors may consider first testing for the response symmetry following each stimulus and then combining ROIs from the two hemispheres to calculate a single averaged measurement per region per fish for comparisons of regional dF/F.

(2) I found the topographical mapping of activated and inhibited ROIs very informative. There appear to be two subpopulations of Rl: a posterior-medial population often activated by negative valence stimuli, and an anterior-lateral population that is frequently inhibited. I wonder if it is possible to decode the valence or category of a stimulus based on the topography and response profiles of these neurons? These results would provide additional evidence for the Rl's roles of threat evaluation.

(3) Findings in this paper, especially differential responses of the Rl to full and partial looming, deserve an expanded discussion. The authors should better anchor these findings to established literature to emphasize their significance in the Discussion. For example, how might this potential categorization mechanism contribute to, or differ from, the mechanisms underlying habituation (Fotowat & Engert, 2023, eLife); what are the possible connections between the pallium and the hindbrain escape circuits that could relay these Rl signals (Kunst et al., 2019, Curr Biol)?

(4) The authors make conservative claims associating the tiam2a+ cluster with Rl neurons activated by noxious stimuli, and their data support this conclusion. However, this link could be further strengthened by testing whether the tiam2a+ cluster shows differential responses to full vs partial looming. This could be achieved by performing pERK staining following the stimulus paradigm. While future tools may allow for direct functional imaging of this population, I believe such experiments are beyond the scope of this paper.

(5) Figure 1E/J, Figure 3E/J: Please clarify whether the dashed red vertical lines indicate the onset or the offset of the stimuli. Additionally, different time windows were used for AUC calculations across these experiments; the authors should provide a rationale for these varying windows in the Results or Methods.

Reviewer #1 (Public review):

Summary:

This study makes a significant and timely contribution to the field of attention research. By providing the first direct neuroimaging evidence for the integration-segregation theory of exogenous attention, it fills a critical gap in our understanding of the neural mechanisms underlying inhibition of return (IOR). The authors employ a carefully optimized cue-target paradigm combined with fMRI to elegantly dissociate the neural substrates of cue-target integration from those of segregation, thereby offering compelling support for the integration-segregation account. Beyond validating a key theoretical hypothesis, the study also uncovers an interaction between spatial orienting and cognitive conflict processing, suggesting that exogenous attention modulate conflict processing at both semantic and response levels. This finding shed new light on the neural mechanisms that connect exogenous attentional orienting with cognitive control.

Strengths:

The experimental design is rigorous, the analyses are thorough, and the interpretation is well grounded in the literature. The manuscript is clearly written, logically structured, and addresses a theoretically important question. Overall, this is an excellent, high-impact study that advances both theoretical and neural models of attention.

Comments on revisions:

I appreciate the authors' thorough and thoughtful revisions, which have successfully addressed all of my prior concerns.

Reviewer #2 (Public review):

This study provides neuroimaging evidence supporting the integration-segregation theory of inhibition of return (IOR), a widely studied attentional phenomenon. It also explores the neural interactions between IOR and cognitive conflict, demonstrating that conflict processing is potentially modulated by attentional orienting.

The integration-segregation theory was investigated using a sophisticated, well-executed experimental task that accounted for cognitive conflict processing, which is phenomenologically related to IOR but is non-spatial. The behavioral and neuroimaging data were carefully analyzed.

The authors have thoughtfully addressed all my previous concerns. By demonstrating how attentional orienting can modulate neural processing of cognitive conflict, this study helps to advance a more unified and mechanistic understanding of the cognitive and neural processes that govern our visual perception and response selection.

Reviewer #3 (Public review):

Summary:

This study provides direct neuroimaging evidence relevant to the integration-segregation theory of exogenous attention-a framework that has shaped behavioral research for more than two decades but has lacked clear neural validation. By combining an inhibition-of-return (IOR) paradigm with a modified Stroop task in an optimized event-related fMRI design, the authors examine how attentional integration and segregation processes are implemented at the neural level and how these processes interact with semantic and response conflicts. The central goal is to map the distinct neural substrates associated with integration and segregation and to clarify how IOR influences conflict processing in the brain.

Strengths:

The study is well-motivated, addressing a theoretically important gap in the attention literature by directly testing a long-standing behavioral framework with neuroimaging methods. The experimental approach is creative: integrating IOR with a Stroop manipulation expands the theoretical relevance of the paradigm, and the use of a genetic-algorithm-optimized fMRI design ensures high efficiency. Methodologically, the study is rigorous, with appropriate preprocessing, modeling, and converging analyses across multiple contrasts. The results are theoretically coherent, demonstrating plausible dissociations between integration-related activity in the fronto-parietal attention network (e.g., FEF, IPS, TPJ, dACC) and segregation-related activity in medial temporal regions (e.g., PHG, STG). Importantly, the findings provide much-needed neural support for the integration-segregation framework and clarify how IOR modulates conflict processing.

Revisions and Evaluation:

The authors have responded thoroughly and convincingly to the concerns raised in the previous round of review. In particular, issues related to the interpretation of dACC activity, the functional characterization of PHG and STG, and reporting clarity have been carefully addressed. The manuscript has been improved in terms of transparency, consistency of reporting, and overall readability.

As a result, I no longer see any major weaknesses. The study is now clearly presented, methodologically sound, and theoretically informative. It makes a valuable contribution to the literature on attention and cognitive control.

Comments on revisions:

I appreciate the authors' efforts in addressing the previous comments. They have responded thoroughly to the concerns raised in the prior round of review. The work is well executed and makes a meaningful contribution to the field.

Reviewer #1 (Public review):

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

Summary:

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

Strengths:

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

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

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

Weaknesses:

(1) The sample size for the ex vivo electrophysiology conducted on the calb1+ lamina I projection neurons (Figure 5) is limited to a total of six recorded neurons. Given the difficulty and complexity of the preparation, this is understandable. Notably, since approximately 87% of lamina I projection neurons heavily innervated by Trpm8+ terminals are calb1+, these six recordings of such neurons in Figure 4E could also be calb1+.

Reviewer #2 (Public review):

Summary:

In this study, the authors took advantage of a semi-intact ex vivo somatosensory preparation that includes hindlimb skin to characterize the response of projection neurons in the dorsal horn of the spinal cord to peripheral stimulation, including cold thermal stimuli. The main aim was to characterize the connectivity between peripheral afferents expressing the cold sensing receptor TRPM8 and a set of genetically tagged neurons of the anterolateral system (ALS). These ALS neurons expressed high levels of the calcium binding protein calbindin 1.

In addition, combining different viral tracing methods, the authors could identify the anatomical targets of this specific subset of projection neurons within the brainstem and diencephalon.

Strengths:

The use of a relatively new (seldom used previously) transgenic line to label TRPM8-expressing afferents, combined with the genetic characterization of a previously identified subset of projections neurons add specificity to the characterization. The transgenic line appears to capture well the subpopulation of Trpm8-expressing neurons.

In addition, the use of electron microscopy techniques makes the interpretation of the structural contacts more compelling

The writing is clear and the presentation of findings follows a logical flow.

Overall, this study provides solid, novel information about the brain circuits involved in cold thermosensation.

Weaknesses:

In the characterization of recorded neurons in close contact or in the absence of this contact with TRPM8 afferents, the number of recordedd neurons is relatively low. In addition, the strength of thermal stimuli is not very well controlled, preventing a more precise characterization of the connectivity.

The authors acknowledge that, technically, this is a very difficult preparation with very low yield as far as obtaining successful recordings. Moreover, the tissue needs to be maintained at room temperature which is obviously not ideal when characterizing cold thermoreceptors due to the unavoidable effects of low temperature on cold-activated receptors.

Reviewer #3 (Public review):

Summary:

Razlan and colleagues provide a detailed anatomical characterization of lamina I projection neurons in the mouse spinal cord that are densely innervated by primary afferents activated by cooling of the skin. The authors validate a Trpm8-Flp mouse line, show synaptic contacts between Trpm8⁺ boutons and projection neurons at the ultrastructural level, and demonstrate at the physiological level that these neurons specifically respond to cooling stimuli. Next, by taking advantage of previous transcriptomic analysis of ALS neurons, the authors identify calbindin as a marker for cold activatetd lamina I projection neurons and map their ascending projections to the rostral lateral parabrachial area, caudal periaqueductal gray, and ventral posterolateral thalamus, well-known thermosensory and thermoregulatory centers. Altogether, these findings provide strong anatomical and functional evidence for a direct line of transmission from Trpm8⁺ sensory afferents through Calb1⁺ lamina I neurons to key supraspinal centers controlling perception of cold and thermoregulatory responses.

Strengths:

The combination of mouse genetics, electron microscopy, ex-vivo physiology, optogenetics and viral tracing provides convincing evidence for a direct cold pathway. The work validates the Trpm8-Flp line by extensive anatomical and molecular characterization. Integration with previous transcriptomic and anatomical data, neatly links the cold-selective lamina I neurons to a molecularly defined cluster of ALS neurons, strengthening the bridge between molecular identity, anatomy, and physiological function.

Weaknesses:

The main limitation remains the relatively small number of neurons that could be recorded electrophysiologically. While understandable given the complexity of the preparation, this necessarily limits generalization.

Reviewer #1 (Public review):

In this manuscript, Hinojosa and colleagues analysed the changes in V1 visual responses induced by locomotion in head-fixed mice using two-photon calcium imaging. The authors observe that locomotion strongly increases the visual responses of V1 excitatory neurons that exhibit sensitizing responses to visual stimuli. Also, there is an increased response in VIP interneurons, and to a lesser extent, PV interneurons and SST interneurons (non-significant). The authors used a model fitted with data presented in the manuscript, as well as previous knowledge on cortical connectivity among different neuron types. The model suggests that the major component of the increased responses during locomotion is an increase in excitatory drive from external inputs (feedforward, feedback and modulatory), most importantly onto VIP interneurons and excitatory neurons. However, the excitatory drive of local excitatory neurons onto other surrounding excitatory and inhibitory cells is reduced.

The manuscript is well presented and represents a valuable analysis of how locomotion modulates the activity of different subtypes of cortical neurons. However, major issues should be addressed to strengthen the results.

Major issues:

(1) Speed and mismatch between locomotion and visual stimulation.

The authors do not clearly describe the definition of locomotion versus the resting state. The speed should, by itself, have an impact on neuronal responses, especially at the onset of locomotion. Several published studies show that the mismatch between a visual stimulus and the speed of the animal induces specific responses in V1, both in excitatory and subtypes of inhibitory neurons. The authors should address these points upfront in the manuscript, since it is likely a major variable explaining their results

(2) Use of deconvolution with MLSpike.

Some results (Figure 2) exclusively depend on the deconvolution of calcium signals into spikes (since the initial peak is not seen in calcium transients). The authors should validate this result either with electrophysiological recordings or with the use of another deconvolution method (e.g. CASCADE), emphasising the limitations of this approach and the limitations of the time resolution of calcium imaging.

(3) The manuscript is centred around a specific increase in visual responses in sensitizing neurons during locomotion, both in the fraction of responsive neurons and response magnitudes.

It is hard to tell whether this difference is due to a greater scaling effect of locomotion, a difference in responses during the resting state, or both. The manuscript should further explore and discuss the differences in responses between sensitizing and depressing neurons, both during the resting state and locomotion. Adding metrics and direct comparisons of the magnitudes of fast responses, slow responses, and time integrals between sensitizing and depressing neurons in resting and locomotion states would help to clarify this. Same for fractions of responsive neurons of each type in each condition. E.g., the slow phase is harder to judge from the plots, but the DeltaF/F integral shown in Figure 1G seems to suggest the difference in response magnitude between sensitizing and depressing neurons is largest in locomotion state, rather than resting state. How do these integrals look for inferred firing rates shown in Figure 2?

(4) There is something counterintuitive about how the changes in inhibition onto sensitizing and depressing neurons during locomotion explain the reported activity changes.

Sensitizers receive reduced SST input and increased PV input during locomotion. If SSTs depress and PVs sensitize (and this is the main reason why sensitizers, which receive dominant input from SSTs sensitize, and vice-versa), how is it possible that this switch does not alter the sensitizing or depressing nature of these neurons' responses in locomotion? Are these changes insufficient to flip the dominant SST-PV drive? Figure 6D-E seems to show there is a flip, at least for sensitizers. How do authors explain this? Do authors think this is related to the narrowing of the adaptive index distribution shown in Figure 1C?

(5) Presentation of the experimental data and the model.

The manuscript introduces the results of interneuron recordings during the description of the model. Similarly, the results of optogenetic manipulations are presented inside the model's description. It would be clearer to present all experimental data first and introduce the model later, fitting it to all experimental evidence previously presented.

Reviewer #2 (Public review):

This is an interesting paper with important results. The authors, working in V1, have previously, in a 2022 paper, defined sensitizing and depressing excitatory (E) cells as those whose response increases or decreases, respectively, across the 10 seconds of showing a drifting grating stimulus. They showed that sensitizing E cells are dominantly inhibited by SST inhibitory cells, which are dominantly depressing, and that depressing E cells are dominantly inhibited by PV inhibitory cells, which are very largely sensitizing. It's been well established that locomotion greatly increases E-cell firing rates in V1 compared to rest, but much remains to be worked out as to the mechanism. Here, they find that locomotion increases the responses of the sensitizing E cells much more than depressing cells. They develop a model of changes in synaptic weights between rest and locomotion to account for the changes. One reason that sensitizers are increased more by locomotion than depressors is that PV cells, which more strongly inhibit depressors, have increased firing for locomotion, whereas SST cells, which more strongly inhibit sensitizers, don't change their firing rates with locomotion. However, in the mode,l a complex array of postulated changes in connection strengths is also involved.

I have, though, a number of concerns: with the model, with the lack of proper discussion of connection to some previous works, and with an overall unclear and confusing presentation and certain controls that should be done.

In the model, they postulate that synapses within the 6-cell-type network - sensitizing, intermediate, and depressing E cells, and PV, SST, and VIP I cells - and from three sources of external input to each of the six types all change between rest and locomotion (except that connections between the E cells don't depend on their types). There are a lot of degrees of freedom, and this makes interpretation of the results difficult. I would have liked to have seen more efforts to constrain the degrees of freedom. For example, there seems to be very little difference between the three E cell types in any of the three types of external input received. Why not constrain them all to get the same external input and see if it significantly affects model fit? Or what if synapses from the three types of external input are left unchanged, and only change their strengths between rest and locomotion? How well could this do? During optimization, why not constrain the changes between rest and locomotion, for example, by putting an L1 penalty on the changes or the relative changes, trying to force them to be sparse, and see whether there are roughly equally good fits? And then, if the main changes are in a small set of synapses, can the authors isolate changes to that small set and do roughly equally well? What about looking at the principal components of the weight changes across models, to isolate patterns of change that are most important?

In terms of comparing to previous works, when optogenetic manipulations of SST and PV are done to test various hypotheses, I would like to see some discussion of what is already known from the authors' 2022 paper and what they are adding or testing that wasn't known or tested from that paper. And Dipoppa et al (2018) also found weight changes to account for the difference between rest and locomotion. They were looking at a fixed point of responses of neurons across retinotopic space to stimuli of various sizes with only one E-cell type, whereas they are accounting for trajectories across time considering 3 E-cell subtypes but without variation in stimuli or retinotopic position of neurons, so the efforts are somewhat different, but still, it would be good to see a bit more discussion of what is in agreement or in contradiction in the conclusions.

In terms of presentation and controls, I have many concerns, which include:

(1) The main result is that sensitizers increase their responses with locomotion ~2X (for dF/F) or about 3.5X (for spikes) more than depressors. But there are other differences between sensitizers and depressors, for example sensitizers have smaller initial stimulus responses at rest, and depressors have larger. What if cells were divided into tertiles by initial stimulus response at rest? Would the authors see the same differences in the effects of locomotion? If so, can they establish whether the difference is really attached to the adaptation properties rather than to, for example, the initial responses, for example, by comparing the regression of response increase against AI vs the regression of response increase against initial resting response? And there might be other controls to be done for other features in which sensitizers and depressors differ.

(2) Lines 103 and following: the authors refer to a "second notable change" which is the narrower distribution of adaptive effects, but I think this is trivial. The adaptive index is AI=(R1-R2)/(R1+R2), where R1 is response 0.5-2.5s after stimulus onset and R2 over 8-10s. But if the change is additive, as suggested by the dF/F figures (and I believe the distributions of AI here are based on dF/F measurements) -- adding the same constant to R1 and R2 will shrink |AI| without changing the sign of AI. So this would seem to just be a signature of a change that is primarily additive rather than multiplicative.

Also, if the authors do decide that they are going to focus on spikes after showing the raw dF/F, then this analysis should be repeated for spikes.

(3) Figure 2, F is supposed to be D minus E, but it doesn't look like it. For example, the initial response under locomotion is very similar in sensitizers and depressors, so the initial difference in F should be small, but it's not; and at rest, depressors initially have larger responses than sensitizers, whereas later depressors have smaller responses than sensitizers, yet the difference at rest is positive at all times. Something seems wrong here.

Reviewer #3 (Public review):

This study aimed to understand the depressing and sensitizing effects of adaptation in mice visual cortex during different behavioral states: locomotion and stationary. There is an impressive characterisation of the responses in different cortical cell types and with different optogenetic manipulations to the inhibitory populations. These form a very interesting dataset to understand the effects of the state on the circuits and gain insight into the mechanisms. This data is then used to constrain a model of the responses. Unfortunately, the model appears to be too flexible, and it was difficult to interpret the insights gained from the different model fits.

Strengths:

The data is impressive. There is a characterisation of responses of PCs and VIP, SST and PV interneurons. Additionally, there is the characterisation of some responses to specific optogenetic manipulations, VIP inactivation, SST or PV activation or inactivation. These data will help develop a good insight into the system. The principle of using the optigenetic manipulations to constrain model parameters is very interesting.

Weaknesses:

Many of the analyses have some concerns in the methodology used, which we list in detail below. Further, the model used to gain insight into the mechanism appears overly complicated and seems hard to gain clear insights from.

Major concerns:

(1) Key concern is the usage of dF/F signals for all analyses, especially when comparing responses.

1a) Figure 1G: Comparison of sensitisers and depressors. It is important to consider what the baseline rates are when making these comparisons, especially when comparing the degree of effects between different cell types. For example, if baseline rates for sensitizers were overall higher, it would mean the difference in gain of response would be lower, and could affect the results in the opposite direction of what is claimed. One option to account for this would be to z-score the overall responses, using the same normalization for locomotion and rest. We also suggest plotting differences in sensitisers, intermediates, and depressors as a function of firing rate. Matching for firing rate across each PC categorization and calculating delta AI for each matched firing rate bin.

1b) Figure 2A-F: The above is an even more significant issue when it comes to estimating spiking rates. The methods do not state how dF/F is calculated. If these are based on using the pre-stim as the reference, the algorithms for spike rate used might not be appropriate if this were used. Using pre-stimulus referencing could result in the estimate going into the wrong range in the calculation of the spike rate.

1c) In both cases above, it could be a problem if baseline firing rates are different between cell types, or states (locomotion/stationary). The latter is established to have effects on many cell types measured, and so needs to be accounted for very carefully.

1d) It would be informative to see per-neuron comparison for adaptive indices during rest and locomotion states. This could be visualized using a scatter plot with AI-rest vs. AI-locomotion for Figures 1D- 1F and 2J- 2L.

1e) Are neurons more strongly modulated between locomotion and rest, also more likely to experience a shift in AI indices (i.e. delta AI). Is there a correlation between the change in firing rate between behavioral states and Delta AI (Loco-Rest)? If so, is this present for all neuron subtypes (e.g. VIP, SST, and PV)?

1f) Optogenetic inhibition of VIP neurons on average abolished the slow depressive effects of adaptation in SST (Figure 3). The strength and prevalence of this effect are unclear. Perhaps one can perform a bootstrap control and opto AI indices and calculate whether AI was significantly reduced following optogenetics inhibition, and if so, on average, how likely was this to occur for the recorded SST neurons? This is important in knowing that the average effects (Figure 3D) aren't driven by a portion of SST neurons, especially as this is later used to confirm the region of parameter space and affects the subsequent results in Figure 4.

(2) Statistics for the effects. There is a mention of Liner mixed models, but no information is given on the actual models being used and tested. This is particularly for the case of Figure 1G, where there is a composition of effect sizes between different populations. What precise significance test is being used? Are the stats on paired cells when considering locomotion and rest?

(3) Model parameters: It is acknowledged that there is a large range of parameters that can model the responses effectively, up to 11% of initial conditions. At 9000 initial conditions, this is around 1000. The parameter estimates are then considered as the mean of each parameter. This seems like a strange choice for a few different reasons:

3a) A mean solution might not be one of the solutions. Let's say the parameters range over a large dimensional space. They could occupy non-overlapping / discontinuous subspaces. In that case, the mean parameters do not necessarily fall within the solution subspaces. Therefore, this reduction to means might not be valid.

3b) Compare distributions rather than means. There are multiple distributions of parameters between conditions. All stats should be on the comparison of distributions rather than just the means.

(4) Visualizing weight matrices: It is very challenging to interpret the weight matrices. Furthermore, it appears that the stationary and locomotion conditions fit independently, and given the large parameter spaces, it is even harder to interpret. Can the fitting instead be done by fitting on one and using those at the initial conditions for the other state? Figure 7 shows an initiative cartoon, but it is not clear how the matrices in Figures 5 and 6 lead to the summary shown in Figure 7. It is also not clear why the connections between inhibitory neurons are not shown in Figure 7. One option is to perhaps run some kind of dimensionality deduction on the parameter space to better interpret the data. When showing deltaWeights, was the model initialised with 'Rest' weights and allowed to change? It is not obvious what the difference is between 'relative change in connection weights' and 'relative change in synaptic weights'.This needs to be clarified.

4a) Model parameters were reduced differently for locomotion and rest (Figure 4). We suggest evaluating the results for locomotion and rest using the same chi-square value of 3 for both behavioral states (at least in controls).

Reviewer #1 (Public review):

Summary:

The authors focused on medaka retinal organoids to investigate the mechanism underlying the eye cup morphogenesis. The authors succeeded to induce lens formation in fish retinal organoids using 3D suspension culture with minimal growth factor-containing media containing the Hepes. At day 1, retinal precursor cells expressing Rx3:H2B-GFP appear in the surface region of organoids. At day 1.5, Prox1+ cells appear in the interface area between the organoid surface and the core of central cell mass, which develops a spherical-shaped lens later. So, Prox1+ cells covers the surface of the internal lens cell core. At day 2, foxe3:GFP+ cells appear in the Prox1+ area, where early lens fiber marker, LFC, starts to be expressed. In addition, foxe3:GFP+ cells show EdU+ incorporation, indicating that foxe3:GFP+ cells have lens epithelial cell-characters. At day 4, cry:EGFP+ cells differentiate inside the spherical lens core, whose surface area consists of LFC+ and Prox1+ cells. Furthermore, at day 4, the lens core moves towards the surface of retinal organoids to form an eyecup like structure, although this morphogenesis "inside out" mechanism is different from in vivo cellular "outside -in" mechanism of eye cup formation. From these data, the authors conclude that optic cup formation, especially the positioning of the lens, is established in retinal organoids though the different mechanism of in vivo morphogenesis.

In the revised manuscript, the authors have added new data on dissociation and re-aggregation of day one organoids and revealed that differentially adhesive property of lens and retinal precursors cells enables the formation of a spherical lens in the center of the organoid and later movement of lens toward the peripheral region of the organoid for lens evagination. Furthermore, the authors showed that BMP and FGF signaling are required for lens precursor induction and subsequent lens fiber differentiation in the organoid, respectively. In the revised manuscript, they have added new data on target tissue of BMP and FGF signaling pathways by showing phosphorylated Smad1/5/8 and phosphorylated ERK1/2, respectively, and revealed that lens precursor cells formed in the center of day one organoid are target of BMP signaling, whereas lens fiber cells formed in the center of day 1.5 to 2 organoid are targeted by FGF signaling. Finally, the authors conducted bulk RNA-seq analysis of 1-4 dpf embryonic eyes and day 1-4 eye organoids and revealed that lens organoids show a similar temporal profile of gene transcription. These data suggest that, although induction and morphogenesis of lens are differentially regulated between eye organoids and in vivo embryonic eyes, their molecular mechanism seems to be shared.

Significance:

Strength: This study is unique. The authors examined eye cup morphogenesis using fish retinal organoids. Eye cup normally consists of the lens, the neural retina, pigment epithelium and optic stalk. However, retinal organoids seem to be simple and consists of two cell types, lens and retina. Interestingly, a similar optic cup-like structure is achieved in both cases; however, cellular mechanism of lens induction and morphogenesis are different between retinal organoid and in vivo eyes, although their molecular mechanism is conserved.

Limitation: In the revised manuscript, the authors clarified almost obscure points; however, a couple of unclear points are still retained. First, there is one unknown cell-type population located in the interface area between foxe3:GFP+ cells and rx2:H2B-RFP+ cells at day 2 organoid. Second, the authors showed that removal of HEPES from the organoid culture media inhibits lens induction and differentiation. However, the role of HEPES in lens induction and differentiation in the organoid remains to be elucidated.

Advancement: In the revised manuscript, the authors have provided precise description of inductive and morphogenetic process of lens induction and differentiation in retinal organoid as well as their molecular evidence, which impact the research field of cell biology and regenerative medical science using human organoid.

Audience: The target audience of current study are still within ophthalmology and neuroscience community people, maybe translational/clinical rather than basic biology. To beyond specific fields, need to formulate a general principle for cell and developmental biology.

Reviewer #2 (Public review):

Summary:

In this study from Stahl et al., the authors demonstrate that medaka pluripotent embryonic cells can self-organise into eye organoids containing both retina and lens tissues. While these organoids can self-organize into an eye structure that resembles the vertebrate eye, they are built from a fundamentally different morphogenetic process - an "inside-out" mechanism where the lens forms centrally and moves outward, rather than the normal "outside-in" embryonic process. This is a very interesting discovery, both for our understanding of developmental biology and the potential for tissue engineering applications. The study would benefit from some additional experiments and a few clarifications. The authors suggest that the lens cells are the ones that move from the central to a more superficial position. Is this an active movement of lens cells or just the passive consequence of the retina cells acquiring a cup shape? Are the retina cells migrating behind the lens or the lens cells pushing outwards? High-resolution imaging of organoid cup formation, tracking retina cells in combination with membrane labeling of all cells would help elucidate the morphogenetic processes occurring in the organoids. Membrane labeling would also be useful as Prox1 positive lens cells appear elongated in embryos while in the organoids, cell shapes seem less organised, less compact and not elongated (for example as shown in Fig 3f,g).

The organoids could be a useful tool to address how cell fate is linked to cell shape acquisition. In the forming organoids, retinal tissue initially forms on the outside, while non-retinal tissue is located in the centre; this central tissue later expresses lens markers. Do the authors have any insights into why fate acquisition occurs in this pattern? Is there a difference in proliferation rates between the centrally located cells and the external ones? Could it be that highly proliferative cells give rise to neural retina (NR), while lower proliferating cells become lens?

What happens in organoids that do not form lenses? Do these organoids still generate foxe3 positive cells that fail to develop into a proper lens structure? And in the absence of lens formation, does the retina still acquire a cup shape?

The author suggest that lens formation occurs even in the absence of Matrigel. Is the process slower in these conditions? Are the resulting organoids smaller? While there are indeed some LFC expressing cells by day2, these cells are not very well organised and the pattern of expression seems dotty. Moreover, LFC staining seems to localise posterior to the LFC negative, lens-like structure (e.g. Fig.S1 3o'clock).

How do these organoids develop beyond day 4? Do they maintain their structural integrity at later stages?

The role of HEPES in promoting organoid formation is intriguing. Do the authors have any insights into why it is important in this context? Have the authors tried other culture conditions and does culture condition influence the morphogenetic pathways occurring within the organoids?

Significance:

This is a very interesting paper, and it will be important to determine whether this alternative morphogenetic process is specific to medaka or if similar developmental routes can be recapitulated in organoid cultures from other vertebrate species.

Comments on revised version:

The revised manuscript is much improved and addresses all of the points raised by the reviewers.

Reviewer #3 (Public review):

Major Comments on first version:

- The manuscript presents a beautiful set of high-quality images showing expression of lens differentiation markers over time in the organoids. The set of experiments is very robust, with high numbers of organoids analysed and reproducible data. The mechanism by which lens specification is promoted in these organoids is, however, poorly analysed, and the reader does not get a clear understanding of what is different in these experiments, as compared to previous attempts, to support lens differentiation. There is a mention to HEPES supplementation, but no further analysis is provided, and the fact that the process is independent of ECM contradicts, as the authors point out, previous reports. The manuscript would benefit from a more detailed analysis of the mechanisms that lead to lens differentiation in this setting.

- The markers analysed to show onset of lens differentiation in the organoids seem to start being expressed, in vivo, when the lens placode starts invaginating. An analysis of earlier stages is not presented. This would be very informative, allowing to determine whether progenitors differentiate as placode and neuroepithelium first, to subsequently continue differentiating into lens and retina, respectively. Could early placodal and anterior neural plate markers be analysed in the organoids? This would provide a more complete sequence of lens vs retina differentiation in this model.

- The analysis of BMP and Fgf requirement for lens formation and differentiation is suggestive, but the source of these signals is not resolved or mentioned in the manuscript. Are BMP4 and Fgf8 expressed by the organoids? Where are they coming from?

- The fact that the lens becomes specified in the centre of the organoid is striking, but it is for me difficult to visualise how it ends up being extruded from the organoid. Did the authors try to follow this process in movies? I understand that this may be technically challenging, but it would certainly help to understand the process that leads to the final organisation of retinal and lens tissues in the organoid. There is no discussion of why the morphogenetic mechanism is so different from the in vivo situation. The manuscript would benefit from explicitly discussing this.

Significance:

This study describes a reproducible approach to differentiate ocular organoids composed of lens and retinal tissues. The characterisation of lens differentiation in this model is very detailed, and despite the morphogenetic differences, the molecular mechanisms show many similarities to the in vivo situation. The manuscript however does not highlight, in my opinion, why this model may be relevant. Clearly articulating this relevance, particularly in the discussion, will enhance the study and provide more clarity to the readers regarding the significance of the study for the field of organoid research, ocular research and regenerative studies.

Comments on revised version:

The authors presented substantial additional experimental evidence that further strengthens their manuscript and addressed with these experiments and their revised results/discussion in the manuscript the comments and suggestions from the reviewers. I think the manuscript has been greatly improved with the additions presented.

Reviewer #1 (Public review):

Summary:

This study identifies a conserved phosphorylation event on Hsp70, at human T495 that is triggered by DNA damage. The authors show that this modification arises in response to MMS and is temporally associated with cell cycle progression through mitosis. Using biochemical analysis, they further argue that the phosphomimetic Hsc70(T495E) adopts an open-like conformation with impaired J protein-stimulated ATP hydrolysis while still retaining client binding. In yeast, both phosphomimetic and phosphonull mutants perturb growth and cell cycle progression, supporting the idea that dynamic regulation of this site helps coordinate DNA damage responses with G1/S control.

Strengths:

A major strength of the paper is that it links prior work on Legionella-mediated Hsp70 phosphorylation to a normal cellular DNA damage response. The study is also commendably multi-level, combining mammalian cell biology, in vitro biochemistry, and yeast genetics to support the central model. Together, the authors provide a coherent story that this Hsp70 site has functional importance in checkpoint-like control rather than being a passive phosphosite, adding to our understanding of the chaperone code.

Minor Weaknesses:

The authors acknowledge that the direct kinases/phosphatases for this site remain unknown. Some conclusions are therefore still somewhat inferential, especially the model that pHsp70 acts as a reversible molecular brake on S-phase entry. These limitations do not undermine the importance of these exciting findings, but they do leave the paper somewhat short of a fully resolved mechanism.

Comments on revisions:

The authors have done a great job in addressing all the previous reviewer concerns. They have provided additional data and refined the text, stating limitations of their proposed model. In doing so, they have produced a much-improved version of the manuscript.

Reviewer #2 (Public review):

The revised manuscript offers little new information and fails to address the critical weaknesses identified in the original submission.

While we can agree that phosphorylation of Thr495 would likely affect Hsp70 function-given the known biochemistry of Hsp70s and the author's previous work on LegK4-the significance of this finding hinges on whether it is a regulated process. If a meaningful fraction of Hsp70 were phosphorylated in a regulated manner triggered by DNA damage or cell cycle progression, it would constitute an important discovery, regardless of its specific impact on fitness in a given context.

However, beyond highlighting the temporal profile of Hsp70 phosphorylation in MMS-treated cells (Figure 4e), the paper fails to rule out the possibility that this correlation is merely an irrelevant side reaction. This "bystander" phosphorylation could simply be caused by the activation of kinases during the experimental MMS treatment and subsequent washout. The authors' claim-that the fraction of phosphorylated Hsp70 increases in a "regulated, cell-cycle dependent manner"-does not sufficiently counter the possibility of it being a non-functional side effect.

This concern could be resolved if the authors had identified the specific kinase, demonstrated its specificity, and manipulated it either genetically or pharmacologically. While I acknowledge this is a "tall order," the lack of such data limits the paper's significance. Furthermore, the current data fails to meet a much lower bar: confirming that a substantial fraction of Hsp70 is actually phosphorylated under the tested conditions. Such a finding would at least suggest the event is capable of impacting the overall Hsp70 pool.

It is surprising that the authors have not provided a ratiometric assay to settle this, such as an immunoblot of total Hsp70 separated on a Phos-tag or IEF gel. Instead, they rely on indirect evidence and data subject to alternative interpretations. Specifically, they argue that the fitness cost of the Thr495Ala mutation (or the phosphomimetic mutation) is due to the loss of regulatory phosphorylation (or deregulated phosphorylation); however, it is equally plausible that the mutations create Hsp70 hypomorphs whose defects are only exposed under stressful experimental conditions.

Reviewer #3 (Public review):

In this manuscript Moss et al. demonstrate that Hsp70 phosphorylation at a conserved threonine residue integrates DNA damage responses with cell-cycle control. The authors present unbiased biochemical, cell-based, and yeast genetic analyses showing that phosphorylation of human Hsp70 at T495 (and the analogous Ssa1 T492 in yeast) is triggered by base-excision-repair intermediates and downstream DDR kinase activity, leading to delayed G1/S progression after DNA damage. They used orthogonal approaches such as ATPase assays, phospho-specific detection, kinase-inhibition studies, synchronization experiments, and phenotypic analyses of phosphomutants. They presented robust data which collectively supported the conclusion that dynamic Hsp70 phosphorylation functions as a conserved "molecular brake" to prevent inappropriate S-phase entry under genotoxic stress.

Comments on revisions:

The authors have addressed all my questions and concerns.

Reviewer #1 (Public review):

Summary:

The authors investigated the extent to which phase-amplitude coupling (PAC) of respiratory and electrophysiological brain activity recordings was related to episodes of life-threatening apnoea in human newborns.

Strengths:

I want to commend the authors for acquiring unique and illuminating data; the difficulty in recording and handling these data has to be appreciated. As far as I can tell, Zandvoort and colleagues are the first to provide robust evidence for respiration-brain coupling in newborns. Their creative use of the phase-slope index for peripheral-central interactions is innovative and credible. If proven to be robust, the authors' findings have important implications well beyond the field of brain-body research.

Comments on revisions:

I would like to thank the authors for a careful revision and additional clarifications; I have no further questions.

Reviewer #2 (Public review):

Summary:

The author's central hypothesis was that the strength of cortico-respiratory coupling in infants is negatively associated with apnoea rate. To prove this, they first investigated the existence of cortico-respiratory coupling in premature and term-born infants, the spatial localisation of the cortical activity and its relationship with the phase of the respiratory cycle, and the directionality of coupling.

Strengths:

The researchers used synchronised EEG and impedance pneumography to detect the phase amplitude coupling.

They have studied a wide range of gestations, from 28 weeks to 42 weeks, including males and females. Their exclusion criteria ensured that healthy babies were studied and potential confounders of impaired respiratory activity were avoided. Their sequential approach in addressing the objectives was appropriate.

Weaknesses:

As a neonatal clinician and neuroscientist, I have commented based on my expertise. I have not commented on signal processing.

There are no major weaknesses to the study. Some minor weaknesses include:

(1) Data relating to the cortical oscillations and the respiratory phase is given. However, whether this would lead to their hypothesis that the strength of cortico-respiratory coupling is negatively associated with apnoea rate is unclear. What preceding data enabled the authors to link the strength of coupling to the rate of apnoea?

(2) If we did not know of data showing the existence of cortico-respiratory coupling in newborn infants, then should it not be the first research question to examine?

(3) What are the characteristics of the infants who contributed data to establish the cortico-respiratory coupling (Figures 2 and 3)?

(4) Although it is the most plausible direction of the relationship, with neural activation driving respiratory muscle contraction, how can the authors prove this with their data? Given that they show coherence between signals, how do we know that the cortical signal precedes the respiratory muscle contraction?

(5) Apgar score is an ordinal variable. The authors should summarise this as median (range).

Comments on revisions:

All the weaknesses are adequately addressed. No more comments

Reviewer #1 (Public review):

Summary:

The authors investigate how stochastic and deterministic factors are integrated in cell fate decisions, using *Dictyostelium discoideum* as a model system. They show that cells in different cell cycle phases (a deterministic factor) are predisposed to different fates, albeit with deviations, when exposed to the same environmental stimulus. However, gene expression variability due to asynchrony in cell cycle phase across cells in the populations and stochasticity of biochemical processes enhances the robustness of cellular responses to environmental cues that disrupt the cell cycle.

Using a simple, tractable mathematical model, the authors characterize the response of cell fate decisions as dependent on a combination of deterministic (cell cycle phase) and stochastic factors (variability in gene expression). They then identify Set1 - a key regulator of gene expression variability - and indicate the mechanism of histone methylation, through which it modulates the variability. Finally, they confirm that gene expression variability contributes to the robustness of cells' response (at the population level) by comparing and contrasting the predictions from the mathematical model versus the outcomes in wild type and set1- mutants.

Strengths:

The authors are careful in their choice of experiments and in measuring gene expression variability, using methods that account for expected trends with average gene expression. The mathematical model chosen is simple to follow intuitively and yet predictive enough (at a qualitative level) of the effects of stochastic-deterministic combination of factors, and burst size/frequency.

Weaknesses:

While the authors show that gene expression variation is a feature of genes associated with fate choice and cell type proportioning, it remains somewhat unclear if this kind of variation, or any amount of it, is always beneficial for robustness or there is some optimum level of it.

