Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

Reviewer #3 (Public review):

Summary:

This study uses bulk mRNA sequencing to profile transcriptional changes in intestinal cells during the early postnatal period in mice - a developmental window that has received relatively little attention despite its importance. This developmental stage is particularly significant because it parallels late gestation in humans, a time when premature infants are highly vulnerable to necrotizing enterocolitis (NEC). By sampling closely spaced timepoints from birth through postnatal week four, the authors generate a resource that helps define transcriptional trajectories during this phase. Although the primary focus is on murine tissue, the authors also present limited data from human fetal intestinal biopsy samples and organoids. In addition, they discuss potential links between observed gene expression changes and factors that may contribute to NEC.

Strengths:

The close temporal sampling in mice offers a detailed view of dynamic transcriptional changes across the first four weeks after birth. The authors leverage these close timepoints to perform hierarchical clustering to define relationships between developmental stages. This is a useful approach, as it highlights when transcriptional states shift most dramatically and allows for functional predictions about classes of genes that vary over time. This high-level analysis provides an effective entry point into the dataset and will be useful for future investigations. The inclusion of human fetal intestinal samples, although limited, is especially notable given the scarcity of data from late fetal timepoints. The authors are generally careful in their presentation of results, acknowledging the limitations of their approach and avoiding over-interpretation. As they note, this dataset is intended as a foundation for their lab and others, with secondary approaches required to more fully explore the biological questions raised.

Weaknesses:

One limitation of the study is the use of bulk mRNA sequencing to draw conclusions about individual cell types. It has been documented that a few genes are exclusively expressed in single cell types. For instance, markers such as Lgr5 and Olfm4 are enriched in intestinal stem cells (ISCs), but they are also expressed at lower levels in other lineages and in differentiating cells. Using these markers as proxies for specific cell populations lowers confidence in the conclusions, particularly without complementary validation to confirm cell type-specific dynamics.

Validation of the sequencing data was itself limited, relying primarily on qPCR, which measures expression at the same modality rather than providing orthogonal support. It is unclear how the authors selected the subset of genes for validation; many key genes highlighted in the sequencing data were not assessed. Moreover, the regional differences reported in Lgr5, Olfm4, and Ascl2, appearing much higher in proximal samples than in distal ones, were not recapitulated by qPCR validation of Olfm4, and this discrepancy was not addressed. Resolving such inconsistencies will be important for interpreting the dataset.

The basis for linking particular gene sets to NEC susceptibility rests largely on their spatial restriction to the distal intestine and their temporal regulation between early (day 0-14) and later (weeks 3-4) developmental stages. While this is a reasonable approach for generating hypotheses, the correlations have limited interpretive power without experimental validation, which is not provided here. Many factors beyond NEC may drive regional and temporal differences in intestinal development.

Finally, the contribution of human fetal biopsy samples is minimal. The central figure presenting these data (Figure 4A) shows immunofluorescence for LGR5, a single stem cell marker. The staining at day 35 is not convincing, and the conclusions that can be drawn are limited to confirming the localization of LGR5-positive cells to crypts as early as 26 weeks.

Joint Public Review:

In this study, the authors sought to characterize the relationship between the timescales of evidence integration in an auditory change detection task and neural activity dynamics in the rat posterior parietal cortex (PPC), an area that has been implicated in the accumulation of sensory evidence. Using the state-of-the-art Neuropixel recording techniques, they identified two subpopulations of neurons whose firing rates were positively and negatively modulated by auditory clicks. The timescale of click-related response was similar to the behaviorally measured timescale for evidence evaluation. The click-related response of positively modulated neurons also depended on when the clicks were presented, which the authors hypothesized to reflect a time-dependent gain change to implement an urgency signal. Using muscimol injections to inactivate the PPC, they showed that PPC inactivation affected the rats' choices and reaction times.

There are several strengths of this study, including:

(1) Compelling evidence for short temporal integration in behavioral and neural data for this task.

(2) Well-executed and interpretable comparisons of psychophysical reverse correlation with single-trial, click-triggered neuronal analyses to relate behavior and neural activity.

(3) Inactivation experiments to test for causality.

(4) Characterization of neural subpopulations that allows for complex relationships between a brain region and behavior.

(5) Experimental evidence for an interesting way to use sensory gain change to implement urgency signals.

There are also some concerns, including:

(1) The work could be better contextualized. From a normative Bayesian perspective, the observed adaptation of timescales and gain aligns closely with optimal strategies for change detection in noisy streams: placing greater weight on recent sensory samples and lowering evidence requirements as decision urgency grows. However, the manuscript could go further in explicitly connecting the experimental findings to normative models, such as leaky accumulator or dynamic belief-updating frameworks. This would strengthen the broader impact of the work by making clear how the observed PPC dynamics instantiate computationally optimal strategies.

(2) It is unclear how the rats are performing the task, both in terms of the quality of performance (they only show hit rates, but the rats also seem to have high false alarm rates), and in terms of the underlying strategy that they seem to be using.

(3) A major conceptual weakness lies in the claim that PPC "dynamically modulates evidence evaluation in a time-adaptive manner to suit the behavioral demands of a free-response change detection task." To support this claim, it would require direct comparison of neural activity between two task demands, either in two tasks or in one task with manipulations that promote the adoption of different timescales.

(4) Some analyses of neural data are lacking or seem incomplete, without considering alternative interpretations.

(5) The muscimol inactivation results did not provide a clear interpretation about the link between PPC activity and decision performance.

Reviewer #1 (Public review):

Summary:

(1) Introduction Hybridogenesis involves one genome being clonally transmitted while the other is replaced by backcrossing. It results in high heterozygosity and balanced ancestry proportions in hybrids. Distinguishing it from other hybrid systems requires a combination of nuclear, mitochondrial, and population-genetic evidence. Hybridogenesis has been identified in only a few taxa (e.g., some fish, frogs, and stick insects), but no new cases have been reported in over a decade. Advancements in high-throughput sequencing now allow for the detection of high individual heterozygosity, which can indicate hybridization, but it is difficult to distinguish hybridogenesis from other similar asexual systems based solely on genome-wide data. To differentiate these systems, researchers look at several key indicators: Presence of pure-species offspring from hybrids (possible only in hybridogenesis); sex ratio (male presence in hybridogenetic systems); nuclear and mitochondrial haplotype sharing with co-distributed parental species; geographic distribution patterns, especially the lack of both parental species in hybrid populations.

(2) What the authors were trying to achieve The paper studies Quasipaa Frogs. Q. robertingeri (narrowly endemic) and Q. boulengeri (widespread), which are morphologically similar and found sympatrically in parts of China. Preliminary RAD-seq data revealed bimodal heterozygosity in Q. boulengeri samples. Some individuals had extremely high heterozygosity, consistent across loci and suggestive of F1 hybrids. These high-heterozygosity individuals had one haplotype from each species. The study investigates the high heterozygosity observed in Quasipaa frogs, particularly in individuals morphologically resembling Q. boulengeri but genetically appearing to be F1 hybrids with Q. robertingeri. The goal is to determine whether these patterns are consistent with hybridogenesis, rather than other atypical reproductive modes. The authors also suggest the hypothesis that hybridogenesis could enable range expansion of an endemic species through hybridization with a widespread relative.

(3) Methods A total of 107 individuals from 53 localities were collected for the study. This sample included 58 sexed adults-27 males and 31 females-as well as a majority of tadpoles. Of these individuals, 31 had previously determined karyotypes. DNA was extracted and sequenced. Individual heterozygosity and ancestry were estimated using bioinformatics tools. F1 hybrids were compared to one of the parental species to examine patterns of fixed heterozygous loci. Mitochondrial DNA was also extracted from sequencing data, and phylogenetic trees were constructed

(4) Results Two groups of individuals were detected based on heterozygosity: one group exhibited high heterozygosity and consisted of F1 hybrids, while the other group showed low heterozygosity, representing pure-species types. The F1 hybrids demonstrated approximately equal ancestry from Q. robertingeri and Q. boulengeri, consistently maintaining a high proportion of heterozygous loci at around 16.7%. In contrast, pure individuals had much lower heterozygosity, approximately 2.9%. F1 hybrids were found across 21 different sites, including both male and female individuals. The presence of numerous fixed heterozygous loci in F1 hybrids confirmed their hybrid origin, and these loci were absent in pure Q. boulengeri samples. F1 individuals typically carried one haplotype from each parental species. There was minimal haplotype sharing between the two pure species, but extensive sharing was observed between F1 hybrids and co-occurring pure-species individuals. In fact, F1 types shared haplotypes with local Q. boulengeri in over 90% of cases, which supports the occurrence of local backcrossing and parental contribution. In terms of mitochondrial DNA, F1 hybrids possessed mitochondrial haplotypes that clustered with Q. boulengeri and often shared these haplotypes directly. Genetic structure and phylogenetic analyses, revealed three distinct genetic clusters corresponding to F1 hybrids, Q. boulengeri, and Q. robertingeri. The F1 hybrids positioned themselves intermediate between the two pure species. Neighbor-joining trees and TreeMix analyses confirmed a strong separation between pure-species types, with F1 hybrids clustering alongside local Q. boulengeri subpopulations, indicating local formation of hybrids.

(5) Discussion In summary, the study reveals hybridogenesis (a reproductive system where hybrids clonally transmit one parental genome) in Quasipaa boulengeri and Q. robertingeri. Hybrids show high genetic heterozygosity and coexist with parental species, ruling out other reproductive modes like parthenogenesis or kleptogenesis. Evidence suggests hybridogenesis enables Q. robertingeri genomes to appear far outside their normal range, possibly aiding range expansion. Chromosomal abnormalities are linked to hybrid hybrids, supporting clonal genome transmission. The genetic divergence between parental species fits patterns seen in other hybridogenetic systems, highlighting a unique, understudied case in East Asia.

Strengths:

Overall, the authors carefully interpret their genetic data to support hybridogenesis as the reproductive mode in this system and propose that this mechanism may aid range expansion. They also appropriately acknowledge the need for further cytogenetic and ecological studies, demonstrating scientific caution. In summary, the discussion reasonably follows from the results, offering cautious interpretation where necessary.

Weaknesses:

Direct reproductive or cytological evidence is still lacking. While alternative reproductive modes are discussed and mostly ruled out logically, some require further empirical testing. The authors maintain a cautious interpretation, appropriately suggesting further research. Some outstanding questions remain.

(1) The elevated heterozygosity and presence of fixed heterozygous loci in hybrids compared to parental species strongly indicate hybridogenesis. However, alternative explanations such as repeated F1 hybridization or some form of balanced polymorphism, while less likely, are not fully excluded.