Reviewer #2 (Public review):

Summary:

A fundamental problem in developmental biology is how a group of apparently identical cells breaks symmetry and differentiates into, for instance, type A and type B cells in the absence of any external influence such as a gradient of something causing cells at the left side of the group to become type A cells. The authors use the model system Dictyostelium to explore the interplay between a known cell-cycle-dependent musical chairs mechanism (cells are at random phases of the cell cycle, and a signal that hits all the cells causes cells that happen to be in one set of cell cycle phases to become the A cells, and cells that happen to be in other phases become the B cells), and stochastic gene expression. They identified genes whose expression is stochastic (unusually high cell-cell variation). Using a very clever and elegant genetic screen, they then show that these genes often are associated with cell fate choice. The authors then show that the stochastic genes have reduced levels of histone (H3K4) Me3 methylation, and that a histone methylase called Set1 is important for this process. They then bring the work together to show that the cell-cycle-dependent mechanism and stochastic gene expression work in combination to generate the observed differentiation of Dictyostelium cells.

Strengths:

Combination of theory, clever genetic screens, single-cell RNA-seq, and molecular and cell biology to dive into the fundamental problem of cell fate choice.

Results support the conclusions.

Very significant contribution to developmental biology.

Weaknesses:

Because the paper is co-written by people doing theoretical work and people doing experimental work, the theory sections will be difficult for an experimentalist and vice versa, but it is very much worth the effort to read this paper, there is a lot in here. There are no weaknesses of the methods and results.

Reviewer #1 (Public review):

Summary:

Badarnee and colleagues analyse fMRI data collected during an associative threat-learning task. They find evidence for parallel processes mediated by the mediodorsal, LGn and pulvinar nuclei of the thalamus. The evidence for these conclusions is promising, but limited by a lack of clarity regarding the preprocessing and statistical methods.

Strengths:

The approach is inventive and novel, providing information about thalamocortical interactions that are scant in the current literature.

Weaknesses:

(1) There are not sufficient details present to allow for the direct interrogation of the methods used in the study.

(2) The figures do not contain sufficiently granular details, making it challenging to determine whether the observed effects were robust to individual differences.

Comments on revisions:

I continue to recommend the plotting of individual data points. While there may be individual variance, it is important to quantify this in publication so that future studies can appreciate the uncertainty surrounding test statistics.

Reviewer #2 (Public review):

Summary:

The authors quantify human fMRI BOLD responses in pulvinar and mediodorsal thalamic nuclei during a fear conditioning and extinction task across two days, in a large sample size (hundreds of participants). They show that the BOLD responses in these areas differentiate the conditioned (CS+) and safety (CS-) stimulus. Additionally this changes with repeated trials which could be a neural correlate of fear learning. They show that the anterior pulvinar is most correlated with the MD, and that this is not due to anatomical proximity. They perform graph analysis on the pulvinar sub nuclei which suggests that the medial pulvinar is a hub between the sensory (lateral/inferior) and associative (anterior) pulvinar. They show different patterns of thalamic activity across conditioning, extinction, recall, and renewal.

Strengths:

The data has a large sample size (n=293 in some measures, n=412 in others). This is a validated human fear conditioning/extinction task that Dr Milad's group has been working with for several years. Few labs have investigated the thalamus activity during fear conditioning and extinction, particularly with a large sample size. There is an independent replication of the pulvinar network structure (Fig. 3), which suggests that the processing in the more sensory-related inferior and lateral pulvinar is relayed to the anterior pulvinar (and possibly thereby to more action-related prefrontal areas) via an intermediate step in the medial pulvinar - potentially a novel discovery but that needs more validation.

Weaknesses:

(1) The authors cannot make causal claims about their results based on correlational neuroimaging evidence. Causal claims should be pared back. E.g. Sentence 1 in results "The anterior pulvinar and MD contribute to early associative threat learning, as evidenced by increased functional activation in response to CS+ compared to CS- at the block level (Fig. 1b-c)." needs to be reworded to something like 'the anterior pulvinar and MD have increased functional activation... This suggests that these areas may contribute to early associate threat learning"

(2) Fig .1 The fact that the difference in BOLD activity between CS+ and CS- goes away on the third trial is not addressed. This is a very large effect in the data.

(3) Fig. 3 Could the observed network structure be due to anatomical proximity? Perhaps the authors should do an analogous analysis to what they did in Fig. 2 for this intra-pulvinar analysis. This analysis doesn't take into account the indirect connections through corticothalamic and thalamocortical connections with visual cortex and the pulvinar. There is an implicit assumption that there are interconnections between the pulvinar sub nuclei, but there are few strong excitatory projections between these sub nuclei to my knowledge. If visual areas are included in the graph, it would make things more complex, but would probably dramatically change the story. In this way, the message is somewhat constructed or arbitrary.

(4) In the results section describing Fig. 4-7, there are no statistics supporting the claims made.<br /> There needs to be a set of graphs comparing the results across the study sessions and days, with statistical comparisons between the different experiments to confirm differences.

(5) FIg. 7 does not include the major corticothalamic and thalamocortical projections from early, mid-level, and higher visual cortex to the different pulvinar nuclei. I doubt that there are strong direct projections between the pulvinar nuclei, rather the functional connections are probably mediated through interconnections with cortical visual areas.

(6) Stylistic: There are a lot of hypotheses and interpretations presented in this primary literature paper which may be better suited for a review or perspective piece.

(7) In the discussion there is an assumption that the fMRI BOLD responses to CS+ and CS- need to be different to indicate that an area is processing these distinctly, but the BOLD signal can only detect large scale changes in overall activity. It's easy to imagine that an area could be involved in processing these two stimuli distinctly without showing an overall difference in the gross amount of activity.

(8) There is strong evidence that the BOLD responses to the threat-related and safety-related stimuli are different, modest evidence for their claims of learning/plasticity in these pathways, and circumstantial evidence supporting their hypothesized graph network models. Overall most of the claims made in the discussion are better considered possible interpretations rather than proven findings - this is not a criticism, as these experiments and subject matter are extremely complex.

(9) This study continues to validate the power and utility of this in human fear conditioning/extinction paradigm, and extends this paradigm to investigating fear learning beyond the traditional limbic system pathways. It's possible that their models for the pulvinar nuclei interconnections could guide future neuromodulation or DBS studies that could provide more causal evidence for their hypotheses.

Comments on revisions:

The reviewers addressed my major concerns appropriately in the modified manuscript. As long as the MRI analysis concerns of Reviewer 3 are satisfied (MRI analysis is not my expertise), I am satisfied with the modified manuscript.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

(1) The manuscript has improved methodologically and analytically after the review. Several weaknesses remain, in my opinion, but still findings are valuable and the evidence can be considered as convincing.<br /> a) fMRI data have low resolution (3 cubic mm), which certainly limits the examination of small nuclei such as the ones investigated here, and especially the examination of the LGN and inferior pulvinar.<br /> b) fMRI was normalized to standard space. Analyzing the data in individual-subject space would have given you the options of avoiding altering every participant's brain and of using more precise atlases than the normalized AAL for ROI selection.<br /> c) Motion during scanning was poorly controlled. Including the motion parameters as covariates of no interest in the GLM/analysis does not fully guarantee that motion is not influencing the results, and that motion is not differentially influencing some experimental conditions more than others.

Reviewer #1 (Public review):

The authors have considered a panel of antibodies that target epitopes at the gp120/gp41 interface (8ANC195 and PGT151), the fusion peptide in the gp41 domain (VRC34), and the MPER region of gp41 (DH511.2_K3 and VRC42). They also investigate 10E8.4/iMab, which is an engineered bispecific antibody that targets the MPER and the CD4 receptor. On a technical note, they have applied a double amber codon-readthrough strategy to incorporate the non-natural TCO*A amino acid, which gets labeled through click chemistry. This approach should result in less disruption of the native Env structure as compared to the peptide insertion previously used for smFRET imaging of Env. Furthermore, previous implementations of smFRET imaging of HIV-1 Env, which focus on gp120 conformation, have yielded limited information on antibodies that target gp41. Altogether, through the cutting-edge application of smFRET imaging, the study provides novel insights into the mechanisms of action of interesting and clinically relevant antibodies.

In validating the functionality of the S401TAG/R542TAG Env, the authors performed infectivity assays and observed 20% infectivity as compared to wild-type (Figure S2A). However, the text equates this with "20% dual-amber suppression efficiency". This would benefit from some explanation. Why do the authors interpret infectivity as reporting on amber suppression efficiency, and not the functional cost of modifying Env, which is probably unavoidable? Or a combination of both? Is there data to suggest that 100% amber suppression would leave Env 100% functional? If so, this would be valuable to show. If not, the text should be clarified.

The authors state that the contour plots in Figure 2E reveal "dynamic sampling" of the observed FRET states. Strictly speaking, as presented, the contour plots (and FRET histograms) provide no information on dynamics per se. They indicate only the relative thermodynamic stabilities of the FRET states; transitions between states are a matter of interpretation. The TDPs, shown later in Figure 5A, nicely display the dynamics. More importantly, interpretation of the contour plots is challenging, as some seem to suggest an evolution toward lower FRET states. This is especially evident in Figures 2F and 3D, which suggest that the system evolves into a stable 0.1-FRET state (CO) after about 3 sec. Unless the authors want to conclude something from this, I would suggest that they consider removing the contour plots, since their interpretations are fully supported by the FRET histograms alone.

The data indicating that Env conformation is manipulated by 10E8.4/iMab is interesting. If I understand correctly, 10E8.4/iMab is an engineered antibody with one Fab targeting MPER and the second Fab targeting CD4. In the absence of CD4, could the difference between 10E8.4/iMab and the other MPER antibodies be due to 10E8.4/iMab being monovalent with respect to MPER binding?

Reviewer #2 (Public review):

Summary:

In this paper, Xu and co-workers unveil two distinct modes of neutralisation by gp41-targeted broadly neutralizing antibodies on HIV-1 Env. So far, it was unclear as to how the mechanism of neutralisation occurred for this subset of neutralising antibodies (that can target the fusion peptide or the membrane proximal external region of the gp41 subunit). Thanks to single-molecule FRET, the authors show that the majority of broadly neutralizing antibodies stabilize the closed Env conformation (named State 1 since the original work by Munro and colleagues PMID: 25298114). Interestingly, the bivalent 10E8.4/iMab stabilized in turn a CD4-bound open state of Env. The two modes of neutralization described for these antibodies show previously unknown allosteric mechanisms that stabilize closed and open Env conformation, stressing the importance of Env conformational dynamics and its efficiency during the process of fusion.

Strengths:

The article is well-written, and the figures fully depict the data in a convincing way. The authors have used smFRET, which is now established in the field as a good tool to assess Env dynamics.

Weaknesses:

(1) The limited controls on how click chemistry affects Env (as labelled Env HIV virions were not evaluated).

(2) Photobleaching of donor and acceptor molecules occurs right after 10sec exposure.

(3) Other limitations are well described in the corresponding section.

Reviewer #1 (Public review):

Summary:

The authors used single-nucleus RNA sequencing (snRNA-seq) to investigate accelerated tooth replacement following tooth plucking in cichlid fish. They analyzed four stages of regeneration using elegant and well-designed approaches to characterize cellular trajectories and interactions within the dental epithelium and mesenchyme during the accelerated replacement process. Their analyses identified cell-type-specific gene expression profiles and intercellular signaling interactions associated with whole-tooth regeneration.

Strengths:

This is a highly interesting and thoughtfully executed study that provides compelling and convincing insights into the mechanisms underlying accelerated tooth regeneration.

Weaknesses:

The manuscript currently lacks experimental validation of the single-nucleus RNA-seq data.

Reviewer #2 (Public review):

Summary:

Mubeen and colleagues studied the cellular basis of tooth regeneration in cichlid fish. Using an elegant tooth plunking strategy followed by single-nucleus RNA-sequencing, the authors were hoping to achieve an atlas of cellular and transcriptional changes that occur within and between cells during whole tooth replacement.

Strengths:

The major strengths of the methods and results are high novelty in the approach in a vertebrate with continuous tooth replacement, the temporal analysis of analyzing at plucking and three later time points, the thorough and sophisticated analysis of the snRNA-seq data, including the inference of trajectories and signaling events, and the robust signal of transcriptional differences induced by tooth plucking.

Weaknesses:

The major weaknesses of the methods and results are no validation of any of the inferred cell types, no functional tests of whether any of the changes in signaling pathways affect the plucking-induced tooth replacement process, and perhaps no clear takeaway message for biologists not necessarily interested in tooth replacement.

Conclusion:

The authors achieved their aims of identifying the changes in gene expression and cellular composition that occur during whole tooth replacement accelerated by plucking. Overall, the results support their conclusions, although some slight semantic qualifiers should probably be added (e.g., referring to "cell types" as "putative cell types").

The work should have a high impact in the field of tooth and organ regeneration, and the novel methodological paradigm established here of accelerating tooth replacement three-fold by plucking has great promise for future follow-up studies to further study this process. The work could also have a strong impact through the computational methods used here to infer trajectories and signaling interactions. Specific pathways, genes, and cell types could be tested in other fish, such as zebrafish, to test function during tooth replacement.

The work is unique and interdisciplinary, and also has significance by establishing that robust phenotypically plastic accelerations in regeneration rates occur upon tooth removal. There are very few studies like this one that combine genetic and environmental studies of regeneration. The result that three different species of cichlid fish that normally have very different tooth patterns all accelerate tooth replacement threefold upon tooth plucking also has significance in revealing a highly conserved plucking response.

Reviewer #3 (Public review):

Summary:

This is an interesting paper. The process of tooth exfoliation and replacement in vertebrates remains an intriguing and fascinating subject of inquiry. As the scientists noted, there are no mammalian models that can be used to examine signaling pathways in real time.

Strengths:

This work integrates in vivo and high-resolution transcriptomics. The study confirms previous findings and emphasizes the need for additional research into the processes that drive the restoration of missing teeth for future therapeutic uses.

Weaknesses:

I disagree with the use of the phrase "plucking". Instead, the authors use tooth extraction or tooth removal, which is clinically more correct for the procedure they are doing.

The title is rather broad and appears to be more appropriate for a review than an original research work. I would advise specifying the species under research and/or the sort of damage model used in the transcriptome analysis.

It's uncertain whether the findings are exclusively based on regeneration. The presence of tooth remnants, as well as unintended harm to surrounding tissues, may have triggered repair mechanisms, thereby biasing the current data. How did the authors handle this issue? The oral cavity was under severe manipulation, increasing the inflammatory stimuli, a situation that does not take place in physiological exfoliation.

The authors indicated the use of microCT analysis; however, no such information appears in the main text. In fact, this manuscript lacks anatomical information. It is required to conduct histological examinations of the regenerated teeth at various time points.

Although the current findings confirm previously found and verified signaling pathways, the absence of functional data lends uniqueness to this work.

Reviewer #1 (Public review):

The paper by Gao et al. describes the effect of capsaicin on the NRF2/KEAP1 pathway. The authors carried out a set of in vitro and in vivo experiments that addressed the mechanisms of the protective effect of capsaicin on ethanol-induced cytotoxicity.

The authors conclude that capsaicin activates NRF2, which leads to the induction of cytoprotective genes, preventing oxidative damage. The paper shows that capsaicin may directly bind to KEAP1 and that it is a noncovalent modification of the Kelch domain.

The authors also designed new albumin-coated capsaicin nanoparticles, which were tested for the therapeutic effect in vivo.

Comments on latest version:

The manuscript has been substantially improved. I have no further comments.

Reviewer #2 (Public review):

Summary:

The paper by Gao et al. describes that capsaicin (CAP) might act as a novel NRF2 agonist capable of suppressing ethanol (EtOH)-induced oxidative damage in the gastric mucosa by disrupting the KEAP1-NRF2 interaction. Initially the authors established and validated a cell model for EtOH-induced oxidative stress which they used to experiment with different CAP concentrations and to determine changes in a variety of parameters such as cell morphology, ROS production, status of redox balance to mitochondrial function, amongst others.

The proposed mechanism by which CAP activates NRF2 and mitigates oxidative stress is thought to be via non-covalent binding to the Kelch-domain of KEAP1. A variety of assays such as BLI, CETSA, Pull-down, Co-IP, and HDX-MS were employed to investigate the KEAP1 binding behavior of CAP both in vitro and in GES1 cells. Consequently, the authors developed in vivo nanoparticles harboring CAP and tested those in a rat model. They found that pretreatment with the CAP-nanoparticles led to significant upregulation of NRF2 and subsequent effects on pro- (suppression of IL-1β, TNF-α, IL-6 and CXCL1) and anti-inflammatory (activation of IL-10) cyotkines pointing to a resolved state of inflammation and oxidative stress.

Strengths:

The work comprises a comprehensive approach with a variety of in vitro assays as well as cell culture experiments to investigate CAP binding behaviour to KEAP1. In addition, the authors employ in vivo validation by establishing an ethanol-induced acute gastric mucosal damage rat model and providing evidence of the potential therapeutic effect of CAP.

The study further provides novel insights into the mode of CAP action by elucidating the mechanism by which CAP promotes NRF2 expression and downstream antioxidant target gene activation.

The design of IR-Dye800 modified albumin-coated CAP nanoparticles for enhanced drug solubility and delivery efficiency demonstrates a valuable practical application of the research findings.

In summary the study's findings suggest that CAP could be a safe and novel NRF2 agonist with implications for the development of lead drugs for oxidative stress-related diseases. Collectively, the data support the significance and potential impact of CAP as a therapeutic agent for oxidative stress-related conditions.

Weaknesses:

While the study provides valuable insights into the molecular mechanisms and in vivo effects of CAP, further clinical studies are needed to validate its efficacy and safety in human subjects. The study primarily focuses on the acute effects of CAP on ethanol-induced gastric mucosa damage. Long-term studies are necessary to assess the sustained therapeutic effects and potential side effects of CAP treatment.

While the design of CAP nanoparticles is innovative, further research is needed to optimize the nanoparticle formulation for enhanced efficacy and targeted delivery to specific tissues.

Addressing these weaknesses through additional research and clinical trials can strengthen the validity and applicability of CAP as a therapeutic agent for oxidative stress-related conditions.

Reviewer #1 (Public review):

Summary:

The Drosophila wing disc is an epithelial tissue which study has provided many insights into the genetic regulation of organ patterning and growth. One fundamental aspect of wing development is the positioning of the wing primordia, which occurs at the confluence of two developmental boundaries, the anterior-posterior and the dorsal-ventral. The dorsal-ventral boundary is determined by the domain of expression of the gene apterous, which is set early in the development of the wing disc. For this reason, the regulation of apterous expression is a fundamental aspect of wing formation.

In this manuscript the authors used state of the art genomic engineering and a bottom-up approach to analyze the contribution of a 463 base pair fragment of apterous regulatory DNA. They find compelling evidence about the inner structure of this regulatory DNA and the upstream transcription factors that likely bind to this DNA to regulate apterous early expression in the Drosophila wing disc.

Strengths:

This manuscript has several strengths concerning both the experimental techniques used to address a problem of gene regulation and the relevance of the subject. To identify the mode of operation of the 463 bp enhancer, the authors use a balanced combination of different experimental approaches. First, they use bioinformatic analysis (sequence conservation and identification of transcription factors binding sites) to identify individual modules within the 463 bp enhancer. Second, they identify the functional modules through genetic analysis by generating Drosophila strains with individual deletions. Each deletion is characterized by looking at the resulting adult phenotype and also by monitoring apterous expression in the mutant wing discs. They then use a clever method to interfere in a more dynamic manner with the function of the enhancer, by directing the expression of catalytically inactive Cas9 to specific regions of this DNA. Finally, they recur to a more classical genetic approach to uncover the relevance of candidate transcription factors, some of them previously known and others suggested by the bioinformatic analysis of the 463 bp sequence. This workflow is clearly reflected in the manuscript, and constitutes a great example of how to proceed experimentally in the analysis of regulatory DNA.

Weaknesses:

The previously pointed weakness (vg expression, P compartment specific effects, early vs late analysis of ap expression in mutants) has been thoroughly and satisfactorily addressed by the authors.

Reviewer #3 (Public review):

In this manuscript, authors use the Drosophila wing as model system and combine state-of-the-art genetic engineering to identify and validate the molecular players mediating the activity of one of the cis-regulatory enhancers of the apterous gene involved in the regulation of its expression domain in the dorsal compartment of the wing primordium during larval development. The paper is subdivided into the following chapters/figures:

(1) In the first couple of figures, authors describe the methodology to genetically manipulate the apE enhancer (a cartoon summarizing all the previous work with this enhancer might help) and identify two well-conserved domains in the OR463 enhancer required for wing development (the m3 region whose deletion phenocopies OR463 deletion: loss of wing, and the m1 region, whose deletion gives rise to AP identify changes in the P compartment).

(2) In the following three figures, authors characterize the m1 regulatory region, identify HOX and ETS binding sites, functionally validate their role in wing development and the activity of the genes/proteins regulating their activity (eg-. Hth and Pointed) by their ability to phenocopy (when depleted) the m1 loss of function wing phenotype. Authors conclude that Hth and Pointed regulate apterous expression through the m1 region.

(3) In the last few figures, the authors perform similar experiments with the m3 regulatory region to conclude that the Grn and Antennapedia regulate apterous expression through the m3 enhancer.

Comments on revised version:

The authors have adequately addressed my major concerns.

Reviewer #1 (Public review):

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

Summary:

The manuscript "Adapting Clinical Chemistry Plasma as a Source for Liquid Biopsies" addresses a timely and practical question: whether residual plasma from heparin separator tubes can serve as a source of cfDNA for molecular profiling. This idea is attractive, since such samples are routinely generated in clinical chemistry labs and would represent a vast and accessible resource for liquid biopsy applications. The preliminary results are encouraging, and likely to benefit the research community.

Comments on previous revisions:

The concerns raised have been addressed. The heparin separator-based cfDNA method described in this study is likely to benefit the research community. I have no further scientific concerns.

Reviewer #2 (Public review):

Summary:

The authors propose that leftover heparin plasma can serve as a source for cfDNA extraction, which could then be used for downstream genomic analyses such as methylation profiling, CNV detection, metagenomics, and fragmentomics. While the study is potentially of interest, several major limitations reduce its impact; for example, the study does not adequately address key methodological concerns, particularly cfDNA degradation, sequencing depth limitations, statistical rigor, and the breadth of relevant applications.

Strengths:

The paper provides a cheap method to extract cfDNA, which has broad application if the method is solid.

Reviewer #2 (Public review):

Summary:

Marinescu et al. combine in vivo imaging with circuit-specific optogenetic manipulation to characterize the anatomic heterogeneity of the medial nucleus accumbens shell in the control of food intake. They demonstrate that the inhibitory influence of dopamine D1 receptor-expressing neurons of the medial shell on food intake decreases along a rostro-caudal gradient while both rostral and caudal subpopulations similarly control aversion. They then identify Stard5 and Peg10 as molecular markers of the rostral and caudal subregions, respectively. Through the development of a new mouse line expressing the flippase under the promoter of Stard5, they demonstrate that Stard5-positive neurons recapitulate the activity of D1-positive neurons of the rostral shell in response to food consumption and aversive stimuli.

Strengths:

This study brings important findings for the anatomical and functional characterization of the brain reward system and its implication in physiological and pathological feeding behavior. In the revision, the authors provided additional data that strengthen the specificity of their behavioral effects. It is a well-designed study, technically sound, with clear and reliable effects. The generation of the new Stard5-Flp line will be a valuable tool for further investigations. The paper is very well written, the discussion is very interesting, addresses limitations of the findings and proposes relevant future directions.

Weaknesses:

Identification and characterization of the activity of Stard5-positive neurons will require further characterization as this population encompasses both D1- and D2-positive neurons as well as interneurons. While they display a similar response pattern as D1-neurons, it remains to determine whether their manipulation would result in comparable behavioral outcomes.

令人惊讶的是，尽管新应用数量激增，苹果声称其应用审核团队能够在48小时内处理90%的提交，并且在过去12周内每周处理超过20万个应用提交，平均审核时间为1.5天。这表明苹果可能已经大幅扩展了其审核能力或提高了自动化程度以应对AI带来的应用激增。

Joint Public Review:

Summary:

This is an excellent, timely study investigating and characterizing the underlying neural activity that generates the neuroendocrine GnRH and LH surges that are responsible for triggering ovulation. Abundant evidence accumulated over the past 20 years implicated the population of kisspeptin neurons in the hypothalamic RP3V region (also referred to as the POA or AVPV/PeN kisspeptin neurons) as being involved in driving the GnRH surge in response to elevated estradiol (E2), also known as the estrogen positive feedback. However, while former studies used cfos coexpression as a marker of RP3V kisspeptin neuron activation at specific times and found that this correlates with the timing of the LH surge, detailed examination of the live in vivo activity of these neurons before, during, and after the LH surge, remained elusive due to technical challenges. In this exciting study, Zhou and colleagues use fiber photometry to measure the long-term synchronous activity of RP3V kisspeptin neurons across different stages of the mouse estrous cycle, including on proestrus when the LH surge occurs, as well as in a well-established OVX+E2 mouse model of the LH surge. For this they used kiss-Cre female mice that were injected with a Cre-dependent AAV injection containing GCaMP6, in order to measure the neuronal activation of RP3V Kiss1 cells.

The authors report that RP3V kisspeptin neuronal activity is low on estrous and diestrus, but increases on proestrus several hours before the late afternoon LH surge, mirroring prior reports of rising GnRH neuron activity in proestrus female mice. The measured increase in RP3V kisspeptin activation is long, spanning ~13 hours in proestrus females and extending well beyond the end of the LH secretion, and is shown by the authors to be E2 dependent. In addition, an intriguing cyclical oscillation in kisspeptin neural activity every 90 minutes exists, which may offer critical insight into how the RP3V kisspeptin system operates.

The compelling methodology allowed the authors to measure RP3V neuronal activation across multiple ovarian cycles in the same mouse, which demonstrated that the timing of the LH surge is variable across cycles, even within the same mouse. In addition, the authors demonstrated using the same females, that ovariectomy resulted in very little neuronal activity in RP3V kisspeptin neurons. When these ovariectomized females were treated with estradiol benzoate (EB) and an LH surge was induced, there was an increase in RP3V kisspeptin neuronal activation, as was seen during proestrus. However, the magnitude of the change in activity was greater during proestrus than during the EB-induced LH surge. Interestingly, the authors noted a consistent peak in activity about 90 minutes prior to lights out on each day of the ovarian cycle and during EB treatment, but not in ovariectomized females. The functional significance of this consistent neuronal activity at this time remains to be determined. In summary, the data from these experiments is compelling and supports the hypothesis in the field that the RP3V kisspeptin neurons regulate the LH surge.

Strengths:

- The study is well designed, uses proper controls and analyses, has robust data, and the paper is nicely organized and written.

- The study is well done and complete, looking at neuronal activation at each stage of the ovarian cycle and then additionally, how neuronal activation in ovariectomized and ovariectomized + EB females compares to that of gonad-intact females. Though not part of this study, the comparison of neuronal activation of GnRH neurons during the LH surge to the current data was convincing, demonstrating a similar pattern of increased activation that precedes the LH surge.

- The authors provide a technical advance for the field in the ability to accurately measure RP3V kisspeptin neuron activity in actively awake, live mice for long periods of time, spanning different cycle stages. This approach offers novel and useful insights into the impact of E2 and circadian cues on the electrical activity of RP3V kisspeptin neurons.

- The within-subjects design used in these experiments is a major strength because it allowed the authors to collect data across multiple ovarian cycles, following ovariectomy, and then with EB treatment. The variability in neuronal activity surrounding the LH surge across ovarian cycles in the same animals is interesting and could not be achieved without this within-subjects design.

- The inclusion and comparison of ovary-intact females and OVX+E2 female is valuable to help test mechanisms under these two valuable LH surge conditions, and allows for further future studies to tease apart minor differences in the LH surge pattern between these 2 conditions.

- The discovery of cyclical oscillation in RP3V kisspeptin neural activity every 90 minutes is intriguing and interesting, and may offer critical insight into how the RP3V kisspeptin system operates, which can be further tested in future studies.

Weaknesses:

- LH levels were not measured in many mice or in robust temporal detail, to allow a more detailed comparison between the fine-scale timing of RP3V neuron activation with onset and timing of LH surge dynamics. While the "peak LH" occurred 3.5 hours after the first RP3V kisspeptin neuron oscillation, it is likely that LH values start to increase several hours before the peak LH, closer to when the first RP3V kisspeptin neuron activity first occurs. Therefore, the onset of the LH surge is likely to be closer to the beginning of the RP3V kisspeptin activity, but future studies are needed to study this timing.

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

- The authors nicely show that there is some variation (~2 hours) in the peak of the first oscillation in cycling proestrus females. By contrast, the small sample size for OVX+E2 females did not permit a similar rigorous analysis of temporal variability under such estrogen-controlled conditions, which will need to be studied in future projects.

Comments on revisions:

The authors have revised the manuscript adequately. There are no further recommended edits or revisions.

Reviewer #2 (Public review):

Summary:

The manuscript entitled "Mitochondrial Protein FgDML1 Regulates DON Toxin Biosynthesis and Cyazofamid Sensitivity in Fusarium graminearum by affecting mitochondrial homeostasis" identified the regulatory effect of FgDML1 in DON toxin biosynthesis and sensitivity of Fusarium graminearum to cyazofamid. The manuscript provides a theoretical framework for understanding the regulatory mechanisms of DON toxin biosynthesis in F. graminearum and identifies potential molecular targets for Fusarium head blight control.

Comments on revised version:

I have no further comments on the revision.

Reviewer #1 (Public review):

Summary:

In this work the authors investigate the molecular dynamics of MinD, a component of the Bacillus subtilis Min system, in vitro and in vivo. In Escherichia coli the Min system is highly dynamic and displays rapid pole to pole oscillation whereby a time average minimum of the Min proteins at mid cell is established. However, in B. subtilis, this is not the case, and there is no MinE present. MinD in B. subtilis dynamically relocalizes from the poles to division sites, and binds to MinC and MinJ, which mediates its interaction with DivIVA. This paper reports biochemical characterization of B. subtilis MinD in vitro and dynamics of MinD variants in vivo, providing mechanistic insight into the mechanism of dynamic localization.

Strengths:

In the current study, the authors perform a detailed biochemical characterization of the in vitro ATPase activity of MinD and demonstrate that rapid hydrolysis is elicited by adding phospholipids. They further show using a collection of substitution mutants of MinD that both monomers and dimers bind to the membrane, and ATP occupancy changes the on and off rates. Identification, quantification, and tracking of discrete Halo-MinD populations was nicely done and showed that mutations in MinD alter dynamic localization, correlating with PL binding on and off rates in vitro.

- In the revised manuscript, the authors now demonstrate localization and tracking data for minC and minJ deletion strains, which suggest that MinJ impacts MinD membrane cycling, but MinC does not. Additional in vitro work showed that the PDZ domain of MinJ modifies MinD ATP hydrolysis rates, and the authors propose that MinJ may promote MinD dimer formation.

Weaknesses of the revised version: No major weaknesses.

Reviewer #2 (Public review):

Summary:

Feddersen & Bramkamp determined important characteristics of how MinD protein binds/dissociates to/from the membrane, and dimerizes in relation to its ATPase activity. The presented data clearly shows the differences in function of MinD homologs from B. subtilis and E. coli.

Strengths:

The work presents well-executed experiments that lead to interesting conclusions and a new model of how Min system works during B. subtilis mid-cell division. Importantly, this model is supported by in vitro characterization of well-chosen mutants in the functional domains of MinD. Outstandingly, most of the in vitro data are confirmed by single-molecule localization microscopy.

Weaknesses:

The authors immobilized liposomes, for which they used E. coli total lipids, to measure ATPase activity and liposome association and dissociation of B. subtilis MinD. For these experiments would be more suitable to use B. subtilis total lipids as more biologically relevant data could be gained.

Although the work is in detail and nicely compares the function of B. subtilis Min system with E. coli Min system, it lacks the comparison of the Min system function in other rod-shaped Gram-positive bacteria. I would suggest including in the Discussion the complexity of other Min systems. Especially, this complexity is seen in other rod-shaped and spore formers such as Clostridial species in which one of these Min systems or both are present, an oscillating E. coli Min system type and more static as in B. subtilis.

Comments on revisions:

I'm satisfied with the authors response to my private recommendation points. However, I thought that they would also respond to my points mentioned in Public Review part, weaknesses as shown above and update the revised version accordingly.

Reviewer #1 (Public review):

Summary:

This study presents a technically sophisticated intravital two-photon calcium imaging approach to characterize meningeal macrophage Ca²⁺ dynamics in awake mice. The development of a Pf4Cre:GCaMP6s reporter line and the integration of event-based Ca²⁺ analysis represent clear methodological strengths. The findings reveal niche-specific Ca²⁺ signaling patterns and heterogeneous macrophage responses to cortical spreading depolarization (CSD), with potential relevance to migraine and neuroinflammatory conditions. Despite these strengths, several conceptual, technical, and interpretational issues limit the impact and mechanistic depth of the study. Addressing the points below would substantially strengthen the manuscript.

Strengths:

The use of chronic two-photon Ca²⁺ imaging in awake, behaving mice represents a major technical strength, minimizing confounds introduced by anesthesia. The development of a Pf4Cre:GCaMP6s reporter line, combined with high-resolution intravital imaging, enables long-term and subcellular analysis of macrophage Ca²⁺ dynamics in the meninges.

The comparison between perivascular and non-perivascular macrophages reveals clear niche-dependent differences in Ca²⁺ signaling properties. The identification of macrophage Ca²⁺ activity temporally coupled to dural vasomotion is particularly intriguing and highlights a potential macrophage-vascular functional unit in the dura.

By linking macrophage Ca²⁺ responses to CSD and implicating CGRP/RAMP1 signaling in a subset of these responses, the study connects meningeal macrophage activity to clinically relevant neuroimmune pathways involved in migraine and other neurological disorders.

Weaknesses:

The manuscript relies heavily on Pf4Cre-driven GCaMP6s expression to selectively image meningeal macrophages. Although prior studies are cited to support Pf4 specificity, Pf4 is not an exclusively macrophage-restricted marker, and developmental recombination cannot be excluded. The authors should provide direct validation of reporter specificity in the adult meninges (e.g., co-labeling with established macrophage markers and exclusion of other Pf4-expressing lineages). At minimum, the limitations of Pf4Cre-based labeling should be discussed more explicitly, particularly regarding how off-target expression might affect Ca²⁺ signal interpretation.

The manuscript offers an extensive characterization of Ca²⁺ event features (frequency spectra, propagation patterns, synchrony), but the biological significance of these signals is largely speculative. There is no direct link established between Ca²⁺ activity patterns and macrophage function (e.g., activation state, motility, cytokine release, or interaction with other meningeal components). The discussion frequently implies functional specialization based on Ca²⁺ dynamics without experimental validation. To strengthen the conceptual impact, a clearer framing of the study as a foundational descriptive resource, rather than a functional dissection, would improve alignment between data and conclusions.

The GLM analysis revealing coupling between dural perivascular macrophage Ca²⁺ activity and vasomotion is technically sophisticated and intriguing. However, the directionality of this relationship remains unresolved. The current data do not distinguish whether macrophages actively regulate vasomotion, respond to mechanical or hemodynamic changes, or are co-modulated by neural activity. Statements suggesting that macrophages may "mediate" vasomotion are therefore premature. The authors should reframe these conclusions more cautiously, emphasizing correlation rather than causation, and expand the discussion to explicitly outline experimental strategies required to establish causality (e.g., macrophage-specific Ca²⁺ manipulation).

The authors conclude that synchronous Ca²⁺ events across macrophages are driven by extrinsic signals rather than intercellular communication, based primarily on distance-time analyses. This conclusion is not sufficiently supported, as spatial independence alone does not exclude paracrine signaling, vascular cues, or network-level coordination. No perturbation experiments are presented to test alternative mechanisms. The authors can either provide additional experimental evidence or rephrase the conclusion to acknowledge that the source of synchrony remains unresolved.

A major and potentially important finding is that the dominant macrophage response to CSD is a persistent decrease in Ca²⁺ activity, which is independent of CGRP/RAMP1 signaling. However, this phenomenon is not mechanistically explored. It remains unclear whether Ca²⁺ suppression reflects macrophage inhibition, altered viability, homeostatic resetting, or an anti-inflammatory program. Minimally, the discussion should be more deeply engaged with possible interpretations and implications of this finding.

The pharmacological blockade of RAMP1 supports a role for CGRP signaling in persistent Ca²⁺ increases after CSD, but the experiments are based on a relatively small number of cells and animals. The limited sample size constrains confidence in the generality of the conclusions. Pharmacological inhibition alone does not establish cell-autonomous effects in macrophages. The authors should acknowledge these limitations more explicitly and avoid overextension of the conclusions.

Comments on revisions:

The authors have answered the questions well.

Reviewer #2 (Public review):

Using chronic intravital two-photon imaging of calcium dynamics in meningeal macrophages in Pf4Cre:TIGRE2.0-GCaMP6 mice, the study identified heterogeneous features of perivascular and non-perivascular meningeal macrophages at steady state and in response to cortical spreading depolarization (CSD). Analyses of calcium dynamics and blood vessels revealed a subpopulation of perivascular meningeal macrophages whose activity is coupled to behaviorally driven diameter fluctuations of their associated vessels. The analyses also investigated synchrony between different macrophage populations and revealed a role for CGRP/RAMP1 signaling in the CSD-induced increase, but not the decrease, in calcium transients.

This is a timely study at both the technical and conceptual levels, examining calcium dynamics of meningeal macrophages in vivo. The conclusions are well supported by the findings and will provide an important foundation for future research on immune cell dynamics within meninges in vivo. The paper is well written and clearly presented.

Reviewer #3 (Public review):

Summary:

The authors of this report wish to show that distinct populations of meningeal macrophages respond to cortical spreading depolarization (CSD) via unique calcium activity patterns depending on their location in the meningeal sub compartments. Perivascular macrophages display calcium signaling properties that are sometimes in opposition to non-perivascular macrophages. Many of the meningeal macrophages also displayed synchronous activity at variable distances from one another. Other macrophages were found to display calcium signals in response to dural vasomotion. CSD could induce variable calcium responses in both perivascular and non-perivascular macrophages in the meninges in part due to RAMP1 dependent effects. Results will inform future research on the calcium responses displayed by macrophages in the meninges under both normal and pathological conditions.

Strengths:

Sophisticated in vivo imaging of meningeal immune cells is employed in the study which has not been performed previously. A detailed analysis of the distinct calcium dynamics in various subtypes of meningeal macrophages is provided. Functional relevance of the responses are also noted in relation to CSD events.