(2) The coexistence of hybrids and parental species, along with high nuclear and mitochondrial haplotype sharing between hybrids and Q. boulengeri, argues against reproductive modes like parthenogenesis, gynogenesis, or kleptogenesis. However, the assumption that hybrid sterility or multiple local hybrid origins are unlikely could be challenged if undetected local variation or cryptic reproductive strategies exist.

(3) The presence of Q. robertingeri nuclear genomes far outside their known geographic range, genetically linked to nearby populations, fits a hybridogenetic-mediated dispersal model. Although the authors dismiss human-mediated or accidental transport as explanations, these scenarios are not necessarily unlikley.

Reviewer #2 (Public review):

This study describes F1 hybrid frog lineages that use an "unusual" form of reproduction, perhaps hybridogenesis. Identifying such species is important for understanding the biodiversity of reproduction in animals, and animals that do not reproduce via "canonical" sex can be useful model systems in ecology and evolution. The conclusion of the study are based on reduced representation sequencing (RAD-seq with a de-novo assembly of loci) of 107 wild-caught individuals from 53 localities (plus 4 outgroup individuals), including 27 males, 31 females, and 49 juveniles of unknown sex. Conclusive inferences of unusual forms of reproduction typically require breeding studies and parent-offspring genotype comparisons but such information is not available (and perhaps impossible to generate) for the focal frog lineages.

(1) Conclusion 1: there are two pure species and F1 hybrids

The authors infer that there are two lineages RR and BB (corresponding to two named species), and F1 interspecific hybrids RB. This inference is based on the results presented in Figure 1 (PCA, admixture, and heterozygosity analyses) as well as analyses of fixed SNP differences between R and B. I think that this conclusion is well supported; my only comment on this part is that it would be useful to have the admixture plots & cross-validation for the 107 samples with other k values (not only k=2) as a supplemental figure. The plots in the supplemental file S1 are for the subset of 55 inds inferred to be BB only.

(2) Conclusion 2: F1 hybrids most likely reproduce via hybridogenesis

This conclusion is based on the sex ratio of hybrids and haplotype sharing between species and lineages at different, ~150 bp long loci. Parthenogenesis (including sperm-dependent parthenogenesis) is unlikely to generate males, yet sexed F1 hybrid individuals include 18 females and 10 males which prompts the exclusion of parthenogenesis in the present paper. Specific haplotype-sharing patterns are also discussed in the study and used as further support, but these arguments (and the related main and supplementary figures) are difficult to read/interpret. To clarify the arguments related to haplotype sharing and haplotype diversities, I suggest that the authors phase the R and B haplotypes from all their hybrids by using their pure (RR and BB individuals) as references. The concatenated lineage-specific haplotypes can then be used to reconstruct a single phylogenetic tree for all loci (easier to visualize and interpret that the separate haplotype networks for the loci). The authors can then draw cartoon phylogenies for what would be the expected pattern for haplotype clustering and diversity for different reproductive modes, and discuss their observed phylogenies in this regard. Similarly, the migration weights (represented in Figure 4) can then also be computed for separate haplotypes in the hybrids.

However, independently of the outcome of the phasing, it is important to note that there is no a priori reason why all F1 hybrid individuals would reproduce via the same reproductive mode. Notably, work by Barbara Mantovani and Valerio Scali on stick insects has shown that different F1 hybrid lineages involving the same parental species reproduce via hybridogenesis or parthenogenesis. I don't see how the presented data can allow excluding that some F1 hybrid frogs are parthenogenetic while others are hybridogenetic for example.

(3) Conclusion 3: Crosses between hybridogenetic RB males and hybridogenetic RB females gave rise to a new population of RR individuals outside of the RR species range (this new population would correspond to location 30 from Figure 1).

It is not entirely clear to me which data this conclusion is based on, I believe it is the combination of known species ranges for the species R (location 30 being outside of this) and the relatively low heterozygosity of RR individuals at location 30.

However, as the authors point out, the study focuses on an understudied geographic range. Isolated or rare populations of the R species may easily have been overlooked in the past, especially since the R and B species are morphologically difficult to distinguish. Furthermore, an isolated, perhaps vestigial population may also likely be inbred/feature low diversity. It seems most appropriate to discuss different (equally likely) scenarios for the RR population at location 30 rather than implying a hybridogenetic origin of RR individuals. I would also choose a title that does not directly imply this scenario but reflects the solid (not speculative) findings of the study.

Reviewer #3 (Public review):

Summary:

This work reports a new case of hybridogenetic reproduction in the frog genus Quasipaa. Only one other example of this peculiar reproductive mode is known in amphibians, and fewer than a dozen across the tree of life. Interestingly, a population of one of the parental species (Q. robertingeri) was found away from the core of its distribution, within the distribution of the hybridogens. This range expansion might have been mediated by hybridogenesis, whereby two copies of the same parental genome came together again after many generations of hybridogenesis.

Strengths:

Evidence for hybridogenesis is solid. The state of the art would be to genotype parents and offspring, but other known alternative scenarios have been considered carefully and can be ruled out convincingly. In addition, the authors are very careful in their phrasing and made sure to never overinterpret their data.

The explicit predictions under different reproductive modes (and Table 1) are a useful resource for future studies and could inspire new findings of unusual reproductive modes in other taxa.

The sampling is very impressive, with over 50 populations sampled across a very large area.

The comparison of p-distances between pairs of species involved in hybridogenesis is interesting.

Weaknesses:

The current phylogenetic reconstruction with the F1s does not enable to infer the number of origins of hybridogenesis, nor whether the population of Q. robertingeri that was found far from the core of the species' distribution indeed derives from hybridogenesis. This is because some of the signal is driven by the Q. boulengeri haplome, which is replaced every generation and therefore does not reflect the evolutionary history of the lineage.

All known reproductive modes except hybridogenesis can be excluded, but without genotyping parents and offspring, it is impossible to rule out another, yet undescribed reproductive mode.

Analyse du "Bon Pays" : Mondialisation, Coopération et Intérêt National

https://hyp.is/go?url=https%3A%2F%2Findex.goodcountry.org%2F&group=world

Résumé

Ce document de synthèse analyse les thèses centrales présentées par Simon Anholt concernant les défis de la mondialisation et la nécessité d'une nouvelle approche de la gouvernance mondiale.

Le problème fondamental identifié est un décalage critique : alors que les problèmes les plus urgents de l'humanité (changement climatique, pandémies, crises économiques) sont mondialisés, les systèmes de gouvernance restent ancrés dans des cadres nationaux égoïstes.

Trois obstacles majeurs à la coopération internationale sont identifiés : la demande des électeurs pour des politiques nationalistes, une forme de "psychopathie culturelle" qui limite l'empathie envers les étrangers, et la fausse croyance des dirigeants que les agendas nationaux et internationaux sont incompatibles.

La solution proposée repose sur une découverte issue d'une analyse de données à grande échelle sur la perception des pays (l'Indice des Marques Nationales). Cette recherche révèle que les pays les plus admirés ne sont pas les plus riches ou les plus puissants, mais ceux perçus comme "bons" – c'est-à-dire ceux qui contribuent de manière significative au bien commun de l'humanité.

Cette découverte lie directement la "bonté" d'un pays à son "intérêt personnel", car une réputation positive attire investissements, tourisme et talents, rendant la collaboration internationale un levier de compétitivité nationale.

Pour matérialiser ce concept, Anholt a créé "l'Indice des Bons Pays", qui mesure la contribution de chaque nation à l'humanité.

L'Irlande se classe au premier rang, démontrant qu'un pays peut honorer ses devoirs internationaux tout en gérant ses propres défis économiques.

L'appel à l'action final est d'intégrer le terme "bon" (défini comme le contraire d'égoïste) dans le discours public et politique, afin de créer une pression citoyenne pour que les gouvernements adoptent des politiques plus collaboratives et tournées vers l'extérieur.

1. Le Paradoxe de la Mondialisation : Problèmes Mondiaux, Solutions Nationales

La mondialisation a profondément interconnecté le monde, créant un système où des événements locaux peuvent avoir des répercussions mondiales quasi instantanées. Des exemples frappants illustrent cette réalité :

• Sanitaire : "Il y a 20 ou 30 ans, si un poulet attrapait froid, éternuait et mourait dans un petit village d'Extrême-Orient, c'était tragique pour le poulet [...] mais c'était peu probable qu'on ait peur d'une pandémie mondiale".

• Économique : "si une banque américaine prêtait trop d'argent à des clients non solvables et que la banque faisait faillite, c'était néfaste [...] mais nous ne pensions pas que ça amènerait un effondrement du système économique pendant presque dix ans."

Cette interconnexion a apporté des bénéfices, comme le succès des Objectifs du Millénaire, prouvant que "l'espèce humaine peut arriver à d'extraordinaires progrès en se montrant unie et persévérante".

Cependant, la mondialisation a également amplifié les problèmes : réchauffement climatique, terrorisme, épidémies, trafic de drogue, et bien d'autres.

Le problème central est que l'humanité n'a pas adapté ses structures de gouvernance à cette nouvelle réalité.

L'organisation mondiale est toujours fragmentée en environ 200 États-nations dont les gouvernements sont programmés pour se concentrer quasi exclusivement sur leurs intérêts nationaux.

Citation clé : "Il faut que nous arrivions à nous reprendre et trouver comment améliorer la mondialisation des solutions pour éviter de devenir une espèce victime de la mondialisation des problèmes."

2. Les Obstacles à la Coopération Internationale

Simon Anholt identifie trois raisons principales qui expliquent la lenteur des progrès sur les enjeux mondiaux et la persistance de l'approche nationaliste.

2.1 La Demande des Électeurs

La première raison est que les citoyens eux-mêmes exigent de leurs gouvernements une focalisation interne.

En élisant ou en tolérant des gouvernements, le message envoyé est clair : la priorité est la prospérité, la croissance, la compétitivité et la justice à l'intérieur des frontières nationales.

Les politiciens, en regardant "dans un microscope" plutôt que "dans un télescope", ne font que répondre à cette demande.

2.2 La "Psychopathie Culturelle"

Le deuxième obstacle est un biais psychologique collectif qu'Anholt nomme la "psychopathie culturelle".

Il s'agit d'un manque de capacité à ressentir une véritable empathie pour les personnes qui sont culturellement différentes.

• L'empathie fonctionne bien avec ceux qui "nous ressemblent, marchent, parlent, mangent, prient et s'habillent comme nous".

• En revanche, les autres, ceux qui sont différents, sont souvent perçus comme des "personnages en carton", des figures bidimensionnelles plutôt que des êtres humains complexes.

Ce manque d'empathie à grande échelle empêche une véritable solidarité mondiale.

2.3 La Fausse Dichotomie des Agendas

Le troisième obstacle est la croyance, particulièrement ancrée chez les dirigeants, que les agendas nationaux et internationaux sont mutuellement exclusifs. Anholt qualifie cette idée de "grand n'importe quoi".

Fort de son expérience de conseiller politique auprès de nombreux gouvernements, il affirme n'avoir jamais vu "un seul problème national qui ne pouvait être résolu de façon plus inventive, plus efficace et plus rapide qu'en le traitant comme un problème international".

3. L'Intérêt Personnel comme Levier du Changement

Pour surmonter ces obstacles et la résistance naturelle de l'être humain au changement, il est nécessaire de démontrer qu'un comportement plus collaboratif sert l'intérêt personnel des nations. C'est le cœur de la découverte d'Anholt.

3.1 La Recherche sur la Réputation des Nations

En 2005, Anholt a lancé l'Indice des Marques Nationales, une étude à très grande échelle recueillant les perceptions du public mondial sur les différents pays.

Cette base de données de 200 milliards de points de données a révélé un fait économique crucial :

• Les pays dépendent "énormément de leurs réputations afin de survivre et de prospérer dans le monde".

• Une bonne image (ex : Allemagne, Suède, Suisse) facilite tout : tourisme, investissements, exportation.

• Une mauvaise image rend tout "difficile et [...] cher".

3.2 La Découverte Clé : Admiration et "Bonté"

En interrogeant cette base de données pour comprendre pourquoi certains pays sont plus admirés que d'autres, la réponse fut surprenante.

Ce n'est ni la richesse, ni la puissance, ni la modernité qui est le facteur principal.

Citation clé : "les pays que nous préférons sont les bons pays. [...] nous admirons surtout un pays parce qu'il est bon."

Un "bon pays" est défini comme un pays qui "contribue au monde dans lequel nous vivons", le rendant "plus sûr, meilleur, plus riche ou plus juste".

Cette découverte crée un lien direct et puissant entre l'altruisme et l'égoïsme : pour réussir économiquement (servir son intérêt national), un pays doit "faire le bien" et contribuer à l'humanité.

"Plus vous collaborez, plus vous devenez compétitif."

4. L'Indice des Bons Pays : Une Nouvelle Mesure du Succès

Pour concrétiser cette idée, Anholt et son équipe ont développé l'Indice des Bons Pays ("The Good Country Index").

• Objectif : Mesurer la contribution exacte de chaque pays, non pas à ses propres habitants, mais au reste de l'humanité.

• Définition de "Bon" : Le terme n'a pas une connotation morale ("bon" vs "mauvais"), mais est utilisé comme le contraire de "égoïste".

Un "bon" pays est un pays qui se préoccupe des intérêts de tous.

4.1 Classement et Enseignements

Les résultats de l'indice offrent des perspectives importantes :

Rang

Pays

Observations Clés

1

Irlande

Le pays qui, par habitant ou par dollar de PIB, contribue le plus au monde. Salué pour sa capacité à maintenir ses devoirs internationaux tout en se relevant d'une grave récession.

2

Finlande

Très proche de l'Irlande, avec des scores globalement élevés.

13

Allemagne

21

États-Unis

66

Mexique

95

Russie

Pays en développement focalisé sur sa construction interne.

107

Chine

Pays en développement focalisé sur sa construction interne.

• Domination Européenne : Le top 10 est majoritairement composé de pays riches d'Europe occidentale (à l'exception de la Nouvelle-Zélande).

• L'Importance de l'Attitude : La présence du Kenya dans le top 30 est cruciale.

Elle prouve que la contribution au monde n'est pas qu'une question d'argent, mais "d'attitude", de "culture" et de volonté politique de se tourner vers l'extérieur.

Les données complètes de l'indice sont accessibles sur le site goodcountry.org.

5. Appel à l'Action : Redéfinir le Discours Politique

La finalité de ce projet n'est pas seulement de classer les pays, mais de changer radicalement le dialogue public et politique.

5.1 Changer le Vocabulaire du Succès

Anholt exprime sa lassitude face à un vocabulaire centré sur l'égoïsme national : "J'en ai assez d'entendre parler de compétitivité.

J'en ai assez d'entendre parler de prospérité, de richesse, de croissance rapide. J'en ai assez d'entendre parler de pays heureux parce que ça reste quand même égoïste."

Il propose de réinjecter le mot "bon" (au sens de "non-égoïste") dans la conversation.

5.2 Un Outil pour les Citoyens

Ce mot doit devenir un "bâton qui s'abattrait sur nos politiciens".

Les citoyens sont invités à utiliser ce critère pour juger les politiques et les dirigeants en se posant la question :

Question clé : "Est-ce qu'un bon pays ferait ça ?"

L'objectif ultime est de faire évoluer les mentalités, pour que le désir principal des citoyens ne soit plus de vivre dans un pays riche ou compétitif, mais dans un "bon pays".

Un pays dont on peut être fier à l'international, car il est reconnu pour sa contribution positive au monde entier.

Synthèse du MIPEX 2025 : Politiques d'Intégration en France

Résumé

L'analyse des politiques d'intégration de la France dans le cadre du Migrant Integration Policy Index (MIPEX) 2025 révèle un tableau contrasté.

Avec un score global de 56 sur 100, la France se positionne à mi-chemin, appliquant des politiques qui offrent des opportunités mais aussi des obstacles significatifs à l'intégration.

Cette note, inchangée depuis 2019, masque des évolutions divergentes :

des progrès notables dans le domaine de l'éducation sont contrebalancés par des reculs en matière d'accès aux soins de santé et de résidence permanente.

L'approche française est classée comme "Intégration Temporaire", un modèle qui accorde des droits fondamentaux aux citoyens non-européens mais leur refuse la sécurité à long terme nécessaire pour s'établir durablement et participer pleinement à la vie citoyenne.

Les points forts de la France résident dans son cadre législatif solide en matière de lutte contre les discriminations et dans les récentes améliorations de l'accès à l'enseignement supérieur.

Cependant, ces avancées sont minées par des politiques restrictives concernant la résidence permanente, le regroupement familial et un processus d'accès à la nationalité jugé discrétionnaire et politisé.

La loi "Immigration & Intégration" de janvier 2024 et les décrets d'application subséquents marquent un tournant vers une approche plus sélective et exigeante, renforçant les exigences linguistiques et civiques.

Pour améliorer son modèle, il est recommandé à la France d'adopter une approche plus cohérente, alignant ses politiques sur un objectif d'intégration à long terme et traitant les immigrés comme de futurs citoyens plutôt que comme des résidents temporaires.

Analyse Détaillée des Politiques d'Intégration

Score Global et Classification

Avec un score de 56 sur 100, les politiques d'intégration de la France sont jugées "à mi-chemin" (halfway to promote societal integration).

Ce score place la France dans la catégorie de l'"Intégration Temporaire". Selon la typologie du MIPEX, ce modèle se caractérise par :

• L'octroi de droits fondamentaux et de certaines mesures favorisant l'égalité des chances.

• Le refus de la sécurité à long terme indispensable pour s'installer de manière permanente, investir dans l'intégration et participer pleinement en tant que citoyen.

• La perpétuation d'une perception des immigrés comme étant partiellement égaux, mais restant fondamentalement des étrangers (outsiders).

Cette approche contraste avec celle des pays du "Top Ten" du MIPEX, qui traitent les immigrés comme des égaux, des voisins et des citoyens potentiels, investissant dans l'intégration comme un processus mutuel bénéfique pour l'ensemble de la société.

Évolutions Récentes des Politiques (Depuis 2019)

Le score global de la France est stable depuis 2019, mais cette stabilité cache des changements contradictoires dans différents domaines politiques.

Changements Positifs :

• Accès à l'enseignement supérieur : Des programmes ciblés ont été mis en place pour améliorer l'accès des migrants à l'enseignement supérieur.

• Intégration dans le corps enseignant : Des initiatives soutiennent l'intégration des migrants dans la profession d'enseignant.

• Projets spécifiques :

◦ AIMES+ (depuis 2023) : Vise à améliorer la qualité des cours de français pour les étudiants immigrés.   

◦ L'Université en Exil (UXIL) : Offre un parcours académique aux étudiants et chercheurs en exil.

Changements Négatifs :

• Résidence permanente : Les conditions de renouvellement du statut de résident permanent ont été durcies, notamment par la réduction des périodes d'absence autorisées hors du territoire français.

• Accès aux soins de santé (depuis 2020) : Les demandeurs d'asile et les immigrés non-européens font face à des obstacles accrus, avec des conditions supplémentaires et des délais d'attente plus longs pour la couverture santé.

Un changement juridique clé en 2019 a introduit un délai de carence de trois mois et une condition de résidence minimale pour l'éligibilité à la Protection Universelle Maladie (PUMa).

• Loi "Immigration & Intégration" (janvier 2024) : Cette loi, dont le score n'est pas encore intégré au MIPEX, a centralisé et renforcé les exigences en matière de langue, de civisme et d'emploi.

Elle introduit des limites au renouvellement des titres de séjour temporaires et des tests de langue et de valeurs plus stricts pour la résidence et la citoyenneté.

Les décrets et circulaires de mi-2024 et début 2025 ont activé ce cadre, augmentant la pression administrative et les obligations d'intégration.

Analyse par Domaine Politique

Domaine Politique

Classification MIPEX

Résumé des Constatations

Mobilité sur le Marché du Travail

Halfway favourable (Moyennement favorable)

Les résidents permanents et les familles ont accès au marché du travail, mais sont exclus de plus de professions réglementées que dans tout autre pays.

Les nouveaux arrivants ont accès aux services généraux d'emploi mais souvent pas à la reconnaissance de leurs diplômes ou à des bourses d'études.

Regroupement Familial

Halfway favourable (Moyennement favorable)

Les exigences (économiques, logement) sont strictes et le processus peut être long et discrétionnaire.

Cependant, une fois réunies, les familles bénéficient de droits socio-économiques égaux et d'un soutien à l'intégration, avec une augmentation des heures de cours de langue (jusqu'à 400h, et 600h pour les personnes analphabètes).

Éducation

Halfway favourable (Moyennement favorable)

La France a renforcé son soutien, notamment via des programmes ciblés depuis 2015 (AIMES+, UXIL).

Tous les élèves, quel que soit leur statut, ont les mêmes droits à l'éducation.

Le point faible reste l'absence de valorisation de la diversité dans l'éducation à la citoyenneté.