Weaknesses:

Specificity of the methods used to target both meningeal macrophages and RAMP1 are limited. A discussion section on potential pitfalls is included to address this.

Reviewer #1 (Public review):

Processing in the primary visual cortex (V1) of mice is not only based on sensory inputs but also strongly modulated by locomotion. In this study, Meier et al. ask whether neurons that are modulated by locomotion form clusters in V1. Their work is based on previous studies from their lab establishing a modularity in the organization of primary visual cortex based on M2-muscarinic-acetylcholine-receptor-positive patches and interpatches (Ji et al. 2015, D'Souza et al. 2019). In these studies, they have highlighted the clustering of specific visual pathways and inhibition. In the current study, they extend this modularity to motor inputs, confirming a clustering of locomotion modulated neurons but also show that these clusters overlap with the M2-negative interpatches of layer 1. Finally, they establish a blueprint for visual processing streams in V1, segregating projections to and from lateral visual areas (LM, AL, and RL) from projections to and from the lateral areas, including the visual area PM, the retrosplenial cortex (RSP), and the secondary motor area (MOs).

Conceptually, this study provides an important finding in the organization of locomotion-related signaling in primary visual cortex, which clearly has substantial implications for sensory processing in visual cortex. While the anatomical data are solid, the link to physiology is incomplete. In conclusion, there are numerous issues that leave the main findings in some doubt, so the authors have some work to do before I find this story convincing.

Major issues:

(1) The major results in this study rely on proper quantification of neuronal responses during resting and running. Recently, it has been reported that hemodynamic occlusion can strongly influence measurements of fluorescent changes using two-photon imaging (Yogesh et al. 2025, doi.org/10.1101/2024.10.29.620650). Since it is unclear whether there is an inherent bias in vasculature and hemodynamic occlusion in M2 patches and interpatches, a quantification of the effect of hemodynamic occlusion would be necessary. This control would ideally be done using mice with GFP expression to test if there is still a clustering of locomotion-modulated neurons that overlaps with M2-negative interpatches. Alternatively, the authors should at the very least quantify the vascularization in M2 patches and interpatches.

(2) To assess the effects, the authors use a correlation analysis for many of their findings (e.g., Figures 2b,c, 4j,k, ...). This, however, is inappropriate to assess the significance of the results. I suggest redoing all statistics with hierarchical bootstrap sampling (Saravanan et al. 2020, PMID: 33644783) or similar.

(3) The authors use two different measures to assess whether and to what extent a neuron is locomotion sensitive, the LMI and "locomotion-responsive". While the LMI is defined based on recording in the light and dark (Figure 2), the "locomotion-responsiveness" is defined only in the dark (Figure 3a,c,d). The link between the two measures should be clarified.

a) Additionally, Figure 2b shows higher average LMI for interpatches, but the locomotion-responsive fraction is similar in interpatches and patches (relative number of pairs in Figure 3c and Figure 3d). How do the authors explain this discrepancy?

b) How is the LMI calculated - based on the average or the maximum response over stimuli? One particular stimulus? If the LMI is defined for each stimulus separately, what is plotted in Figure 2b?

(4) In the last panels of Figures 4-7, the authors analyze the alignment of cell bodies with the M2 patches. While in superficial layers it might be straightforward to align the cell body locations with the M2 patches and interpatches in layer 1, this alignment does not appear to be trivial for deeper layers. The authors should provide additional material to convince the reader of the proper alignment.

(5) Related to point 4 above - Given the importance of a proper alignment of M2 patches with the in vivo imaging, the in vivo - ex vivo alignment should be more convincing than Figure 1 C-E. Measuring M2 patches in vivo (as the authors have tried to do) would have provided more solid evidence. Have the authors tried to remove the dura for their in vivo imaging to increase signal-to-noise? In any case, more examples of proper alignment are necessary.

(6) The authors state that locomotion selectively affects M2-/M2- pairs based on Figure 3c. However, to make this claim, there should be a significant difference between the correlation of stimulus-driven noise of M2-/M2- locomotion-responsive pairs and M2-/M2- locomotion-unresponsive pairs, AND no significant difference in the same analysis for M2+/M2+ pairs (i.e., testing the differences between the bars in Figure 3c and Figure 3d).

Reviewer #2 (Public review):

Summary:

Meier et al. explore the variability of locomotion-related modulations in mouse area V1. They present 4 major findings: V1 L2/3 neurons beneath M2- interpatches are more strongly locomotion-modulated than those beneath M2+ patches, while V1 L2/3 neurons are more strongly orientation tuned. They then use viral tracing to examine the relationship of M2- interpatches and M2+ patches with inputs from and outputs to HVOs, MO, RSP, and LP, and find evidence for different closed-loop subnetworks within L1; these relationships, however, are more complicated for cell bodies in L2/3. Finally, they also describe an overlap between M2- interpatches and SOM+ dendrites/axons.

Strengths:

The strength of the manuscript is the detailed anatomical quantification of closed-loop connectivity, and the description of the organizing principles of M2- interpatches and M2+ patches.

Weaknesses:

The major weakness of the manuscript is the lack of a direct connection between the functional and the anatomical data, and the somewhat puzzling effects observed in the analysis of noise correlations. The former issue might be alleviated by modelling, where the authors could explore the space of possibilities that could explain the functional data based on the anatomical connectivity. Some control analyses could be done, for the comparison of noise correlations.

Reviewer #3 (Public review):

The authors build on the large body of their previous research, which showed that the mouse primary visual cortex is organised into two types of clusters, M2+ and M2-, which exhibit distinct input patterns from thalamus and higher visual cortical areas and distinct visual tuning preferences. The current study reveals that a like-to-like projection from within-cluster neurons to the areas that provide feedback projections and, furthermore, that neurons in the M2- clusters are more strongly affected by non-visual signals about the locomotion of the animal.

The study adds fundamental insights to our understanding of the principles of cortical organisation and computation, specifically how the cortex integrates sensory and action-related signals.

While the tracing data are very convincing, data analysis should be strengthened to support the claims:

(1) The locomotion modulation index (LMI) compares the mean activity during running and not running but does not seem to account for differences between visual stimuli, so that the LMI could be influenced by the neuron's visual tuning rather than its sensitivity to locomotion, e.g. if the mouse was running more when the neuron's preferred stimulus was presented. Trials should first be averaged per stimulus, and then across stimuli. Alternatively, only the preferred stimulus could be considered.

The significance test (unpaired t-test) suffers from the same flaw. Instead an ANOVA (with stimulus parameter as factor) would resolve the problem, or testing whether fitting the data with two tuning curves (one per locomotion state) or a single curve results in a lower error (using cross-validation).

Given that there is evidence that specific visual stimuli can induce more or less running in mice, this issue is very important to account for behavioural differences across stimuli.

(2) All bars in Figure 2b show a lower LMI than the reported mean LMI of 0.19. This should be checked.

(3) Correlation tests: Pearson correlation is only meaningful when applied to continuous data. A more suitable test for discrete data like the M2 patch quantile is a rank test like Kendall's coefficient of rank correlation. This applies to data in Figure 2b,c, 4j,k, Figure 2 - Supplement 2,1a, etc.

(4) How OSI was determined should be clarified. Specifically, were R_pref and R_ortho the mean responses to the two opposite movement directions? Similarly, how was the half-width at half-maximum of orientation determined? From the fits in Figure 2a, it looks like the widths of both Gaussians can be different.

(5) The correlation measures in Figure 3 would greatly benefit from additional analyses to help interpretation of the results.

a) Correlations between neurons typically increase with increasing firing rates (e.g., de la Rocha J, Doiron B, Shea-Brown E, Josić K, Reyes A. 2007. Correlation between neural spike trains increases with firing rate. Nature 448:802-6. doi:10.1038/nature06028). Could the higher correlations in M2+ pairs (Figure 3a) be explained by higher firing rates in M2+ compared to M2- neurons?

b) To determine correlations in Figure 3a, trials during locomotion and stationarity were pooled. As locomotion impacts the firing rate of the neurons, it would be helpful to separate correlations between the two states, locomotion vs stationarity, so the measures reflect something closer to "noise correlations" rather than tuning to locomotion.

c) Similarly, in Figure 3b, I wonder whether the large correlations in M2- pairs are driven by locomotion rather than functional connectivity. As suggested in b, a better test of noise correlations would be to account for locomotion, i.e., separate trials by stimulus identity and locomotion state. To prevent conditions with few trials from having greater weight in the overall noise correlations, I suggest the authors first z-score responses per condition, then determine noise correlations across all trials (as explained in Renart et al., 2010).

d) Correlations in Figure 3a,b should be tested with an ANOVA and a control for multiple tests.

(6) In plots like Figure 4j-l, it would be very informative to show individual measures (per ROI and mouse) in addition to mean +- SEM. As the counts are low (<10) it wouldn't obstruct the plot.

(7) The caption of Figure 4l says that most retrogradely labelled cells are located in L2/3. However, the plot only shows data from L2/3 and a single section of L4, so one cannot compare it to other layers. Can the authors corroborate the claim with data from other layers?

(8) Methods:<br /> The authors should provide more details on the visual stimuli: What was the background on which gratings were presented? How long was the inter-stimulus interval? What was presented during the inter-stimulus interval? How large were gratings used to map tuning to SF, TF, and orientation?

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate mechanisms of acquired resistance (AR) to KRAS-G12C inhibitors (sotorasib) in NSCLC, proposing that resistance arises from signaling rewiring rather than additional mutations.

Strengths:

Using a panel of AR models-including cell lines, PDXs, CDXs, and PDXOs-they report activation of KRAS and PI3K/AKT/mTOR pathways, with elevated PI3K levels. Pharmacologic inhibition or CRISPR-Cas9 knockout of PI3K partially restores sotorasib sensitivity, and p-4EBP1 upregulation is implicated as an additional contributor, with dual mTORC1/2 inhibition more effective than mTORC1 inhibition alone.

Weaknesses:

While the study addresses an important clinical question, it is limited by several weaknesses in experimental rigor, data interpretation, and presentation. The mechanistic findings are not entirely novel, since the role of PI3K-AKT-mTOR signaling in therapeutic resistance is already well-established in the literature. Several key conclusions are not entirely supported by the data. Furthermore, while the authors use CRISPR-Cas9 to knock out PI3K and 4E-BP1 in H23-AR and H358-AR cells to restore sotorasib sensitivity, they do not perform reconstitution experiments to confirm that re-expressing PI3K or 4E-BP1 reverses the sensitization. This prevents full characterization of PI3K and p-4EBP1 upregulation as contributors to resistance.

Comments on revised version:

The authors have addressed some but not all of my concerns and suggestions. The authors do acknowledge some of the limitations. It would be useful to include a limitations paragraph in the Discussion.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors focus on the identification of the mechanisms involved in the acquired resistance to Sotorasib in non-small lung KRASG12C mutant cells. To perform this study, the authors generate different clones of cell lines, cell-derived xenografts, patient-derived xenograft organoids and patient-derived xenografts. In all these models, the authors generate resistant forms (i.e., resistant cell lines PDXs and organoids) and the genetic and molecular changes were characterised using whole-exome sequencing, proteomics and phospho-proteomics. This analysis led to the identification of an important role of the PI3K/AKT/mTORC1/2 signalling network in the acquisition of resistance in several of the models tested. Molecular characterisation identified changes in the expression of some of the proteins in this network as key changes for the acquisition of resistance, and in particular, the authors show that changes in 4E-BP1 are common to some of the cells downstream of PI3K. Using pharmacological testing, they show that different drugs targeting PI3K, AKT and MTORC1/2 sensitise some of the resistant models to Sotorasib. The analyses showed that the PI3K inhibitor copanlisib has an effect in NSCLC cells that, in some cases, seems to be synergistic with Sotorasib. Based on the work performed, the authors conclude that the PI3K/mTORC1/2 mediated 4E-BP1 phosphorylation is one of the mechanisms associated with the acquisition of resistance to Sotorasib and that targeting this signalling module could result in effective treatments for NSCLC patients.

The work as presented in the reviewed manuscript is still very interesting, provides cell models that benefit the community, and can be used to expand our knowledge of the mechanism of resistance to KRAS targeting therapies. Some changes suggested by reviewer 1 and this reviewer have been made to the text, including changes to text and figures, including quantification of some blots. But for most of it, this version is very similar to the first submission and many of the weaknesses and suggestions I made remain the same.

Strengths:

- One of the stronger contributions of this article is the different models used to study the acquisition of resistance to Sotorasib. The resistant cell lines, PDXs and PDXOs and the fact that the authors have different clones for each, made this collection especially relevant as they seem to show different mechanisms that the cells used to become resistant to Sotorasib. Although logically, the authors focus on one of these mechanisms, the differential responses of the different clones and models to the treatments used in this work show that some of the clones used additional mechanisms of resistance that can be explored in other studies. Importantly, as they use in vitro and in vivo models, the results also consider the tumour microenvironment and other factors in the response to the treatments.

- Another strength is the molecular characterisation of the different Sotorasib-resistant tumour cells by WES, which shows that these cells do not seem to acquire secondary mutations.

- The use of MS-based proteomics also identifies proteome signatures that are associated with the acquisition of resistance, including PI3K/mTORC1/2. The combination of proteomics and phospho-proteomics results should allow the identification of several mechanisms that are deregulated in Sotorasib-resistant cells

- The results show a strong response of the NSCLC cells and PDXs to copanlisib, a drug for which there is limited information in this cancer type.

- The way they develop the PDX-resistant and the PDXO seems to be appropriate.

- The revised manuscript includes the information for the whole exosome sequence, making the finding clearer for the reader.

Weaknesses:

In general, the data is of good quality, but due to the sheer amount of data included and the way it is presented and discussed, several of the claims or conclusions are not clear.

- The abstract is mainly the same, and the authors only indicate that they will update it.

- The tables with the proteomics data are still not included, and again, there is only a comment from the authors that it will be made available. Thus, the way the data is presented in Figure 3 still does not allow the reader to get an idea of many of the findings from this experiment.

- In Figure 3, the authors indicate that the raw data will be included in the revised version, which should improve the understanding of the reader, but this is not included yet. As in the previous version, the MS-based Phosphoproteome is still not really presented in the current manuscript.

- The authors still do not specify where the proteomics data will be deposited, and whether it will be made public to comply with FAIR principles. They indicate that they will comply with the journal requests, but it is still not clear what will be deposited.

- The experiments in Figure 4 are very confusing, and some controls are missing. There is no blot where they show the effect of Sotorasib treatment in H23 and

- The authors do not address the important point made in the previous review about the effect of copanlisib in parental cells. I might not have been clear, so the data in Figure 4D-F seem to support that PI3K treatment of parental cells is as effective as in the resistant cells. Therefore, it is not clear whether the effect shown in the resistant cells is related to the acquisition of resistance to sotorasib or if these cells are simply sensitive to the drug because the parental cells were already sensitive.

Reviewer #1 (Public review):

Summary:

This carefully executed study uncovers the functional relevance of curl signals that impinge on the retina every time an observer's gaze direction and movement direction are not aligned.

Strengths:

This finding is important, highlighting the functional role of an abundant incidental signal (curl in retinal motion) that has thus far believed to be a nuisance that needs to be filtered out of the retinal motion stream.

The study's evidence is compelling: a combination of psychophysical experiments and critical manipulations, control theory and neural modeling, which together make an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction.

This study uncovers the functional relevance of curl signals that occur on the retina when an observer is moving, and gaze is not straight ahead. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Another clear strength is that the study uses tightly controlled experimental manipulation to provide strong test cases for the hypothesis that curl is used for visual navigation. These conditions are important to constrain the proposed model (and future models) of heading control.

The modeling is very clearly described, and the modeling and analysis code is published and freely available. The authors go beyond a back-of-the-envelope control model and show how it might be implemented at the neural-circuit level. The model is biologically plausible.

Weaknesses:

The discussion would benefit from an extension of the implications of the study and predictions of their model.

Reviewer #2 (Public review):

This study examines how curl in the retinal flow field can be used as a control variable for estimating and controlling the heading of a moving observer. The basic idea (which is not entirely new, see Matthis et al. 2022) is that translation along a path with eccentric gaze (meaning that the subject is not heading toward the point they are looking at) produces a pattern of optic flow on the retina with a rotational component around the point of fixation (which can be captured by the mathematical "curl" operator). The sign and magnitude of retinal curl vary with heading relative to the point of fixation, such that curl can be used as a control variable to steer rightward or leftward to move toward the fixated target. The authors perform behavioral experiments and show that there are biases in perceived heading that seem to be largely governed by retinal curl. They also show that a simple controller model can use curl to steer toward a target, and they provide a neural network model that provides a biologically plausible implementation of the controller (although there are some questions about that).

There is a core of interesting work here that I think can be important to the field. However, there is a lack of clarity on several important fronts, including design of the behavioral experiments, presentation of the behavioral data, conceptual framing of what curl can and cannot do, etc. Equally importantly, the manuscript is not written in a manner that will make it accessible to most vision scientists. I consider myself to be pretty knowledgeable about optic flow, and I had to read most of the manuscript 3 or 4 times to be able to understand the bulk of it. And my experience is that most vision scientists do not understand optic flow well, so I fear that most of the readers that the authors should want to reach would struggle to understand the work. As written, this is mainly going to make an impact on a handful of optic flow gurus. Thus, I consider that this manuscript will need a major overhaul to clarify important issues and make it more accessible.

Major issues:

(1) The manuscript contains inconsistent, if not misleading, messaging about what information retinal curl does, and does not, provide regarding heading estimation. In the Abstract, the authors state: "We propose an alternative: the visual system utilizes retinal curl directly to estimate heading, rendering the explicit recovery of the FOE unnecessary." Based on my understanding of the rest of the manuscript, I find this statement to be a misrepresentation for two main reasons:

a) To "directly estimate heading" relative to what? When not qualified, most people interpret "heading" to mean an observer's heading relative to the world (or some allocentric reference frame). But retinal curl only gives information about an observer's heading relative to the point on which their eyes are fixated. Moreover, that point of fixation will change every few hundred milliseconds in natural viewing, so the retinal curl will change with each new fixation even as heading relative to the world remains unchanged. So I think most readers would grossly misinterpret the claim that retinal curl can be used "directly to estimate heading". Indeed, in the authors' controller model, the initial heading needs to be given, and then the controller can work. But from where does the visual system get the initial heading, since it does not come from curl? These issues are left hanging. Thus, while curl can provide a very useful input for steering toward a fixated target, other signals are needed to estimate heading relative to the world. This has to be made much clearer early on, and a conceptual schematic diagram might help. Also, the authors generally do not specify the reference frame of the variables they are talking about, leaving lots of room for misinterpretations. It should be clear each time they are talking about a variable, such as heading, whether it is relative to the fixation target, body, world, etc.

b) It seems to me that retinal curl will depend on other variables, in addition to heading relative to the fixation target. For example, it seems to me that the magnitude of retinal curl will depend on self-motion speed, the depth structure of the scene, the angle of elevation of the fixated target, and perhaps others. This is not discussed at all, and many readers would get the misguided impression that there is a 1:1 mapping from curl to heading (relative to fixation). If I am right that this is not correct, it means that retinal curl can tell the observer whether to steer right or left to move toward the fixated target, but it cannot tell them how much to steer. Indeed, in the authors' controller model, there is a free parameter that calibrates curl to angle. It makes sense that this works to fit trajectory data that are given from a fixed environment, but it is unclear how the brain would use retinal curl to control steering when these other variables are uncertain or changing unpredictably. Moreover, how does the system change the mapping from curl to steering command as the location of fixation changes relative to the current heading? These are issues that need to be brought up in framing the problem and discussed at some length. If the authors can show mathematically that retinal curl is only dependent on heading (relative to fixation) and not any of these other variables, it would be very valuable to show the equations for this relationship.

(2) The description of the behavioral experiment and presentation of behavioral data leaves a lot to be desired.

a) First, it is stated (line 158) that "Participants continuously reported their perceived direction of self-motion while maintaining fixation on the yellow dot." Again, the reference frame is completely unspecified. Participants were reporting their perceived heading relative to what? The fixation target? The world? What exactly were the instructions given to the subjects to perform the task? Based on the description of how perceived paths are computed (line 166-), it seems to be presumed that subjects are reporting their heading relative to the world because those angles are then converted into x and z coordinates in what I presume is a world-centered reference frame. But how do we know that subjects are accurately reporting their heading relative to the world? What if they are biased in their reports by the location of the fixation target relative to the scene, or by some other reference signal? Is it possible for the authors to rule out the possibility that perceptual biases seen in the unaltered curl condition result from observers not fully adopting the assumed reference frame of the task? If this cannot be firmly excluded, it seems to create problems for the rest of the study.

b) I also feel that there is a mismatch between what the behavioral task requires and what the controller model does. Subjects are apparently asked to report their heading relative to the world, but the controller model only controls their heading relative to the point that they are fixating. I understand how this is resolved in the model, but I think this type of distinction is buried and will not be apparent to most readers. Again, the reference frames of what is being measured and controlled need to be specified explicitly in all parts of the paper, and the authors need to explain how the system would combine curl-based control with some other measures of (at least initial) heading for world-centered heading to be computed. All of the assumptions need to be clearly specified.

c) In addition, I found it frustrating that the authors never present raw perceptual data from the observers. Rather, in Figure 2, we see reconstructed trajectories that are perfectly smooth with no indications of noise whatsoever. Since these paths are computed from the perceptual reports, there must be some noise inherent in them. The figures should represent this uncertainty somehow, and it should be explained how these perfectly smooth trajectories are obtained.

(3) "...the magnitude of retinal curl in the fovea can specify the body trajectory relative to gaze (Matthis et al., 2022)." The main idea put forward by the authors here seems to overlap heavily with this statement that they attribute to Matthis et al. 2022. While I think this paper still adds importantly to the topic, the authors do not discuss how their findings are different from those of Matthis et al. 2022, why they are an important extension, etc. Readers should not have to go read this other paper to have any idea how the present findings are placed in importance relative to the literature.

(4) The analysis and treatment of eye movements is extremely weak. The authors discarded trials for which gaze deviated from the fixation point by more than 3 degrees (which is a LOT given that the eye speeds are generally in the neighborhood of 0.5 deg/sec), and they provide basic stats on the distribution of positions. But this largely misses the point: it is not small position errors that are likely to matter, but rather velocity errors. Even a small amount of retinal slip of the target while it is being pursued will cause image motion that is going to alter the optic flow field around the fixation target. So, for example, the retinal curl field may no longer be centered on the fixation target. How do we know that some of the perceptual biases are not influenced by image motion resulting from imperfect tracking of the fixation target? This needs to be analyzed and discussed.

(5) I found the sections of text comparing the separate and joined fits (starting line 287) to be a bit too rosy. The authors show the separate fits in the main text, and it is not very surprising that these fits are good, given that the model has 30 parameters, and these data are pretty low-dimensional. The authors only show the joined fits in the supplement, and they say that they are almost as good as the separate fits (indeed, they are better in a model comparison sense, but this is 30 parameters vs. 2 parameters). However, when I look at the fits of the joined model in the supplement, I don't find them to be very impressive. In particular, the model grossly misses the data for the straight paths for several subjects (e.g., id5, id6, id8, id10). And fitting the straight paths would presumably be easiest. This implies that the joined model is really missing something and that fitting the curved paths interacts strongly with fitting the data for different fixation target locations on the straight path. I think that the authors should discuss the results a bit more soberly and tone down their conclusions here.

(6) The section of the paper on neural simulations (starting line 387) has a few weaknesses. First, why are only straight paths simulated here? This does not seem to provide a very rigorous test of the model. Second, it is awkward that the simulation results are presented in units of pixels, rather than degrees. Third, the authors seem to downplay the fact that the neural estimates of heading seem to oscillate rather wildly (over a range of hundreds of pixels, whatever that means, see especially Figure S16). It was far from clear to me how an estimate of heading with these large oscillations is useful. It would seem to require that heading estimates are integrated over substantial lengths of time to be reliable. It was therefore unclear how the model produces such smooth paths from these oscillating estimates.

Reviewer #3 (Public review):

Summary:

This manuscript uses a novel paradigm to demonstrate that rotational motion patterns in the retinal image, called curl, directly influence perception of heading direction. This means that it is not necessary to recover the focus of expansion, defined by the point of zero motion when moving along a straight trajectory toward a target, as is commonly thought.

Strengths:

It has long been accepted that the focus of expansion of the optic flow field generated by self-motion is used to guide heading direction. While there have been many challenges to the need to recover the focus of expansion when gaze is not in the direction of travel, it is still not well understood how retinal motion patterns contribute to heading perception. Recent work has demonstrated the complexity of the retinal motion patterns during natural walking, where body motion adds a rotational component. A rotational component also results from curved paths as well as gaze off the direction of travel. This rotational component is called curl. The primary contribution of this manuscript is to demonstrate convincingly that curl influences perception of heading, and that it is not necessary to recover the focus of expansion.

A strength of the manuscript is that realistic retinal motion patterns are generated by recording the image sequences generated by a walker in a virtual environment, and then using those patterns as stimuli in the experiment. This allows the creation of the more complex flow patterns that are a consequence of the bob and sway of natural walking, which are often considered a minor factor. The elegant experimental design allows direct manipulation of the curl signal, and this in turn directly influences measured heading perception. Another strength is that the authors ground their findings in control theory and neural computations, using a model that produces human-like path trajectories.

The study is timely, given the long history of this question, together with the growing understanding of the complexity of naturally generated retinal motion and the absence of direct evidence for the way that these motion patterns are used in heading perception. It adds an important piece of evidence for how retina-centered optic flow may be used by the visual system, which is critical for our understanding of motion processing in the brain.

Weaknesses:

The primary limitation of the paper is that it avoids discussion of some of the inevitable complexities of heading perception. The main issue is what exactly is meant by heading. Different behaviors evolve over different timescales. The geometry of retinal motion defines instantaneous heading, which varies widely through the gait cycle. Time-varying information like this is known to be important in the momentary control of balance. Heading can also be thought of as steering the body toward a distant goal, which evolves over longer timescales. The current manuscript appears to be concerned with heading information integrated over a few seconds and seems to provide evidence that heading is indeed integrated over the gait cycle. The issue of the time scale of the computation is touched on, but it is not related to how it might be used in normal walking or what situations it might apply to. Steering toward a distant goal during walking is not a very difficult problem and may not require evaluation of retinal motion, but control of balance is more challenging and may depend critically on curl. Consequently, the timescale of the computation needs to be considered in order to understand what is meant by heading.

Reviewer #1 (Public review):

Summary:

The study is technically extensive and employs a wide range of experimental approaches, including in vivo analyses, cell-based assays, and transcriptomic data integration. The authors provide a detailed characterization of inflammatory and stress-related pathways activated following IMQ exposure in mouse skin. These datasets may be informative for researchers specifically interested in IMQ-induced dermatitis or in stress responses triggered by chemical skin irritants.

Strengths:

The study is technically extensive and employs a wide range of experimental approaches, including in vivo analyses, cell-based assays, and transcriptomic data integration. The authors provide a detailed characterization of inflammatory and stress-related pathways activated following IMQ exposure in mouse skin. These datasets may be informative for researchers specifically interested in IMQ-induced dermatitis or in stress responses triggered by chemical skin irritants.

Weaknesses:

A major limitation of the manuscript is its exclusive reliance on the IMQ model, which does not adequately represent the immunological drivers, cellular interactions, or therapeutic responsiveness of human psoriasis, despite the manuscript's framing. IMQ-induced inflammation is dominated by innate immune activation and mouse-specific pathways, whereas human psoriasis is driven primarily by IL-23/IL-17-mediated interactions between keratinocytes and Th17/Tc17 cells. As a result, conclusions drawn entirely from IMQ-based experiments have limited relevance to human disease biology.

Consistent with this issue, the manuscript places strong emphasis on pathways such as TLR signaling, inflammasome activation, and IL-1-associated responses, none of which are established as central drivers of plaque psoriasis in patients. Therapeutic strategies targeting these pathways have failed to achieve clinical efficacy comparable to IL-23 or IL-17 blockade, yet this translational gap is not adequately addressed.

The in vitro keratinocyte experiments further limit interpretability. Stimulation of keratinocytes with IMQ is not an accepted model of psoriasis-relevant keratinocyte activation, and the study does not demonstrate induction of well-established psoriasis signature gene programs. Without this validation, it is difficult to assess the relevance of the observed cellular stress responses to human disease.

The RNA-sequencing analyses raise additional concerns regarding rationale and interpretation. The basis for selecting specific mouse and human datasets is unclear, including the use of unpublished or non-psoriasis inflammatory datasets. Key methodological details related to data processing, normalization, cross-species comparison, and statistical analysis are insufficiently described. In addition, the limited number of differentially expressed genes identified does not align with the extensive psoriasis transcriptomic literature, raising concerns about analytical rigor.

Finally, the manuscript emphasizes a small number of genes described as "psoriasis-associated" while failing to demonstrate regulation of widely accepted psoriasis signature genes known to correlate with disease activity and therapeutic response in patients.

Reviewer #2 (Public review):

Summary:

This paper shows that imiquimod, a compound often used to induce a psoriasis-like skin inflammation in mice, has a TLR7-independent effects that induce the unfolded protein response and amplify cytokine expression in dendritic cells. Although these effects of imiquimod have been described in the literature before, this study provides more detailed evidence and different contexts to this observation. These findings add to existing literature that imiquimod has a pleotropic mechanism of action involving changes in mitochondrial functions and cellular stress responses. Specifically, the authors show that imiquimod can induce calcium signaling in immune cells and potentiate two branches of the unfolded protein response in a TLR7-independent and MyD88-independent manner. They also show that some of these effects might be partially mediated by direct binding of imiquimod to Gelsolin. These findings expand our understanding of imiquimod-mediated inflammation and are useful for the field of experimental skin immunology and mouse models of psoriasis. However, the molecular and cellular mechanisms connecting Gelsolin to the unfolded protein response and skin inflammation presented in this paper require further investigation in the context of TLR-mediated inflammation.

Strengths:

(1) TLR7-independent effects of imiquimod on the expression of genes and proteins involved in the unfolded protein response are well demonstrated.

(2) Gelsolin is identified as a new imiquimod-binding protein in mouse cells.

Weaknesses:

(1) Effects of imiquimod on mitochondrial Ca signaling are not clear from the presented data.

(2) The mechanism of action connecting imiquimod to Gelsolin on the unfolded protein response and cytokine production remains not fully explained.

(3) It remains unclear if Gelsolin contributes to regulating TLR7 (or other types of TLR-mediated) inflammation in vivo.

Reviewer #1 (Public review):

Porte et al. investigate how observers form confidence judgments about the presence vs absence of near-threshold audiovisual stimuli. In two psychophysical detection experiments, human participants judged whether a stimulus (visual, auditory, or audiovisual) was present or absent, reported amodal confidence, and then gave modality-specific detection and confidence ratings using a bidimensional scale. The authors report that audiovisual (AV) stimuli are detected more accurately than unimodal stimuli, but that multisensory stimulation does not improve metacognitive efficiency. Participants are more confident in absence than in presence judgments. They extend a previously proposed model to an audiovisual setting, assuming evidence is available only for presence and that absence is inferred via counterfactual detectability. Detection is modeled with a disjunctive integration rule across modalities, while confidence is explained by a combination of conjunctive (for presence) and disjunctive/negation-of-disjunction (for absence) rules.

There are several points I wish to have clarified, outlined below:

(1) Framing of bimodal vs unimodal detection

On p.3, the introduction states that "Adults typically show higher detection rates and faster reaction times for bimodal than for unimodal stimuli." This is broadly consistent with the literature, but as written, it obscures the fact that these effects depend critically on experimenter-defined stimulus strengths. It is trivial to construct cases where a strong unimodal stimulus is more detectable than a bimodal stimulus made of two very weak unimodal stimuli. If "bimodal" is understood as the co-presentation of two unimodal components matched in detectability, then Bayes-rule-based arguments indeed predict better detection for the bimodal case; how much better is theoretically interesting, but not quantified in this paper. There is an entire literature on the combination of two unimodal stimuli, which is not touched on. For a pertinent reference, see Ernst & Banks 2002. I recommend clarifying that the statement assumes comparable unimodal intensities.

(2) Relationship to signal detection theory and counterfactual perceptibility

In the introduction, the authors write, "If sensory evidence is only available for presence," motivating counterfactual perceptibility as a necessary ingredient to infer absence. However, standard signal detection theory (SDT) already provides a widely accepted framework in which a continuous internal response is present on both signal and noise (absent) trials, with absence corresponding to the noise distribution and decisions implemented by a criterion.

Thus, there is no logical need to invoke counterfactual perceptibility simply to define absence; rather, the Mazor-style framework adds an explicit belief model about detectability and an optimal stopping policy. It would strengthen the paper to more clearly state how the proposed model goes beyond SDT conceptually, acknowledge that SDT can account for presence/absence decisions without counterfactuals, and position the counterfactual account as a hypothesis about how observers actually compute absence/confidence, not as a necessity. One of the central claims of the paper is that detection in the case of absence requires counterfactual reasoning. The authors should demonstrate whether or not an SDT-based generative model can describe these amodal and uni- and bi-modal stimulus decisions. In such an SDT model, an SDT-based generative model in which the noise distribution is shared across conditions, and unimodal vs bimodal differences are captured by changes in the mean or variance of the signal+noise distribution.

(3) Confidence vs performance: is AV confidence special?

The paper's central claims about multisensory confidence and metacognition would be stronger if the authors showed that AV confidence deviates from what is expected given performance alone. From the reported results, AV accuracy is around 80%, with visual and auditory at about 60% and 40%, respectively. Given that confidence typically monotonically scales with accuracy, the first question is whether AV confidence is entirely explained by improved performance, or whether there is an additional multisensory contribution. A simple, informative analysis would be for each subject, plot mean confidence vs per cent correct for AV, V, A, and absent conditions, and to test whether AV confidence lies above the trend predicted by accuracy alone.

(4) Metacognitive measures: logistic regression slopes vs meta-d′/d′

In the "Multisensory effects on metacognitive performance" section, the authors define "metacognitive sensitivity" as the slope of a Bayesian logistic regression predicting accuracy from confidence. There is substantial literature showing that logistic-slope measures of metacognitive sensitivity are criterion-dependent and can be affected by both task and confidence criteria (for one example, see Rausch & Zehetleitner, 2017). In contrast, meta-d′/d′ was specifically developed to provide a bias-invariant measure of metacognitive efficiency. Though this, too, is dated (see Boundy-Singer et al., 2023). Given that the authors already estimate HMeta-d-based M-ratios, it is unclear why they rely on logistic regression slopes as their primary "metacognitive sensitivity" metric in Figure 4A. I suggest either replacing the logistic-slope metric with SDT-based measures (meta-d′, meta-d′/d′) or providing a clear justification for using logistic slopes, along with a discussion of their known limitations.

Additionally, Figure 3 reports M-ratios without showing the corresponding d′ or meta-d′ for judge-present vs judge-absent conditions. Presenting these would help contextualize the metacognitive efficiency results and clarify whether differences are driven mainly by changes in metacognitive sensitivity, changes in task performance, or both. The d' values per condition could be added to Figure 2A.

(5) Interpretation of confidence in absence vs presence

The authors emphasise that it is surprising subjects are more confident in absence than in presence judgments, both at amodal and modality-specific levels. However, Figure 2B suggests that absent responses are very accurate: absent is reported as present only in about 10% of absent trials, implying a high correct rejection rate. If confidence tracks outcome probability, higher confidence for absence may be at least partly expected. Before attributing this asymmetry primarily to counterfactual reasoning, it would be important to explicitly relate confidence to accuracy for hits, misses, false alarms, and correct rejections and show whether absence confidence remains elevated relative to presence after controlling for accuracy differences across judgment types and conditions. Without this, the interpretation that higher absence confidence is inherently "unexpected" seems overstated.

(6) Model: integration rules, confidence, and evidence strength

The modeling section extends the Mazor et al. ideal observer to two modality-specific sensors, with disjunctive integration for detection and then disjunctive vs conjunctive integration rules for confidence. I have a few comments.

First, the detection rule is disjunctive and is reported as a finding. However, the conclusion that detection relies on a disjunctive rule ("present if A or V") closely mirrors the task instructions-participants are explicitly told to respond "present" if they detect the stimulus in any modality. As such, this seems more like a sanity check than a novel empirical finding.

Relatedly, the conjunctive detection is a weak null. The conjunctive rule ("present only if both A and V") is behaviorally implausible given the task instructions. A more informative baseline would be an SDT-style scalar-evidence model (see comment 2), rather than a conjunctive rule that participants would have to actively violate the instructions to follow.

Second, confidence in the model is defined as the probability of being correct at the time of the detection decision. However, this implies a fixed amount of evidence at decision time unless additional mechanisms are invoked. This issue is well known in diffusion modeling (see Kiani et al. 2014) and deserves explicit discussion; otherwise, it is unclear how the model produces graded confidence from a bound-crossing rule alone.

Third, the authors do not consider a straightforward evidence-strength account of confidence. When both modalities indicate presence, there is, on average, more total sensory evidence than in unimodal trials, making correct decisions more likely and, under most frameworks, confidence higher. Likewise, weak evidence in both modalities can be stronger evidence for absence than moderate in one and weak in the other. Many of the patterns that motivate the presence-conjunctive/absence-disjunctive mix could arise from a model where confidence simply reflects the amount of evidence for the chosen option, without positing distinct logical integration rules for presence vs absence. As the authors note, purely disjunctive or purely conjunctive confidence rules fail to capture the trends in confidence reports in Figure 7, leading them to adopt a combined presence-conjunctive / absence-disjunctive rule. A more parsimonious alternative-that confidence scales with evidence magnitude and cross-modal agreement-should be explicitly considered and, ideally, implemented as a competing model.


Finally, if the model is intended as a good account of the data, it would be useful to report whether it also reproduces the metacognitive efficiency patterns (M-ratios) beyond the mean confidence patterns shown in Figures 7-8. At present, the model appears systematically over-confident, which should be acknowledged and quantified.

(7) Confidence asymmetry index (CAI) and modality weighting

The confidence asymmetry index (CAI) is defined as the difference between auditory and visual confidence on AV vs absent trials, and the authors report strong correlations between observed and simulated CAI across participants. They interpret this as evidence that subjects place different weights on auditory vs visual signals. Several questions arise. First, does CAI capture asymmetries beyond what is expected from accuracy differences between modalities and conditions? Second, because the simulated data are generated from model fits to the observed data, a correlation between observed and simulated CAI is expected: the model is built to reproduce the individual patterns it is then compared to. A stronger test would compare CAI from data simulated with modality-specific belief parameters, versus CAI from data simulated with constrained equal belief parameters (same θs). Relatedly, the paper would benefit from a plot showing the distribution of θs for A and V- present stimuli across subjects. These values could also be related to unimodal sensitivity measured in the calibration/training phases. A natural prediction is that higher unimodal sensitivity should correspond to higher belief parameters for presence.