Santé

Slightly favourable (Légèrement favorable)

Le système de santé est inclusif, mais il ne répond que faiblement aux besoins spécifiques des patients migrants.

Depuis 2020, les barrières à l'accès se sont renforcées pour les demandeurs d'asile et les immigrés non-UE (conditions plus strictes, délais d'attente allongés).

Participation Politique

Halfway favourable (Moyennement favorable)

Les étrangers sont peu informés et consultés par les autorités.

La France est l'un des rares grands pays de destination sans droit de vote local pour les étrangers.

Une consultation accrue des groupes de réfugiés est notée au niveau national depuis 2018.

Résidence Permanente

Halfway favourable (Moyennement favorable)

L'accès au statut sécurisé de 10 ans est conditionné par des exigences linguistiques, d'intégration et parfois économiques parmi les plus restrictives.

Bien que le statut lui-même soit protecteur, il est très difficile à obtenir et à renouveler (notamment depuis 2024).

Accès à la Nationalité

Slightly favourable (Légèrement favorable)

Le parcours est similaire à d'autres pays occidentaux (5 ans de résidence, double nationalité possible).

Cependant, le processus est de plus en plus politisé, discrétionnaire et décourageant pour certains candidats.

Les exigences strictes (stabilité financière, niveau B1 en langue, entretien d'assimilation subjectif) constituent des barrières importantes.

Antidiscrimination

Slightly favourable (Légèrement favorable)

Il s'agit du plus grand point fort de la France en matière d'intégration.

La législation est solide et l'organe de défense (Défenseur des Droits) est efficace pour informer le public et aider les victimes.

Ces politiques semblent avoir eu un impact positif à long terme sur les mentalités publiques en Europe.

Conclusions et Recommandations

Le modèle d'intégration français est marqué par une incohérence fondamentale :

ses forces reconnues en matière de lutte contre la discrimination et

ses progrès dans l'éducation sont sapés par une approche restrictive et précaire concernant les piliers de l'intégration à long terme que sont la résidence, la famille et la nationalité.

La trajectoire politique récente confirme cette tendance restrictive.

La loi de 2024, les nouvelles instructions préfectorales sur la naturalisation (mai 2025) et une proposition de 2024 remettant en cause le droit du sol témoignent d'un changement de discours vers des politiques d'intégration plus exclusives.

Pour renforcer son modèle, la France devrait :

1. Adopter une Approche Cohérente : Aligner les politiques restrictives de résidence et de regroupement familial sur ses mesures plus inclusives en matière d'éducation et d'antidiscrimination.

2. Sécuriser les Parcours d'Intégration : Réduire le caractère discrétionnaire et les exigences excessives dans les procédures d'accès à la résidence permanente et à la nationalité pour offrir la stabilité nécessaire à une intégration réussie.

3. Traiter les Immigrés comme de Futurs Citoyens : Mettre en œuvre une vision de l'intégration comme un processus à double sens qui renforce la confiance mutuelle et bénéficie à l'ensemble de la société.

Comme le démontrent 130 études scientifiques indépendantes utilisant les données du MIPEX, la manière dont les gouvernements traitent les immigrés est un facteur déterminant qui influence non seulement l'acceptation par le public, mais aussi le sentiment d'appartenance, la participation et même la santé des immigrés dans leur nouveau pays.

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Prisons have GOT to radically change

Because of the random nature of AI content generation, it can theoretically create anything and often needs guiding to avoid truly random creation.

The internet made copyright infringement way easier. Platforms such as YouTube needing to implement copyright for any unauthorized use of audio.

There should be three steps to configuring "Prompt Firewall".

1. Add AccuKnox Prompt Firewall as proxy
2. Create Application on Control Plane
3. Configure Prompt Firewall policies for the application and monitor it.

Vitamins that are associated with brain function.

The Antarctic peninsula is one of the fastest warming regions on Earth, in the past 50 years, the temperatures have risen nearly 3 degrees, raising sea levels and in turn threatening animals who rely on that environment to survive.

Insert Table in Word to correctly align the columns of your numbers in your TOC.

Нетривиальный корнейкейс: система заставляет Server 1 упасть, хотя он мог бы подождать Server 3 и нормально восстановиться... Решение очень элегантное: смотреть sequence numbers. Если у неупавшего сервера sequence number выше, он гарантированно видел все апдейты и может безопасно пересобрать группу

these articles are nonsense

left off 3.2.2 come back mid parah

weakens credibility - violates ethical standards on inclusive speaking and proper sourcing

ohhh

This is a curious claim that I cannot substantiate. After looking around, I see the same claim in Wikipedia's article for Copyright renewal in the United States, but the cited source (Fishman, Stephen; 2010. The Public Domain: How to Find & Use Copyright-Free Writings, Music, Art & More) doesn't appear to provide a robust citation. (On page 287 in Chapter 15, Fishman writes, "The Copyright Office estimates that only about 15% of pre-1964 published works were ever renewed", unaccompanied by a footnote.) One also notices that 1961 and 1964 are not the same year.

I stumble across a similar claim ("a 1961 report from the U.S. Copyright Office estimates that 85% of the books never had the copyrights renewed") on lcgsco.org, which disappointingly turns out to be the website for "Larimer County Genealogical Society", with no link to the report in question.

The Copyright Office does make available its historical archive of annual reports at https://www.copyright.gov/history/annual_reports.html. Could its annual report be the one referred to? Searching for the string "renewal" in the 1961 annual report turns up two noteworthy occurrences (out of a reported nine):

1. on page 2, a claim related to renewals mentioning a "15 percent" figure

2. on page 16, a description of "Studies 29–31 [… including …] 31. Renewal of Copyright"

The full sentence on page 2 where the "15 percent" figure appears is actually that "The year's increase in registrations was nearly 3 percent, this was counting a 15 percent decrease in renewal registrations, the result of the corresponding decrease in original regisrations 28 years previously." This is, troublingly, not the same thing as fewer than 15% of copyright registrations until 1961 being renewed. (It is not even the same as only 15% of registrations up for renewal in 1961 being renewed.) It is a 15% decrease in renewals relative to the prior year, i.e., number of renewals in 1961 compared to the number of renewals in 1960. If this is the report that the is meant to be the source for those claims of 85% non-renewal, it then it fails verification.

I have also come across a reference for renewal figures in a 2008 article in D-Lib Magazine (Peter B. Hirtle, "Copyright Renewal, Copyright Restoration, and the Difficulty of Determining Copyright Status"). Footnote 4 there cites the same Study 31 (attributed to a Barbara Ringer). Interestingly, it is not cited as a way to substantiate the 15% claim, but instead that "only 7% of registered copyrighted books" were renewed. It's not clear from context whether that claim is meant to be about the full range of potential renewals up to 1961, or merely the subset of works up for renewal in 1961.

At the time of this comment, I have not yet looked into Study 31 itself. I expect it to contain answers.

Reviewer #1 (Public review):

Liver cancer shows a high incidence in males than females with incompletely understood causes. This study utilized a mouse model that lacks the bile acid feedback mechanisms (FXR/SHP DKO mice) to study how dysregulation of bile acid homeostasis and a high circulating bile acid may underlie the gender-dependent prevalence and prognosis of HCC. By transcriptomics analysis comparing male and female mice, unique sets of gene signatures were identified and correlated with HCC outcomes in human patients. The study showed that ovariectomy procedure increased HCC incidence in female FXR/SHP DKO mice that were otherwise resistant to age-dependent HCC development, and that removing bile acids by blocking intestine bile acid absorption reduced HCC progression in FXR/SHP DKO mice. Based on these findings, the authors suggest that gender-dependent bile acid metabolism may play a role in the male-dominant HCC incidence, and that reducing bile acid level and signaling may be beneficial in HCC treatment. This study include many strengths: 1. Chronic liver diseases often proceed the development of liver and bile duct cancer. Advanced chronic liver diseases are often associated with dysregulation of bile acid homeostasis and cholestasis. This study takes advantage of a unique FXR/SHP DKO model that develop high organ bile acid exposure and spontaneous age-dependent HCC development in males but not females to identify unique HCC-associated gene signatures. The study showed that the unique gene signature in female DKO mice that had lower HCC incidence also correlated with lower grade HCC and better survival in human HCC patients. 2. The study also suggests that differentially regulated bile acid signaling or gender-dependent response to altered bile acids may contribute to gender-dependent susceptibility to HCC development and/or progression. 3. The sex-dependent differences in bile acid-mediated pathology clearly exist but are still not fully understood at the mechanistic level. Female mice have been shown to be more sensitive to bile acid toxicity in a few cholestasis models, while this study showed a male dominance of bile acid promotion of HCC. This study used ovariectomy to demonstrate that female hormones are possible underlying factors. Future studies are needed to understand the interaction of sex hormones, bile acids, and chronic liver diseases and cancer.

Reviewer #2 (Public review):

Summary:

This manuscript presents a thoughtful and well-executed study of critical period plasticity in the Drosophila larval motor circuit. The authors examined how transient heat, 32 {degree sign}C, during the embryonic stage, altered network properties, showing that premotor interneurons A27h increase excitatory drive onto motoneurons, which respond with a reduction in excitability. At the NMJ, synaptic terminals expand and GluRIIA distribution shifts, yet synaptic transmission remains largely unaffected. Despite these local compensations, the treated larvae display slower crawling and prolonged recovery from seizures, indicating that the network is functionally compromised.

Strengths:

(1) One of the major strengths of this study is the elegant dissection of a defined circuit, tracking changes from premotor interneurons through motoneurons to the NMJ. The multimodal approach provides a comprehensive view of how connected elements respond to CP perturbations.

(2) An interesting finding is that NMJ morphology changes dramatically without corresponding deficits in synaptic transmission, challenging the common assumption that larger boutons necessarily indicate stronger synapses.

(3) Another intriguing result is that even with two layers of homeostatic compensation, locomotor behavior is still impaired, highlighting the limits of compensation and underscoring the critical role of CP timing.

(4) Beyond these scientific insights, the study benefits from a well-defined, tractable system and simple experimental manipulations, which together make the results highly interpretable and reproducible.

Weaknesses:

There are a few areas where the manuscript could be strengthened.

(1) Although A27h premotor neurons are well characterized, the claim that they are the causal driver of downstream changes would be strengthened by additional experiments or a clearer discussion of the temporal hierarchy.

(2) While 32 {degree sign}C heat stress is presented as ecologically relevant, it produces maladaptive behavioral outcomes, raising questions about the ecological and mechanistic interpretation of the model. In particular, most experiments, with the exception of Figure 1, used prolonged (24h) heat treatments, which could introduce developmental effects beyond the CP itself. Comparing shorter and longer heat exposures would help clarify the specificity of the CP response.