Reviewer #2 (Public review):

Summary:

In this study, across two experiments, the authors wrestle with the question: What is the profile of confidence judgments in presence/absence decisions for audiovisual stimuli? After thresholding observers to 50% target detection rates in each modality, the authors conducted one experiment that included 75% target presence (spread equally across bimodal, auditory, and visual targets) and one experiment with 50% overall target presence. Results showed that, overall, detection performance was higher for audiovisual stimuli compared to unimodal ones, and that a recent model for stimulus detection could be extended to this multisensory scenario. By incorporating a disjunctive rule for absence judgments and a conjunctive rule for presence judgments, the model was able to qualitatively reproduce some of the trends observed in the human data regarding confidence.

Strengths:

(1) The paper makes novel contributions to the study of multisensory confidence judgments for yes/no target detection.

(2) The paper further extends the use of a leading model of stimulus detection (from Mazor et al., 2025).

(3) Pre-registration of the study was implemented, and the code is publicly available (although the GitLab link requires registration to access the materials).

(4) One of the empirical results (higher confidence for absence compared to presence judgments) is especially interesting, contributing another empirical finding to a very mixed literature on this topic (as the authors note).

Weaknesses:

(1) Page 5 - I have concerns about the use of the equal-variance model from Signal Detection Theory to analyze the data. For example, the authors should read the recent paper by Miyoshi, Rahnev, and Lau in iScience, found at this link: https://www.cell.com/iscience/fulltext/S2589-0042(26)00373-1. In this paper, the authors note how the equal variance model should be used with caution in yes/no detection tasks, since the variances of the "stimulus present" and "stimulus absent" distributions are often different from one another. In a revision, I highly recommend that the authors explicitly discuss this paper and review whether the assumptions for the equal-variance model have been met (e.g., since they have confidence data, one way to do this would be to evaluate if the slope of the line in zROC space differs from 1). The authors may also want to incorporate methods from this iScience paper into the current manuscript, or potentially move to using an unequal variance SDT model and compute d'a and c'a.

(2) Related to the computation/measurement of the response criterion, the authors note on page 18 in the Methods that for Experiment 1, signals are actually present on 75% of trials, since a bimodal stimulus is present on 25% of trials, the visual circle only occurs on 25% of trials, the sinusoidal tone occurs on 25% of trials, and then only noise is present on 25% of trials. Did the authors have any a priori hypotheses about the response criteria that participants would exhibit in Experiment 1, considering the unbalanced target presentation rate in this task? Also, in Experiment 2, what did it mean to equate target present and target absent trials? Is it that they broke 50% target present trials down into 16.67% bimodal targets, 16.67% visual targets, and 16.67% auditory targets? A few more details would be good to explicitly note for those trying to replicate the task.

(3) It is important to plot the individual data for Figure 2. If the authors didn't match detection performance for the visual and auditory modalities, it would be good to see the individual data to know why. Is it that the thresholding procedure didn't work for some of the participants in the visual modality, and that's why the "yes" response rate is (on average) ~60% or higher across the two experiments? Similarly, in the auditory domain, do the authors have participants that are at floor? Or is it simply that the staircases failed to successfully target 50% detection on average?

(4) The authors mentioned that data were collected on the Prolific platform. What checks did they conduct to ensure that this data wasn't produced by bots? There are recent high-profile publications in PNAS and Behavioral Research Methods that indicate how online data collection is problematic (e.g., https://www.pnas.org/doi/10.1073/pnas.2535585123 and https://link.springer.com/article/10.3758/s13428-025-02852-7). What analyses or quality checks are there to ensure that humans were the ones completing the task?

(5) Page 7 - Since confidence was collected on a continuous scale, the authors should say a bit more about how they were able to compute measures of metacognitive efficiency. My understanding is that to compute meta-d', the data has to be binned. How was the binning implemented? With whatever bin size the authors chose, would it make any difference to the results if they changed the number of the bins in the analysis?

(6) Page 8 - Is there a prior precedent for using slope of the Bayesian logistic regression predicting accuracy from confidence as a measure of metacognitive sensitivity? If so, can the authors cite those papers as a reference? If not, can they place this analysis within the context of other measures of metacognitive sensitivity that exist? (meta-d', AUROC (Type 2), etc.)

(7) Page 8 - Another one of the results on page 8 is worth reflecting further upon: the authors note how in Experiment 1, no credible difference was found between unimodal and bimodal trials (DeltaM = -0.25 [-0.59, 0.10]), but in Experiment 2, "we observed higher metacognitive efficiency in unimodal compared to bimodal trials (DeltaM = -0.28 [-0.54, -0.02]. Those DeltaM values are nearly identical, so without a power analysis motivating the number of participants the authors collected, how certain are they that the results from these two experiments are really that distinct? It reminds me a bit of the Andrew Gelman blog post, "The difference between significance and non-significance is not significant".

(8) Is there any way to look at whether the presence of multisensory hallucinations (or perhaps that word is too strong, and we should simply consider them miscategorizations) increased as the task progressed? That is, the authors have repeated presentations of audiovisual stimuli for at least some percentage of the trials. Since the percentages for auditory stimuli being correctly categorized as auditory are at 85% in Experiment 1 and 79% in Experiment 2, were the trials where they miscategorized these stimuli equally spread throughout the task? Or did they come later in the experiment, after being repeatedly exposed to multisensory trials?

(9) Would the authors obtain the same results if they got rid of the amodal confidence judgment in their task, and simply had participants report the bimodal confidence following the presence/absence judgment? Part of the reason for asking this is that, according to page 11, the model is only fitted to amodal detection accuracy and response time data. This surprised me. I would have expected that the bimodal confidence would provide more useful information for the model fit. The authors should further explain this rationale in the paper. It seems odd to me to have the multisensory confidence ratings and not have them play a central role in the modeling work.

(10) In Figure 6, it appears the model is a bit off in its estimate of auditory responses (panel B, E) in the AV condition. Do the authors have any intuitions about why this might be happening?

(11) The authors talk about how the model is reproducing effects in the human data, but there's no systematic comparison, quantitatively, of how the two things relate. The authors should include some quantitative measure that reflects this.

(12) Related to this, I am not sure I agree with the characterization in Figure 7 that "when confidence followed a disjunctive rule, the model failed to capture important aspects of the data. On the other hand, when confidence followed a conjunctive rule, it reproduced confidence in presence judgments but failed to capture variability in confidence ratings for absence judgments." What, quantitatively, is the basis of this claim? This applies to Figure 8, too. I am not clear how, specifically, and quantitatively, the authors are justifying their claims about model fits. I don't think the confidence asymmetry index in Figure 8 is enough to quantify the quality of the model fitting procedure.

(13) Is there any chance the higher metacognitive efficiency for auditory trials is simply driven by differences in the d' values across the modalities? It might be good to probe this effect further.

(14) Lastly, I think it would be interesting to look at how instructions about modality-specific attention could modulate these findings, in terms of how unimodal (unimodal visual, unimodal auditory) or bimodal attention might modulate these effects. This is an idea for future work.

Reviewer #3 (Public review):

Summary:

This study used a pre-registered novel behavioural paradigm and computational modelling to investigate multi-sensory influences on detection and confidence. Participants performed amodal detection of auditory and visual stimuli (indicating that a stimulus was there when either an auditory stimulus or a visual stimulus or both were present), followed by amodal and unimodal confidence ratings. Detection was higher when both stimuli were present, and the presence of one modality increased the confidence in the presence of the other modality. In contrast to previous detection studies, confidence was higher for absent than for present judgements, but metacognitive efficiency was higher for present judgements. Metacognitive sensitivity was higher for bimodal stimuli, but this was not the case for metacognitive efficiency, suggesting that the sensitivity might be driven by first-order performance. The computational model showed that both detection and confidence in absence followed a disjunctive evidence integration rule, while confidence in presence followed a conjunctive integration rule.

Strengths:

The paper has several major strengths. Firstly, it addresses a novel research question using an innovative and well-controlled paradigm. Furthermore, the paradigm and analyses were pre-registered, and all effects that were interpreted were replicated in two independent samples. Finally, the paper uses an advanced computational model to capture counterintuitive patterns in the data.

Weaknesses:

The major weakness of the paper is the narrative structure. It is not always clear how the different analyses relate to the main research question. Many different effects are reported in terms of detection accuracy, bias, confidence and metacognition, as well as cross-modal and unimodal versus bimodal effects. It would help readability if the paper were streamlined in terms of the research question that is being answered, which I believe is specifically about multimodal absence judgements. Relatedly, for a reader not intimately familiar with the metacognition literature, the difference between MRatio, metacognitive sensitivity and metacognitive efficiency is not obvious. It would be good to clarify this more in the manuscript.

In general, the conclusions drawn by the authors seem to be supported by the results. However, I was missing quantitative model comparisons between the conjunctive and the disjunctive models and an explanation of why the models systematically overestimated the confidence ratings. Furthermore, the 'perceptual multisensory interference' section reports on very interesting effects, but these are not supported by statistical tests in the main text. It would help to assess the strength of the claims if the statistical evidence in favour of these claims were presented together in the main text.

One other concern is that in real-world multi-sensory perception, such as the mosquito example in the introduction, the auditory and visual signals have a strong natural association, which means that if you hear the auditory signal, you expect that you will see the visual signal soon and vice versa. As far as I understood, this association was not present in the current paradigm, which might influence the type of effects that one would expect to see.

Reviewer #1 (Public review):

Summary:

Bot et al. introduce GeneSLand, a computational framework to quantify and visualize gene expression specificity across diverse transcriptomic datasets. The method leverages expression level-breadth (L-B) relationships to construct multi-level specificity landscapes and derives metrics such as lbSpec and dRate to characterize gene specificity in a threshold-independent manner. The authors showed the applicability of the approach across bulk RNA-seq, single-cell datasets, and cross-species primate brain data, showing that specificity patterns captured by this approach reflect both tissue-specific expression and evolutionary distances. Overall, the framework represents an interesting and potentially useful contribution to the analysis of gene expression specificity.

Strengths:

(1) Introduces an original conceptual framework based on expression level-breadth relationships to characterize gene specificity.

(2) Provides a threshold-independent approach that could overcome some limitations of classical specificity metrics.

(3) Demonstrates the applicability of the framework across different biological datasets.

Weaknesses:

(1) The method relies on predefined binning thresholds for expression levels, and the sensitivity of the derived metrics to this parameter is not fully explored.

(2) The advantages of lbSpec relative to established metrics could be more clearly shown with some biological examples.

(3) The robustness of the framework with noisy datasets, small sample sizes, or lower sequencing depth is not well evaluated.

Reviewer #2 (Public review):

Summary:

Bot & Davila-Velderrain present a new method to understand expression specificity, based on an analysis of the relation between expression level and breadth for each gene. They show that the method captures biological differences across organs, diverse cell types, and specific cell subtypes, for different biological processes and across species.

Strengths:

This manuscript addresses an important question in an original manner, and was a pleasure to read. The authors frame the question very clearly: gene expression is a complex trait, which can be summarized in an informative manner by its specificity. The method the authors propose (which I'll call "LB" in this review) has several attractive features, summarising different specificity profiles in a more nuanced manner than the widely used tau. They show convincingly that their method captures relevant biology at different scales. I especially appreciated the comparative analyses of specificity within broad cell types and within neuronal subtypes.

Weaknesses:

Surprisingly, while the method works well, the authors never compare it to the state-of-the-art. Thus, comments 1 and 2 are my only "major" comments.

(1) In the Introduction, the authors should explain which shortcomings of existing methods motivate the development of a new one.

(2) In the Results section, the authors should compare the results of LB with other methods, at least tau and Gini (which is conceptually quite similar to LB).

(3) It would be good to show the sensitivity of LB to different numbers of bins.

(4) The conservation of specificity across primates was already reported in Kryuchkova-Mostacci 2016 (https://doi.org/10.1371/journal.pcbi.1005274). But also see Dunn et al 2018 (https://doi.org/10.1073/pnas.1707515115) for criticism of this type of naive pairwise comparisons.

Reviewer #1 (Public review):

Summary:

This manuscript investigates the degradation dynamics of extracellular DNA in soils and its impact on estimates of microbial abundance and diversity. By combining a broad geographic sampling design with a primer-labeling strategy, qPCR quantification, amplicon sequencing, and PMA treatment, the authors aim to disentangle total versus intracellular DNA signals and explore sequence-specific degradation patterns. The topic is relevant, particularly given the increasing awareness of relic DNA as a confounding factor in microbial ecology. The experimental design is ambitious and potentially impactful. However, several conceptual inconsistencies, methodological ambiguities, and statistical limitations currently weaken the robustness of the conclusions. These issues need to be addressed.

Strengths:

The manuscript addresses a timely and important question in microbial ecology, particularly given the growing recognition that relic DNA can bias interpretations of community composition derived from amplicon sequencing. The study is ambitious in scope, incorporating a broad geographic sampling design across multiple soil types, which enhances the generalizability of the findings. The use of a controlled microcosm experiment combined with a primer-labeling strategy to track extracellular DNA dynamics is conceptually innovative and provides a structured framework to investigate degradation processes.

In addition, the integration of multiple approaches, including qPCR for absolute quantification, high-throughput sequencing for community profiling, and PMA treatment to differentiate extracellular from intracellular DNA, represents a comprehensive attempt to disentangle complex sources of bias in soil microbiome analyses. The effort to link degradation dynamics with environmental variables and to explore sequence-level patterns further demonstrates the authors' intent to move beyond descriptive analyses toward a mechanistic understanding.

Weaknesses:

Several conceptual and methodological issues currently limit confidence in the study's conclusions. Key terms such as "sequence-specific degradation" are not clearly defined or supported by a mechanistic or structural hypothesis, making it difficult to interpret the biological meaning of the results. In addition, the bioinformatic workflow presents inconsistencies, particularly the use of ASVs followed by clustering at 97% similarity, which undermines the resolution required to support sequence-level inferences. Statistical analyses are also insufficiently described, including unclear definitions of "T values," a lack of detail on pairing structure, and no indication of multiple testing correction.

Furthermore, important methodological details are missing or unclear, including primer design (e.g., GAPDH tag vs ACTF), Illumina library preparation (e.g., adapter and indexing strategy), and validation of PMA treatment efficiency. The interpretation of PMA-treated samples as representing "living communities" is likely overstated, given the known limitations of the method in soil systems. Finally, typographical errors, inconsistent terminology, and unclear phrasing throughout the manuscript reduce readability and further complicate interpretation.

Reviewer #2 (Public review):

Summary:

This manuscript describes the results of an interesting study examining the rate of degradation of extracellular DNA in soil ecosystems using a clever experimental approach. 16S ribosomal RNA genes were amplified from soil samples, and then purified PCR amplicons, containing a 5' linker sequence on the forward primer, were introduced to soils and monitored over time using real-time quantitative PCR and NGS amplicon sequencing. The study was able to measure rates of overall extracellular DNA degradation, but also sequence-specific degradation rates. I like the idea and execution of the study, and the results are interesting. The manuscript needs some help to improve the overall readability. Please see general and editorial comments below.

Strengths:

Innovative experimental design that is well deployed across a large number of soil types, revealing interesting variability in extracellular DNA degradation.

Weaknesses:

(1) The manuscript needs another review to improve the readability of the document.

(2) The authors have used 16S genes to look at sequence-specific degradation. But 16S rRNA genes are actually pretty well conserved, and there isn't as much genetic variation across this gene among organisms as there is for other genes. It might be more relevant to look at metagenomic DNA degradation from high AT, high GC organisms, etc. This would be more generalizable than 16S genes.

(3) Consideration of differential cell lysis during soil DNA extraction needs to be considered as well.

(4) It is not clear why the authors didn't put GAPDH linkers on the reverse primer as well. This would have given an easier amplicon to amplify (no degeneracies at all).

Reviewer #1 (Public review):

Summary:

Sugarman, Vanselow et al. adapted a microCT instrument to permit imaging of an entire organism, a hatchling octopus. In the resulting 3D dataset, they segmented the major organ systems, including the vascular, respiratory, digestive, and nervous systems. The authors released the dataset through a public web interface, and present some observations about body-wide neuroanatomy.

Strengths:

- The dataset is of good quality and access to a whole-cephalopod anatomical resource will be useful for the scientific community.

- The interactive web interface facilitates exploration of the dataset.

Weaknesses:

- The authors identify a series of bundles of nerve fibers between the suckers and the central brain and propose that these structures together constitute the chemotactile pathway, linking sensation to learning and memory. This is an over-interpretation of the available evidence. The data is not presented in a way that allows the reader to independently assess the proposed anatomical relationships: many images include near-opaque colored overlays on the fibers of interest, making it difficult to determine whether these bundles truly merge or interface. Additionally, the mesoscale resolution achieved here reveals the presence of large nerve bundles but cannot resolve the origin or synaptic relationships of the neurons in the bundles - including those from the chemotactile receptors of the suckers. There are likely multiple synapses between the periphery and the central brain, and the location and connectivity of individual neurons are not visible at this resolution. Consequently, this dataset does not demonstrate structural connectivity. Elucidating a neural circuit would require complementary approaches such as neuronal tracing or electron microscopy connectomics.

- The language used in the manuscript is often overly complex and convoluted, making it difficult to follow the main arguments and to assess the strength of the claims. In addition, some vocabulary in the main text is overly technical (e.g. related to microCT or anatomy), making it difficult for a general biology or cephalopod audience to understand, while some neuroscience vocabulary is used imprecisely or in ways that overstate what can be concluded from anatomical data. A substantial rewrite using clearer, more cautious language is recommended. Additionally, a deeper discussion of the observed octopus arm anatomy, and how this may relate to its semi-autonomous function would make this article of greater interest to a broader audience.

Reviewer #2 (Public review):

Sugarman et al show a major advance in the volumetric imaging of the cephalopod body and nervous system, using wide field high resolution micro-CT imaging. The new detection optics are striking in their performance, and the conclusions made from the images seem well-founded. The technical advance and the conclusions both justify the reader's attention, but the authors should make the figures and the text teach the reader so that the findings are more accessible and convincing.

The paper is now written in a style that will impress those ready to be impressed and fail to impress many of the readers, although it should.

(1) The authors must improve the text so that it cleanly states what was known previously, and how the current results extend this. For example. putting a section in the middle of the results section (page 3) that states: "Long-range connections between sucker and brain were demonstrated by fine chemical and tactile sensing by suckers in behavioral experiments with live O. bimaculoides (Buresch et al., 2022, 2024; Sepela et al., 2025; van Giesen et al., 2020; Wells, 1978a; Wells & Young, 1969) and by loss of chemotactile learning and memory observed after ablation of the "inferior frontal system" (i.e., inferior frontal/subfrontal/buccal lobe complex) (Wells, 1978a)..." seems a bit confusing to me. Similarly, putting in a reference to optical imaging approaches for combining data sets (Preibisch et al., 2009) as only the citation does little to make the work accessible. Please expand the text so that it teaches what the authors are thinking.

(2) The authors must improve the figures so the work is more digestible. The data is a pyramid, and the "google earth" range of magnifications and details is not clear in the figures. In short, the figure will impress those who know to be impressed and fail to impress the majority.

(3) The videos are far more useful in this contribution that in almost any other paper. Use them more so the reader realizes how key they are. Revising them to demonstrate the amazing range of scales in the data would be wise.

(4) The demonstration of the data visualization tool is excellent as far as it goes. Expanding the treatment of the multi-scale rendering would be wise.

With proper expansion of the text and the figures, it will become far more obvious that this is landmark work.

Reviewer #3 (Public review):

Summary:

Sugarman et al. present a microCT scan of a hatchling octopus from the species Octopus bimaculoides. The scan is publicly available and poses as a valuable tool for the field of cephalopod biology. Using this scan, the authors uncover two undescribed neural pathways: the intermediate longitudinal tract (iLTs) in the axial nerve cord linking the suckers to the brain, and the arm-to-arm u-tracts (AAUTs) interconnecting neighboring arms. How the eight sucker-lined octopus arms are coordinated with one another and with the brain have been long standing questions in the octopus motor control field, and the results presented here have promise for addressing these questions. However, major weaknesses addressed below limit the interpretability of the dataset.

Strengths:

The authors have publicly published a scan of an entire hatchling octopus, with major organs and subdivisions of the nervous system already segmented. Accessing the data is straightforward, and the authors provide adequate instructions on how to navigate the dataset.

The authors provide validation of the AAUTs using lucifer yellow and wheat germ agglutinin. To overcome motion artifact in the hatchling dataset, the connections between the iLTs and the suckers are validated with a microCT scan of a distal section of adult arm.

Weaknesses:

Given the resolution of the dataset, neural connectivity is determined by texture differences alone, which can be misleading. As such, any claims of anatomical connectivity will need further validation, ideally with tracing techniques. While the authors investigated the AAUTs with other techniques, no such validation exists for the iLTs. Furthermore, the authors themselves state that as the iLTs converge with the brachial nerve, they become indistinguishable from other fibers. Any connections of the iLTs to the brain are only hypothesized, despite their claim of demonstrating a clear pathway from the suckers to the brain.

The relevant prior research on octopus neurobiology is not well explained, making it challenging to understand the significance of the results in a broader context.

Reviewer #1 (Public review):

Mutations in CDHR1, the human gene encoding an atypical cadherin-related protein expressed in photoreceptors, are thought to cause cone-rod dystrophy (CRD). However, the pathogenesis leading do this disease is unknown. Previous work has led to the hypothesis that CDHR1 is part of a cadherin-based junction that facilitates the development of new membranous discs at the base of the photoreceptor outer segments, without which photoreceptors malfunction and ultimately degenerate. CDHR1 is hypothesized to bind to a transmembrane partner to accomplish this function, but the putative partner protein has yet to be identified.

The manuscript by Patel et al. makes an important contribution toward improving our understanding of the cellular and molecular basis of CDHR1 associated CRD. Using gene editing, they generate a loss of function mutation in the zebrafish cdhr1a gene, an ortholog of human CDHR1, and show that this novel mutant model has a retinal dystrophy phenotype, specifically related to defective growth and organization of photoreceptor outer segments (OS) and calyceal processes (CP). This phenotype seems to be progressive with age. Importantly, Patel et al, present intriguing evidence that pcdh15b, also known for causing retinal dystrophy in previous Xenopus and zebrafish loss of function studies, is the putative cdhr1a partner protein mediating the function of the junctional complex that regulates photoreceptor OS growth and stability.

This research is significant in that it:

(1) provides evidence for a progressive, dystrophic photoreceptor phenotype in the cdhr1a mutant and, therefore, effectively models human CRD; and

(2) identifies pcdh15b as the putative, and long sought after, binding partner for cdhr1a, further supporting the theory of a cadherin-based junction complex that facilitates OS disc biogenesis.

Comments on the revised version of the manuscript:

The authors adequately addressed previous comments related to lack of details on quantitative and statistical analyses and methods. In this regard, I believe the revised manuscript presents a stronger analysis of the data. I also appreciated the revised discussion section, which better contextualizes their new data with previous observations in different animal models.

The authors provided additional evidence in Fig 1C-H for the co-localization of pcdh15b and actin within CPs using immunolabeling with super resolution imaging. This data firmly supports their other observations. A similar approach tends to also show co-localization of actin and cdhr1a, although the authors suggest that the pattern of expression is less overlapping, which would be expected if cdhr1a is predominately expressed in the OS membranes whereas pcdh15b is predominantly expressed in the CP membranes. In my opinion the data presented to support this separation is still not that convincing. Moreover, the authors show that both cdhr1a and pcdh15b are expressed in CPs using immuno-TEM (Fig 1I). This is a difficult question to address experimentally, and it is, of course, still plausible that pcdh15b within the CP membrane and cdhr1a within the OS membrane are interacting in trans. However, I just don't think that the data unequivocally support mutually exclusive localization of these proteins as suggested by the authors and depicted in the model in Fig 1J.

Reviewer #2 (Public review):

Summary:

The goal of this study was to develop a model for CDHR1-based Con-rod dystrophy and study the role of this cadherin in cone photoreceptors. Using genetic manipulation, a cell binging assay, and high- resolution microscopy the authors find that like rods, cones localize CDHR1 to the lateral edge of outer segment (OS) discs and closely opposes PCDH15b which is known to localize to calyceal processes (CPs). Ectopic expression of CDHR1 and PCDH15b in K652 cells indicate these cadherins promote cell aggregation as heterophilic interactants, but not through homophilic binding. This data suggests a model where CDHR1 and PCDH15b link OS and CPs and potential stabilize cone photoreceptor structure. Mutation analysis of each cadherin results in cone structural defects at late larval stages. While pcdh15b homozygous mutants are lethal, cdhr1 mutants are viable and subsequently show photoreceptor degeneration by 3-6 months.

Strengths:

A major strength of this research is the development of an animal model to study the cone specific phenotypes associate with CDHR1-based CRD. The data supporting CDHR1 (OS) and PCDH15 (CP) binding is also a strength, although this interaction could be better characterized in future studies. The quality of the high-resolution imaging (at the light and EM levels) is outstanding. In general, results support the conclusions of the authors.

Weaknesses:

While the cellular phenotyping is strong, the functional consequences of CDHR1 disruption is not addressed. While this is not the focus of the investigation, such analysis would raise the impact of the study overall. This is particularly important given some of the small changes observed in OS and CP structure. While statistically significant, are the subtle changes biologically significant? Examples include cone OS length (Fig 4F, 6E) as well as other morphometric data (Fig 7I in particular). Related, for quantitative data and analysis throughout the manuscript, more information regarding the number of fish/eyes analyzed as well as cells per sample would provide confidence in the rigor. The authors should also not whether analysis was done in an automated and/or masked manner.

Comments on revisions:

Most of my concerns were addressed in this revised version.

Reviewer #3 (Public review):

Summary:

The manuscript by Patel et al investigates the hypothesis that CDHR1a on photoreceptor outer segments is the binding partner for PCDH15 on the calyceal processes, and the absence of either adhesion molecule results in separation between the two structures, eventually leading to degeneration. PCDH15 mutations cause Usher syndrome, a disease of combined hearing and vision loss. In the ear, PCDH15 binds CDH23 to form tip links between stereocilia. The vision loss in less understood. Previous work suggested PCDH15 is localized to the calyceal processes, but the expression of CDH23 is inconsistent between species. Patel et al suggest that CDHR1a (formerly PCDH21) fulfills the role of CDH23 in the retina.

The experiments are mainly performed using the zebrafish model system. Expression of Pcdh15b and Cdhr1a protein is shown in the photoreceptor layer through standard confocal and structured illumination microscopy. The two proteins co-IP and can induce aggregation in vitro. Loss of either Cdhr1a or Pcdh15, or both, results in degeneration of photoreceptor outer segments over time, with cones affected primarily.

The idea of the study is logical given the photoreceptor diseases caused by mutations in either gene, the comparisons to stereocilia tip links, and the protein localization near the outer segments. The work here demonstrates that the two proteins interact in vitro and are both required for ongoing outer segment maintenance. The major novelty for this paper would be the demonstration that Pcdh15 localized to calyceal processes interacts with Cdhr1a on the outer segment, thereby connecting the two structures. Unfortunately, the data as presented are inadequate proof of this model.

Strengths:

The in vitro data to support the ability of of Pcdh15b and Cdhr1a to bind is well done. The use of pcdh15b and cdhr1a single and double mutants is also a strength of the study, especially being that this would be the first characterization of a zebrafish cdhr1a mutant.

This is a large body of data.

Weaknesses:

(1) I have serious concerns about the quality of the imaging here. The premise that cdhr1a/pcdh15 juxtaposition is evidence for the two proteins mediating the connection between outer segments and calyceal processes requires very careful microscopy. The SIM images have two major issues - one being that the red and green channels are misaligned and the other being evidence of bleed through between the channels. This is obvious in Fig 2A but likely true across all the panels in Fig 2, and possibly applies to confocal images in Fig 1 as well. The co-labelling with actin shows very uneven, punctate staining for actin bundles.

(2) The newly added TEM and transverse sections include colored regions that obscure the imaging.

(3) The quantification should be done with averages from individual fish. Counting individual measurements as single data points artificially inflates the significance. Also, the cone subtypes are still lumped together for analysis despite their variable sizes.

(4) I highlighted previously that the measurement of calyceal processes was incorrect. The redrawn labels in Fig 7 are now more accurate, although still difficult to interpret. However, the quantification in Fig 7O is exactly the same. How can that be if the measurement region is now different?

(5) Lower magnification views would provide context for the TEM data.

(6) The statement describing the separation between calyceal processes and the outer segment in the mutants is still not backed up by the data.

(7) The authors state "from the fact that rod CPs are inherently much smaller than cone CPs". This is now referenced, but incorrectly. Also, the issue of pigment interference was not addressed.

(8) The images in panels B-F of the Supplemental Figure are uncannily similar, possibly even of the same fish at different focal planes.

Reviewer #1 (Public review):

Summary:

The manuscript by Tsukamoto et al. describes a compelling approach to understanding whether inter-species differences in social behavior might emerge from differential expression patterns of the oxytocin receptor (Oxtr) in the brain. To this end, they genetically engineer BAC transgenic mouse lines with insertions of a large construct incorporating prairie vole Oxtr gene and surrounding regulatory elements. They name these lines Koi lines. They first evaluate if prairie vole-like Oxtr expression is reproduced in the Koi mouse lines, and they find heterogenous patterns across different lines that do not depend on the number of insertions. While they found that Koi mice can reproduce vole-like expression in PFC, NAc, and BLA, the reproduction was never complete: one Koi line had NAc and mPFC expression, another had BLA expression, etc. They confirmed major expression patterns across 3 methods: crossing with LacZ reporter line, in situ hybridization, and ligand binding (autoradiography). To determine the expression pattern of the BAC insert but not endogenous Oxtr, the authors generated new mouse lines by crossing Koi lines with Oxtr -/- line. Importantly, they found that Oxtr expression pattern in the mammary gland was similar across all lines, and wild-type mice.

The authors used Koi:Oxtr-/- lines to test social behavior, specifically partner preference ( a behavior specific to prairie voles) and maternal behavior. They find that different Koi lines showed different changes in these behaviors compared to wild-type mice. Moreover, while some lines showed changes in partner preference, others seemed to show changes in maternal behavior. For one of the lines (Koi4), the partner preference and the maternal behavior were incongruent.

The manuscript then hypothesizes that the Oxtr gene is positioned in different 3D chromatin structures across species and across tissues, leading to more rigid expression in the mammary glands, but more flexible expression patterns in the brain.

Strengths:

This study has major implications in the field of oxytocin research, and more broadly in the field of neuromodulation. It is novel, bold, and rigorous.

Weaknesses:

(1) The expression in the brain and mammary gland (Figure 2) was not quantified, preventing a more objective conclusion that the brain has flexible expression and mammary gland expression is rigid.

(2) In Figure 7, a similar heatmap for the mammary gland is missing.

(3) Partner preference in males was not tested.

(4) It is unclear if in the behavioral testing the stimulus animals were the same genotype as the focal female or were wild-types. This could have an impact on the behavioral outcome.

Reviewer #2 (Public review):

Summary:

This is a bold and important study and addresses an important question in the field: how species-specific variation in brain oxytocin receptor expression relates to differences in social behavior.

Tsukamoto et al. generated eight independent transgenic mouse lines (Koi lines) carrying a bacterial artificial chromosome (BAC) encompassing the prairie vole Oxtr locus along with flanking intergenic regions, with the goal of probing the behavioral consequences of species-specific variation in brain Oxtr expression. Across these "volized" lines, the authors claim conserved Oxtr expression in the mammary gland but strikingly divergent patterns of brain expression, none of which fully recapitulate endogenous prairie vole Oxtr distribution, and instead exhibit expression patterns that diverge from both mouse and prairie vole brain Oxtr distribution. Nevertheless, some lines exhibit partial overlap with vole Oxtr expression pattern reported in the literature within specific brain regions, and one line displays partner preference behavior reminiscent of prairie voles. The authors further report line-dependent differences in maternal pup retrieval and crouching behaviors, which they interpret as evidence that variation in brain Oxtr expression can drive variation in social behaviors. Together with analyses of topologically associating domain (TAD) architecture, the authors conclude that brain, but not peripheral- Oxtr expression, is shaped by distal regulatory elements beyond the BAC insert, and propose that such regulatory flexibility underlies evolutionary diversification of social behavior.

Strengths:

A particular strength of the study is the generation of multiple independent transgenic lines, which provides a valuable resource for probing regulatory influences on Oxtr expression.

Weaknesses:

While the study addresses an important question, I have several methodological and conceptual concerns regarding the study in its current form. Some aspects of the study fall outside my primary area of expertise, and I am therefore not in a position to fully evaluate the technical difficulty or rigor of those components, or to judge whether my suggestions would be feasible to implement. I defer to reviewers with relevant expertise for a more detailed assessment of these aspects.

(1) Each independent Koi line exhibits a distinct brain expression pattern that differs from both wild-type mouse and prairie vole Oxtr expression, complicating the interpretation of the results. The manuscript does not include a direct comparison of brain Oxtr expression patterns in these transgenic lines with those of prairie voles. Instead, expression similarity is inferred primarily from regional localization and compared indirectly with prior literature (Figures 2-5). For those lines that show partial resemblance to prairie vole Oxtr expression patterns, the authors do not assess whether Oxtr-expressing neurons share comparable anatomical projections or transcriptomic identity with prairie vole Oxtr-expressing neurons. Quantification of expression remains largely descriptive, illustrating expression patterns (Figure 2), OXTR protein distribution (Figure 3; images are difficult to evaluate due to low contrast), or Oxtr mRNA levels across selected brain regions in Koi lines, wild-type mice, and mOxtr-/- mice (Figures 4-5), without directly testing similarity to prairie vole expression. In addition, whole-brain expression data are lacking, with analyses restricted to selected sections. While such analyses may be beyond the scope of the present study, these limitations nonetheless complicate interpretation of the central question - namely, whether the observed behavioral phenotypes arise from vole-like Oxtr circuits rather than from distinct, line-specific expression configurations.

(2) The authors state that Oxtr expression in the mammary gland is similar across all Koi lines and the mOxtr-IRES-Cre knock-in line. However, the images presented in Figure 2 appear to show differences in anatomical detail across lines, and no quantitative analysis is provided to support the claim of equivalence.

(3) The conclusion that integration site rather than copy number determines the observed BAC transgene expression patterns (Lines 202-203) is not fully supported by the data. First, the authors did not compare multiple copy numbers at the same genomic insertion site, making it impossible to disentangle copy-number effects from position effects. Second, BAC copy number does not necessarily scale linearly with expression; higher copy numbers can have a repressive effect on gene expression (Garrick et al, Nat Genet, 1998).

(4) While I am not an expert in TAD analysis, the observed differences in 3D architecture around Oxtr are consistent with a role for long-range regulatory interactions. However, these analyses appear largely descriptive and correlative, and establishing a causal contribution of 3D chromatin organization to Oxtr regulation by distal elements would likely require direct perturbation of TAD boundaries or looping interactions. I recognize that such experiments may be beyond the scope of the present study, but clarifying this limitation in the interpretation would be helpful.

Reviewer #1 (Public review):

Summary:

The authors describe co-regulated gene modules underlying stage differentiation in Leishmania donovani through a system-level analysis of multiple molecular layers. Using amastigotes isolated from infected hamster spleens and corresponding culture-derived promastigotes, they analyzed genomic variation, transcript abundance, protein levels, phosphorylation states, and metabolite profiles. By combining these, the study identified potential regulatory mechanisms associated with parasite differentiation and generated hypotheses regarding how gene expression is coordinated across different levels.

Strengths:

A major strength of the study is the breadth of the dataset generated. The integration provides an unusually comprehensive view of molecular changes associated with Leishmania differentiation in vitro. Such multi-layer datasets involving bona fide vertebrate host stages remain relatively rare in parasitology and will likely become a valuable resource for the molecular parasitology community. In addition, the use of amastigotes isolated from infected hamsters rather than relying on axenic models provided a biologically relevant framework for the analyses.

The revised manuscript improved several aspects of the original. The RNA-seq analysis is described with a clearer pipeline, and several claims regarding causal regulatory feedback associations have been appropriately toned down. Among the observations reported, the association between parasite differentiation and proteasome-mediated protein degradation is particularly remarkable. The combination of quantitative proteomics with pharmacological inhibition of the proteasome with lactacystin provides support for a role for protein turnover in developmental transitions and paves the way for future mechanistic studies.

Weaknesses:

Most regulatory interpretations remain largely inferential or indirect. The integration identifies correlations between different levels, but direct functional validation is limited/absent. Many of the descriptions should not be interpreted as validated. As highlighted by the authors in this revised version, the mechanistic studies will be part of future work and are beyond the scope of the current work. Of note, the attempt to confirm lactacystin-induced inhibition of proteasomal activity via anti-polyUb immunoblotting did not demonstrate the expected outcome of increase in overall poly-ubiquitylation.

Comments on revised version:

The authors have appropriately addressed my comments and questions from the initial review process. My remaining concern relates to the lack of evidence to confirm proteasomal inhibition by lactacystin in both promastigotes and amastigotes. The immunoblotting experiment newly presented does not reveal a clear increase in the levels of poly-ubiquitylated proteins in treated parasites. In fact, poly-Ub levels were lower at both the 4h and 18h timepoints of treatment. If alternative antibodies or additional immunoblots are not available, the manuscript would benefit from an expanded discussion of this observation and potential explanations. In particular, the interpretation that lactacystin stabilizes ama- and pro-specific degradation would be greatly strengthened by such validation.

Reviewer #2 (Public review):

Pescher and colleagues present a revised manuscript detailing the multi-omic characterisation of Leishmania donovani amastigote to promastigote differentiation and integration of this data. The molecular pathways that regulate Leishmania life-stage transitions are still poorly understood, with many approaches exploring single proteins/RNAs etc in a reductionist manner. This paper takes a systems-scale approach and does a good job of integrating the disparate -omics datasets to generate hypotheses about the intersections of regulatory proteins that are associated with life-cycle progression. The differentiation step studied is from amastigote to promastigote using hamster-derived amastigotes which is a major strength. The use of hamsters permits the extraction of parasites that are host adapted and represent "normal", host-adapted Leishmania ploidy; the promastigote experiments are performed at a low passage number. Therefore, this is a strength or the work as it reduces the interference from the biological plasticity of Leishmania when it is cultured outside the host for prolonged periods. The multi-omics datasets presented are robust in their acquisition and analysis and will form an excellent resource for researchers studying the molecular events (particularly proteasomal protein degradation, and phosphorylation) during life-stage progression.

General comments on the revisions:

My view is that the authors have made significant, satisfactory changes that address the comments and queries I made on the original manuscript (Review Commons).

There are two areas where the authors had to make major changes/justifications where further comment is merited, these were:

RNA-seq.<br /> The most significant issue was the originally underpowered RNA-seq which had only two replicates. This has been repeated with four replicates now. This has not led to changes in the interpretation of the data between the original study and this one. One comment that the authors make in the response to this was : "Given the robustness of the stage-specific transcriptome, and the legal constrains associated with the use of animals, we chose to limit the number of replicates to the necessary". Ensuring that animal experiments are properly powered and that maximum robustness of the data from the minimum sample size is an important part of experimental design for ethical use of animal models. Essentially the replication here could have been avoided if the original study had used 1 more animal. However, the new version of RNA-seq brings appropriate confidence to the interpretation of the data.

Phosphoproteomics.<br /> The authors provide a robust justification of their strategy for the phosphoproteomics and highlight the inclusion criteria for phosphosites: "Phosphosites were only considered if detected with high confidence (identification FDR<1%) and high localisation confidence (localisation probability >0.75) in at least one replicate". The way missing values were dealt with is explained "For statistical analyses, missing values within a given condition were imputed with a well-established algorithm (MLE) only when at least one observed value was present in that condition." This fills in some of the gaps I was missing from the original manuscript, and I am satisfied that the data analysis is entirely appropriate for a discovery/system -based approach such as this one. The authors also edit the manuscript to reflect that "occupancy" or "stoichiometry" might not be the best description of what they were presenting and switched to the terminology of "normalised phosphorylation level" - I think this is an appropriate response.

Overall, in the absence of follow up experiments on specific individual examples, some of the claims in the original submission were toned down and reflect a more neutral description of the data now. Significantly, the data still underpin a key role for regulation of the ribosome between the amastigote and promastigote stages (and during the differentiation process). The recursive and reciprocal links between the phosphorylation and ubiquitination systems are interesting and present many opportunities for future investigation.

Reviewer #3 (Public review):

Summary:

The authors proposed to use 5-layer systems level analysis (genomics, transcriptomics, proteomics / protein degradation, metabolomics, phosphoproteomics) to uncover how post-transcriptional mechanisms regulate stage differentiation in Leishmania donovani.<br /> This enabled the identification of several potential regulatory networks, including the regulation of stage-specific gene clusters by RNA stabilisation or decay, proteasomal degradation and protein phosphorylation.<br /> In the new version of this manuscript, the authors have addressed all questions raised by the reviewers.

Strengths:

Although some observations in this study have already been described in the literature, the integrated analysis applied here provides a novel view on how different levels of post-transcriptional networks regulate Leishmania differentiation. This "5-layer system" represents the first analysis of this depth in kinetoplastid parasites.<br /> The revised version with an increased sample number for the RNA-seq now made the authors assumptions adequate to their obtained data.<br /> The use of a proteasomal inhibitor adds an interesting insight in how protein degradation is involved in the parasite differentiation, confirming previous observations in the literature, and help to explain the discrepancies between mRNA and protein expression in the different stages.

Weaknesses:

While this work provides an impressive and foundational dataset, it opens the door for future research to rigorously validate these initial findings and conclusions.

Significance and Impact in the field.

The different datasets generated in this study will be of great interest to the parasitology community, either to be used for hypothesis generation, to validate data from other sources, etc.

The multi-layered analysis performed here identified a series of potential feedback loops and regulatory networks to be further explored in organisms that lack transcriptional control.

Reviewer #1 (Public review):

Summary:

The authors aim to study mutational paths connecting WW domains with different binding specificities. Their approach combines an unsupervised sequence generative model based on RBMs with a path-sampling algorithm. The key result is that most intermediate sequences along the designed transition paths retain measurable binding activity in wet-lab assays, whereas paths containing the same mutations introduced in a randomized order are largely non-functional. This difference is attributed to epistatic interactions captured by the RBM model.

Strengths:

Exploring mutational paths in high-dimensional protein sequence space is a challenging problem. The computational framework used here is state-of-the-art and is strengthened by systematic experimental characterization of binding activity. The study is comprehensive in scope, including multiple transition paths both within and across WW specificity classes, and the integration of modeling with high-throughput experimental validation is a clear strength.

Weaknesses:

A major concern is whether the stated goal of specificity switching is fully achieved. Along the sampled transition paths, most intermediate variants appear to retain specificity close to either the initial or the final class, rather than exhibiting gradually shifting specificity. For example, in Figure 4G (Class I to Class II/III), binding appears largely binary, with intermediates behaving similarly to one of the endpoints. A similar pattern is observed in Figure 3H for the Class I to Class IV transition, where binding responses are close to 0 or 1. In this sense, the specificity-switching objective is only partially realized by assigning two endpoints with different specificity. This raises a broader conceptual question: is it possible that different WW specificities evolved from a common ancestor without passing through intermediates that exhibit mixed or intermediate specificity? If so, then inferring specificity-switching pathways purely from extant natural sequences may be fundamentally challenging.

Reviewer #2 (Public review):

This is an extremely important work that shows how one can use generative models to construct specificity-switching mutational paths in complex fitness landscapes. The experimental evidence is very clear, and the theoretical tools are innovative.

The work will likely have a deep impact on future research aimed at understanding how evolution navigates fitness landscapes as well as reconstructing ancestral sequences.

The manuscript is extremely clear and well written, the experimental evidence is strong, and the methods are clearly described, so I do not have major issues to raise. A few minor issues are listed below.

(1) I consider the WW domain as an 'easy' case from the point of view of generative modelling. The domain is rather short, epistatic effects are not very strong (e.g. Boltzmann learning usually converges very quickly to a very paramagnetic state), and the resulting models are well interpretable (e.g. the hidden units of the RBM correlate well with subclasses).

This is not always (not often?) the case, however. In more complex proteins, the learning procedures can be slower and the resulting models less interpretable. Just for completeness, perhaps the authors could comment on the generality of the results and what they would expect for other systems based on their experience.

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

In this paper, the authors investigate the effects of Miro1 on VSMC biology after injury. Using conditional knockout animals, they provide the important observation that Miro1 is required for neointima formation. They also confirm that Miro1 is expressed in human coronary arteries. Specifically, in conditions of coronary diseases, it is localized in both media and neointima and, in atherosclerotic plaque, Miro1 is expressed in proliferating cells.

However, the role of Miro1 in VSMC in CV diseases is poorly studied and the data available are limited; therefore, the authors decided to deepen this aspect. The evidence that Miro-/- VSMCs show impaired proliferation and an arrest in S phase is solid and further sustained by restoring Miro1 to control levels, normalizing proliferation. Miro1 also affects mitochondrial distribution, which is strikingly changed after Miro1 deletion. Both effects are associated with impaired energy metabolism due to the ability of Miro1 to participate in MICOS/MIB complex assembly, influencing mitochondrial cristae folding. Interestingly, the authors also show the interaction of Miro1 with NDUFA9, globally affecting super complex 2 assembly and complex I activity.<br /> Finally, these important findings also apply to human cells and can be partially replicated using a pharmacological approach, proposing Miro1 as a target for vasoproliferative diseases.

Comments on revisions:

The authors have adequately addressed all the concerns raised by the reviewers, and the manuscript has been substantially improved

Reviewer #2 (Public review):

Summary:

This study identifies the outer‑mitochondrial GTPase MIRO1 as a central regulator of vascular smooth muscle cell (VSMC) proliferation and neointima formation after carotid injury in vivo and PDGF-stimulation ex vivo. Using smooth muscle-specific knockout male mice, complementary in vitro murine and human VSMC cell models, and analyses of mitochondrial positioning, cristae architecture and respirometry, the authors provide solid evidence that MIRO1 couples mitochondrial motility with ATP production to meet the energetic demands of the G1/S cell cycle transition. However, a component of the metabolic analyses are suboptimal and would benefit from more robust methodologies. The work is valuable because it links mitochondrial dynamics to vascular remodelling and suggests MIRO1 as a therapeutic target for vasoproliferative diseases, although whether pharmacological targeting of MIRO1 in vivo can effectively reduce neointima after carotid injury has not been explored. This paper will be of interest to those working on VSMCs and mitochondrial biology.

Strengths:

The strength of the study lies in its comprehensive approach assessing the role of MIRO1 in VSMC proliferation in vivo, ex vivo and importantly in human cells. The subject provides mechanistic links between MIRO1-mediated regulation of mitochondrial mobility and optimal respiratory chain function to cell cycle progression and proliferation. Finally, the findings are potentially clinically relevant given the presence of MIRO1 in human atherosclerotic plaques and the available small molecule MIRO1.

Weaknesses:

(1) High-resolution respirometry (Oroboros) to determine mitochondrial ETC activity in permeabilized VSMCs would be informative.

(2) Therapeutic targeting of MIRO1 failed to prevent neointima formation, however, the technical difficulties of such an experiment is appreciated.

Comments on revisions:

The authors have addressed the concerns I previously raised.

Reviewer #3 (Public review):

Summary:

This study addresses the role of MIRO1 in vascular smooth muscle cell proliferation, proposing a link between MIRO1 loss and altered growth due to disrupted mitochondrial dynamics and function. While the findings are useful for understanding the importance of mitochondrial positioning and function in this specific cell type, the main bioenergetic and mechanistic claims are not strongly supported.

Strengths:

- This study focuses on an important regulatory protein, MIRO1, and its role in vascular smooth muscle cell (VSMC) proliferation, a relatively underexplored context.<br /> - This study explores the link between smooth muscle cell growth, mitochondrial dynamics, and bioenergetics, which is a significant area for both basic and translational biology.<br /> - The use of both in vivo and in vitro systems provides a useful experimental framework to interrogate MIRO1 function in this context.

Weaknesses:

- Some key bioenergetic aspects may require further investigation.

Comments on revisions:

The authors have adequately addressed most of the concerns I raised. I would suggest adding some of the justifications provided to the reviewers to the manuscript to further clarify and aid interpretation of the data, especially for the bioenergetic part (e.g., the proposed interaction with CI components, which might otherwise appear implausible to readers).

Reviewer #1 (Public review):

Summary:

This article provides new insights into the organisational changes of the X4-tropic HIV-1 co-receptor CXCR4 upon binding of the viral receptor-binding protein X4-gp120, either in its soluble form or when displayed as Env on virus-like particles (VLPs). The study employs single-particle tracking total internal reflection fluorescence (SPT-TIRF) microscopy to quantify the dynamics and clustering of CXCR4 on CD4+ T cells. The data show that CXCR4 clusters in the presence of X4-gp120 and VLPs, a phenomenon that is also observed for the primary HIV-1 receptor CD4. The authors also show that a WHIM mutant of CXCR4 (CXCR4-R334X) that does not cluster in the presence of its natural ligand, CXCL12, clusters in the presence of X4-gp120 and VLPs.

Major strengths:

The data are well presented, discussed, and supported by solid evidence. Literature is cited appropriately.

Major weaknesses:

The authors have addressed my concerns in the revised manuscript.

Significance:

In summary, the work is presented in a clear fashion, and the main findings are properly highlighted. The paper will be of interest to the broader virology community as well as to researchers studying cell receptor clustering. The findings are not entirely surprising because it has been shown previously that the binding of Env to CD4 mediates CD4 clustering, which would also suggest clustering of the co-receptor. Nonetheless, the paper provides strong evidence that CXCR4 clusters and changes its dynamics in the presence of CD4 and X4-gp120. Moreover, the evidence that X4-gp120 clusters CXCR4-R334X is of high interest as it suggests a different binding mechanism for X4-gp120 from that of the natural ligand CXCL12, raising questions for further research.

Reviewer #2 (Public review):

Summary:

The author investigates how the HIV-1 Env glycoprotein modulates the nanoscale organisation and dynamics of the CXCR4 co-receptor on CD4⁺ T cells. The author demonstrates that HIV-1 Env induces CXCR4 clustering distinct from that triggered by its natural ligand (CXCL12), implicating spatial receptor organization as a determinant of infection. This study investigates how HIV-1 Env (specifically X4-tropic gp120) alters the membrane organization and dynamics of the chemokine receptor CXCR4 and its WHIM-associated mutant, CXCR4R334X, in a CD4-dependent manner. Using single-particle tracking total internal reflection fluorescence microscopy (SPT-TIRF-M), the authors demonstrate that both soluble gp120 and virus-like particles (VLPs) displaying gp120 induce CXCR4 nanoclustering, reduce receptor diffusivity, and promote immobile nanoclusters of CXCR4 at the membrane of Jurkat T cells and primary CD4⁺ T cell blasts. The work offers new insights into the spatial organisation of receptors during HIV-1 entry and infection. The manuscript is well-written, and the findings are significant.

Significance:

Nature and significance of the advance:<br /> This work marks a conceptual and mechanistic breakthrough in understanding HIV-1 entry. It goes beyond the static view of Env-co-receptor interaction to show that nanoscale reorganization of CXCR4, distinct from chemokine-induced clustering, occurs during HIV-1 Env engagement and may be essential for infection.

Context within existing literature. Previous studies established Env-induced CD4 clustering (Yin et al., 2020) and chemokine-induced CXCR4 nanocluster formation (Martínez-Muñoz et al., 2018), but the exact nanoscale rearrangement of CXCR4 in the context of HIV-1 Env and physiological Env densities remains unquantified. This study addresses this gap using SPT-TIRF, STED microscopy, and functional assays.

Audience and influence: The findings will be of interest to researchers in HIV virology, membrane receptor biology, viral entry mechanisms, and therapeutic target development. The receptor-clustering aspect could also influence broader fields of study, such as GPCR organization and immune receptor signalling.

Reviewer expertise: I can evaluate HIV-1 entry mechanisms, viral glycoprotein-host-host-host receptor interactions, single-molecule fluorescence microscopy, and membrane protein dynamics. I am less equipped to evaluate the deep structural modelling aspects, though the in silico AlphaFold results are straightforward to interpret in context.

Reviewer #3 (Public review):

Summary:

The authors investigate how HIV-1 Env engagement affects the nanoscale organization and dynamics of the CXCR4 coreceptor on target cells. Using single-particle tracking TIRF microscopy, they analyze CXCR4 distribution following exposure to gp120 or HIV virus-like particles, including both wild-type CXCR4 and the WHIM-associated CXCR4.R334X variant. The study further examines the role of CD4-CXCR4 heterodimerization and contrasts Env-induced receptor organization with that elicited by the natural ligand CXCL12.

Evaluation:

A major strength of this work is the integration of high-resolution imaging with functional and comparative analyses that distinguish Env-induced CXCR4 clustering from chemokine-driven effects. The experiments are clearly described, include appropriate controls, and are supported by quantitative analyses that are consistent across experiments. The revised manuscript appears to have addressed many of the technical and interpretive issues raised during initial review, improving clarity around data analysis and strengthening confidence in the conclusions.

I am not an expert in TIRF microscopy or single-molecule tracking and defer to other reviewers regarding limits of imaging and tracking methods. However, I did not identify major inconsistencies between the biological data presented and the conclusions drawn.

The authors data support the conclusion that HIV-1 Env, delivered as gp120 or virus-like particles, promotes CD4-dependent nanoscale clustering of CXCR4, including the CXCR4.R334X variant associated with WHIM syndrome, in a manner distinct from CXCL12-induced receptor organization. The authors are generally careful to frame their conclusions in proportion to the evidence and avoid overinterpretation.

Overall, this study builds on prior work on CXCR4 distribution and HIV entry by providing higher-resolution insight into receptor nanoclustering and its modulation by Env. The findings provide a mechanistic refinement rather than a conceptual paradigm shift but is a valuable dataset useful to researchers studying HIV entry, coreceptor biology, and membrane receptor organization.

Reviewer expertise: HIV-1 Envelope glycoproteins and entry assays, HIV broadly neutralizing antibodies, HIV vaccine design

Comments on revised version:

This reviewer has no further recommendations and thanks the authors for clarifying that the Env content in gp120-VLPs was lower than the NL4-3deltaIN particles but that the percentage of mature particles in the gp120-VLPs was higher.

Reviewer #4 (Public review):

Summary:

The authors investigate the impact of surface bound HIV gp120 and VLPs on CXCR4 dynamics in Jurkat T cells expressing WT or WHIM syndrome mutated CXCR4, which has a defective response to CXCL12. Jurkat cells were transfected with CXCR4-AcGFP. Images were acquired and a single particle tracking routine was applied to generate information about nanoclustering and diffusion, and FRET was used to investigate CD4-CXCR4 proximity. They compare effects of soluble gp120 to immature and mature VLPs, which include varying degrees of gp120 clustering. They find that solid phase gp120 or VLP can increase CXCR4 clustering size and decrease diffusion in Jurkat cells. Surprisingly, VLP lacking gp120 could increase CXCR4 clustering and speed, which is paradoxical as there were no known ligands on the VLPs, but they likely carry many cellular proteins with potential interactions. The impact of CXCL12 and gp120 binding to CXCR4 was different in terms of clustering and receptor down-regulation.

Significance:

The strengths are that it's an important question and the reagents are well prepared and characterised. They are detecting quantitative effects that will likely be reproducible. The information generated is potentially useful for those studying HIV infection processes and strategies to prevent infection.

The major weakness is that the conditions for the SPT experiments are not ideal in that the density of particles is too high for SPT and the single molecule basis for assessing nanoclusters is not clear. This means that the data is getting at complex molecules phenomena and less likely be generating pure single molecules measurements.

Comments on revised version:

The authors should make the tracking data available and this will aid others in following up on it.

Reviewer #1 (Public review):

Summary:

Yang et al. investigate the central pathways underlying nociceptive responses in Drosophila. The authors employ a behavioral platform they previously developed, which uses laser stimulation to deliver nociceptive stimuli while enabling automated tracking of fly behavior. By combining large-scale behavioral screening with circuit tracing approaches, the study identifies a set of dopaminergic neurons (DANs) and mushroom body output neurons (MBONs) that participate in the transmission of nociceptive signals. Nociceptive escape behavior has generally been regarded as largely reflexive. It is therefore intriguing that the mushroom body, a neural circuit classically associated with learning, is involved in this process. In particular, the recruitment of dopaminergic neurons typically linked to both appetitive and aversive valence is noteworthy and raises interesting questions about how nociceptive information is integrated within the circuits. Overall, the findings are conceptually interesting and may provide useful insights into dissecting the nociceptive escape behavior.

Strengths:

The behavioral assay used in this study is high-throughput and appears reproducible. The authors screened a large number of genetic lines, and the behavioral responses were carefully quantified. The trans-Tango tracing results are consistent with the behavioral screening results. And the observation that circuits typically associated with learned behaviors (mushroom body) contribute to a nociceptive escape response, generally considered a hard-wired reflex, is conceptually interesting.

Weaknesses:

The use of laser stimulation to induce nociceptive stimuli makes the paradigm difficult to combine with calcium imaging or optogenetic manipulations. As a result, the study lacks functional and temporally precise tests of the proposed circuit mechanisms.

Several aspects of the Methods section require additional detail:

(1) How was the behavioral potency level calculated? Since some of the split-GAL4 lines label multiple neurons, and the individual neurons may innervate multiple compartments. It is therefore unclear how a single "behavioral potency level" value was assigned to a compartment.

(2) Additional details are needed on how velocity was calculated, particularly the time window used for the analysis. In the Kir-silenced condition, the variation in velocity appears smaller than in the control group, which would benefit from clarification.

(3) Connectome analysis. More details are needed regarding how DAN-MBON connectivity was quantified in Figure 5. For example, were only DAN → MBON connections considered, or were bidirectional connections included?

Reviewer #2 (Public review):

Summary:

This manuscript aims to identify the central nervous system circuitry, specifically within the mushroom body (MB), that mediates nociception-induced escape behavior in adult Drosophila. The authors provide a detailed map of the neural pathways underlying defensive actions in flies. Overall, the study is technically solid, clearly written, and conceptually<br /> interesting.

Strengths:

The authors present compelling evidence by integrating multiple complementary approaches. The ALTOMS laser system enables precise, automated measurement of escape latency, allowing for high-throughput and objective behavioral quantification. Neuronal silencing experiments assess functional necessity and demonstrate that specific dopaminergic neurons (DANs) and mushroom body output neurons (MBONs) are critical for escape behavior. Trans-Tango anatomical mapping further supports the proposed circuit by identifying putative synaptic connections consistent with the authors' model.

Weaknesses:

A central limitation of the study is its heavy reliance on chronic Kir2.1-mediated neuronal silencing as the primary functional manipulation. This approach raises concerns about potential developmental compensation and indirect network effects. The authors could strengthen their conclusions by incorporating more temporally precise, reversible silencing strategies, such as recently developed optogenetic- or chemogenetic-based methods.

In addition, the study relies on the trans-Tango system to identify downstream synaptic partners, which has several inherent limitations. Trans-Tango detects only chemical synapses and cannot reveal electrical coupling. The system may also yield false negatives due to reporter sensitivity, and anatomical labeling alone does not establish functional connectivity in the context of the specific behavior examined.

Reviewer #3 (Public review):

Summary:

Yang et al sought to describe central brain circuits that underlie nociception-induced escape in Drosophila using a combination of neurogenetic tools to silence subsets of neurons and to trace their postsynaptic connections. They present interesting data that identify subsets of DANs and MBONs that are required for a jumping response to an aversive stimulus, but not for baseline locomotion, and present a model for linking peripheral nociception to MB- dependent escape behavior.

Strengths:

They use an innovative avoidance assay to elicit a robust behavioral response and use trans-tango to identify downstream targets of painless and TrpA1-expressing neurons.

Weaknesses:

This reviewer's enthusiasm for the study is lowered due to an incomplete description of methods, methods section, appropriate behavioral controls, immunohistochemistry data, and a complete behavioral screen of DANs and MBONs. Below I list my suggestions, questions, and criticisms.

(1) Behavioral studies are interesting. The assay is simple, yet innovative. However, there is no power analysis or explanation of how sample sizes were selected. I commend the authors for including a positive control; however, although UAS-controls are present, there are no GAL4-controls included in the study. Given that many of the lines used for behavior are split-GAL4's, it's unclear if the additional transgene influenced behavior. This should be addressed.

(2) It is also not clear from the methods how the behavior was run and how it was analyzed. Was baseline locomotion recorded before the laser was introduced? I assume this is the case; however, more importantly, how long after the flies were introduced to the arena were baseline recordings collected? How much data was used to calculate velocity? Were the experimenters blind to the conditions they were assessing? More detail in the methods is essential for understanding the data and providing an opportunity to replicate results.

(3) At times, the authors describe "locomotion velocity" as baseline locomotion, but other times, they describe it as escape velocity (see reference to Figure 1F). The authors should clarify whether escape velocity was calculated.

(4) Immunohistochemistry: There is a lack of detail regarding a description of the flies used for trans-tango experiments. How many brains were evaluated? Was there variability across brains? Were the flies males or females? This is an important detail as sex could impact the level of expression of the ligand and therefore the results. It is also not clear at what age these flies were dissected and at what temperature they were raised. This can also significantly affect the post-synaptic signal that is measured (see Talay et al 2017).

(5) Figure 2 shows the overlap of trans-tango and dopamine signal, but there is no signal for the GAL4-line to evaluate the overlap between presynaptic signal and postsynaptic signal. This expression is an important consideration and should be included.

(6) Expression of the GAL4 lines in the central brain is also important to show because the authors suggest that, because painless and TrpA1 expression does not fully overlap in peripheral tissue, it might converge in the central brain. Does that central brain expression of painless and TrpA1 overlap?

(7) Further, although the authors clearly label the different dopamine subsets (PPL1, PAL, and PAM), some orientation with regard to where these images were taken would be helpful. I recommend a stack showing the location of the cell bodies and then a zoom in to see the overlap.

(8) Behavioral data for DANs and MBONSs: I recommend that the authors discuss the results by the neurons that are targeted and not the driver lines. For instance, the authors suggest they get the largest effects for 433B, 434B, and 298B, but all of these lines target very similar neuronal subsets y4>y1y2. It's also not clear why different split-lines were selected. Several of the lines have overlapping expression, and other compartments were not included at all. In order to determine which MBONs and DANs are required for escape behavior, all MBONs and DANs should be included. See Aso et al for a list of recommended lines for behavior based on specificity and intensity.

(9) Based on trans-tango data, it is not clear why the authors focus exclusively on PPL1 and PAM when PAL, PPM1, 2, 3, and PPL2 also overlap with painless and trpA1. Certainly, PPL1 and PAM DANs innervate the MB, but so do some of the other DANs identified.

(10) For Figure 5, the titles of A and B are DANs and MBONs, but it is really showing the average jumping response when neurons that innervate MB compartments are silenced. Many DANs and MBONs innervate multiple compartments (PPL1-a`2a2, etc.); thus, if the intention is to identify neural circuits that modulate escape response, the analysis should focus on the neurons, not the MB compartments. I recommend reorganizing this data so it highlights the DANs and MBONs instead of the MB compartments. I also recommend showing error bars for averages and/or raw data and organizing the x-axes so DAN and MBON compartments can be easily compared.

(11) Lastly, nuance is lost here in the Behavioral Potency Level, given that some of these compartments are over-represented and not adjusted for the strength of expression in different split-GAL4 lines. Aso et al. (2014) recommended specific split-GAL4 lines based on specificity and intensity. Some of the lines that are included in the average Behavioral Potency are not recommended for behavior based on the intensity of expression, which could significantly influence the potency score.

Reviewer #1 (Public review):

Summary:

This paper presents a wireless device for closed-loop control of optogenetic stimulation based on behavioral triggers. The authors demonstrate the device through two behavioral experiments in mice, showcasing the device's capabilities and emphasizing open accessibility and using off-the-shelf components.

Strengths:

The paper presents a device that is open access and easily reproducible for wireless stimulation in a closed loop based on behavioral triggers. Other strengths of the device include the simultaneous use of multiple devices in parallel and the claimed ease of integration with existing frameworks. The paper shows to behavioral experiments on multiple mice along with some device validation results.

Weaknesses:

The main weakness of the presented device lies in the lack of flexibility in stimulation power. For a device that is intended for stimulation only, having to physically change a component on the board to adapt stimulation power is a major downside. Reprogrammable stimulation current is not complex to implement and should really have been included on this device. Another weakness lies in the limited battery life of the device. While using a battery-powered device decreases spatial constraints, allowing for the maze experiment presented in the paper, it also means the lifespan of the device is limited compared to an inductively powered device, limiting its ability for long-term experiments.

Reviewer #2 (Public review):

Summary:

The authors have developed an elegant, lightweight, open-source system that should be able to be widely disseminated to the community. They have used this system in multiple experimental paradigms and demonstrate its functionality quite elegantly. One of these experiments involves two of three animals in the arena being stimulated, a situation that clearly requires an untethered approach. They have appropriately quantified key system parameters (latency and battery life).

Strengths:

The introduction places this work in a broader context. That context includes a number of previous solutions, many of which are smaller or more technically complex. However, I agree with the authors that there is a need for something that is easy for labs to acquire and deploy in terms of both what goes on the head and the broader infrastructure (i.e., not needing complex wireless power delivery approaches).

The paper does an excellent job of describing the system architecture. And the architecture is good! Their system comprises more than just the bluetooth enabled head-mounted devices - they also have built an interface that allows for TTL triggers that link into existing workflows.

The key metrics for a device like this are weight, battery life, and latency. The weight is 1.4g, which is appropriate for adult mice; the battery life is ~100 minutes of continuous stimulation, which should be sufficient for many experiments, and the latency is typically less than 30 ms, which is fine for all but the most demanding closed-loop experiments.

Performance is demonstrated in two experiments, a continuous Y-maze, which elegantly demonstrates how transfected animals learn to sense optogenetic closed-loop stimulation to drive their choice behavior in a way that control-stimulated animals do not. While authors claim that the ~2m diameter apparatus is "large scale", the second behavior more convincingly demonstrates the need for wireless stimulation.

They used closed-loop monitoring of animal pose to selectively stimulate animals for approaching the tails of a dominant conspecific (based on pre-experimental pairwise assessments). It seems that the original hope was that the increases in following that they observe would result in long-lasting changes in the hierarchy of a cage, but as they report, this was not observed. Critically, their supplementary video demonstrates that they conducted this experiment with two instrumented animals simultaneously. This is a situation where a tether would have been hopelessly tangled within a few moments!

The online documentation seems complete, and it seems quite possible for other labs to adopt and deploy the system.

Weaknesses:

The battery life is highly dependent on the stimulation paradigm. It makes sense that the LED is a major component of power consumption. It would have been elegant to measure the total optical energy that can be provided by the system. In addition, Bluetooth transmission is probably a major consumer of power, and receiving may not be "free". Quantifying power as a function of Bluetooth message rates would have been useful.

Presumably, the major constraint on latency is that the Bluetooth receiver polls at ~10 Hz, resulting in latency blocks of 20+, 30+, or 40+ ms. Why latency is never less than 10 ms is unclear. Could latency be reduced by changing a setting? Having a low-latency option would be very helpful for some experimental situations. Latency is probably the primary weakness of the system.

The programming process sounds quite complicated. It would be nice if they had OTA updates. But described and open source. Similarly, the configuration process (Arduino IDE) seems a bit complex. It would be nice if there were a dedicated cross-platform application.

It is unclear what the maximum number of devices that could be used without wireless interference is. The base station has two charging stations, but it would have been nice to understand the limits beyond this number.

There is a very nice website for the system, but there is some concern that the code and design files are not archived. Could they be deposited with the paper?

Reviewer #3 (Public review):

Summary:

This study presents a novel device for wireless control of optogenetic stimulation of the mouse brain, the Blueberry, using Bluetooth Low Energy (BLE) communication for parallel activation of up to 4 devices through an Arduino interface. The authors also present two types of brain implants for light delivery that can be connected to the Blueberry: one using uLEDs for surface cortical stimulation, and another using optical fibers for intra- or sub-cortical implants. The architecture of the system, including electronics, communication, and programming, is thoroughly described. Because the system was especially designed to be integrated with existing software used for neuroscience behavioral experiment for closed-loop experiments, validation of the system is shown on two different scenarios: a learning task in a "infinite" Y-maze, where light delivery at precise locations conditions arm choice for navigation; and a social interaction analysis where 3 animals are simultaneously stimulated in order to alter social dynamics among the group.

Strengths:

(1) The full system can be built by individual labs with simple PCB printing, off-the-shelf components, and readily available hardware (Arduino) for widespread dissemination.

(2) Four headstages can be controlled in parallel for simultaneous experiments with multiple mice.

(3) Validation across different relevant behavioral tests, demonstrating the potential of integrating Bluberry in closed-loop setups.

Weaknesses:

(1) Some details in the manuscript regarding system characterization (latency, battery life, etc) are included only in the supplementary materials.

(2) The practical details of integration with other commercial and open-source software used for the closed-loop experiments, which could help third-party researchers interested in using the system, are lacking sufficient detail.

(3) System range (3 meters reported) is limited for a BLE device.

(4) Light output amplitude is not programmable, limiting the choice of stimulation protocols and LEDs used.

(5) Thermal modeling of the cortical surface stimulator was not performed, and it is unclear if the brain implant for this purpose is within the safety limits.

(6) The paper is missing a comparison with other state-of-the-art devices for wireless control of optogenetic stimulation in mice.

Reviewer #1 (Public review):

Summary:

Mancl et al. present an integrative structural and mechanistic analysis of the human insulin-degrading enzyme (IDE), combining cryo‑EM, time‑resolved cryo‑EM, SEC‑SAXS, enzymatic assays, all-atom molecular dynamics (MD) simulations, and coarse‑grained MD simulations. Their study delineates how IDE undergoes coordinated open-close transitions and interdomain rotations, how these motions relate to its unfoldase and protease activities, and how a single residue, R668, acts as a molecular latch governing these conformational changes. Through expanded structural datasets and computational analyses, the authors propose a mechanistic model for how IDE captures, unfolds, and degrades diverse amyloidogenic substrates such as insulin and Aβ.

Strengths:

A major strength of this study is its integration of structural, biophysical, biochemical, and computational approaches. The authors now provide six cryo‑EM structures, including a new time‑resolved O/O state captured 123 ms after substrate mixing, which clarifies the early structural response of IDE to insulin binding. The combination of multibody analysis, 3D variability analysis, all‑atom MD, and coarse‑grained Upside simulations yields a coherent picture in which rotational interdomain motions and charge‑swapping events at the IDE‑N/C interface underpin substrate unfolding and repositioning.

The identification of R668 as a central determinant of the open-close transition, supported by MD, HDX‑MS data from prior work, SEC‑SAXS, and functional assays on the R668A mutant, represents a significant mechanistic advance. The inclusion of Aβ degradation assays adds biological breadth and supports the conclusion that R668 modulates activity in a substrate‑dependent manner.

The authors have also substantially improved clarity by reorganizing figures, refining section headers, and adding introductory structural schematics. Taken together, the revised manuscript now provides a rigorous and accessible framework for understanding IDE dynamics and their relevance to amyloid peptide turnover.

Weaknesses:

At this stage, remaining limitations are modest and inherent to the system rather than the approach. While the study convincingly demonstrates substrate‑dependent modulation of IDE dynamics, it does not experimentally assess additional endogenous substrates (e.g., amylin, glucagon), which would be needed to fully generalize the role of R668 across the substrate spectrum of IDE. Furthermore, the timescale mismatch between MD simulations and catalytic turnover, which the authors clearly acknowledge, means that correlations between simulated motions and enzymatic kinetics remain inferential. Finally, some flexible cryo‑EM states (particularly O/pO) continue to exhibit moderate local resolution, which constrains atomic interpretation of highly dynamic regions, although this is addressed transparently.

Reviewer #2 (Public review):

Summary:

The manuscript describes various conformational states and structural dynamics of the Insulin degrading enzyme (IDE), a zinc metalloprotease by nature. Both open and closed state structures of IDE have been previously solved using crystallography and cryo-EM which reveal a dimeric organization of IDE where each monomer is organized into N and C domains. C-domains form the interacting interface in the dimeric protein while the two N-domains are positioned on the outer sides of the core formed by C-domains. It remains elusive how the open state is converted into the closed state but it is generally accepted that it involves large-scale movement of N-domains relative to the C-domains. Authors here have used various complementary experimental techniques such as cryo-EM, SAXS, size-exclusion chromatography and enzymatic assays to characterize the structure and dynamics of IDE protein in the presence of substrate protein insulin whose density is captured in all the structures solved. The experimental structural data from cryo-EM suffered from high degree of intrinsic motion amongst the different domains and consequently, the resultant structures were moderately resolved at 3-4.1 Å resolution. Total five structures were generated in the originally submitted manuscript using cryo-EM. Another cryo-EM reconstruction (sixth) at 5.1Å resolution was mentioned after first revision which was obtained using time-resolved cryo-EM experiments. Authors have extensively used Molecular dynamics simulation to fish out important inter-subunit contacts which involves R668, E381, D309, etc residues. In summary, authors have explored the conformational dynamics of IDE protein using experimental approaches which are complemented and analyzed in atomic detail by using MD simulation studies. The studies are meticulously conducted and lay the ground for future exploration of the protease structure-function relationship.

Strengths:

The manuscript presents a powerful integrative structural biology study that combines high-resolution cryo-EM, particle heterogeneity analysis, time-resolved cryo-EM, multiscale molecular dynamics simulations, SAXS, and biochemical assays to dissect the conformational dynamics of human insulin-degrading enzyme. A major strength is the identification of a previously unappreciated rotational component of IDE-N relative to IDE-C and the discovery of R668 as a molecular latch governing the open-close transition, supported consistently by structural, computational, mutational, and functional data. The work provides a coherent mechanistic framework linking IDE dynamics to substrate unfolding, allostery, and substrate-dependent catalysis, with clear relevance to diabetes and Alzheimer's disease biology.

Weaknesses:

Despite its depth, several key mechanistic conclusions-particularly substrate unfolding and the proposed "β-grabbing" mechanism-rely heavily on coarse-grained and all-atom MD simulations rather than direct experimental observation. Cryo-EM density for insulin is limited and heterogeneous, restricting definitive structural interpretation of substrate binding modes. The time-resolved cryo-EM experiment captures only a single dominant state at modest resolution, limiting insight into transient intermediates. In addition, the study focuses primarily on insulin, leaving the generality of the proposed mechanism for other IDE substrates insufficiently tested, and the therapeutic implications remain largely speculative without direct pharmacological modulation data.

Reviewer #2 (Public review):

Summary

This study addresses the hypothesis that the higher prevalence of autoimmune diseases in women could result from sex-dependent differences in thymic generation or selection of TCR repertoires. The biological question is important and the dataset is valuable. However, the study has major conceptual and analytical limitations.

In particular:

- The conclusions cannot be generalized to autoimmune diseases as a whole, as only type 1 diabetes (T1D) and celiac disease (CeD) antigens were analyzed.<br /> - The central interpretation is not supported by the data, as the observed signal is strongly influenced by TCRs associated with T1D, which shows a male-biased incidence and therefore does not align with the female bias the study aims to explain.

Strengths

The key strength of this work is the newly generated dataset of TCR repertoires from sorted thymocyte subsets (DP and SP populations). This approach enables the authors to distinguish between biases in TCR generation (DP) and thymic selection (SP). Bulk TCR sequencing allows deeper repertoire coverage than single-cell approaches, which is valuable here. However, the absence of TRA-TRB pairing and HLA context limits the interpretability of antigen specificity analyses.

Weaknesses

The authors did not adequately address the central concerns raised in the previous review. As a result, the major issues remain unresolved.

(1) Generalization to autoimmune diseases is not justified.

The study aims to explain the higher prevalence of autoimmune diseases in females. The main conclusion is based on enrichment in females of TCRs annotated as autoimmune-associated using database matching.<br /> However, these matches correspond exclusively to TCRs specific for T1D and CeD. This already limits the conclusions to these two diseases and does not justify generalization to autoimmune diseases as a whole.

(2) Contradiction with epidemiology of T1D which is male-biased

T1D and CeD have opposite sex biases in European populations. While CeD is more frequent in females (~60%; doi:10.1016/j.cgh.2018.11.013), T1D is more frequent in males (male:female = 1.11 in France; doi:10.1111/dom.70124).<br /> Importantly, T1D constitutes a substantial fraction of the autoimmune-associated dataset (42 out of 48 epitopes; 83 out of 185 TRB sequences). Therefore, the observed signal is strongly influenced by a disease that does not follow the female bias the study aims to explain.