(3) While there are schematics for experimental procedures, a circuit diagram tracing information flow and indicating where structural and functional changes occur would help readers better understand the findings.

(4) Finally, the main paradox of the study, that robust homeostatic compensations occur yet behavior remains impaired, could be explored in more depth in the Discussion.

Reviewer #3 (Public review):

Summary:

During development, neural circuits undergo brief windows of heightened neuronal plasticity (e.g., critical periods) that are thought to set the lifelong functional properties of underlying circuits. These authors, in addition to others within the Drosophila community, previously characterized a critical period in late fly embryonic development, during which alterations to neuronal activity impact late-stage larval crawling behavior. In the current study, the authors use an ethologically-relevant activation paradigm (increased temperature) to boost motor activity during embryogenesis, followed by a series of electrophysiology and imaging-based experiments to explore how 3 distinct levels of the circuit remodel in response to increases in embryonic motor activity. Specifically, they find that each level of the circuit responds differently, with increased excitatory drive from excitatory pre-motor neurons, reduced excitability in motor neurons, and no physiological changes at the NMJ despite dramatic morphological differences. Together, these data suggest that early life experience in the motor neuron drives compensatory changes at each level of the circuit to stabilize overall network output.

Strengths:

The study was well-written, and the data presented were clear and an important contribution to the field.

Weaknesses:

The sample sizes and what they referred to throughout the distinct studies were unclear. In the legends, the authors should clearly state for each experiment N=X, and if N refers to an NMJ, for example, instead of an individual animal, they should state N=X NMJs per N=X animals. This will help readers better understand the statistical impact of the study.

Reviewer #1 (Public review):

Summary:

A study researching the relationship between affective shifts and cognitive performance in a daily life setting.

Strengths:

The evidence provided is compelling: the findings are conceptually replicated in three samples of adequate size and statistical rigor in analyzing the data, with methods beyond the current state of the art in applied research. For example, using two-step multilevel vector autoregressive models that were adopted to allow the inclusion of covariates, and contemporaneous effects corrected for temporal relations and background covariates. In addition, the authors use beautiful visualizations to convey the different samples used (Figure 1) and intuitive and rich figures to convey their obtained results.

In summary, the authors were able to convincingly show that higher negative affect is linked to slower cognitive processing speed, with results supporting their conclusions.

Weaknesses:

I have one major concern. Although a check for careless responding has been conducted on the basis of long reaction times, I wonder whether, beyond long response times, any other sanity checks with respect to, e.g., careless responding were done? For example, a lack of variability of EMA items over subsequent occasions, e.g., say 15, is often seen as an indicator of careless responding, especially when using VAS items. In line 693, it is stated, "We added a small amount of random noise, ranging from -0.1 to +0.1, to each EMA time series to allow models to converge when EMA time series showed minimal variance over time", which I understand, but this lack of variability could also be caused by participants stopping to take the study seriously. For datasets 1 and 2, this might be more difficult to assess (due to the limited response values), but maybe the authors can get an indication of this in dataset 3?

Plug-in hybrid vehicles (PHEVs) are not as environmentally friendly as previously claimed, according to recent EU data.

• Real-world emissions from PHEVs average 135 g CO₂/km, only 19% less than conventional petrol and diesel cars which average 166 g CO₂/km.
• Official WLTP tests significantly underestimate emissions; PHEVs often run on the combustion engine, even in electric mode, increasing real fuel consumption.
• In electric-only mode, PHEVs still burn around 3 liters of petrol per 100 km, resulting in emissions of 68 g CO₂/km—over eight times higher than manufacturers’ claims.
• Hidden fuel consumption costs drivers approximately 500 euros per year more than official manufacturer data suggests.
• PHEVs are among the most expensive on the market; average price in 2025 in Germany, France and the UK is 55,700 euros, about 15,000 euros higher than average battery-only electric cars.
• Increasing electric range through larger batteries does not reduce emissions; heavier vehicles consume more fuel and energy overall.
• PHEVs with electric ranges above 75 km emit more CO₂ on average than those with ranges of 45–75 km.
• The biggest discrepancies are noted for Mercedes-Benz plug-in hybrids; in 2023, their real emissions exceeded official figures by an average of 494%, with the GLE-Class over by 611%.
• Automotive industry is lobbying to have PHEVs recognized as low-emission beyond 2035, despite new EU regulations that require the transition to zero-emission vehicles.
• Weakening EU regulations could undermine the development and adoption of truly zero-emission technologies.
• According to Transport & Environment, plug-in hybrids are “one of the biggest myths in motoring,” often emitting almost as much as combustion-engine cars and much more than test results indicate.
• EEA and T&E data casts doubt on PHEVs as effective solutions for transportation decarbonization; their environmental benefits are increasingly questioned.

Reviewer #1 (Public review):

Summary:

Millet et al. show that C. elegans systematically prefers easy-to-eat bacteria but will switch its choice when harder-to-eat bacteria are offered at higher densities, producing indifference points that fit standard economic discounting models. Detailed kinetic analysis reveals that this bias arises from unchanged patch-entry rates but significantly elevated exit rates on effortful food, and dop-3 mutants lose the preference altogether, implicating dopamine in effort sensitivity. These findings extend effort-discounting behavior to a simple nematode, pushing the phylogenetic boundary of economic cost-benefit decision-making.

Strengths:

Extends the well-characterized concept of effort discounting into C. elegans, setting a new phylogenetic boundary and opening invertebrate genetics to economic-behavior studies.

Elegant use of cephalexin-elongated bacteria to manipulate "effort" without altering nutritional or olfactory cues, yielding clear preference reversals and reproducible indifference points.

Application of standard discounting models to predict novel indifference points is both rigorous and quantitatively satisfying, reinforcing the interpretation of worm behavior in economic terms.

The three-state patch-model cleanly separates entry and exit dynamics, showing that increased leaving rates-rather than altered re-entry-drive choice biases.

Demonstrates that _dop-3_ mutants lose normal effort discounting, firmly tying monoaminergic signaling to this behavior and paralleling vertebrate findings.

Demonstration of discounting in wild strain (solid evidence).

Weaknesses:

Only _dop-3_ shows an effect, whereas _cat-2_/_dat-1_ do not, leaving the broader role of dopamine synthesis and reuptake ambiguous.

With only five wild isolates tested, and only one clearly showing clear evidence of preference for the easy to eat bacteria, it's hard to conclude that effort discounting isn't a lab-strain artifact or how broadly it varies in natural populations.

Reviewer #2 (Public review):

Summary:

Here Millet et al. adapted a t-maze paradigm for use in C. elegans to understand whether nematodes exhibit effort discounting behaviors comparable to other species. C. elegans worms were reliably sensitive to how effortful the food was to consume, allowing for the application of standard economic models of decision-making to be applied to their behavior. The authors then demonstrated the necessity of dopamine signaling for this behavior, identifying dop-3 mutants in particular as insensitive to effort. Together, this work establishes a new model system for the study of discounting behavior in cost-benefit decision-making.

Strengths:

The question is well-motivated and the approach taken here is novel; it is uncommon for worms to undergo such behavioural procedures (although this lab has previously been integral to pushing the extent of the complexity of behaviours studied in C. elegans). The authors are careful in their approach to altering and testing the properties of the elongated bacteria. Similarly, they go to some effort to understand what exactly is driving behavioural choices in this context, both through application of simple standard models of effort discounting and a kinetic analysis of patch leaving. The comparisons to various dopamine mutants further extends the translational potential of their findings. I also appreciate the comparison to natural isolate strains as the question of whether this behaviour may be driven by some sort of strain-specific adaptation to the environment is not regularly addressed in mammalian counterparts to this work.

Weaknesses:

The authors have now addressed concerns about whether the mechanisms underlying the choice behavior here are generalizable to other organisms. Specifically, their work speaks to foraging-inspired effort discounting paradigms in rodents and humans in which the decision is whether to stay or leave a given resource, rather than to simultaneous decision-making across two options in a T-maze.

The dopamine results are interesting but still difficult to interpret. As the authors discuss, the lack of an effect in the cat-2 and dat-1 mutants is surprising given the effect in the dop-3 mutants. Understanding what exactly the role of dop-3 is here therefore requires further study.

Reviewer #3 (Public review):

Summary:

The authors establish a behavioral task to explore effort discounting in C. elegans. By using bacterial food that takes longer to consume, the authors show that for equivalent effort, as measured by pumping rate, animals obtain less food, as measured by fat deposition.

The authors formalize the task by applying a neuroeconomic decision making model that includes, value, effort, and discounting. They use this to estimate the discounting C. elegans apply based on ingestion effort by using a population level 2-choice T-maze.

They then analyze the behavioral dynamics of individual animals transitioning between on-food and off-food states. Harder to ingest bacteria led to increased food patch leaving.

Finally, they examined a set of mutants defective in different aspects of dopamine signaling, as dopamine plays a key role in discounting in vertebrates and regulates certain aspects of C. elegans foraging.

In their response to the first set of reviews, the authors take care to ensure their task is analogous to at least some of those used in mammals and make changes to the text to better clarify some of their conclusions. My view is the same--that this is an interesting paper for methodological and scientific reasons that brings an important theoretical framework to bear on C. elegans foraging behavior. While I think the mutant results are somewhat unsatisfying, this is not the principal contribution of the work.

Strengths:

The behavioral experiments and neuroeconomic analysis framework are compelling and interesting and make a significant contribution to the field. While these foraging behaviors have been extensively studied, few include clearly articulated theoretical models to be tested.

Demonstrating that C. elegans effort discounting fits model predictions and has stable indifference points is important for establishing these tasks as a model for decision making.

Weaknesses:

The dopamine experiments are harder to interpret. The authors point out the perplexing lack of an effect of dat-1 and cat-2. dop-3 leads to general indifference. I am not sure this is the expected result if the argument is a parallel functional role to discounting in vertebrates. dop-3 causes a range of locomotor phenotypes and may affect feeding (reduced fat storage), and thus there may be a general defect in the ability to perform the task rather than anything specific to discounting.

That said, some of the other DA mutants also have locomotor defects and do not differ from N2. But there is no clear result here-my concern is that global mutants in such a critical pathway exhibit such pleiotropy that it's difficult to conclude there is a clear and specific role for DA in effort discounting. This would require more targeted or cell-specific approaches. The authors state these experiments are outside the scope of the current study, and that at minimum their results implicate dopamine signaling in some form. I tend to agree but still think locomotion defects of DA mutants complicate this question.

Meanwhile, there are other pathways known to affect responses to food and patch leaving decisions-5HT, PDF, tyramine, etc. in their response the authors state they focus on dopamine because of its role in discounting behavior in mammals.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1(Public Reviews):

Summary: 

Here, Millet et al. consider whether the nematode C. elegans 'discounts' the value of reward due to effort in a manner similar to that shown in other species, including rodents and humans. They designed a T-maze effort choice paradigm inspired by previous literature, but manipulated how effortful the food is to consume.C. elegans worms were sensitive to this novel manipulation, exhibiting effort-discountinglike behaviour that could be shaped by varying the density of food at each alternative in order to calculate an indifference point. This discounting-like behaviour was related to worms' rates of patch leaving, which differed between the low and high effort patches in isolation. The authors also found a potential relationship to dopamine signalling, and also that this discounting behaviour was not specific to lab-based strains of C. elegans . 

Strengths: 

The question is well-motivated, and the approach taken here is novel. The authors are careful in their approach to altering and testing the properties of the effortful, elongated bacteria. Similarly, they go to some effort to understand what exactly is driving behavioural choices in this context, both through the application of simple standard models of effort discounting and a kinetic analysis of patch leaving. The comparisons to various dopamine mutants further extend the translational potential of their findings. I also appreciate the comparison to natural isolate strains, as the question of whether this behaviour may be driven by some sort of strain-specific adaptation to the environment is not regularly addressed in mammalian counterparts. The manuscript is well-written, and the figures are clear and comprehensible. 

Weaknesses: 

Discounting is typically defined as the alteration of a subjective value by effort (or time, risk, etc.), which is then used to guide future decision-making. By adapting the standard t-maze task for C. elegans as a patch-leaving paradigm, the authors observe behaviour strongly consistent with discounting models, but that is likely driven by a different process, in particular by an online estimate of the type of food in the current patch, which then influences patch-leaving dynamics (Figure 3). This is fundamentally different from decision-making strategies relating to effort that have been described in the rodent and human literatures. 

We agree that in our study worms are likely making an on-line estimate of food quality in the current patch, but we wish to point out that rodents and humans also use on-line estimates in some significant effort-discounting paradigms. With respect to rodents, we call attention to effort discounting studies involving the widely used progressive ratio task (references in Discussion). In this task, animals can either lever-press for a preferred food or consume a less preferred food that is freely available nearby. However, the number of lever presses required to obtain preferred food increases as a function of the cumulative number of lever presses until the effort-cost of obtaining preferred food becomes too high and the animal switches to a freely available food. In essence, the lever and the freely available food are patches and the animal decides whether or not to leave the “lever” patch. It seems inescapable that the progressive ratio task involves an on-line assessment of the cost/benefit relationship associated with lever pressing. With respect to humans, one highly cited study (reference in Discussion) presented participants with a series of virtual apple trees. They could see how many apples are in the current tree and how much effort (squeezing a handgrip) is required to gather them. Their task was to decide whether or not to gather apples from that tree based on the perceived cost and benefit. Thus, on-line estimation is a common strategy used by animals and humans as shown in the effort discounting literature. We now make this point in the Discussion section titled A model of effort-discounting like behavior.

Similarly, the calculation of indifference points at the group instead of at the individual level also suggests a different underlying process and limits the translational potential of their findings. The authors do not discuss the implications of these differences or why they chose not to attempt a more analogous trial-based experiment.  

It is not clear to us why changing the read-out –– from the individual level to the population level –– necessarily suggests that a different biological mechanism is at work. In our view, there is one mechanism and it can be seen from different perspectives (e.g., individual vs population). Furthermore, the analogous trial-based experiment, as we understand it, would be to record behavior one worm at a time in the T-maze. This design is not practical because it entails recording a large number of single worms in the T-maze for 60 min each. 

In the case of both the dopamine and natural isolate experiments, the data are very noisy despite large (relative to other C. elegans experiments) sample sizes. In the dopamine experiment, disruption of dop1, dop-2, and cat-2 had no statistically significant effect. There do not appear to be any corrections for multiple comparisons, and the single significant comparison, for dop-3, had a small effect size. 

An ANOVA followed by a Dunnett test was used to test differences between groups in Fig. 4 and 5. The Dunnett test is a multiple comparison test comparing experimental groups to a single control group. It is used to minimize type I error while maintaining statistical power and does not require further correction for multiple comparisons. We have clarified the use of the Dunnett test in the statistical table.  The effect size for dop-3 is 0.5 (Cohen’s d), which is typically interpreted as a medium, not small, effect size.(e.g. Cohen, Psychological Bulletin, 1992, Vol. 112. No. 1,155-159). 

More detailed behavioural analyses on both these and the wild isolate strains, for example by applying their kinetic analysis, would likely give greater insight as to what is driving these inconsistent effects. 

More detailed behavioral analysis could reveal why we observe a difference in effort discounting in some strains and not others. However, it is not obvious what type of behavioral analysis would be needed to differentiate between pleiotropic effects of the mutations/natural isolates and more specific effects on effort discounting. A simple kinetic analysis in particular may not be enough to reveal relevant differences between mutants/natural isolates. For this reason, we think that such experiments may be better suited for future follow up studies.

Reviewer #2 (Public Reviews)

Summary: 

Millet et al. show that C. elegans systematically prefers easy-to-eat bacteria but will switch its choice when harder-to-eat bacteria are offered at higher densities, producing indifference points that fit standard economic discounting models. Detailed kinetic analysis reveals that this bias arises from unchanged patch-entry rates but significantly elevated exit rates on effortful food, and dop-3 mutants lose the preference altogether, implicating dopamine in effort sensitivity. These findings extend effortdiscounting behavior to a simple nematode, pushing the phylogenetic boundary of economic costbenefit decision-making. 

Strengths: 

(1) Extends the well-characterized concept of effort discounting into C. elegans , setting a new phylogenetic boundary and opening invertebrate genetics to economic-behavior studies. 

(2) Elegant use of cephalexin-elongated bacteria to manipulate "effort" without altering nutritional or olfactory cues, yielding clear preference reversals and reproducible indifference points. 

(3) Application of standard discounting models to predict novel indifference points is both rigorous and quantitatively satisfying, reinforcing the interpretation of worm behavior in economic terms. 

(4) The three-state patch-model cleanly separates entry and exit dynamics, showing that increased leaving rates-rather than altered re-entry-drive choice biases. 

(5) Investigates the role of dopamine in this behavior to try to establish shared mechanisms with vertebrates. 

(6) Demonstration of discounting in wild strain (solid evidence). 

Weaknesses: 

(1) The kinetic model omits rich trajectory details-such as turning angles or hazard functions-that could distinguish a bona fide roaming transition from other exit behaviors. 

The overarching goal of present paper was to develop a simple model for effort discounting in a small, genetically tractable organism.  Accordingly,  we focused on quantitative assays that are easy to implement and analyze. The patch-leaving assay and its associated kinetic analysis are one such assay. To keep things simple in this assay, we counted the number of  transitions between the three states shown in Fig. 3A. We chose not to analyze the data in terms of turning angles or hazard functions because the metrics we developed seemed sufficient. Finally, we note that there are new modeling data showing that the presumptive transitions into the roaming state can be explained in terms of a one-state stochastic model in which there is no discrete roaming state (Elife. 2025 Jul 30;14:RP104972. doi:

10.7554/eLife.104972.PMID: 40736321).

(2) Only dop-3 shows an effect, and the statistical validity of this result is questionable. It is not clear if the authors corrected for multiple comparisons, and the effect size is quite small and noisy, given the large number of worms tested. Other mutants do not show effects. Given these two concerns, the role of dopamine in C. elegans effort discounting was unconvincing. 

An ANOVA followed by a Dunnett test was used to test statistical significance in figures 4 and 5 (see above for a discussion of these tests). We believe this approach is rigorous, and the use of these tests is statistically valid. We note that the effect size for this comparison was medium.

(3) With only five wild isolates tested (and variable data quality), it's hard to conclude that effort discounting isn't a lab-strain artifact or how broadly it varies in natural populations. 

The fact that four of the five natural isolates tested display levels of effort discounting similar to N2 (only one natural isolate does not display effort discounting) argues against effort discounting being a laboratory adaption.  We have nevertheless weakened the claim regarding natural isolates. We now say effort discounting-like behavior may not be an adaptation to the laboratory environment.  

(4) Detailed analysis of behavior beyond preference indices would strengthen the dopamine link and the claim of effort discounting in wild strains. 

Going beyond preference in the behavioral analysis might or might not reveal new phenotypes that strengthen the link with dopamine. At present, however, we think such experiments are beyond the scope of the paper.

(5) A few mechanistic statements (e.g., tying satiety exclusively to nutrient signals) would benefit from explicit citations or brief clarifications for non-worm specialists. 

We are unable to identify a mechanistic statement tying satiety to nutrient signals in our manuscript.

Reviewer #3 (Public Reviews)

Summary: 

The authors establish a behavioral task to explore effort discounting in C. eleganss . By using bacterial food that takes longer to consume, the authors show that, for equivalent effort, as measured by pumping rate, they obtain less food, as measured by fat deposition. The authors formalize the task by applying a formal neuroeconomic decision-making model that includes value, effort, and discounting. They use this to estimate the discounting that C. elegans applies based on ingestion effort by using a population-level 2-choice T-maze. They then analyze the behavioral dynamics of individual animals transitioning between on-food and off-food states. Harder to ingest bacteria led to increased food patch leaving. Finally, they examined a set of mutants defective in different aspects of dopamine signaling, as dopamine plays a key role in discounting in vertebrates and regulates certain aspects of C. elegans foraging. 

Strengths: 

The behavioral experiments and neuroeconomic analysis framework are compelling, interesting, and make a significant contribution to the field. While these foraging behaviors have been extensively studied, few include clearly articulated theoretical models to be tested. 

Demonstrating that C. elegans effort discounting fits model predictions and has stable indifference points is important for establishing these tasks as a model for decision making. 

Weaknesses: 

The dopamine experiments are harder to interpret. The authors point out the perplexing lack of an effect of dat-1 and cat-2. dop-3 leads to general indifference. I am not sure this is the expected result if the argument is a parallel functional role to discounting in vertebrates. dop-3 causes a range of locomotor phenotypes and may affect feeding (reduced fat storage), and thus, there may be a general defect in the ability to perform the task rather than anything specific to discounting.

That said, some of the other DA mutants also have locomotor defects and do not differ from N2. But there is no clear result here - my concern is that global mutants in such a critical pathway exhibit such pleiotropy that it's difficult to conclude there is a clear and specific role for DA in effort discounting. This would require more targeted or cell-specific approaches. 