The authors argue that T1D sex bias varies globally, including female-biased incidence in East Asia and Africa. However, this argument does not resolve the issue, as the cohort analyzed in this study was derived from France, where T1D shows a male-biased incidence. Thus, the interpretation remains inconsistent with the population context of the dataset.

(3) Lack of disease-level and donor-level resolution

The authors combine T1D and CeD into a single "autoimmune" category and do not provide per-disease, per-donor or per-epitope distributions, despite explicit reviewer's requests.

This prevents evaluation of whether the observed signal is driven by:<br /> - a specific disease (T1D or CeD), or<br /> - a small number of donors

Without this analysis, the conclusions cannot be properly interpreted.

(4) Use of "polyspecificity" concept is not supported by experimental evidence

The authors extensively use the concept of "polyspecific TCRs," defined as single-chain CDR3 sequences annotated across databases as recognizing distinct and unrelated antigenic categories. This concept is not supported by experimental evidence (except for a single TCR in Quiniou et al., as acknowledged by the authors).

In the absence of robust validation, a more parsimonious explanation for such ambiguously annotated TCR chains is the presence of false-positive annotations in public databases (see, e.g., Ton Schumacher's preprint https://www.biorxiv.org/content/10.1101/2025.04.28.651095.abstract) or alternatively, distinct TRA pairing for identical TRB sequences resulting in different specificities.

The observation that these TCRs have high generation probability is expected, as TCRs found in independent studies are likely to have high generation probability. The interpretation of these sequences as biologically meaningful entities (e.g., a "first line of defense") is therefore speculative and not supported by the data.

The authors also refer to in silico-generated polyspecific TCRs (ref. to Nature Machine Intelligence). However, such sequences are generated ex vivo and do not undergo thymic selection. A TCR capable of recognizing multiple unrelated foreign antigens would likely also recognize self-antigens and be eliminated during negative selection. Therefore, this argument does not support the biological relevance and in vivo existence of the proposed polyspecific TCR class.

(5) Insufficient statistical analysis of diversity

The absence of statistically significant differences in repertoire diversity between sexes (Figure 3), despite an apparent visual trend, may reflect limited sample size and insufficient statistical power rather than a true absence of differences. A more appropriate statistical approach, such as mixed-effects modeling, was requested in the previous review but was not performed.

Reviewer #1 (Public review):

Summary:

Age-related synaptic dysfunction can have detrimental effects on cognitive and locomotor function. Additionally, aging makes the nervous system vulnerable to late-onset neurodegenerative diseases. This manuscript by Marques et al. seeks to profile the cell surface proteomes of glia to uncover signaling pathways that implicated in age-related neurodegeneration. They compared the glial cell-surface proteomes in the central brain of young (day 5) and old (day 50) flies and identified the most up- and down-regulated proteins during the aging process. 48 genes were selected for analysis in a lifespan screen, and interestingly, most sex-specific phenotypes. Among these, adult-specific pan-glial DIP-β overexpression (OE) significantly increased the lifespan of both males and females and improved their motor control ability. To investigate the effect of DIP-β in the aging brain, Marques et al. performed snRNA-seq on 50-day old Drosophila brains with or without DIP-β OE in glia. Cortex and ensheathing glia showed the most differentially expressed genes. Computational analysis revealed that glial DIP-β OE increased the cell-cell communication, particularly with neurons and fat cells.

Strengths:

(1) State-of-the-art methodology to reveal the cell surface proteomes of glia in young and old flies.

(2) Rigorous analyses to identify differentially expressed proteins. 3

(3) Examination of up- and down-regulated candidates and identification of glial-expressed mediators that impact fly lifespan.

(4) Intriguing sex-specific glial genes that regulate life span.

(5) Follow-up RNA-seq analysis to examine cellular transcriptomes upon overexpression of an identified candidate (DIP-β).

(6) A compelling dataset for the community that should generate extensive interest and spawn many project.

Weaknesses:

(1) DIP-β OE using flySAM:

a) These flies showed a larger increase in lifespan compared to using UAS-DIP-β (Figure 2 C,D). Do the authors think that flySAM is a more efficient way of OE than UAS? Also, the UAS construct would be specific to one DIP-β isoform while flySAM likely would likely express all isoforms. Could this also contribute to the phenotypes observed?

b) The Glial-GS>DIP-β flySAM flies without RU-486 have significantly shorter lifespans (Figure 2C) than their UAS-DIP-β counterparts. flySAM is lethal when expressed under the control of tubulin-GAL4 (Jia et al. 2018) likely due to toxicity of such high levels of overexpression. Is it possible that larger increase in lifespan is due to the already reduced viability of these flies?

c) Statistics: It is stated in the Methods that "statistical methods used are described in the figure legend of each relevant panel." However, there is no description of the statistics or sample sizes used in Figure 2.

(2) Figure 3: The authors use a glial GeneSwitch (GS) to knock down and overexpress candidate genes. In Figure 3A, they look at glial-GS>UAS-GFP with and without RU. Without RU, there is no GFP expression, as expected. With RU, there is GFP expression. It is expected that all cell body GFP signal should colocalize with a glial nuclear marker (Repo). However, there is some signal that does not appear to be glia. Also, some many glia do not express GFP, suggesting the glial GS driver does not label all glia. This could impact which glia are being targeted in several experiments.

(3) It is interesting that sex-specific lifespan effects were observed in the candidate screen.

a) The authors should provide a discussion about these sex-specific differences and their thoughts about why these were observed.

b) The authors should also provide information regarding the sex of the flies used in the glial cell surface proteome study.

c) Also, beyond the scope of this study, examining sex-specific glial proteomes could reveal additional insights into age-related pathways affecting males and females differentially.

(4) The behavioral assay used in this study (climbing) tests locomotion driven by motor neurons. The proteomic analysis was performed with the central adult brain, which does not include the nerve cord where motor neurons reside. While likely beyond the scope of this study, it would be informative to test other behaviors including learning, circadian rhythms, etc.

(5) It is surprising that overexpressing a CAM in glia has such a broad impact on the transcriptomes of so many different cell types. Could this be due to DIP-β OE maintaining the brain in a "younger" state and indirectly influencing the transcriptomes? Instead of DIP-β OE in glia directly influencing cell-cell interactions? Can the authors comment on this?

Comments on revisions:

The authors have conducted additional experiments, updated text/figures, and included discussions to address the concerns raised by the reviewers. I commend the authors on a thorough, rigorous study that will undoubtedly impact the field and spawn many projects for years to come.

One minor comment: In Figure S2, the figure legend states "A-C"; however, the figure itself only has an A and B.

Reviewer #3 (Public review):

Summary:

Nigro et al examine how the locus coeruleus (LC) influences the medial prefrontal cortex (mPFC) during attentional shifts required for behavioral flexibility. Specifically, they propose that LC-mPFC inputs enable mice to shift attention effectively from texture to odor cues to optimize behavior. The LC and its noradrenergic projections to the mPFC have previously been implicated in this behavior. The authors further establish this by using chemogenetics to inhibit LC terminals in mPFC and show a selective deficit in extradimensional set shifting behavior. But the study's primary innovation is the simultaneous inhibition of LC while recording multineuron patterns of activity in mPFC. Analysis at the single neuron and population levels revealed broadened tuning properties, less distinct population dynamics, and disrupted predictive encoding when LC is inhibited. These findings add to our understanding of how neuromodulatory inputs shape attentional encoding in mPFC and are an important advance. There are some methodological limitations and/or caveats that should be considered when interpreting the findings and these are described below.

Strengths:

The naturalistic set-shifting task in freely-moving animals is a major strength, and the inclusion of localized suppression of LC-mPFC terminals builds confidence in the specificity of the behavioral effect. Combining chemogenetic inhibition of LC while simultaneously recording neural activity in mPFC with miniscopes is state-of-the-art. The authors apply analyses to population dynamics, in particular, that can advance our understanding of how the LC modifies patterns of mPFC neural activity. The authors show that neural encoding at both the single cell level and the population level are disrupted when LC is inhibited. They also show that activity is less able to predict key aspects of the behavior when the influence of LC is disrupted. This is quite interesting and adds to a growing understanding of how neuromodulatory systems sharpen tuning of mPFC activity.

Weaknesses:

Weaknesses are mostly minor, but there are some caveats that should be considered. First, the authors use a DBH-Cre mouse line and provide histological confirmation of overlap between HM4Di expression and TH immunostaining. While this strongly suggests modulation of noradrenergic circuit activity, the results should be interpreted conservatively as there is no independent confirmation that norepinephrine (NE) release is suppressed and these neurons are known to release other neurotransmitters and signaling peptides. In the absence of additional control experiments, it is important to recognize that effects on mPFC activity may or may not be directly due to LC-mPFC NE.

Another caveat is that the imaging analyses are entirely from the extradimensional shift session. Without analyzing activity data from the intradimensional shift (IDS) session, one cannot be certain that the observed changes are to some feature of activity that is specific to extradimensional shifts. Future experiments should examine animals with LC suppression during the IDS as well, which would show whether the observed effects are specific to an extradimensional shift and might explain behavioral effects.

Comments on revisions:

The authors overall do a nice job of addressing reviewer comments, and I believe the manuscript is significantly improved.

Reviewer #1 (Public review):

Genetically encoded fluorescent proteins expressed in specific cell types allow recognising them in vivo and, if the protein is a functional indicator, as in the case of genetically encoded calcium indicators (GECIs), to record activity from the same cellular ensemble. Ideally, if proteins (fluorophores) have perfectly distinct spectral properties, signals can be distinguished from as many cell types as the number of employed fluorophores. In practice, fluorescent proteins have non-negligible crosstalk both in absorption and emission bands. In addition, fluorescence contribution of each fluorophore normally varies from cell to cell and therefore spectral properties of cells expressing two or more proteins are different. The work of Phillips et al. addresses this challenge. The authors present an approach defined as "Neuroplex", allowing identification of up to nine cell types from the same number of fluorophores. The fingerprint of each cell is then associated with functional fluorescence from the GECI GCaMP, allowing recording calcium activity from that specific cell. The method is implemented in vivo using head-mounted miniscopes.

The authors used a mouse line expressing GCaMP in cortical pyramidal neurons and developed an experimental pipeline. First, they injected the nine AAV viruses, causing expression of fluorophores in a different brain area. The idea was not to image that area, but a non-infected medial prefrontal cortex (mPFC) section where neurons could be infected by their axons projecting in an injected area, in this way being identified by their targeting region(s). A GRIN lens, allowing spectral analysis, was mounted in the mPFC section, and GCaMP fluorescence was then recorded during behavioural tasks and analysed to identify regions of interest (ROIs) corresponding to neuron somata. After functional imaging, the head of the mouse was fixed, spectral analysis was performed, and after necessary correction for chromatic distortions, the fluorophore contribution was determined for each ROI (neuron) from where GCaMP signals were detected. Notably, the procedures for estimation and correction of chromatic aberration and light transmission (described in Figure 2) were a major challenge in their technical achievements. The selection of the nine fluorophores was another big effort. This was done by combining computer simulations and direct measurement of spectra from individual proteins expressed in HEK293 cells. It is important to say that the authors could simulate arbitrary combinations of two or more different fluorophores and evaluate the ability of their algorithm to detect the correct proteins against wrong estimations of false-negative (absence of an expressed protein) or false-positive (presence of a non-expressed protein). Not surprisingly, this ability decreases with the level of GCaMP expression. The authors underline that most errors were false-negatives, which have a milder impact in terms of result interpretation, but the rate of false positives was, nevertheless, relevant in detecting a second fluorophore from a cell expressing only one protein. The experimental profiles of fluorophores were dependent both on the specific fluorescent protein and on the projecting area, and the distribution of double-labelled did not match anatomical evidence. This result should be taken as the limitation of the present pioneering experiments, presented as proof-of-principle of the approach, but Neuroplex may provide far improved precision under different experimental conditions.

In my view, the work of Phillips et al. represents a significant advance in the state-of-the-art of the field. The rigorous analysis of limitations in the use of Neuroplex must be considered an important guideline for future uses of this approach.

Comments on revision:

The authors have adequately addressed my comments.

Reviewer #2 (Public review):

Summary:

The manuscript introduces Neuroplex, a pipeline that integrates miniscope Ca²⁺ imaging in freely moving mice with multiplexed confocal and spectral imaging to infer projection identities of recorded neurons. This technical approach is promising and could broaden access to projection-resolved population imaging. However, the core quantitative analyses apply a winner-take-all single-label assignment per neuron even when multiple fluorophores exceed threshold, with additional labels treated descriptively as "secondary hits." While the authors acknowledge and simulate dual labeling, the extent to which this single-label decision rule affects subtype fractions and behavioural comparisons remains uncertain without a multi-label (or probabilistic) sensitivity analysis and propagation of classification uncertainty.

Strengths:

(1) Conceptual advance and practicality: Decoupling acquisition from identity readout constitutes an innovative approach that is, in principle, applicable in laboratories currently using single-color miniscopes.

(2) Engineering thoroughness: The manuscript offers detailed consideration of GRIN optics, spectral libraries, registration procedures, and simulations that address signal-to-noise ratio, background, and class imbalances.

(3) Immediate community value: If demonstrated to be robust, the pipeline could enable projection-resolved analyses without reliance on specialized multicolor miniscopes.

Comments on revision:

The authors have addressed my comments, and I have no further remarks.

Reviewer #1 (Public review):

In this study, the authors investigated a specific subtype of SST-INs (layer 5 Chrna2-expressing Martinotti cells) and examined its functional role in motor learning.

Most of the issues remain unaddressed. The findings across experiments are inconsistent, and it is unclear how the authors performed their analyses or why specific time points and comparisons were chosen. The study will require major re-analyzing and additional experiments to substantiate its conclusions.

After reading the reviewers' responses, my major concerns about the manuscript remain unresolved, particularly regarding the arbitrarily defined stages of learning in the motor learning task and how the calcium imaging data align with the animal's movements.

- In line 331, the authors refer to session 5 as "training," describing it as the final spoon session, and session 6 as "re-training," because it is the first session in which the pellet is presented on the plate rather than on the spoon. However, in Fig. 1F-H, even in the Ctrl group, it is clear that the performance drops significantly in session 5, which is supposed to be the easiest session before switching to the more difficult plate condition.

- In the classic pellet-reaching task, the spoon sessions would typically be considered "shaping", while the plate sessions would represent the actual training phase. However, in this manuscript, the authors still insist on referring to session 2 as "learning" and session 5 as "training." I don't understand the difference between session 2 and session 5, especially when session 5's performance is lower than session 2 (even in Fig 1H when you compare succ ratio).

- Since session 6 (on the plate) is considered as "retraining," why don't the authors present the behavioral results beyond session 6? As a result, it remains unclear whether the animals improved their performance during the retraining phase.

- Lastly, in Fig. 4B the authors present only the success ratio and claim that performance improves with CLZ application. However, when comparing sessions 8-10 between the Ctrl and Cre⁺ groups, there already appears to be a baseline difference. CLZ treatment in Cre⁺ mice seem to bring performance only to the WT level rather than producing a clear improvement beyond baseline.

- Regarding the alignment between imaging and behavior, the authors report ~100 prehensions per minute. However, the calcium imaging traces show fewer than 20-30 spikes over 150 seconds (~2.5 min; Fig. 1E). This discrepancy raises concerns about whether the authors can truly isolate calcium signals corresponding to individual prehension events (either successful ones or multiple combined events for unsuccessful attempts). The manuscript still does not present behavioral data that directly aligns prehension events with calcium imaging activity. Although the authors performed analyses suggesting that prehension-related activity does not systematically alter non-prehension epochs, this claim is difficult to evaluate without seeing the underlying traces. It is therefore unclear how the authors selected the example calcium traces aligned to prehension onset, given that there are more than 100 prehension events per minute.

- In Fig. 1I, the authors also did not address why neural activity during successful trials is already lower one second before movement onset. The longer traces provided do not help to explain this observation or clarify the origin of this pre-movement reduction in activity. It actually further suggests that there may be some artifacts in the imaging that could affect the analysis.

- Overall, because it remains difficult to understand exactly what the authors are analyzing (and because the definitions of the motor learning stages appear arbitrary) it is difficult to agree with the authors' conclusion that Ma2s cells reduce PyrN cell assembly plasticity during learning, thereby possibly facilitating already acquired motor skills.

Reviewer #2 (Public review):

Summary:

In this manuscript, Malfatti et al. study the role of Chrna2 Martinotti cells (Mα2 cells), a subset of SST interneurons, for motor learning and motor cortex activity. The authors trained mice on a forelimb prehension task while recording neuronal activity of pyramidal cells using calcium imaging with a head mounted miniscope. While chemogenetically increasing Mα2 cell activity did not affect motor learning, it changed pyramidal cell activity such that activity peaks become sharper and differently timed than in control mice. Moreover, co-active neuronal assemblies become more stable with a smaller spatial distribution. Increasing Mα2 cell activity in previously trained mice did increase performance on the prehension task and led to increased theta and gamma band activity in the motor cortex. On the other hand, genetic ablation of Mα2 cells affected fine motor movements on a pasta handling task while not affecting the prehension task. While overall this study addresses an important and timely question, limitations in the design of the motor learning task and data analysis significantly weaken the conclusions drawn in this manuscript.

Strengths:

The proposed question of how Chrna2-expressing SST interneurons affect motor learning and motor cortex activity is important and timely. The study employs sophisticated approaches to record neuronal activity and manipulate the activity of a specific neuronal population in behaving mice over the course of motor learning. The authors analyze a variety of neuronal activity parameters, comparing different behavior trials, stages of learning, and the effects of Mα2 cell activation. The analysis of neuronal assembly activity and stability over the course of learning by tracking individual neurons throughout the imaging sessions is notable, since technically challenging, and yielded the interesting result that neuronal assemblies are more stable when activating Mα2 cells.

Overall, the study provides compelling evidence that Mα2 cells regulate certain aspects of motor behaviors, likely by shaping circuit activity in the motor cortex.

Weaknesses:

While the authors addressed some of the concerns raised by the reviewers, several major limitations still exist in the revised manuscript.

(1) I appreciate the authors now showing more measures of the prehension task (total reaches, success reaches/min, and success ratio) and providing more details on the task design. However, it is unclear why the authors chose a task design that is somewhat different from the commonly used approach. Here they increase the distance of the food pellet each session and are thus making the task increasingly harder, whereas commonly the target distance is kept stable (See 10.1038/nature08389 for example). The result is that important readouts of learning (e. g. success rate) thus remain stable, making it impossible to judge if learning has occurred, without a control group of non-trained mice. This makes it impossible to judge if the task is affected by increased Mα2 cell excitability, since there is no reference of how these measurements are supposed to change in a mouse that learns or doesn't learn the task.

(2) Regarding the analysis of the calcium imaging data, it is still unclear why the authors cannot report a commonly used dF/F0 or z-score value, as recommended by both reviewers. The authors state the 1 sec time window prior to the prehension cannot be used as a baseline (F0), as there might be preparatory motor activity. In that case an even earlier window (such as -2 to -1sec) or z-scores should be used. The current version relabeling the background subtracted fluorescence signal as dF/F0 is misleading. Relatedly, it is unclear why the authors don't think the 1 sec window before prehension cannot be used as baseline, but at the same time use the difference in calcium activity before and after prehension onset as a cut-off criterion for defining cells as modulated during prehension and including in the analysis.

(3) While the authors have improved their statistical reporting, key information is still missing in several places. For example, no N-numbers are listed in legends for figure 3, and there is no mention of the number of mice for analysis in figures 2 and 3. For clarity, the authors should also include the statistical test performed in the figure legends for any p-values shown in the figure.

Reviewer #1 (Public review):

Nio and colleagues address an important question about how the cerebellum and ventral tegmental area (VTA) contribute to extinction learning of conditioned fear associations. This work tackles a critical gap in the existing literature and provides new insights into this question in humans through the use of high-field neuroimaging with robust methodology. The presented results are novel and will broadly interest both the extinction learning and cerebellar research communities. As such, this is a very timely and important contribution.

Strengths:

The core finding - coupling of cerebellum and VTA as a reward-like prediction errors during fear extinction - is novel and addresses a genuine gap in the literature. Also the paradigm spanning several sessions, a well-powered sample, 7T imaging and complementary analytical approaches to target the question is commendable.

Weaknesses:

The authors have satisfactorily addressed the concerns raised in the previous version of the manuscript. Several results, as well as conclusions drawn from them, still rest on trend-level evidence, although the revised presentation of the results now provides a more balanced interpretation of these findings.

Reviewer #1 (Public review):

Summary:

The aim of this paper is to model the spontaneous emergence of sequences in networks of plastic spiking neurons. By spontaneous, they mean that the inputs have no structure, no sequences, but the network nevertheless generates sequences. To obtain this, they assume several synaptic plasticity and single neuron plasticity rules. The primary findings are that sequences can emerge, that they slowly drift over time, that weights also constantly change over time, but that very strong weights are more stable. The main driver of this result is the plasticity rules assumed.

Strengths:

The paper is based on simulations of a relatively large network of conductance based integrate and fire neurons. There are two different pair-based STDP rules assumed for excitatory-to-excitatory synapses and for inhibitory-to-excitatory synapses. In addition, weights are normalized, and there is an adaptation due to plasticity of the spiking threshold. The network is analyzed via simulations and data processing akin to what would be done for physiological data. The simulations are extensive, and the analysis seems rigorous.

Weaknesses:

There are several fundamental problems with the paper:

(1) The plasticity mechanisms used assumed that pair-based STDP is sufficient to account for synaptic plasticity in vivo. This is unrealistic. Various different papers have shown that pair-based STDP models do not account well for experimental data. If this model is a simulation of the visual cortex (unclear), then firing rates can be sufficiently high, such that firing rates are more important than spike times. We already know that firing rates matter due to the original Markram et al paper from 1997. Even if pair-based STDP is used, we already know from Bi and Poo 1998 that there is a weight dependence of synaptic plasticity such that strong weights potentiate less and decay more. This additional assumption alone might completely change the results in this study. We don't really know how to model realistic synaptic plasticity, but we know pair-based STDP is a bad model. Would these results be robust enough for a change in the learning rule, for example, to triplet-based, calcium-based, or voltage-based? Are the results shown even robust enough to include slight modifications to the learning rule, for example, weight dependence of pair-based STDP?

(2) The first stage of training, in which the network reaches a steady state, is unclear. What type of activity is exhibited in this network? Does most of it arise from the external inputs? What firing rates are obtained? What are the spike statistics? This is important because this activity is responsible for generating the emergent sequences, and also depends (I think) on the plasticity mechanisms. Does the 'spontaneous activity' in the network depend strongly on the external input? Figure 1E is where we see a raster plot, but we see only neurons within a sequence, and it seems neurons within the sequence fire almost only once. Before showing sequences that more general structure of the spiking activity and how it evolves should be explained and quantified.

(3) Do these sequences really emerge without structured inputs? Is there any evidence to suggest that such sequences emerge without a structured input? If yes, please cite it. It makes sense that it would, because the time scale of these sequences is much faster than the sensory or behavioral time scale. However, experimental evidence to support this will make the paper much more interesting.

(4) This paper is a phenomenological paper. It does not really say what these sequences might be good for, except for a cite or two, and it does not model any specific experiment. There is a medium here (a plastic spiking network) which generates a phenomenon (sequences). It also generates other measurable phenomena, such as connectivity motifs. Such motifs have been quantified in animals. It would be natural to compare the motif statistics found here to motifs characterized experimentally. This would make these results more substantial.

(5) There are implicit predictions in the work. For example, about the stability of strong vs. weak efficacies or the stability of different motifs. Such predictions should be made more explicit.

Reviewer #2 (Public review):

Summary:

This paper investigates how a combination of spike-timing-dependent plasticity rules in recurrent spiking networks leads to the spontaneous emergence of repeating neuronal sequences. The authors show that despite the weight distribution reaching a steady state, individual synaptic connections undergo constant turnover with timescales that depend on connection strength. The plasticity rules promote fan-in/out connectivity motifs that appear to support sequence generation.

Strengths:

The question addressed is important and biologically relevant. The most interesting finding of the paper is the coexistence of a stable weight distribution with constant turnover of individual synaptic connections.The simulations seem to be carefully executed.

Weaknesses:

The paper does not make a sufficient attempt to explain why the observed phenomena arise under the specific learning rules employed. There is no theoretical reduction, no analytical argument, and no mechanistic intuition. As it stands, this reads as a descriptive simulation study.

It is never made clear which results reflect robust qualitative phenomena and which are specific to the particular hyperparameter choices of these simulations. Specific percentages and parameter values are reported throughout the main text without justification of their importance or generality.

The finding that sequence composition undergoes continual turnover while the global weight distribution remains stable is interesting, but the authors should more carefully situate this result within the existing theoretical literature on synaptic drift and sequence stability under ongoing plasticity. Several modeling papers have addressed related phenomena, and the novelty of the present contribution relative to this body of work is not clearly established.

Reviewer #3 (Public review):

Summary:

This modelling study connects synaptic plasticity, connectivity motifs, and representational drift. The authors combine excitatory and inhibitory STDP with weight normalization and intrinsic plasticity in a recurrent spiking network of AdEx neurons. This combination generates heavy-tailed synaptic weight distributions and supports repeating spike sequences under both unstructured and structured inputs. While global network statistics stabilize over time, individual synapses continue to change, creating a form of drift. Structured inputs further stabilize sequences, yet the network retains flexibility to learn new patterns.

Strengths:

(1) Multi-scale turnover analysis:

The authors study the evolution of individual synapses, 3-neuron motifs, follower neurons, and entire neuronal sequences, revealing distinct turnover timescales.

(2) Fan-in/out motif analysis:

A specific connectivity motif (fan-in/out) is shown to be over-represented in the network and preferentially stabilised by the plasticity rules compared to other possible motifs. This generates interesting insights and testable predictions.

(3) Connection to representational drift:

The connection of ongoing synaptic plasticity to drift is timely and interesting, reproducing observations of macro-level stability and synapse-level turnover with a relatively simple mechanism.

(4) Rigour and thoroughness:

The overall quality of the numerical experiments performed in this study is high, with extensive supplementary material performing various controls to solidify the claims.

Weaknesses:

(1) Limited connection to network function:

Sequence detection relies on a rather artificial protocol (forced spiking of a single neuron 1,000 times), which I suspect mostly tests whether the lognormal tail of the weight distribution can propagate activity. This risks being circular. I think performing the same sequence analysis on a random network/a network with the same weight distribution but shuffled would help understand what comes from a generic heavy-tailed weight distribution and the particular weights potentiated by the plasticity rules used here.

The network, which would classically be evaluated as a memory network, is not assessed on this aspect. While the authors do not overclaim, this limits the impact.

Relatedly, the relearning experiment (Figure 5G) shows catastrophic forgetting. This is acknowledged in the discussion, but the suggested solutions (alternating patterns, plastic readout) are speculative without supporting simulations. This limits the applicability of the model as a memory model or, more broadly, as a model of a brain region/function.

Additionally, in the sequence learning experiments with structured input, the ability to learn seems tied to the very specific timescale of pattern presentation (~10 ms per pattern, comparable to the STDP kernel time constants), arguably faster than the timescale of external stimuli. The stability of sequences may also owe more to the normalization scheme than to STDP per se.

(2) Novelty claims and positioning within the literature:

On page 16, the authors write: "Our results demonstrate that spiking sequences can be generated in randomly connected networks trained by synaptic plasticity even under unstructured inputs, which supports STDP being the main actor, while stabilizing mechanisms such as weight normalization and intrinsic plasticity play a complementary role." (c1).

Several aspects of this work are less novel than the presentation suggests:

(a) The fact that STDP can create sequence-like dynamics/asymmetric connectivity matrices in recurrent networks has been studied theoretically [1,2] and in simulations [3,4,5]. While [3] is cited, the manuscript underplays the similarity. [4] (uncited) considers e+iSTDP with a different homeostatic term to represent sequential stimuli in large recurrent spiking networks. [5] (uncited) also considers a recurrent spiking network with several STDP-like rules and shows that many combinations can store and recall sequential inputs.

(b) Lognormal weight distributions emerging from STDP-based plasticity and the autonomous emergence of connectivity structures have extensive literature. While many of these articles are already cited in the manuscript, I fail to see what this work brings to this matter compared to existing work (particularly [6]).

(c) Several published works challenge the manuscript's implicit claim (c1) that sequences require their particular combination of rules. Many other plasticity mechanisms can create sequences [3,4,5,7,8,9]. Some interpretations may also need to be dialed down: [10] (uncited) showed that sequences can be stored and retrieved using EI and IE plasticity alone. iSTDP may be doing more computational work than acknowledged, which complicates the interpretation of which mechanisms are truly driving the phenomena.

Overall, most of the relevant work is already cited in the manuscript, but not necessarily acknowledged adequately.

(3) Justification of plasticity model/robustness analysis:

The parameters in Tables 1 and 2 are quite specific without strong justification (for instance, different sparsity values for each connection type and specific normalization factors). Without parameter sweeps, it is difficult to know whether the key findings are robust or overfit to this particular network configuration. Given the number of parameters, exhaustive sweeps are out of question, and the argument made previously would still prevent the rule combination proposed from being considered as more than one possible mechanism for sequence generation among many others. However, this deserves to be acknowledged, and potentially a few sweeps to be run (e.g., over LTP/LTD ratio, normalization threshold, and network size). I don't think that Figure S12, which shows that removing any component of the model causes it to break down in some way, is enough to cover alternative plasticity rules.

A related concern is that the network is small by current standards (1,200E + 240I neurons), especially with sparse connectivity (6-20%). Small networks with few connections are susceptible to synchronization (other studies typically consider networks of at least 10k neurons). The authors should discuss whether the phenomena they observe would persist at larger scales and under more biologically realistic connectivity. Specifically, are the intrinsic and normalization plasticity terms as crucial in this case?

(4) Fan-in/out motif evidence is correlational:

The evidence linking the fan-in/out motif to sequence stability appears to be correlational. Properly establishing causality would require targeted ablations or rewiring of fan-in/out connections. While designing a clean causal intervention may be difficult, the correlational nature of the evidence should be stated explicitly.

Conclusion:

To summarize, the manuscript would benefit from:

(1) Reframing the contribution:

Multi-scale turnover analysis and the discussion around representational drift as the core novelties. I would reposition sequence emergence and lognormal distributions as reproducing known results under a specific plasticity model and analysis method.

(2) Acknowledging that many rule combinations could produce equivalent outcomes, and not suggesting that the combination chosen here is special.

(3) Adding parameter sensitivity analysis or, at a minimum, discussing robustness.

References:

[1] Kempter, Gerstner and van Hemmen, Hebbian learning and spiking neurons, 1999, PRE

[2] Ocker, Litwin-Kumar and Doiron, Self-organization of microcircuits in networks of spiking neurons with plastic synapses, 2015, plos CB<br /> (Theoretical account of STDP in spiking networks and motifs, though it only looks at 2-synapse motifs (not fan-in/fan-out)).

[3] Fiete et al., Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity, 2010, Neuron

[4] Duarte and Morrison, Dynamic stability of sequential stimulus representations in adapting neuronal networks, 2014, Frontiers in Comp Neuro

[5] Confavreux et al., Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety and degeneracy at the heart of learning, 2025, bioRxiv

[6] Zheng, Dimitrakakis and Triesch , Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex, 2013, plos CB

[7] Zheng and Triesch, Robust development of synfire chains from multiple plasticity mechanisms, 2014, Front Comp Neuro

[8] Ravid Tannenbaum and Burak, Shaping Neural Circuits by High Order Synaptic Interactions, 2016, plos CB

[9] Bell, Duffy, and Fairhall, Discovering plasticity rules that organize and maintain neural circuits, 2024, NeurIPS

[10] Gong and Brunel, Inhibitory Plasticity Enhances Sequence Storage Capacity and Retrieval Robustness, 2024, bioRxiv

Reviewer #1 (Public review):

The wide-ranging serotonergic projections emerging from the Dorsal Raphe nucleus (DRN) are suggestive of a central role in regulating brain-wide activity and behavioural states. DRN activity has been associated with diverse functions, ranging from mood, motivation and pain regulation to sleep and cognitive flexibility. Its far-reaching connectivity made it challenging to assess the brain-wide effect of its activation, especially during behaviour.

The present study by Qi et al. addresses these challenges by combining state-of-the-art tracking microscopy with the whole-brain accessibility of the larval zebrafish model. To investigate the effect of DRN activation, the authors leveraged the Tg(tph2:ChrimsonR) line to optogenetically activate tph2-positive neurons in the DRN, while monitoring changes in brain-wide activity, locomotion and auditory-stimuli evoked responses.

Optogenetic activation had a suppressing effect on locomotion, which the authors distinguished from inducing sleep by the maintenance of posture and its sleep disturbing effect of nighttime stimulations. Further, the authors report a distinct effect of DRN activation on motor-related, but not auditory-related neuronal subspaces, identified by demixed principal component analysis.

In addition, rather than affecting all motor-correlated neurons similarly, tph2+ DRN-mediated suppression focused on neurons encoding high-amplitude or turning motion.

In summary, the work of Qi et al. provides solid evidence for a predominant role of the DRN in wake-state motor suppression by aptly combining the vast data-acquisition possibilities of the larval zebrafish model with computational methods to extract relevant information.

The brain-wide scope of the analysis is a key strength, reducing bias, confirming the involvement of known motor and auditory regions, and providing a valuable dataset for future analyses.

While the results well support the conclusion of the authors, certain biological and technical aspects demand discussion.

Reviewer #2 (Public review):

Summary:

The authors examine the effects of activating the dorsal raphe nucleus serotonergic system using a combination of calcium imaging and optogenetics in freely moving larval zebrafish. Their findings show that optogenetic stimulation induces a state of behavioral quiescence.

They further investigate whether this state corresponds to sleep or reduced motor activity. Analyses of posture and sleep-related paradigms indicate that serotonergic activation primarily suppresses motor output rather than promoting sleep. Notably, this suppression appears to be bout type-dependent, with stronger effects on neurons associated with larger tail amplitudes and turning angles.

In addition, auditory stimulation experiments reveal no significant impact of serotonin on sound encoding.

Strengths:

The study combines advanced experimental techniques with state-of-the-art analytical methods, enabling precise and compelling insights into the role of serotonergic modulation. The experiments and analyses are well aligned with the questions being addressed, and the results appear robust and reliable.

Moreover, the implementation of experiments that combine calcium imaging and optogenetics in freely moving animals is technically challenging and appears well justified in the context of the research questions.

Weaknesses:

While the analytical techniques employed are sophisticated and appear to be appropriately applied, their presentation makes the manuscript difficult to follow. Although the explanations are provided in the Methods section, including more guidance in the main text, such as how to interpret each analytical approach and what outcomes would be expected under different scenarios, would help readers who are less familiar with these techniques.

Providing this context would better guide the reader in navigating the figures, broaden the accessibility of the work, and ultimately increase its impact.

While the authors discuss different quiescent states mediated by serotonin reported in previous studies, their interpretation is limited to stating that "a common feature shared by these distinct behavioral states is a pronounced reduction in movement," and consequently proposing that activation of dorsal raphe nucleus is not sufficient to specify a particular behavioral state, but rather plays a primary role in driving motor suppression.

In my view, a more thorough attempt to determine whether the observed state corresponds to any of the previously described forms of quiescence, or represents a subset or variant of them, would strengthen the manuscript. This would help better integrate the findings with the existing literature.

For example, given that the authors have access to whole-brain activity data, it would be valuable to examine and discuss whether there are shared patterns of activation with previously reported quiescent states.

The manuscript largely avoids discussing the mechanisms underlying the observed motor suppression. For instance, is this effect driven directly by serotonin release onto target neurons? Is it mediated by glial activity, as suggested in other studies? Are additional neuromodulatory systems being recruited?

While addressing these questions may require substantial further work, potentially beyond the scope of the present study, the availability of whole-brain data provides an opportunity to at least explore or discuss these possibilities. In particular, it would be interesting to examine the recruitment of regions not directly stimulated but known to be associated with other neuromodulatory systems or promoting glial activation (e.g., the locus coeruleus).

Reviewer #1 (Public review):

Summary:

This article investigates the application of commonly employed analytic methods in electrophysiological neuroscience to the speech envelope taken from 17 different languages' audio corpora. The findings indicate that features observed in speech-brain tracking responses, specifically theta and gamma oscillations, as well as their phase-amplitude coupling, are actually present within the speech envelope itself. This suggests that the neural data recorded in response to speech primarily reflects an evoked response to the temporal statistical properties of the envelope, rather than an inherent neural mechanism. Data from 18 individuals with epilepsy listening to French speech further support this interpretation: theta and gamma oscillations, along with their phase-amplitude coupling, are absent at rest and are linearly driven by the acoustic envelope during speech perception.

Strengths:

I find these results very interesting and convincing, with a strong take-home message: we should exercise caution when interpreting observed theta/gamma activity and the associated phase-amplitude coupling during speech comprehension tasks.

Weaknesses:

I mostly have comments on clarifications regarding the methods, specifically on the criteria for language exclusion, and on the statistical testing and reporting.

(1) Clarification is needed regarding the rationale for the number of languages analysed: initially, 17 languages were considered, six were excluded due to the absence of PAC in the high gamma range, yet the analysis was ultimately conducted on only nine languages, not eleven. Could you please explain this discrepancy?

(2) Considering the six languages that did not exhibit any statistically significant high-frequency PAC, do you have potential reasons for this result? Might it be related to the fundamental frequency (F0) of the speakers' voices? If six languages out of seventeen do not show PAC, can we argue that this feature is universal across languages?

(3) How is inter-subject variability addressed within the SEEG analysis? The authors report the percentage of SEEG independent components showing significant effects in power spectral changes, PAC, and other measures, but it is unclear whether these components are consistent across participants or whether only a few participants drive the effect. It would be helpful to report how many participants are retained for each selection of SEEG-ICs in the article. Currently, the statistical testing of the SEEG-ICs also appears to assume independent samples. It would be helpful to include group-level statistical tests across subjects, for instance by performing mixed-effects models and including participant as a random factor.