We agree with the reviewer that the results of the dopamine experiments are puzzling and getting a better understanding of the role of dopamine in effort-discounting will require more sensitive assays and different experimental approaches (e.g. cell-specific rescues). However, as mentioned by the reviewer, all the mutations tested have some pleiotropic effects, yet only dop-3 displays a defect in effort discounting. This, in our opinion, points to a specific role of dop-3 in effort-discounting in C. elegans. This point is now made in the Discussion in the section titled Role of dopamine signaling in effort discountinglike behavior.

Meanwhile, there are other pathways known to affect responses to food and patch leaving decisions: serotonin, pigment-dispersing factor, tyramine, etc. The paper would have benefited from a clarification about why these were not considered as promising candidates to test (in addition to or instead of dopamine). 

We focused on DA because of its well-established effect on effort discounting in rodents.

Testing other pathways is a goal for future research.

Reviewer #1 (Recommendations for the authors):

The current results are more a reframing of data gathered from a patch-leaving paradigm, but described in the form of economic choice modelling in which discounting is one possible explanation. One more parsimonious explanation that worms estimate in real-time some rate of reward and leave the patch at some threshold, consistent with canonical foraging models, previous experiments in C. elegans, and the authors' own data (Figure 3). Therefore, I am wary about some of the claims made in this manuscript, such as 'decision-making strategies based on effort-cost trade-offs are evolutionarily conserved'. 

These points are now addressed in the Discussion in a revised section titled A model of effortdiscounting like behavior. (i) We now call attention to the fact that our T-maze assay is a patch-leaving foraging paradigm. (ii) We now propose a revised model in which “worms make an on-line assessment of food value in the current patch which in turn alters patch-leaving dynamics, increasing the exit rates from cephalexin-treated patches as shown in Figure 3.” (iii) We now provide evidence from the rodent and human literature that the strategy of on-line assessment of reward value may be evolutionarily conserved in the case of a class of effort discounting tasks whose solution requires on-line assessments. 

If the reason the authors chose to do a patch-leaving style task rather than a traditional t-maze is because C. elegans is unable to retain the sort of information necessary to make such simultaneous decisions - e.g., if pre-training on the two options isn't possible - then this in itself suggests that mechanisms underlying these decisions in worms and mammals are unlikely to be the same. I mention this because I would like to suggest to the authors an alternative interpretation: that patch foraging is actually 'the' canonical computation that translates across species. This would, in fact, be nicely consistent with some other recent modelling work in humans, e.g., https://www.biorxiv.org/content/10.1101/2025.05.06.652482v1. 

Please see the previous response.

Reviewer #2 (Recommendations for the authors):

Can you provide a picture of the regular and CEPH bacteria? 

Done (see Figure 1––figure supplement 1).

Reviewer #3 (Recommendations for the authors):

I would recommend testing representative mutants in other pathways in the choice task. If possible, more targeted experiments with dop-3, including either cell-specific KOs or rescues, would very much strengthen this aspect of the paper. 

While valuable, these experiments are out of scope for the present study.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

Summary

The manuscript presents IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework for constructing gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. IGNITE utilizes a kinetic inverse Ising model to infer gene interactions from binarized expression data and can predict genetic perturbation effects, such as those from knockout experiments. Although the application of inverse Ising models to network reconstruction is not entirely novel, IGNITE's specific implementation and its application to single-cell RNA sequencing data represent a new development. The method is tested on the transition from naive to formative states in murine pluripotent stem cells, a system the authors are highly knowledgeable about, and its performance is compared to state-of-the-art alternative methods.

Major concerns

My concern regards the generality of the method, particularly the entire pipeline presented, and the fairness of the performance comparison. These concerns can be easily addressed by the authors by better explaining their choices and their general applicability, and by toning down the conclusions about the comparison with existing inference methods.

The pre-processing steps are extensive, and their rationale is not always clear, though the results heavily depend on this analysis. Several steps appear to involve arbitrary choices optimized for specific outcomes, potentially introducing biases. The authors should better explain the rationale behind their choices to mitigate these concerns.

Specifically, part of the pipeline seems to be built to reproduce a specific expression pattern of 24 genes that some of the authors discovered in a previous paper. Although this prior knowledge could be useful and relevant in this specific system, it could limit the generality of the method. For example, the authors selected approximately 2000 genes based on prior knowledge and used a combination of t-SNE and UMAP for dimensionality reduction (although the two techniques have a similar goal). This specific combination seems to reproduce the pseudotime alignment the authors were expecting to find, but such prior information might not be available in general. Therefore, feature selection and the methods used to project data need more justification, especially if the goal is to create a general tool applicable across different biological systems.

Analogously, the clustering seems manually adjusted to match known expression patterns of 24 relevant genes, rather than being the result of an optimized clustering method. Additionally, the clusters overlap with different time points, raising concerns about potential batch effects. These issues should be addressed to strengthen the validity of the method.

The claims about the comparison with existing methods should be toned down. While the comparisons are useful and interesting, they might be biased due to the method's fine-tuning for the specific system studied. The claim that the model requires only scRNA-seq data is misleading, as strong prior biological knowledge was used to select, for example, the genes analyzed.

Significance

The manuscript is scientifically sound, clearly written, and deserves publication. The proposed method is quantitative, novel, theoretically grounded, and was tested in detail with appropriate null models and statistical methods. Moreover, IGNITE can be applied to various biological systems as the availability of scRNA-seq datasets is continuously growing. The paper will be of interest to a broad community of computational biologists and biology labs interested in gene regulation using scRNA-seq data.

The limitation, in my opinion, is the method's (particularly the pre-processing pipeline) fine-tuning for the specific biological system tested. Testing IGNITE on another biological system without pre-selected pre-processing steps or detailed biological priors would be more convincing and make the paper's conclusions much stronger. The comparison with other methods also may be slightly biased due to this fine-tuning.

My background is in statistical physics, with expertise in biological physics, specifically in mathematical modeling and data analysis in molecular biology.

Reviewer #2 (Public review):

Summary:

In this manuscript, Bansal et al examine and characterize feeding behaviour in Anopheles stephensi mosquitoes. While sharing some similarities to the well-studied Aedes aegypti mosquito, the authors demonstrate that mated females, but not unmated (virgin) females, exhibit suppression in their blood-feeding behaviour. Using brain transcriptomic analysis comparing sugar-fed, blood-fed, and starved mosquitoes, several candidate genes potentially responsible for influencing blood-feeding behaviour were identified, including two neuropeptides (short NPF and RYamide) that are known to modulate feeding behaviour in other mosquito species. Using molecular tools, including in situ hybridization, the authors map the distribution of cells producing these neuropeptides in the nervous system and in the gut. Further, by implementing systemic RNA interference (RNAi), the study suggests that both neuropeptides appear to promote blood-feeding (but do not impact sugar feeding), although the impact was observed only after both neuropeptide genes underwent knockdown.

Strengths and/or weaknesses:

Overall, the manuscript was well-written; however, the authors should review carefully, as some sections would benefit from restructuring to improve clarity. Some statements need to be rectified as they are factually inaccurate.

Below are specific concerns and clarifications needed in the opinion of this reviewer:

(1) What does "central brains" refer to in abstract and in other sections of the manuscript (including methods and results)? This term is ambiguous, and the authors should more clearly define what specific components of the central nervous system was/were used in their study.

(2) The abstract states that two neuropeptides, sNPF and RYamide are working together, but no evidence is summarized for the latter in this section.

(3) Figure 1<br /> Panel A: This should include mating events in the reproductive cycle to demonstrate differences in the feeding behavior of Ae. aegypti.<br /> Panel F: In treatments where insects were not provided either blood or sugar, how is it that some females and males had fed? Also, it is unclear why the y-axis label is % fed when the caption indicates this is a choice assay. Also, it is interesting that sugar-starved females did not increase sugar intake. Is there any explanation for this (was it expected)?

(4) Figure 3<br /> In the neurotranscriptome analysis of the (central) brain involving the two types of comparisons, can the authors clarify what "excluded in males" refers to? Does this imply that only genes not expressed in males were considered in the analysis? If so, what about co-expressed genes that have a specific function in female feeding behaviour?

(5) Figure 4<br /> The authors state that there is more efficient knockdown in the head of unfed females; however, this is not accurate since they only get knockdown in unfed animals, and no evidence of any knockdown in fed animals (panel D). This point should be revised in the results test as well. Relatedly, blood-feeding is decreased when both neuropeptide transcripts are targeted compared to uninjected (panel C) but not compared to dsGFP injected (panel E). Why is this the case if authors showed earlier in this figure (panel B) that dsGFP does not impact blood feeding? In addition, do the uninjected and dsGFP-injected relative mRNA expression data reflect combined RYa and sNPF levels? Why is there no variation in these data, and how do transcript levels of RYa and sNPF compare in the brain versus the abdomen (the presentation of data doesn't make this relationship clear).

(6) As an overall comment, the figure captions are far too long and include redundant text presented in the methods and results sections.

(7) Criteria used for identifying neuropeptides promoting blood-feeding: statement that reads "all neuropeptides, since these are known to regulate feeding behaviours". This is not accurate since not all neuropeptides govern feeding behaviors, while certainly a subset do play a role.

(8) In the section beginning with "Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels...", the authors state that there was no change in blood-feeding and later state the opposite. The wording should be clarified as it is unclear.

(9) Just before the conclusions section, the statement that "neuropeptide receptors are often ligand-promiscuous" is unjustified. Indeed, many studies have shown in heterologous systems that high concentrations of structurally related peptides, which are not physiologically relevant, might cross-react and activate a receptor belonging to a different peptide family; however, the natural ligand is often many times more potent (in most cases, orders of magnitude) than structurally related peptides. This is certainly the case for various RYamide and sNPF receptors characterized in various insect species.

(10) Methods<br /> In the dsRNA-mediated gene knockdown section, the authors could more clearly describe how much dsRNA was injected per target. At the moment, the reader must carry out calculations based on the concentrations provided and the injected volume range provided later in this section.

It is also unclear how tissue-specific knockdown was achieved by performing injection on different days/times. The authors need to explain/support, and justify how temporal differences in injection lead to changes in tissue-specific expression. Does the blood-brain barrier limit knockdown in the brain instead, while leaving expression in the peripheral organs susceptible? For example, in Figure 4, the data support that knockdown in the head/brain is only effective in unfed animals compared to uninjected animals, while there is no evidence of knockdown in the brain relative to dsGFP-injected animals. Comparatively, evidence appears to show stronger evidence of abdominal knockdown mostly for the RYa transcript (>90%) while still significantly for the sNPF transcript (>60%).