Reviewer #2 (Public review):

Summary:

This paper nicely demonstrates that "speech tracking" in the auditory cortex extends all the way up to 100Hz-150Hz. Specifically, the study asks whether the fluctuations in sound amplitude found in speech at various time scales relate to fluctuations found in similar time scales in intracranial recordings in auditory brain areas. First, it analyzes amplitude fluctuations in speech of 17 different languages, and characterizes fluctuations due to syllabic rate (2-6Hz), vocalic features (30-50 Hz), and fundamental frequency (100-150 Hz, in male speakers). It then analyzes whether neural activity occurs while listening to male and female speakers in French. By measuring changes in power spectrum relative to rest, it links the sound amplitude fluctuations to fluctuations in neural activity in the same frequency bands, referring to them as "theta", "low-gamma", and "high-gamma". Using Grange "causality," it clearly shows that the neural fluctuations can be predicted linearly from the sound fluctuations. Using a cross-frequency coupling measure, they further show that, in the neural dynamic, high-gamma fluctuations precede theta fluctuations.

Strengths:

(1) Analysis of neural activity (Figure 2 is a very compelling account of how theta, low, and high gamma observed in neural recordings closely follow the properties of the acoustic speech signal itself.

(2) This includes phase amplitude coupling, a property that I had not previously seen described for the speech signal itself, and is here nicely demonstrated in Figure 1.

(3) The Grange "causality" analysis makes a compelling case that neural fluctuations in these frequency bands are driven by the stimulus itself.

(4) The finding in Figure 4 that female fundamental emerges at half the frequency in the neural activity is, to my knowledge, an entirely novel observation, not just in speech but in amplitude modulated sounds in general. This non-linear phenomenon is very interesting and prompts a host of interesting questions for future research: Does this happen only for voiced speech, does it depend on the harmonic stack of speech, or can it be produced with a single AM frequency? Are there preferred frequencies for this phenomenon?

(5) The cross-frequency coupling measure shows a number of directed effects in the neural signal which seem to counter the predominant view in neuroscience, namely, that the phase of the slower fluctuations "organize" or "drive" the faster fluctuations seen in power, e.g. theta→gamma coupling, which here is seen to be reversed as gamma→ theta coupling, and this is not a property of sound itself. This, too, should lead to a number of follow-up studies (although there are some potential confounds here).

Weaknesses:

(1) The claim that different frequency bands are processed in different locations, referred to in the abstract as "multiplexing" is less well supported. The neural analysis is performed on independent components that are spatially distributed, making this claim less transparent than it could be, with other, more direct ways of treating electrode location, such as bipolar referencing.

(2) The writing in the Introduction and Results section obscures the source of sound amplitude fluctuations at different timescales. Instead, it treats these fluctuations as some sort of discovery. This is strange because the abstract and discussions are fairly accurate on this point - namely, they are all due to well-known properties of speech. The descriptions are accurate, although I would put it slightly differently: fluctuations below 6Hz are due to varying length of sentences and words, 25Hz-50Hz are well-established stationary times of the vocal tract, and 100-150Hz are the vibration of the vocal cords in male speakers.

(3) The problem of guiding the analysis of sound by notions from neural signals is most glaring when they restrict their analysis to less than 150Hz, which leaves out female-voiced speech.

(4) Along with this, there is a heavy emphasis on notions of "rhythms" and "oscillations" when clearly, aside from the vocal cords, there is no evidence for rhythmic fluctuations. Any reasonable definition of a rhythm would need at least 2 or 3 cycles of a repeated pattern. A spectral "peak" for the sound envelope is shown at 5Hz. But this is not indicative of a regular rhythm. Instead, the peak appears to be an artifact of displaying power per octave rather than power spectral density. A peak in a power per octave is not a reliable indicator of a coherent oscillation, and the speech envelope does not exhibit a clear 5Hz rhythm. Unfortunately, prior literature has not been clear on this. It would be more accurate if the word "rhythm" were replaced with "fluctuation" and/or "activity" for the case of speech envelope and neural activity, respectively.

(5) The Introduction also omits the literature on neural responses to amplitude-modulated sounds that go up at least to 200Hz and more. So the findings here on "high-gamma" are well in line with prior literature.

(6) The fact that neural analysis was cut off at 150Hz to me is a missed opportunity to test if neural speech tracking goes all the way up to 200Hz of the typical female fundamental.

(7) The gamma→theta effects reported here could be confounded by a simple longer delay in the analysis of theta. In fact, Figure S5 confirms that delay. It is unclear whether the CFD metric captures anything more than a temporal delay between the two signals. The term "functionally interconnected" in the abstract is a bit of a stretch; it may be essentially delayed correlation.

(8) There is a minor concern with the claim that low-gamma drives theta amplitude. While statistics on this are reported, the corresponding figure may be suggesting an alpha-harmonic instead of theta (Figure 5c).

Reviewer #3 (Public review):

Summary:

This manuscript investigates whether the theta-gamma phase-amplitude coupling in the human auditory cortex serves as an intrinsically generated neural mechanism for hierarchically parsing speech or not. By analyzing speech corpora across 17 languages alongside human intracranial EEG recordings, the authors demonstrate that these nested oscillatory dynamics are actually inherent, robust acoustic properties embedded within the speech envelope itself. Consequently, they claim that rather than generating parsing windows internally, the early auditory cortex acts as a temporal demultiplexer that segregates syllabic, vocalic, and pitch features into distinct, stimulus-driven neural channels. Furthermore, the study presents evidence for a reversed functional directionality wherein fast-varying gamma activity drives the phase alignment of slower theta rhythms, fundamentally reframing auditory PAC as a stimulus-evoked alignment to a highly structured external signal rather than an endogenous cognitive parsing tool.

Strengths:

(1) The authors demonstrated robust theta-gamma acoustic structure across languages. They analyzed the acoustic speech envelope across 17 typologically distinct languages. This establishes that the nested theta-gamma acoustic structure is a universal feature of human speech, rather than an artifact of one language's specific phonology.

(2) The use of time-resolved, high-SNR intracranial recordings is a critical strength of this study. This approach provides the precise spatiotemporal fidelity required to confidently separate and delineate multiplexed high-frequency dynamics, particularly the low- and high-gamma bands, that are essential for accurate speech decoding but are typically attenuated or lost in non-invasive scalp recordings.

(3) The authors move beyond standard correlational PAC metrics by employing a suite of converging analyses, including the isolation of true oscillations from aperiodic noise and the directional index. Together, these metrics demonstrate that auditory PAC is a stimulus-evoked alignment to a highly structured external speech signal, rather than an intrinsically generated top-down parsing mechanism.

Weaknesses:

(1) A major methodological concern is the use of ICA across SEEG electrode shafts to define distinct neural sources (SEEG-ICs). SEEG electrodes traverse complex macroanatomy, including multiple cortical layers, sulcal banks, and white matter. By constructing components derived from weights across the entire electrode, and subsequently localizing each component solely to the contact with the maximal contribution, the authors risk generating biologically implausible signals. Such an approach potentially mixes true localized cortical gray matter activity with deep structure or white matter signals. Given that a central claim of this manuscript is the spatial and functional segregation of theta and gamma neural populations, the authors could consider further validating these core findings (such as the gamma-to-theta directionality) using single-channel or bipolar-referenced data.

(2) Another methodological concern is the use of GC to evaluate the directional causality between speech and neural signal. As noted in Bastos & Schoffelen (2015) and indeed acknowledged by the authors' own citation of Nolte et al. (2010), Granger Causality is highly sensitive to SNR imbalances and filtering artifacts. Given the inherent SNR disparity between a cleanly extracted acoustic envelope and noisy SEEG data, coupled with the known distortions introduced by distinct filtering pipelines (Barnett & Seth, 2011), the GC results may reflect methodological artifacts rather than true physiological driving.

(3) The third concern is the study's exclusive reliance on linear metrics applied to the envelopes of band-filtered speech and neural signals, e.g., linear Granger Causality and cross-correlations. The human auditory system is an inherently non-linear dynamical system. Complex acoustic features, such as rapid spectrotemporal transitions or dynamic pitch trajectories, often drive non-linear neural responses and complex phase-locking behaviors. While the linear models provide strong interpretable results, by restricting their connectivity and directionality metrics to linear autoregressive models, the authors may be missing substantial non-linear interactions, or conversely, forcing a linear fit onto non-linear data, which can distort estimations of causality and temporal lags. The authors should consider explicitly addressing this limitation in their discussion. Ideally, they should validate their core directional claims on a subset of the data using an information-theoretic, non-linear metric (e.g., Transfer Entropy or Mutual Information), or apply linear methods to nonlinearly abstracted features (e.g., phonemic, syllabic, intonational-level features), to ensure their linear assumptions are not masking or misrepresenting the true underlying dynamics.

Reviewer #1 (Public review):

Summary:

The authors report results from an EEG study investigating neural oscillations in 8-month-old infants, as well as an adult control group. Participants were presented with cartoon figures flickering at different frequencies, as well as a broadband condition. While adults showed the well-known dominant response at 10 Hz, infants showed dominance resonance at 4 Hz, irrespective of stimulation frequency. The authors interpret this finding as evidence for the fundamental role of 4 Hz oscillations in early development and discuss two conflicting theories regarding the underlying functionality.

Strengths:

Overall, this is a very well-designed and rigorous study, and the results significantly add to our understanding of a very fundamental aspect of early brain activity. The study is embedded in a coherent theoretical framework, and the authors discuss possible implications and next steps with great clarity.

Weaknesses:

I see relatively few weaknesses in this paper. It does not statistically compare infant and adult responses, which would add to the argument that infant responses actually differ from adult ones, but I don't think this is necessary at this point for the authors' argument.

In contrast, I actually like about the paper that the authors had a very clear vision of what they wanted to look at - 4 Hz oscillation responses in 8-month-olds - and this is exactly what they did. Yes, this does not answer all questions one might have, especially about the function of 4-Hz-oscillations in infants, but it goes a long way in characterising properties in 4 Hz oscillations, which provides the starting point for several potential future lines of research.

Reviewer #2 (Public review):

Summary:

This study combines EEG with frequency-tagging and broadband stimulation paradigms to investigate the developmental precursors of brain rhythms in 8-month-old human infants. The manuscript employs state-of-the-art methods, focusing on theta and alpha rhythms to assess their functional significance in visual information processing.

By evaluating responses to visual stimulation at different frequencies and broadband stimulation presented simultaneously with sounds, the authors report a stimulation frequency-independent response at ~4 Hz. They interpret this as the precursor of the adult alpha rhythm involved in perceptual echo mechanisms. However, I have a number of questions regarding the hypotheses, experimental framework, and analytical approach that need to be addressed before confirming the conclusions.

Strengths:

(1) The analyses are innovative, and the frequency-tagging paradigm is particularly well-suited for studying challenging populations with short protocols.

(2) The sample size is adequate.

Weaknesses:

There is a gap between the hypotheses and the experimental paradigm, as well as between the hypotheses and the analytical choices. These gaps could alter the interpretation of the findings and thus require clarification (or perhaps a reformulation of the theoretical framework).

I am not convinced that the conclusion - that the theta rhythm is the functional precursor of the alpha rhythm in the infant visual system - holds without addressing the following questions.

In brief, my specific concerns are the following:

(1) Gap Between Hypotheses and Experimental Paradigm:

The experimental paradigm involves the simultaneous presentation of sound and image, i.e., cross-modal sensory information, which contrasts with the manuscript's theoretical framework and conclusions, all of which are grounded in visual information processing. Previous work has shown that preverbal infants spontaneously engage in cross-modal associative learning in such audiovisual paradigms (e.g., Kabdebon et al., 2019). This raises the question of whether the paradigm taps into different mechanisms - such as associative learning - rather than those hypothesized, and whether these mechanisms might better explain the observed 4 Hz response. Associative learning mechanisms are particularly relevant to theta rhythm, involving hippocampal learning and the engagement of wider networks, including frontal areas.

Given this cross-modal design, I question whether it might alter the interpretation of the paradigm and the conclusions drawn. The current framing of the manuscript suggests that theta/4 Hz is the functional equivalent of the alpha rhythm for visual processing in the 8-month-old brain. However, the use of multisensory input complicates this conclusion for the visual domain and the parallel to adult mechanisms.

Kabdebon, C., & Dehaene-Lambertz, G. (2019). Symbolic labeling in 5-month-old human infants. Proceedings of the National Academy of Sciences, 116(12), 5805-5810.

(2) Analytical Focus - Gap Between Hypothesis and Analysis Choices:

The link between the literature described in the introduction and the hypothesis of a 4 Hz inherent rhythm in the visual system remains unclear. This puzzles me as to why the analyses focused on 4 Hz and a control band that is not adapted to the infant population. The focus of the analyses on 4 Hz (and the control band analyses) overlooks the critical frequency range (~6-8 Hz), which other studies have suggested may serve as proxies for the adult alpha rhythm. This omission does not align with the hypotheses regarding the role of the alpha rhythm in visual information processing.

The introduction discusses both alpha rhythm and its significance in perceptual echo phenomena, and theta rhythm and its role in mnemonic function, but these remain as separate phenomena. While the paradigm aims to assess perceptual echo phenomena in infants, one would expect the hypothesis to relate to precursors of the alpha rhythm in infancy (slower frequencies, yet related to alpha, ~6 Hz; Stroganova et al., 1999). However, the authors hypothesize that theta rhythm (4 Hz) is a precursor of the alpha rhythm in infancy: "Given the prominence of the theta rhythm in infancy, we expected the presence of a 4 Hz theta response and resonant activity in the infant visual system upon periodic stimulation and broadband visual input, respectively."

Why did the authors not study the 6-9 Hz frequency range, which previous work suggests may serve as a proxy for alpha in infants? Currently, the analyses are restricted to the theta range (i.e., 4 Hz) and a control band (adult-classical alpha range [8-14 Hz]), but [8-14 Hz] is not adapted to the infant population. At this age, prior work has reported ~6 Hz as the age-adapted range corresponding to alpha. It would be more appropriate to investigate this range. I can see some trace of this in Figure 2a, but perhaps this is weaker compared to the 4 Hz stimulation due to the cross-modal nature of the paradigm.

Stroganova, T. A., Orekhova, E. V., & Posikera, I. N. (1999). EEG alpha rhythm in infants. Clinical Neurophysiology, 110(6), 997-1012.

In the adult results, we also see similar ("two types of") responses: the main response at 8 Hz, which to me is the upper band of the theta rhythm (related to cross-modal learning), and traces around 10 Hz, which are more in line with perceptual echo mechanisms. The cited literature in adults (VanRullen & Macdonald, 2012), on which the authors base their framework and analysis, indicates a response at 10 Hz (not 8 Hz). This supports the idea that the 8 Hz response observed in this work might be related to the cross-modal presentation of stimuli. The authors could evaluate this more easily through a control group of adults with an unimodal (visual-only) presentation of stimuli.

(3) Methodological Approach and Clarity:

The methodological approach is not sufficiently detailed, which is crucial for reproducibility and wider contribution, especially given the difficulties in studying infants. Key points requiring clarification include preprocessing, choice of electrode clusters, and statistical details.

Reviewer #3 (Public review):

Summary:

The authors aim to characterize the intrinsic temporal dynamics of the infant visual system by examining how it responds to rhythmic visual stimulation. Using EEG in 8-month-old infants, they present visual stimuli that flicker at different periodic frequencies as well as broadband (aperiodic) luminance sequences to probe resonance properties of the visual system. The central goal is to determine whether the infant brain exhibits a characteristic oscillatory response independent of the external stimulation frequency, analogous to the well-known alpha (~10 Hz) resonance of the adult visual system. The results are then compared with data from a small adult sample to assess whether the dominant processing rhythm of the visual system shifts across development.

Strengths:

This manuscript presents a compelling and carefully executed study with intriguing findings, and I greatly enjoyed reading it. Several strengths deserve particular mention:

(1) Clear and focused research approach. The study addresses a well-defined question regarding the intrinsic rhythmic dynamics of the infant visual system and applies an elegant experimental paradigm to probe these dynamics directly.

(2) Well-designed parametric stimulation paradigm. The use of rhythmic visual stimulation across multiple frequencies (2-30 Hz), combined with broadband stimulation, provides a systematic way to characterize resonance properties of the visual system. This parametric approach allows the authors to clearly visualize the relationship between stimulation frequency and neural response, making the key effects easy to grasp.

(3) Strong statistical power in the infant sample. The relatively large infant sample (N = 42) is a major strength, particularly given the challenges of infant EEG research. This sample size provides sufficient power to support the conclusions about the robustness of the ~4 Hz response in infants.

(4) Converging analytical approaches. The authors combine periodic stimulation analysis with impulse-response-function (IRF) analyses of broadband stimulation, which provides complementary evidence for the presence of a ~4 Hz resonance in the infant visual system. This convergence strengthens the interpretation of the results.

(5) Direct developmental comparison. Although the adult sample is small, including adults in the same paradigm provides a useful benchmark showing the expected alpha-band response (~8-9 Hz), thereby contextualizing the infant findings within a developmental framework.

Weaknesses:

(1) Potential oculomotor contribution to the frontal 4 Hz effect. My main concern relates to the interpretation of the prominent ~4 Hz response in infants, particularly at frontal electrodes. The frequency range is close to what might be expected for oculomotor activity such as microsaccades, and the scalp distribution appears suggestive of such a contribution. Notably, the topography of the 4 Hz response differs substantially from the topography of the harmonic responses (Figure 2B), which show the expected occipital dominance. The latter is more clearly visual, whereas the former is more complex, definitely going beyond visual responses. This should be considered more in the discussion.

(2) Differences in topography between periodic and IRF effects. The spatial distribution of the 4 Hz response during periodic stimulation also appears to differ from the topography of the 4 Hz impulse response function (IRF; Figure 2B vs 3D). The IRF response appears not really "visual" in its spatial distribution, as compared to, e.g. the harmonic responses in 2B. This difference could indicate distinct underlying generators, but the implications of this discrepancy are not discussed in detail.

(3) Strength of the interpretation of neural resonance. Taken together, these observations make it difficult to determine conclusively whether the observed 4 Hz activity reflects genuine neural resonance of the visual system or potentially other processes (e.g., oculomotor dynamics). While the current findings remain interesting under either interpretation, the manuscript tends to favor the neural resonance account quite strongly without fully addressing alternative explanations.

(4) Relation to known developmental shifts in resting-state oscillations. The dominance of lower-frequency rhythms (theta range) in infancy is well documented in the resting-state EEG literature. Although this point is briefly mentioned in the discussion, it would be interesting to relate the current findings more directly to this literature. For example, it would be informative to know whether peak frequencies observed here align with resting-state theta peaks in infants and whether similar spatial distributions are observed.

(5) Limited follow-up of the proposed theoretical accounts. The discussion introduces both mnemonic and inhibition accounts for infant theta activity. However, these frameworks are not fully developed in relation to the present data. In particular, the mnemonic account might generate testable predictions within the current dataset, for example, whether theta responses change over time with repeated stimulus exposure or learning.

(6) Characterization of the adult alpha response. A minor point concerns the characterization of the adult resonance frequency. The manuscript often refers to a 10 Hz alpha resonance, whereas the data presented here show a peak around ~8 Hz (Figure 5A). In that frequency range, that is a lot. Also, there seems to be some variability, such that for the topography, the authors use the "individual alpha frequency". It would be interesting to see the distribution of peak frequencies across participants to appreciate the actual range. Interestingly, the spatial distribution of the alpha response also appears quite similar to the infant 4 Hz effect (Figure 5B) and differs from the harmonic responses, which may deserve further discussion. A comparison with resting-state alpha characteristics could also be informative here (e.g., does the peak IAF during visual stimulation relate to IAF recorded at "rest").

Reviewer #1 (Public review):

Summary and Strengths:

Shin et al deepen our understanding of high-frequency oscillations in the frontal cortex during REM in a manner that sheds important light on the roles of these events. In particular, they reveal that cortical HFOs are modulated by theta oscillations, occur in chains and recruit cortical neuronal activation patterns in a manner that is distinct from other high-frequency events during non-REM or in the hippocampus. They also show that these events occur during increased oscillatory cross-talk between hippocampus and cortex and may protect cortical neurons from downregulation of firing during sleep. Overall, this is important work with several novel observations pointing towards an important role for these events that will become increasingly understood over time.

I also wanted to comment that 2D is a beautiful illustration of separate and essentially exclusive communication channels used during HF events in NREM vs REM. They almost perfectly complement each other's frequencies.

Weaknesses:

I have only one major scientific critique: I believe we need to see quantification of how phasic REM theta waves with versus without HFOs differ. What do REM HFOs add to the "normal" theta oscillation? Without this comparison, it is more difficult to interpret the meaning of these events. Given that HFO chains have IEIs around the time of a theta cycle duration, are the repeating spiking activities stronger during HFO repeats than during adjacent theta waves without HFOs? What percentage of theta waves contain HFOs, and what is the firing rate during those theta waves with vs without HFOs? Is there differential firing rate modulation? The authors may even consider that all REM-HFO-specific quantifications should be shown as differential from phasic theta cycles without HFOs.

As a non-scientific comment on the manuscript itself: unfortunately, the paper is difficult to read and understand at times, requiring great effort by the reader. This is to an extent that communication is hindered. The paper is dense with changing methods, often from panel to panel. Unfortunately, the panel quantifications are not explained in the results section in a manner that readers can understand without going to read the methods, often for each individual panel. These measures should be explained in a way that lets readers understand the conclusions of each panel and what gross calculations were used to reach those. Instead, too much jargon is used rather than clear descriptions of the overall calculations being done for each panel. 


The authors mention in the discussion section that they see increased functional connectivity between mPFC and CA1, but most data suggesting this seems to be based on LFP rather than spiking. Functional connectivity is best defined by spiking-spiking relationships. And these authors have spiking data. So I believe either the descriptive language should be pulled back to something like "oscillatory coupling" or more analyses should be dedicated to showing spike-spike coordination across regions.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate high-frequency oscillations (HFOs) in the prefrontal cortex during REM sleep. They identify a specific pattern where these HFOs occur in "chains" that are phase-locked to theta oscillations, primarily during the "phasic" periods of REM. The study contrasts these events with isolated HFOs and NREM ripples, suggesting a unique role for these chains in coordinating activity between the prefrontal cortex and the hippocampus. Most notably, the authors report that a specific subset of hippocampal cells-those that co-fire with the prefrontal cortex during these HFOs-increase their firing rates over the course of sleep, suggesting a potential mechanism for selective memory consolidation.

Strengths:

The study addresses an under-explored area of sleep physiology: the fine-grained temporal coordination between the cortex and hippocampus during REM sleep. The identification of HFO "chains" and their association with higher theta power provides an interesting framework for understanding how the brain might organize information transfer outside of NREM sleep. The observation that specific hippocampal populations show differential firing rate changes based on their participation in these HFO events is a striking finding that warrants further investigation.

Weaknesses:

The primary weakness of the study lies in the lack of a clear distinction between global brain states and the specific events being analyzed. Because the authors compare HFOs across different sleep stages (NREM, tonic REM, and phasic REM) without sufficient controls, it is difficult to determine if the observed differences are intrinsic to the HFOs themselves or simply a reflection of the different physiological states in which they occur.

Furthermore, the evidence for "structured reactivation" is not yet convincing. The temporal alignment of these reactivation events appears inconsistent, with peaks occurring well before the HFO itself, and the analysis does not sufficiently control for pre-existing cellular assembly strengths. Additionally, some of the sleep architecture presented appears atypical, such as very short REM bouts and direct NREM-to-REM transitions that bypass standard progression, raising questions about the consistency of the sleep detection across animals. Finally, the study does not account for potential confounds like baseline firing rates when interpreting the behavior of "high-cofiring" neurons, which may simply be the most active cells in the population.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

(1) Distinction from wake HFOs

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

(2) Link to memory consolidation

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

Reviewer #1 (Public review):

Summary:

The manuscript presents a three-dimensional and molecular atlas of the adult hagfish brain to investigate the evolutionary origin and early diversification of vertebrate brain organization. Using whole-brain tissue clearing, light-sheet microscopy, and computational reconstruction, the authors generate a high-resolution 3D anatomical model of the hagfish brain. They complement this structural analysis with gene-expression profiling of neurotransmitter systems and receptors, including glutamatergic, GABAergic, cholinergic, serotonergic, and dopaminergic markers.

Strengths:

Together, the work aims to establish a modern neuroanatomical reference for the hagfish. Given the phylogenetic importance of hagfish as one of two extant species of cyclostomes (the other being lamprey), and the fact that the hagfish brain has barely been studied in contrast to the lamprey, the atlas provides a foundational resource and should be of interest to evolutionary and comparative neurobiology.

Weaknesses:

However, there are several places where both data presentation and the narrative can be improved and clarified, and particularly some of the homology and evolutionary claims seem to be superlative and need to be toned down. I present more detailed comments below:

(1) The authors spend too much effort trying to convince readers of the monophyly of hagfish and lamprey to stress its importance for evolutionary comparisons. This is now well accepted; instead, there could be more details on some of the specific, unique features of the hagfish brain relevant to a comparative atlas. For instance, the unusual fusion of the telencephalon anteriorly with the olfactory bulb and posteriorly with the diencephalon (Wicht and Northcutt, 1992), the degenerate visual system, the absence of the pineal gland, and the oculomotor system can be discussed in reference to the generated atlas and examined marker expression in related structures and their possible identity.

(2) The assertion that the MGE is absent in the lamprey is incorrect based on Sugahara et al. (2016; 2017), who identified lamprey paralogues of Nkx2.1/2.4 that are expressed in the ventral subpallium. This should be corrected.

(3) The major contribution of this study, in my mind, is the "three-dimensional atlas" of the hagfish brain. However, the atlas itself is not presented; A video of the 3D reconstructed Nissl-stained hagfish brain would be an important data resource and should be added. Annotations of forebrain, midbrain and hindbrain regions and constituent major structures can also be illustrated, which will be a useful resource.

(4) In the pallium, there seems to be an inner GABAergic cell layer and inner and outer glutamatergic cell layers, as noticed in lampreys (Suryanarayana et al., 2017). What are the overall proportions of glutamatergic and GABA neurons? In the images, it does seem that vGlut neurons are present in both P2 and P4, while there appear to be more GAD neurons in P4.

(5) As a general comment, homology claims should be toned down throughout the manuscript. This would at least require some connectivity data or transcriptomic analysis for any possible suggestions; the current data, with few markers, are insufficient for any reasonable comparisons.

(6) Expression of Pax6 and AChE is not sufficient to suggest a cerebellum-like structure. While it is true that embryonic Pax6 expression in the rhombic lip of the hagfish embryo is more comparable to other vertebrates than lamprey, and the presence of a rudimentary cerebellum-like structure would be of great interest, the evidence is too limited for such claims and should be toned down.

(7) Again, expression of Tbr1 and GAD1 in NCvl neurons does not suggest that these could be hippocampal neurons. One would at least need to rule out expression of prethalamic markers and demonstrate the presence of pallial markers through transcriptomic data (as in Lamanna et al., 2023).

(8) Presence of GABAergic neurons in the striatum - is there any data on expression of dopamine receptors, particularly given the seeming loss of the D2 receptor subtype in the hagfish?

Reviewer #2 (Public review):

Summary:

The work of Harada and collaborators fills an important gap in our knowledge of neuronal identities in the adult hagfish brain. There is essentially no modern, cell-type-level characterisation of neuronal identity in the hagfish brain yet. Existing data are limited to classical neuroanatomy (e.g. Nieuwenhuys) and sparse transmitter/gene-expression studies, mostly in embryos (e.g. work from the Kuratani lab). This study reveals a very broad peculiar pattern of dopaminergic identities and a strikingly unusual pattern of serotonergic transmission, with serotonergic cell bodies present in the telencephalon, which is uncommon for vertebrates and contrasts with previous reports (e.g., Kadota, 1991).

Strengths:

The three-dimensional reconstruction of the brain, including the ventricular system, is novel and very useful. Most of the neurotransmitter identity patterns presented here have not been previously described, and those that were published earlier, such as the serotonergic system (e.g. Kadota, Nieuwenhuys, Wicht), are old and would clearly benefit from re-evaluation using more modern approaches.

Weaknesses:

Neurotransmitter identities are highly relevant for interpreting the possible presence of LGE/MGE territories in hagfish (e.g. GABAergic patterns), for characterising the raphe nuclei (e.g. serotonergic system), and for refining our understanding of the central prosencephalic complex in relation to other vertebrate brain architectures. However, the authors do not address these points and overlook recent evidence from the amphioxus brain that could help interpret their results in an evolutionary context. Overall, the results are insufficiently discussed in relation to the current state of the art.

The study would clearly benefit from complementary gene expression profiling to place these neurotransmitter patterns within a broader framework of brain partitions, to enable more direct comparisons with other vertebrates, and, importantly, to interpret them in relation to the prosomeric model. Furthermore, the work lacks appropriate controls for the in situ hybridization experiments; Datx2 does not show any expression, so there is currently no evidence that this probe is functional. Including such controls would also strengthen the overall description of the dopaminergic system, especially given that the expression patterns of the different genes analysed appear very diffuse and somewhat random.

Reviewer #1 (Public review):

Summary:

The manuscript provides a well‑argued discussion of the misalignment between common predictive performance evaluations reported in the literature and actually measuring clinical utility in the context of predictive psychiatry. Specifically, the authors discuss measurement reliability and prevalence as two neglected factors which can substantially inflate the assessment of model performance for clinical practice. To mitigate this, the authors offer a concrete framework and an accompanying web tool, with which to adjust performance metrics and additional predictive‑value and decision‑analytic measures.

Strengths:

The manuscript speaks convincingly about the risk of face validity and the practical irrelevance of seemingly promising predictive models in psychiatry. The authors outline how predictive performance estimations often fail to generalize to clinical contexts and thereby potentially mislead scientific efforts. In the face of ubiquitous biomarker models and incremental improvements in the literature, the reader is reminded that, irrespective of the glory of the proposed model, low reliability of clinical measurements fundamentally affects (and limits) both effect sizes and predictive performance ("garbage in, garbage out"), and that neglecting this can ultimately lead to misinformed decisions in the treatment of individual patients. The provision of an online tool with a user‑friendly interface and clearly worked examples is a major practical asset that will facilitate the adoption of the proposed framework beyond quantitative methodologists.

Weaknesses:

While the outlined issues highlight important aspects in the translational gap, the suggested solutions remain somewhat theoretical. For example, the use of prevalence might not reflect what a model would see in practice, assuming that population prevalence and the composition of actual clinical cohorts are aligned. Accounting for who presents to care, and under which referral or triage patterns, is a crucial determinant of effective base rates. While the authors do acknowledge the importance of using base rates from the target population, these nuances could be emphasized more prominently at the points where practical recommendations are made. Relatedly, the analytical context and the methodological assumptions are not clearly specified. Many arguments and demonstrations are derived in univariate, group‑comparison settings and then discussed in a way that can be read as broadly applicable.

Reviewer #2 (Public review):

Summary and strengths:

The authors present a description of their online tool to estimate real-world performance of predictive models. The authors bring together different calculations to make better-informed implementation choices. It is a very nice tool to go from effect sizes to base rates to decision curve analysis. The paper describes the background and use of the tool with examples and seems like an extended version of their online how-to. The methods themselves are not new, but I think the tool will be valuable for researchers from different fields. Tools already exist for the conversion of effect sizes (my current favorite is https://www.escal.site/), but I haven't seen measurement noise being incorporated previously. The main benefit is the evaluation of performance under different real-world scenarios. Code is available on GitHub, and the manuscript is well-written.

Weaknesses:

While comprehensive explanation and examples are important for correct use of the tool, I don't really see the added value above their online how-to guide, as the software itself has already been published (Karvelis, P. and Diaconescu, A. O. (2025b). E2p simulator: An interactive tool for estimating real world predictive utility of research findings. Journal of Open Source Software, 10(114):8334.)

Reviewer #3 (Public review):

Summary:

This important work provides a web-based tool to contextualize effect sizes in psychiatry with respect to reliability and base rates (collectively referred to as predictive utility analysis). The methods for the tool incorporate established psychometric principles that I think are of use for multiple fields in this seemingly easy-to-use tool. I agree with the critical importance of this tool and the methodological points made in this manuscript. Enthusiasm for the manuscript is weakened by a lack of clarity on the formulation of the paper and stated goals of the examples used, with the inferences and impact on clinical decision making from various parameterizations via this tool left open-ended.

Strengths:

This paper presents a well-considered and, what I think will be highly useful, web-based tool to contextualize effect sizes with respect to reliability and base rates. As the authors rightly point out, such a tool could be used in conjunction with widespread analytic power analysis tools in study planning. The paper also well contexualizes the need for such a tool in the relatively recent history of concerns of power, reliability, and inference in psychiatry specifically, and more general meta-scientific debates in psychology and neuroscience.

Weaknesses:

My primary feedback on this manuscript is the lack of clarity in what the paper itself, specifically, separate from the tool, is hoping to achieve. There is a central, but unresolved, tension in whether the reader is supposed to:

(1) focus on the specifics of the examples used and whether to reevaluate the substantive claims from the studies, (2) buy in to how various reliability and base rate parameters impact modeling outcomes, (3) receive an introduction to the tool itself.

In my estimation, the largest contribution to the field here is in (2) and (3), but currently much of the real estate of the paper is dedicated to several examples of (1). While these specific examples may be illustrative to some degree, I think given the number and brevity of such, they are unlikely to incidentally achieve points (2) and (3) above. Specific examples include the assertion of kappas for DSM diagnoses, without much nuance (e.g., see https://psycnet.apa.org/buy/2015-27500-001). Given the relatively limited space given to this example, however, it's hard to be entirely certain what the reviewer should take away.

A second point of concern is where this tool would be situated in the research pipeline. I agree with the authors that this tool could be used in ways that parallel power analysis. With that in mind, it seems the most common use of this tool for an individual investigator is likely to be in a priori study planning. In contrast, and with my point above in mind, the use of the tool for existing results is likely best done with multiple estimates of effect sizes, reliability, and base rates, as is common in meta-analysis or consensus reviews. Nevertheless, there is no real example or guidance around how this influences new study planning.

A third point is that more nuance would be useful in the introduction about the current state of psychiatry research. For example, I share many of the authors' concerns about reliability, power, reproducibility, and barriers to translation. That said, it is the case that while effect sizes should be considered considerably more, they are widely considered in psychiatry research via the common place of meta-analysis and other data pooling approaches. Another such example that the authors state in the context of reliability: "However, this [reliability] attenuation is rarely accounted for in routine analyses in psychiatry". This is true in practice, but somewhat misleading insofar as the method by which to do this remains unclear. For example, should we all report disattenuated associations, assuming there is no error and everything is perfectly reliable? This, of course, would be unrealistic to expect zero error. That we can achieve this with the new tool is clear, but the nuance of how and under what circumstances it should be done is not clear, and such nuance should be better reflected in the framing of the problem. That is, there is also a lack of clarity on what ought to be best practices and field-wide goals, rather than simply the lack of an ability to model these factors.

Minor point

For conceptual clarity, it would benefit the manuscript to at least briefly mention the role of validity in translational importance. Of course, the current psychometric issues of reliability, base rate, power, etc are critical, but it should at least be mentioned, given the potential wide audience of this manuscript, validity is important as well. For example, highly reliable measures may not be valid indicators of underlying disease etiology (e.g., fMRI head motion is a highly reliable trait-level feature, but typically not considered an important predictor or consequence of mental health worth investing translational resources in). Relatedly, confounding as a general topic would be useful to mention just briefly, to help with the spirit of considering underlying issues in translation.

Reviewer #1 (Public review):

Summary:

Here the authors attempted to test whether the function of Mettl5 in sleep regulation was conserved in Drosophila, and if so, by which molecular mechanisms. To do so they performed sleep analysis, as well as RNA-seq and ribo-seq in order to identify the downstream targets. They found that the loss of one copy of Mettl5 affects sleep, and that its catalytic activity is important for this function. Transcriptional and proteomic analyses show that multiple pathways were altered, including the clock signaling pathway and the proteasome. Based on these changes the authors propose that Mettl5 modulate sleep through regulation of the clock genes, both at the level of their production and degradation, possibly by altering the usage of Aspartate codon.

Comments on revisions:

The authors addressed all my comments satisfactorily.

Reviewer #3 (Public review):

Xiaoyu Wu and colleagues examined a potential role in sleep of a Drosophila ribosomal RNA methyltransferase, mettl5. Based on sleep defects reported in CRISPR generated mutants, the authors performed both RNA-seq and Ribo-seq analyses of head tissue from mutants and compared to control animals collected at the same time point. A major conclusion was that the mutant showed altered expression of circadian clock genes, and that the altered expression of the period gene in particular accounted for the sleep defect reported in the mettl5 mutant. In this revision, the authors have added a more thorough analysis of clock gene expression and show that PER protein levels are increased relative to wild type animals a specific times of day, indicating increased stability of the protein. Given that PER inhibits its own transcription, the per RNA is low in the mutants. The revised manuscript included efforts toward a more detailed understanding of how clock gene expression was altered in the mutants, as well as other clarification of sleep phenotypes.

Comments on revisions:

All critiques have been addressed by the authors; the manuscript is much improved from its original submission. Thank you.

Reviewer #1 (Public review):

Summary:

This study aims to clarify MATR3's function and molecular mechanism in oocyte growth and maturation, explore its association with OMA, and its potential as a diagnostic and therapeutic target using specific knockout mouse models, human OMA samples, and multi-omics technologies. And it has fully achieved preset objectives with results strongly supporting conclusions. Specifically, it addresses the gap in the synergistic mechanism of epigenetic and secretory signals regulated by RNA-binding proteins (RBPs) in oocyte growth and enriches the molecular etiological spectrum of oocyte maturation disorders. It is the first time the conservative function of MATR3 has been revealed in multiple species, providing a paradigm for cross-species research on RBPs in the field of reproductive biology. It also provides a new candidate target for OMA, a clinically refractory infertility disease, and is expected to promote the optimization of assisted reproductive technology and the development of precision medicine.

Strengths:

The strengths of this study are significant and prominent. First, the research system is comprehensive, integrating knockout mouse models, in vitro knockdown models, multi-species (mouse, porcine, and human) verification, combined with scRNA-seq, LACE-seq, CO-IP, and other multi-omics and molecular biology technologies, forming a complete and progressive evidence chain. Second, the mechanism analysis is in-depth, clarifying the dual molecular mechanisms of MATR3 regulating the transcriptional synthesis and secretion of GDF9 through "recruiting KDM3B to regulate H3K9me2 demethylation" and "directly binding to Rdx mRNA", with a clear logical closed loop. Third, the clinical correlation is close. It is the first time to find abnormal nuclear localization of MATR3 in oocytes of OMA patients, providing new clues for clinical disease mechanism research, and verifying the downstream function of GDF9 through rescue experiments, effectively enhancing the translational value of the results.

Weaknesses:

This study included only one OMA patient's oocyte sample. Without clinical screening for MATR3 mutations or abnormal expression, establishing a causal relationship between MATR3 and OMA remains difficult.

Reviewer #2 (Public review):

Summary:

This study investigates the role of MATR3 in oocyte development and folliculogenesis using conditional knockout mouse models together with in vitro follicle culture and molecular analyses. The authors aim to determine whether MATR3 regulates oocyte maturation and follicle development and to explore potential mechanisms linking MATR3 function to transcriptional and epigenetic regulation in growing oocytes.

Strengths:

A major strength of the work is the use of a conditional knockout mouse model combined with complementary in vitro follicle culture approaches, which together provide a useful framework for examining gene function during oocyte development. The study also attempts to integrate cellular phenotypes with molecular analyses of transcriptional activity and epigenetic markers.

Weaknesses:

Several weaknesses limit the strength of the conclusions. These include insufficient validation of key experimental manipulations (such as the efficiency of MATR3 knockdown in siRNA experiments), limited quantification or statistical analysis for some datasets, inconsistencies between the text and presented data in certain figures, and incomplete methodological descriptions that make it difficult to fully evaluate reproducibility.

Reviewer #3 (Public review):

Summary:

The study aims to elucidate the dual molecular mechanisms of the RNA-binding protein MATR3 in oocyte growth and maturation. The authors propose that MATR3, highly expressed in growing oocytes (GOs), regulates oocyte quality through two pathways: epigenetically, by recruiting KDM3B to remove the repressive H3K9me2 mark at the Gdf9 locus to activate transcription; and post-transcriptionally, by binding Rdx mRNA to maintain microvillus structure for GDF9 secretion. This mechanism ensures oocyte-granulosa cell communication and female fertility. The study also explores the link between MATR3 and human oocyte maturation arrest (OMA).

Strengths:

The study proposes an innovative dual-mechanism model encompassing "epigenetic transcriptional activation and cytoskeletal regulation," which not only expands the functional understanding of RNA-binding proteins in chromatin regulation but also reveals the coordination between nuclear transcription and organelle structure. By integrating scRNA-seq and LACE-seq, the authors constructed a comprehensive regulatory network for MATR3, identifying both key targets and numerous potential molecules, thereby providing rich resources for future mechanistic studies. Furthermore, the inclusion of oocyte samples from human OMA patients directly links the basic findings to clinical reproductive disorders. Despite the limited sample size, this approach demonstrates strong translational potential.

Weaknesses:

The partial phenotypic improvement achieved by exogenous GDF9 supplementation suggests that the downstream effector pathways may involve a more complex network regulation, implying that the current interpretation of GDF9's central role could be further explored. Regarding the developmental abnormalities of granulosa cells in the conditional knockout model, their pathological origins require in-depth analysis to determine whether they represent primary alterations or secondary adaptive responses resulting from the loss of oocyte signaling.

Reviewer #1 (Public review):

The manuscript by Fisher et al describes the molecular mechanism underlying how G beta gamma subunits engage with the beta 3 isoform of PLC. The paper used a combination of cryo EM, BRET assays, and biochemical assays of PLC beta activity. A key discovery is that G beta gamma is not sufficient to drive membrane binding by itself, and instead promotes G alpha activation. The work is important, but suffers slightly from some ambiguity in the actual interface that is present in their cryo EM model, as crosslinkers could stabilise a transient and non-native complex. This is somewhat abrogated by the careful mutational analysis, which shows that mutation of any of these three sites does somewhat block PLC beta G beta gamma activation. However, there could be some improvement in the presentation of this data, as well as possible mutant selection. Overall, this paper is a nice complement to the Falzone et al paper, showing the membrane-bound complex of PLCB3 on membranes, with this work building on this work, highlighting the importance this will have in our full understanding of PLC beta activation.

Major concerns:

My biggest concern is the potential that this interface is artefactual based on the crosslinking strategy utilised. Here are thoughts on how this could be better validated, presented in a more convincing way.

(1) The authors' main claim is that there is a degree of plasticity of G beta gamma binding to the PLC beta 3 isoform, with three possible binding sites. The main complication of this is, of course, the possibility that the crosslinking stabilises a non-native complex, driven by a mutated cysteine.

Because of this, any other additional details about this interface are going to be critical for the scientific audience to judge if this is accurate.

What would greatly help Figure 1 is an evolutionary conservation analysis of the novel Gbg interface in PLC, to see how well this is conserved, and compare this to the conservation of the previously annotated sites. Conservation of these sites on both the G beta gamma and PLC side would help justify this as a native complex.

This will also help orient the reader to the identity of the mutated residues assayed in Figure 3.

(2) The g beta gamma orientation is also different than what I have observed in previous g beta gamma effector structures. Is there any precedent for this as an effector interface? A supplemental figure comparing this structure to other g beta gamma interfaces from other enzymes, for example recent Tesmer structure with PI3K.

(3) The mutational analysis in Figure 2D-G seems to give some strange results, and I have some question why certain residues were chosen rather than others. Mutation of the Gbg side will be more complicated, as of course that can affect any of the three surfaces. My main question is that, from the way Figure 2A is oriented, the main salt bridge in their novel interface to me looks like R199-D228, with K183 being in the wrong orientation to E226, and D167 being far from any charged residues. Why did the authors not make the corresponding R199 to D or E mutation?

(4) To help the reader's interpretation of Figure 2A, I would recommend a supplemental figure showing the density for interfacial residues, as that also would increase confidence in the interface.

Reviewer #2 (Public review):

In this manuscript, the authors dissect how Gβγ potentiates PLCβ3 signaling in cells. Using engineered crosslinking to stabilize a Gβγ-PLCβ3 complex, single particle cryo-EM, and cell-based functional assays, they identify and map multiple putative Gβγ interaction surfaces on PLCβ3, including a previously unrecognized binding mode. Structure-guided mutagenesis supports the functional relevance of these interactions and suggests that Gβγ potentiation is not primarily mediated by PLCβ3 membrane recruitment, but instead enhances PLCβ3 activity after the lipase is already at the membrane.

Previous reconstitution work on the membrane surface (Falzone & MacKinnon, 2023) proposed a recruitment/partitioning-centric model in which Gβγ increases PLCβ3 output largely by elevating its membrane surface concentration, whereas Gαq primarily increases catalytic turnover; under those reconstitution conditions, the two inputs can combine approximately multiplicatively. In receptor-driven cellular signaling, however, PLCβ3 is robustly recruited to the plasma membrane upon Gαq activation, which raises the question of whether Gβγ contributes mainly through additional recruitment or through a post-recruitment mechanism once PLCβ3 is already at the membrane.

This manuscript helps address that gap by using membrane-anchored PLCβ3 and complementary cellular readouts to separate "getting PLCβ3 to the membrane" from "boosting activity once PLCβ3 is already there." Their results argue that, in cells, membrane recruitment is largely dominated by Gαq·GTP, while Gβγ can further potentiate PIP2 hydrolysis after membrane association, consistent with a modulatory role at the membrane rather than primary recruitment.

Overall, the work provides a structural and mechanistic framework for Gβγ-PLCβ3 cooperation and helps clarify the basis of Gq pathway amplification. The manuscript is generally strong, but some issues need to be addressed.

Major comments:

(1) BMOE/BM(PEG)2 crosslinking may enforce a non-native docking geometry, potentially compromising the physiological relevance and precision of the Gβγ-PLCβ3 interface as described. Although a >50% 1:1 crosslinked complex is formed and remains active, the solution maps show lower local resolution for Gβγ, consistent with a dynamic, potentially heterogeneous, interface. One interface is captured via a single engineered cysteine pair (PLCβ3 E60C-Gβ C271), which could potentially bias the pose. It would be helpful if the authors could provide additional orthogonal support (e.g., alternative crosslinked sites) and bolster the clarification of its uniqueness and relevance.

(2) In the crosslinked structure, the authors report that GβD228 interacts with PLCβ3 R199 and K183. In Figure 2A, R199 appears closer to Gβ D228 than K183, yet only K183 is functionally tested. Testing R199 (e.g., R199E/R199A) would strengthen the structure-guided validation of this interface.

(3) The mutagenesis strategy appears inconsistent across figures/assays, which makes it difficult to interpret phenotypes and directly link the functional data to the proposed interfaces. For example, in Figure 2E, we see R185L but R215E, while residue L40 is mutated to Gly in the IP accumulation assays but to Glu/Lys (L40E/K) in the BRET assays (Figures 3B/3D/3F). The authors should (i) clearly justify the rationale for each substitution (conservative vs charge-reversal, interface disruption, etc.) and (ii), where possible, test the same mutants across assays (or provide evidence that alternative substitutions yield consistent conclusions).

Reviewer #3 (Public review):

Summary:

PLCβ3 is activated by both Gαq and Gβγ subunits. This paper follows previous solutions and cryoEM studies of PLCβ3 / Gβγ, trying to understand the molecular details of activation using cellular BRET assays and cryoEM.

Strengths:

The authors find evidence for multiple binding sites on PLCβ3 for Gβγ and suggest that Gβγ is not bone fide activator per se but enhances Gαq activation by positioning the catalytic site towards substrate, although this is not completely convincing. Although these sites may not naturally be operative, the authors might want to develop the potential role of these sites.

The authors also find that this activation is not through recruitment of the enzyme to the membrane by Gβγ released upon G protein activation, in accord with other PLCβ enzymes, but not for PLCβ3, and again, the authors might want to develop this point further.

Weaknesses:

(1) I'm confused as to why the authors feel that their mechanism is distinct from the two-state enzyme, the synergistic activation proposed by Ross in 2011, using a primarily thermodynamic argument. As written, the authors appear to be very reliant on structural and BRET studies that do not give the details that would disprove this interpretation. The main issue is that the author's mechanism does not fully explain how Gβγ activation occurs for PLCβ2 in reconstituted systems in the absence of Gαq subunits.

(2) In a recent study, McKinnon presents a model showing that Gαq and Gβγ activate PLCβ3 by two distinct pathways and that activation by Gβγ occurs through membrane recruitment. It is not surprising that the authors find that this is not true since the pelleting method used by McKinnon is subject to error. The authors should directly address the limitations of this previous work and the changes in proteoliposomes with sedimentation that alter partition coefficients. Although the inability of Gβγ to drive membrane binding is in accord with the quantitative studies of Scarlata, showing that the affinity of PLCβ3 to Gβγ is fairly weak as compared to the intrinsic membrane partition coefficient.

(3) It was proposed many years ago that in signaling complexes Gαq - Gβγ may not have to fully dissociate when binding PLCβ, but rather shift their relative orientation when binding to PLCβ to allow activation. Is their model consistent with this? Is it possible that PLCβ3 keeps Gβγ from diffusing to enhance the rate of Gq / Gβγ re-association?

(4) The authors find that Gβγ binds multiple sites, and it is clear that the PH domain site is the primary one in accord with previous work. Could these weaker sites be an artifact of the elevated concentrations used in cryoEM and BRET assays?

(5) Although their assays infer differences in binding affinities, it would strengthen the paper if the authors could estimate the association energies of these different binding sites. This estimation would also address the concern stated above.

Reviewer #1 (Public review):

Summary:

The authors set out to understand the complex regulation of the assembly of the Type 3 Secretion System of S. typhimurium. They found that the gene synteny as well as specific mRNA stem loops were important for the translational coupling of sctS and sctT. Without this regulation, SctT self-oligomerizes, which disrupts the export of effector proteins and leads to a decreased fitness of the pathogen. The work was done using a variety of convincing methods and leads to an updated picture of how T3SS assembly occurs. Since the same genetic synteny is found in a large majority of T3SS in different bacteria, it is likely that this is a general mechanism, but one that needs to be further experimentally validated.

Strengths:

The paper uses an impressive amount of experiments, with different techniques, to describe how they identified the genetic regulation of SctT production.

Weaknesses:

Only minor weaknesses are found.

(1) Regarding the use of the complex being unique. It is not well explained what makes this a unique complex.

(2) The paper would benefit from a discussion regarding how regulation might work in the minority of bacterial strains where the T3SS gene synteny is largely different. One would expect that those bacteria would have a different way of regulating T3SS assembly, but that is not discussed at all by the authors.

Reviewer #2 (Public review):

Summary:

In this manuscript, Samuel Wagner and colleagues describe an elegant mechanism to prevent promiscuous assembly of a core virulence type III secretion system protein, SctS. Starting from a bioinformatic standpoint, they demonstrate that synteny is highly conserved, and sctT occurs immediately downstream of sctS. Secretion is greatly reduced when sctT is removed or scrambled from its genomic context, and sctT expression is accordingly reduced (sctS synteny is also important, though less so). The distance between sctS and sctT is crucial. An elegant series of genetic experiments leads the authors to pinpoint a stem loop structure that occludes the Shine-Dalgarno sequence of sctT. This property is independent of the actual gene preceding sctT. In sum, this means that SctS is already expressed before SctT is expressed, preventing SctT from forming cytotoxic homooligomers.

Strengths:

The manuscript is very well-written, easy to follow, and describes a substantial amount of genetic detective work to identify the underlying mechanism. I have only a number of textual suggestions, mainly for the Introduction text, which I believe could be revised for a flagellar and broader audience.

Weaknesses:

Major concern:

While the work is rigorous and substantial, I am unsure as to whether its findings will appeal beyond a niche audience.

Minor points:

(1) Line 117: The number here seems to be very small. RefSeq has ~200,000 genomes. My guess is that at least 100,000 of these will be bacterial. Many (most?) bacteria have flagella, and some unflagellated strains have injectisomes, meaning I would have guessed that the authors would have ~50,000 genomes with SctRSTU. This estimate is error-prone, but not by too much. Can the authors explain the discrepancy between my estimate and their figure of almost two orders of magnitude? (SctRSTU/FliPQGFlhB should also be easy to pick up by sequence searches, so I don't think this is due to false negatives).

(2) Discussion: I would appreciate some discussion of how species that do not conserve the synteny of sctS and sctT prevent problems of sctT oligomerisation? It doesn't need to be evidence-based at this stage, but I'm sure the authors have thought about this, and the Discussion is an appropriate place to share their speculations.

Reviewer #3 (Public review):

At the core of the bacterial type III secretion system (T3SS), a nanomachine used to inject effector proteins into eukaryotic cells, five highly conserved proteins, SctRSTUV, form the export apparatus, which is the actual gate for effector proteins. Not only are these proteins the most strongly conserved parts of the system, but also their gene order is conserved, which is not the case for most other components of the T3SS. Interestingly, this order does not completely recapitulate the assembly order, which is SctR5-T4-S-U-V. Looking into the reasons for the conserved synteny, the authors noted a stem-loop in the mRNA of the Salmonella SPI-1 sctS gene, which is present in many other T3SS as well (and in fact had been found in Yersinia before). They then use an array of clever gene permutations and modifications to discern the benefit of this order for the bacteria. The combination of thorough sequence analysis with different, partly quantitative, protein expression and secretion assays and growth curves, both in the native Salmonella background and in heterologous systems, provides strong evidence for the interpretation of the authors: The stem-loop in sctS prevents the premature expression of SctT, which can otherwise assemble into "futile multimers" that can lead to ion loss. The presence of stem-loops in many other sctS/T genes gives weight to this finding.

This is a very nice and thorough study addressing an important point in the assembly of type III secretion systems. I only have a few suggestions.

(1) Conserved gene orders have been shown for many complexes, and the findings presented in this manuscript might be applicable to other membrane complexes.

The conservation of gene order and the presence of the stem loop give weight to the authors' findings. However, it is only mentioned quite late in the discussion that a similar stem loop was found in Yersinia upstream sctT earlier, and was interpreted differently. The authors' current discussion is somewhat evasive on this point. Why would these similar structures be used differently? Why would temperature not play a role in Salmonella SPI-1? And wouldn't the stem-loop also couple sctS and sctT expression in Yersinia? This should be addressed, if possible, by experiments (at least, the influence of temperature on the SPI-1 mRNA structure should be testable for the authors) and by a more detailed discussion (given the redundancy of RNA thermometers in the Yersinia T3SS, the interpretation in the current paper might well be the more compelling one).

(2) A point that deserves more attention is that a similar finding in Yersinia has been interpreted differently before (as a temperature sensor rather than translational coupling) - are these systems really different? Testing the different interpretations in the respective other system (at least the influence of temperature in the Salmonella SPI-1 system used in this manuscript) would have made the interpretation even more compelling.

(3) Another point that should be discussed in more detail is why this mechanism is present when replacement of the sctT ATG by weaker start codons and the simple omission of a separate SD sequence upstream sctT would achieve the same outcome. This could be tested in one of the nice heterologous systems, as used in Figure 4.

Reviewer #1 (Public review):

Summary:

This is an interesting and well-written manuscript in which the authors set out to answer a simple, old question with a modern toolkit: where in crab evolution did sideways walking arise, how often has it been lost or regained, and is it plausibly linked to the ecological and taxonomic success of true crabs. To do this, they record locomotion from 50 live species, convert each species' movements into a quantitative index that compares forward versus sideways bouts, and then map the resulting states onto a recent crab phylogeny to infer the most likely evolutionary history of locomotor direction.

Strengths:

The strongest part of the study is the dataset itself. Comparable behavioral measurements across dozens of crab species are rare. The authors have done the field and husbandry work needed to make this possible. The overall pattern they recover, that most true crabs are strongly biased toward sideways movement (while a smaller set of lineages move predominantly forward), is interesting and likely to be useful to others. The phylogenetic mapping is also a reasonable way to address the "how many times" question (although this is peripheral to my expertise). The manuscript makes a convincing case that sideways locomotion is not simply a trivial byproduct of a crab-like body plan.

Weaknesses:

Where I am less convinced is in how strongly the authors describe the discreteness of the behavioral categories and the absence of intermediates. The manuscript states that the Forward-Sideways Index shows a clear separation between two locomotor types with little evidence for intermediates, and it cites a statistical test rejecting a single peak in the distribution. However, the histogram in Figure 3 appears structured within each labeled category, with subclusters inside both the forward and sideways groups rather than a single tight peak per group. This matters because the index is built by first placing each movement bout into "forward" versus "sideways" bins using a fixed angle boundary and then collapsing the result into a single ratio. That approach is simple and transparent enough, but it can also hide mixed strategies. For example, a species that produces substantial amounts of both forward and sideways walking can still end up with a strongly positive or negative index, and therefore be classified as a pure "type," even though the underlying behavior is mixed. In that context, rejecting a single peak in the across-species distribution does not, by itself, justify the stronger claim that intermediates are rare or absent.

Related to this, a key methodological choice is the use of 60 degrees as the cutoff between forward and sideways bouts. This boundary may be reasonable as a convention, but the paper does not explain why it is the right place to draw the line, and there is a plausible biological concern that a fixed angular cutoff does not mean the same thing across taxa.

Crabs vary in body shape and in how the legs are arranged around the body. In my own comparative work, for example, some species show an elliptical stance pattern elongated along the preferred direction of travel, while others show a more circular leg arrangement, and the latter can express more mixed forward and sideways behavior. When limb arrangement and body geometry differ across species, the same measured angle can correspond to different underlying mechanics and different functional "degree of sidewaysness." The practical implication is that the reported binary separation may partly reflect the imposed classification rule, rather than a sharp biological divide.

Another limitation that affects interpretation is the decision to use one individual per species. I understand the logistics, and for some questions, a single representative individual can be a reasonable first pass. But it is not strong support for negative claims about intermediates, especially in a group where individuals can change substantially with growth and allometry. Crabs can grow dramatically, often with pronounced allometric shifts in limb proportions that can alter the center of mass location. Size alone can alter the kinematics and choice of locomotor behaviors in crustaceans. In species where appendage proportions change with size, or where certain legs become disproportionately large (or calcified), it is plausible that locomotor direction and the distribution of movement angles shift across ontogeny. That makes it hard to treat a single individual as a complete description of a species-level strategy, particularly for species that fall closer to the boundary between categories.

In sum, this is a valuable and useful behavioral comparative study with a dataset that many in the field will appreciate. The main conclusions about the likely evolutionary placement of sideways walking are plausible, but several of the stronger claims about discrete locomotor types, the absence of intermediates, and the relationship to diversification would be more convincing if the analysis were less dependent on a fixed angular cutoff and on single individuals per species, or if the manuscript framed those points more cautiously so the conclusions track the strength of the evidence.

Reviewer #2 (Public review):

Summary:

The current work investigates the evolution of sideward locomotion in Brachyura in light of a single evolutionary origin. To this end, the authors first analysed the mode of locomotion in 50 crab species and observed mutually exclusive presence of sideways vs. forward movement. The phylogenetic analysis confirmed that there is indeed a single evolutionary origin for sideways movement, which was sometimes followed by several reversions to forward locomotion. This way, authors demonstrate how locomotor movement modes shape evolutionary diversification in animals by showing that species richness is much higher in side-ways-moving crabs than in the nearest groups. This is an interesting work that integrates behavioural analysis and phylogenetic relations, capitalising largely on crabs. I have a few suggestions and questions.

Firstly, I think the paper spends too much time on a straightforward analysis of the mode of locomotion. I was also wondering whether the phylogenetic analysis could be simply achieved by maximising an objective function in which the modes of movement are inversely coded for two putative groups, with all values calculated at all possible nodes.

Unfortunately, I find that the authors did not sufficiently discuss differences in the ecological niches of species with forward vs. sideways locomotion modes (including challenges of locomotion and substrate).

Likewise, what are the anatomic correlates of forward vs. sideways locomotion? For instance, how are the advantages assumed for sideways movement associated with a flattened body? Is it possible that the mode of motion is secondary to flattened/narrow body structure, which basically limits the distance between legs and thus makes the forward movement difficult - under this logic, the mode of movement would be a secondary phenomenon to body shape traits. How can one differentiate between this alternative and the one that puts the mode of movement in the centre of the story? On a related note, how do different modes of movement relate to the ability to fit into tight spaces - how does it relate to differences in leg joints?

Is it possible that the sideways movement maximises the scanned visual field per unit time/displacement, which may be beneficial for mostly forward-moving predators?

It is really difficult to decipher the information contained in the nodes (circles) in the printed black-and-white version of the manuscript.

Briefly, although I find the study interesting, the presented complexity may not be necessary given the endpoints; it can be achieved much more simply. Furthermore, the degree to which the conceptual analysis of different modes of locomotion was exercised was limited. The general approach may serve as a good model for the evolutionary analysis of other traits. The demonstration of traceability of the relations in question is a major contribution of the work.

Strengths:

The research question and the novel combination of different data types.

Weaknesses:

The complexity of the methods used, along with a limited discussion of the potential dynamics that may underlie the evolution of the sideways movement mode.

通过了同行评审，但独立评估发现了幻觉、伪造结果和夸大新颖性——这个细节极为重要，却经常被忽视。它揭示了一个深刻的系统性漏洞：AI 已经学会了「通过评审」，但没有学会「诚实做科学」。这两件事在人类评审员看来是同一件事，但在 AI 系统的优化目标中可能是分离的。这是 AI 安全在科学领域的具体表现。

史上第一篇完全由 AI 自主生成并通过同行评审的论文——这个里程碑的重要性不亚于 AlphaFold 折叠蛋白质。令人惊讶的是，这篇论文得分超越了 55% 的人类作者投稿（平均分 6.33，高于人类投稿平均录取线）。学术界存在了数百年的「同行评审」制度，第一次被一个 AI 系统悄悄穿越了。

【启发】「AI 天然不在意结构，除非你明确要求」——这个发现定义了人类工程师在 AI 时代最不可替代的职责：做代码结构的「守门人」。这与 Every 文章里「每个人都是管理者」的洞见形成呼应：人类的工作从「执行代码」转变为「审查代码质量并为 AI 设定标准」。对工程团队文化的启发：代码 Review 的重要性不是在下降，而是在上升——因为现在需要 Review 的代码量是以前的 10 倍。

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have submitted a second revision, largely to address a comment from Reviewer 2, which was "The failure to model the neural data with an explicit model is a missed opportunity." The authors have now included a computational model.]

This study makes a fundamental contribution to our understanding of interocular suppression, particularly continuous flash suppression (CFS). Using neuroimaging data from two macaque monkeys, the study provides compelling evidence that CFS suppresses orientation responses in neurons within V1. These findings enrich the CFS literature by demonstrating that neural activity under CFS may prevent high-level visual and cognitive processing.

Comments on previous revisions:

The authors have addressed all my previous comments.

Reviewer #2 (Public review):

Summary:

The goal of this study was to investigate the degree to which low-level stimulus features (i.e., grating orientation) are processed in V1 when stimuli are not consciously perceived under conditions of continuous flash suppression (CFS). The authors measured the activity of a population of V1 neurons at single neuron resolution in awake fixating monkeys while they viewed dichoptic stimuli that consisted of an oriented grating presented to one eye and a noise stimulus to the other eye. Under such conditions, the mask stimulus can prevent conscious perception of the grating stimulus. By measuring the activity of neurons (with Ca2+ imaging) that preferred one or the other eye, the authors tested the degree of orientation processing that occurs during CFS.

Strengths:

The greatest strength of this study is the spatial resolution of the measurement and the ability to quantify stimulus representations during CSF in populations of neurons preferring the eye stimulated by either the grating or the mask. There have been a number of prominent fMRI studies of CFS, but all of them have had the limitation of pooling responses across neurons preferring either eye, effectively measuring the summed response across ocular dominance columns. The ability to isolate separate populations offers an exciting opportunity to study the precise neural mechanisms that give rise to CFS, and potentially provide insights into nonconscious stimulus processing.

Weaknesses:

(The authors have now included a computational model in the second revision.)

Reviewer #3 (Public review):

Summary:

In this study, Tang, Yu & colleagues investigate the impact of continuous flash suppression (CFS) on the responses of V1 neurons using 2-photon calcium imaging. The report that CFS substantially suppressed V1 orientation responses. This suppression happens in a graded fashion depending on the binocular preference of the neuron: neurons preferring the eye that was presented with the marker stimuli were most suppressed, while the neurons preferring the eye to which the grating stimuli were presented were least suppressed. Binocular neuron exhibited an intermediate level of suppression.

Strengths:

The imaging techniques are cutting-edge.

Weaknesses:

The strength of CFS suppression varies across animals, but the authors attribute this to comparable heterogeneity in the human psychophysics literature.

Comments on previous revisions:

The authors have addressed my comments from the previous round of review, and I have no further comments.

Reviewer #1 (Public review):

In this paper, Stanojcic and colleagues attempt to map sites of DNA replication initiation in the genome of the African trypanosome, Trypanosoma brucei. Their approach to this mapping is to isolate 'short-nascent strands' (SNSs), a strategy adopted previously in other eukaryotes (including in the related parasite Leishmania major), which involves isolation of DNA molecules whose termini contain replication-priming RNA. By mapping the isolated and sequenced SNSs to the genome (SNS-seq), the authors suggest that they have identified origins, which they localise to intergenic (strictly, inter-CDS) regions within polycistronic transcription units and suggest display very extensive overlap with previously mapped R-loops in the same loci. Finally, having defined locations of SNS-seq mapping, they suggest they have identified G4 and nucleosome features of origins, again using previously generated data. Though there is merit in applying a new approach to understand DNA replication initiation in T. brucei, where previous work has used MFA-seq and ChIP of a subunit of the Origin Replication Complex (ORC), there are two significant deficiencies in the study that must be addressed to ensure rigour and accuracy.

(i) The suggestion that the SNS-seq data is mapping DNA replication origins that are present in inter-CDS regions of the polycistronic transcription units of T. brucei is novel and does not agree with existing data on the localisation of ORC1/CDC6, and it is very unclear if it agrees with previous mapping of DNA replication by MFA-seq due to the way the authors have presented this correlation. For these reasons, the findings essentially rely on a single experimental approach, which must be further tested to ensure SNS-seq is truly detecting origins. Indeed, in this regard, the very extensive overlap of SNS-seq signal with RNA-DNA hybrids should be tested further to rule out the possibility that the approach is mapping these structures and not origins.

(ii) The authors' presentation of their SNS-seq data is too limited and therefore potentially provides a misleading view of DNA replication in the genome of T. brucei. The work is presented through a narrow focus on SNS-seq signal in the inter-CDS regions within polycistronic transcription units, which constitute only part of the genome, ignoring both the transcription start and stop sites at the ends of the units and the large subtelomeres, which are mainly transcriptionally silent. The authors must present a fuller and more balanced view of SNS-seq mapping, across the whole genome, to ensure full understanding and clarity.

In the revised manuscript, the authors have improved the presentation and analysis of their data, expanding the description of SNS-seq mapping across the genome, and more clearly assessing to what extent there is correlation between SNS-seq signal and previous mapping approaches to predict origins (by MFA-seq and ChiP-chip of ORC1/CDC6). With regard the correlation between SNS-seq and ORC/1CDC6 ChIP-chip, it should be noted that two datasets were generated in distinct strains of T. brucei (Lister 427 and TREU927, respectively), and it is unclear if the latter dataset can be accurately mapped to the strain used here. Notwithstanding this concern, these improvements clarify a number of aspects of the SNS-seq mapping: (1) the signal is more prevalent in the transcribed core of the genome than in the largely transcriptionally silent subtelomeres; and (2) whereas previous work revealed strong correlation between ORC1/CDC6 localisation and MFA-seq peaks at the ends of multigene transcription units, neither of these data show significant overlap with SNS-seq signal, which is not seen at transcription start or stop sites ('SSRs'; supplementary Fig.8D) and shows marked depletion at predicted ORC1/CDC6 sites (supplementary Fig.8C). To the authors' credit, they acknowledge this lack of correlation in the discussion.

The authors have not provided any new data to substantiate their assertion that SNS-seq accurately detects origins in T. brucei, and therefore the work rests on a single experimental approach, without validation. As a result, the suggestion of abundant, previously undetected origins in the intergenic regions of multigene transcription remains a prediction. One key untested limitation of the work lies in the observation that the very large majority of SNS-seq signal overlaps with previously RNA-DNA hybrids; without an experimental test, the suggestion that the authors have 'disclosed for the first time a strong link between RNA:DNA hybrid formation and DNA replication initiation' remains conjecture.

Reviewer #2 (Public review):

Summary:

Stanojcic et al. investigate the origins of DNA replication in the unicellular parasite Trypanosoma brucei. They perform two experiments, stranded SNS-seq and DNA molecular combing. Further, they integrate various publicly available datasets, such as G4-seq and DRIP-seq, into their extensive analysis. Using this data, they elucidate the structure of origins of replications. In particular, they find various properties located at or around origins, such as polynucleotide stretches, G-quadruplex structures, regions of low and high nucleosome occupancy, R-loops, and that origins are mostly present in intergenic regions. Combining their population-level SNS-seq and their single-molecule DNA molecular combing data, they elucidate the total number of origins as well as the number of origins active in a single cell.

Between the initial submission and this revision, the raised major concerns have not been resolved, and no additional validation has been provided.

Strengths:

(1) A very strong part of this manuscript is that the authors integrate several other datasets and investigate a large number of properties around origins of replication. Data analysis clearly shows the enrichment of various properties at the origins, and the manuscript is concluded with a very well-presented model that clearly explains the authors' understanding and interpretation of the data.

(2) The DNA combing experiment is an excellent orthogonal approach to the SNS-seq data. The authors used the different properties of the two experiments (one giving location information, one giving single-molecule information) well to extract information and contrast the experiments.

(3) The discussion is exemplary, as the authors openly discuss the strengths and weaknesses of the approaches used. Further, the discussion serves its purpose of putting the results in both an evolutionary and a trypanosome-focused context.

Weaknesses:

I have major concerns about the origin of replication sites determined from the SNS-seq data. As a caveat, I want to state that, before reading this manuscript, SNS-seq was unknown to me; hence, some of my concerns might be misplaced.

(1) There are substantial discrepancies between the origins identified here and those reported in previous studies. Given that the other studies precede this manuscript, it is the authors' duty to investigate these differences. A conclusion should be reached on why the results are different, e.g., by orthogonally validating origins absent in the previous studies.

(2) I am concerned that up to 96% percent of all SNS-seq peaks are filtered away. If there is so much noise in the data, how can one be sure that the peaks that remain are real? Upon request, the authors have performed a control, where randomly placed peaks were run through the same filtering process. Only approximately twice as many experimental peaks passed filtering compared to random peaks. While the authors emphasize reproducibility between replicates, technical artifacts from the protocol would also be reproducible. Moreover, in other SNS-seq studies, for example, Pratto et al. Cell 2021, Fig. 1B, + and − strand peaks always appear closely paired. This pattern contrasts strongly with Fig. 2A in this manuscript.

Further, I have some minor concerns that do not affect the main conclusions of the manuscript:

- Fig 2C: The regions shown in the heatmap have different sizes, and I presume that the regions are ordered by size on the y-axis? If so, does the cone-shaped pattern, which is origin-less for genic regions and origin-enriched for intergenic regions, arise from the size of the regions? (I.e., for each genic region, the region itself is origin-less and the flanking intergenic regions contain origins.) If this is the case, then the peaks/valleys, centered exactly on the center of the regions on the mean frequency plots, arise from the different sizes of the analyzed regions, not from the fact that origins are mostly found at the center of intergenic regions. This data would be better presented with all regions stretched to the same size. This has not been addressed in the revision.

- Line 123, "and the average length of origins was found to be approximately 150 bp.": To determine origins, the authors filter away overlapping peaks and peaks that are too far from each other. Both restrict the minimal and maximal length of origins that can be observed, and this, in turn, affects the average length. This has not been addressed in the revision.

Are claims well substantiated?:<br /> The identification of origins via SNS-seq appears to be incompletely supported to me.<br /> All downstream analyses depend on the reliability of origin identification.

Impact:<br /> This study has the potential to be valuable for two fields: In research focused on T. brucei as a disease agent, where essential processes that function differently than in mammals are excellent drug targets. Further, this study would impact basic research analyzing DNA replication over the evolutionary tree, where T. brucei can be used as an early-divergent eucaryotic model organism.

Reviewer #1 (Public review):

Summary:

The manuscript "Adapting Clinical Chemistry Plasma as a Source for Liquid Biopsies" addresses a timely and practical question: whether residual plasma from heparin separator tubes can serve as a source of cfDNA for molecular profiling. This idea is attractive, since such samples are routinely generated in clinical chemistry labs and would represent a vast and accessible resource for liquid biopsy applications. The preliminary results are encouraging, and likely to benefit the research community.

Comments on revisions:

The concerns raised have been addressed. The heparin separator-based cfDNA method described in this study is likely to benefit the research community. I have no further scientific concerns.

Reviewer #2 (Public review):

Summary:

The authors propose that leftover heparin plasma can serve as a source for cfDNA extraction, which could then be used for downstream genomic analyses such as methylation profiling, CNV detection, metagenomics, and fragmentomics. While the study is potentially of interest, several major limitations reduce its impact; for example, the study does not adequately address key methodological concerns, particularly cfDNA degradation, sequencing depth limitations, statistical rigor, and the breadth of relevant applications.

Strengths:

The paper provides a cheap method to extract cfDNA, which has broad application if the method is solid.

Weaknesses:

(1) The introduction lacks a sufficient review of prior work. The authors do not adequately summarize existing studies on cfDNA extraction, particularly those comparing heparin plasma and EDTA plasma. This omission weakens the rationale for their study and overlooks important context.

(2) The evaluation of cfDNA degradation from heparin plasma is incomplete. The authors did not compare cfDNA integrity with that extracted from EDTA plasma under realistic sample handling conditions. Their analysis (lines 90-93) focuses only on immediate extraction, which is not representative of clinical workflows where delays are common. This is in direct conflict with findings from Barra et al. (2025, LabMed), who showed that cfDNA from heparin plasma is substantially more degraded than that from EDTA plasma. A systematic comparison of cfDNA yields and fragment sizes under delayed extraction conditions would be necessary to validate the feasibility of their proposed approach.

(3) The comparison of methylation profiles suffers from the same limitation. The authors do not account for cfDNA degradation and the resulting reduced input material, which in turn affects sequencing depth and data quality. As shown by Barra et al., quantifying cfDNA yield and displaying these data in a figure would strengthen the analysis. Moreover, the statistical method applied is inappropriate: the authors use Pearson correlation when Spearman correlation would be more robust to outliers and thus more suitable for methylation and other genomic comparisons.

(4) The CNV analysis also raises concerns. With low-coverage WGS (~5X) from heparin-derived cfDNA, only large CNVs (>100 kb) are reliably detectable. The authors used a 500 kb bin size for CNV calling, but they did not acknowledge this as a limitation. Evaluating CNV detection at multiple bin sizes (e.g., 1 kb, 10 kb, 50 kb, 100 kb, 250 kb) would provide a more complete picture. In addition, Figure 3 presents CNV results from only one sample, which risks bias. Similar bias would exist for illustrations of CNVs from other samples in the supplementary figures provided by the authors. Again, Spearman correlation should be applied in Figure 3c, where clear outliers are visible.

(5) It is important to point out that depth-based CNV calling is just one of the CNV calling methods. Other CNV calling software using SNVs, pair-reads, split-reads, and coverage depth for calling CNV, such as the software Conserting, would be severely affected by the low-quality WGS data. The authors need to evaluate at least two different software with specific algorithms for CNV calling based on current WGS data.

(6) The authors omit an important application of cfDNA: somatic mutation detection. Degraded cfDNA and reduced sequencing depth could substantially impact SNV calling accuracy in terms of both recall and precision. Assessing this aspect with their current dataset would provide a more comprehensive evaluation of heparin plasma-derived cfDNA for genomic analyses.

Comments on revisions:

As suggested previously, the Pearson correlation analysis tends to be overstated; please replace it with Spearman correlation in the whole manuscript. Currently, the authors include both of them in the abstract, method, results, and graphics, all of which are required to be updated to only use Spearman correlation results.

I don't have other concerns about the manuscript.

AI Scientist 研究——一个让 AI 自动化完整科研周期的系统——被 Nature 正式发表了。令人震惊的是：一篇关于「AI 能否替代科学家」的论文，本身就是通过「AI 辅助科研」的过程产生的，并通过了人类同行评审。这个自指性质让 Nature 的认可变成了一个双重背书：既是对内容的认可，也是对方法论的认可。Sakana 将这个成果作为 Marlin 的技术背书，是极为聪明的品牌叙事策略。

「预测反馈」的概念令人惊讶：AI 在报告发出前，预测各位审阅者会说什么、Twitter 会怎么反应、调查结果会是什么——研究者先在「预测反馈」中迭代，只有当预期信息增量足够高时，才真正发出去等待真实反馈。这是一种「反馈的预计算」——把等待时间转化为优化时间，本质上是把「串行等待」变成了「并行模拟」。