Reviewer #3 (Public review):

Summary:

This manuscript investigates the regulation of host-seeking behavior in Anopheles stephensi females across different life stages and mating states. Through transcriptomic profiling, the authors identify differential gene expression between "blood-hungry" and "blood-sated" states. Two neuropeptides, sNPF and RYamide, are highlighted as potential mediators of host-seeking behavior. RNAi knockdown of these peptides alters host-seeking activity, and their expression is anatomically mapped in the mosquito brain (sNPF and RYamide) and midgut (sNPF only).

Strengths:

(1) The study addresses an important question in mosquito biology, with relevance to vector control and disease transmission.

(2) Transcriptomic profiling is used to uncover gene expression changes linked to behavioral states.

(3) The identification of sNPF and RYamide as candidate regulators provides a clear focus for downstream mechanistic work.

(3) RNAi experiments demonstrate that these neuropeptides are necessary for normal host-seeking behavior.

(4) Anatomical localization of neuropeptide expression adds depth to the functional findings.

Weaknesses:

(1) The title implies that the neuropeptides promote host-seeking, but sufficiency is not demonstrated (for example, with peptide injection or overexpression experiments).

(2) The proposed model regarding central versus peripheral (gut) peptide action is inconsistently presented and lacks strong experimental support.

(3) Some conclusions appear premature based on the current data and would benefit from additional functional validation.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Bansal et al. present a study on the fundamental blood and nectar feeding behaviors of the critical disease vector, Anopheles stephensi. The study encompasses not just the fundamental changes in blood feeding behaviors of the crucially understudied vector, but then uses a transcriptomic approach to identify candidate neuromodulation pathways which influence blood feeding behavior in this mosquito species. The authors then provide evidence through RNAi knockdown of candidate pathways that the neuromodulators sNPF and Rya modulate feeding either via their physiological activity in the brain alone or through joint physiological activity along the brain-gut axis (but critically not the gut alone). Overall, I found this study to be built on tractable, well-designed behavioral experiments.

Their study begins with a well-structured experiment to assess how the feeding behaviors of A. stephensi change over the course of its life history and in response to its age, mating, and oviposition status. The authors are careful and validate their experimental paradigm in the more well-studied Ae. aegypti, and are able to recapitulate the results of prior studies, which show that mating is a prerequisite for blood feeding behaviors in Ae. aegypt. Here they find A. Stephensi, like other Anopheline mosquitoes, has a more nuanced regulation of its blood and nectar feeding behaviors.

The authors then go on to show in a Y-maze olfactometer that ,to some degree, changes in blood feeding status depend on behavioral modulation to host cues, and this is not likely to be a simple change to the biting behaviors alone. I was especially struck by the swap in valence of the host cues for the blood-fed and mated individuals, which had not yet oviposited. This indicates that there is a change in behavior that is not simply desensitization to host cues while navigating in flight, but something much more exciting is happening.

The authors then use a transcriptomic approach to identify candidate genes in the blood-feeding stages of the mosquito's life cycle to identify a list of 9 candidates that have a role in regulating the host-seeking status of A. stephensi. Then, through investigations of gene knockdown of candidates, they identify the dual action of RYa and sNPF and candidate neuromodulators of host-seeking in this species. Overall, I found the experiments to be well-designed. I found the molecular approach to be sound. While I do not think the molecular approach is necessarily an all-encompassing mechanism identification (owing mostly to the fact that genetic resources are not yet available in A. stephensi as they are in other dipteran models), I think it sets up a rich line of research questions for the neurobiology of mosquito behavioral plasticity and comparative evolution of neuromodulator action.

We appreciate the reviewer’s detailed summary of our work. We thank them for their positive comments and agree with them on the shortcomings of our approach.

Strengths:

I am especially impressed by the authors' attention to small details in the course of this article. As I read and evaluated this article, I continued to think about how many crucial details could potentially have been missed if this had not been the approach. The attention to detail paid off in spades and allowed the authors to carefully tease apart molecular candidates of blood-seeking stages. The authors' top-down approach to identifying RYamide and sNPF starting from first principles behavioral experiments is especially comprehensive. The results from both the behavioral and molecular target studies will have broad implications for the vectorial capacity of this species and comparative evolution of neural circuit modulation.

We really appreciate that the reviewer has recognised the attention to detail we have tried to put, thank you!

Weaknesses:

There are a few elements of data visualizations and methodological reporting that I found confusing on a first few read-throughs. Figure 1F, for example, was initially confusing as it made it seem as though there were multiple 2-choice assays for each of the conditions. I would recommend removing the "X" marker from the x-axis to indicate the mosquitoes did not feed from either nectar, blood, or neither in order to make it clear that there was one assay in which mosquitoes had access to both food sources, and the data quantify if they took both meals, one meal, or no meals.

We thank the reviewer for flagging the schematic in figure 1F. As suggested, we have removed the “X” markers from the x-axis and revised the axis label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose in the assay. For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data, as it does not capture the variability in the data.

I would also like to know more about how the authors achieved tissue-specific knockdown for RNAi experiments. I think this is an intriguing methodology, but I could not figure out from the methods why injections either had whole-body or abdomen-specific knockdown.

The tissue-specific knockdown (abdomen only or abdomen+head) emerged from initial standardisations where we were unable to achieve knockdown in the head unless we used higher concentrations of dsRNA and did the injections in older females. We realised that this gave us the opportunity to isolate the neuronal contribution of these neuropeptides in the phenotype produced. Further optimisations revealed that injecting dsRNA into 0-10h old females produced abdomen-specific knockdowns without affecting head expression, whereas injections into 4 days old females resulted in knockdowns in both tissues. Moreover, head knockdowns in older females required higher dsRNA concentrations, with knockdown efficiency correlating with the amount injected. In contrast, abdominal knockdowns in younger females could be achieved even with lower dsRNA amounts.

We have mentioned the knockdown conditions- time of injection and the amount dsRNA injected- for tissue-specific knockdowns in methods but realise now that it does not explain this well enough. We have now edited it to state our methodology more clearly (see lines 932-948).

I also found some interpretations of the transcriptomic to be overly broad for what transcriptomes can actually tell us about the organism's state. For example, the authors mention, "Interestingly, we found that  after a blood meal, glucose is neither spent nor stored, and that the female brain goes into a state of metabolic 'sugar rest', while actively processing proteins (Figure S2B, S3)".

This would require a physiological measurement to actually know. It certainly suggests that there are changes in carbohydrate metabolism, but there are too many alternative interpretations to make this broad claim from transcriptomic data alone.

We thank the reviewer for pointing this out and agree with them. We have now edited our statement to read:

“Instead, our data suggests altered carbohydrate metabolism  after a blood meal, with the female brain potentially entering a state of metabolic 'sugar rest' while actively processing proteins (Figure S2B, S3). However, physiological measurements of carbohydrate and protein metabolism will be required to confirm whether glucose is indeed neither spent nor stored during this period.” See lines 271-277.

Reviewer #2 (Public review):

Summary:

In this manuscript, Bansal et al examine and characterize feeding behaviour in Anopheles stephensi mosquitoes. While sharing some similarities to the well-studied Aedes aegypti mosquito, the authors demonstrate that mated females, but not unmated (virgin) females, exhibit suppression in their bloodfeeding behaviour. Using brain transcriptomic analysis comparing sugar-fed, blood-fed, and starved mosquitoes, several candidate genes potentially responsible for influencing blood-feeding behaviour were identified, including two neuropeptides (short NPF and RYamide) that are known to modulate feeding behaviour in other mosquito species. Using molecular tools, including in situ hybridization, the authors map the distribution of cells producing these neuropeptides in the nervous system and in the gut. Further, by implementing systemic RNA interference (RNAi), the study suggests that both neuropeptides appear to promote blood-feeding (but do not impact sugar feeding), although the impact was observed only  after both neuropeptide genes underwent knockdown.

Strengths and/or weaknesses:

Overall, the manuscript was well-written; however, the authors should review carefully, as some sections would benefit from restructuring to improve clarity. Some statements need to be rectified as they are factually inaccurate.

Below are specific concerns and clarifications needed in the opinion of this reviewer:

(1) What does "central brains" refer to in abstract and in other sections of the manuscript (including methods and results)? This term is ambiguous, and the authors should more clearly define what specific components of the central nervous system was/were used in their study.

Central brain, or mid brain, is a commonly used term to refer to brain structures/neuropils without the optic lobes (For example: https://www.nature.com/articles/s41586-024-07686-5). In this study we have focused our analysis on the central brain circuits involved in modulating blood-feeding behaviour and have therefore excluded the optic lobes. As optic lobes account for nearly half of all the neurons in the mosquito brain (https://pmc.ncbi.nlm.nih.gov/articles/PMC8121336/), including them would have disproportionately skewed our transcriptomic data toward visual processing pathways.

We have indicated this in figure 3A and in the methods (see lines 800-801, 812). We have now also clarified it in the results section for neuro-transcriptomics to avoid confusion (see lines 236-237).

(2) The abstract states that two neuropeptides, sNPF and RYamide are working together, but no evidence is summarized for the latter in this section.

We thank the reviewer for pointing this out. We have now added a statement “This occurs in the context of the action of RYa in the brain” to end of the abstract, for a complete summary of our proposed model.

(3) Figure 1

Panel A: This should include mating events in the reproductive cycle to demonstrate differences in the feeding behavior of Ae. aegypti.

Our data suggest that mating can occur at any time between eclosion and oviposition in An. stephensi and between eclosion and blood feeding in Ae. aegypti. Adding these into (already busy) 1A, would cloud the purpose of the schematic, which is to indicate the time points used in the behavioural assays and transcriptomics.

Panel F: In treatments where insects were not provided either blood or sugar, how is it that some females and males had fed? Also, it is unclear why the y-axis label is % fed when the caption indicates this is a choice assay. Also, it is interesting that sugar-starved females did not increase sugar intake. Is there any explanation for this (was it expected)?

We apologise for the confusion. The experiment is indeed a choice assay in which sugar-starved or sugar-sated females, co-housed with males, were provided simultaneous access to both blood and sugar, and were assessed for the choice made (indicated on the x-axis): both blood and sugar, blood only, sugar only, or neither. The x-axis indicates the choice made by the mosquitoes, not the choice provided in the assay, and the y-axis indicates the percentage of males or females that made each particular choice. We have now removed the “X” markers from the x-axis and revised the axis label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose to take.

In this assay, we scored females only for the presence or absence of each meal type (blood or sugar) and are therefore unable to comment on whether sugar-starved females consumed more sugar than sugarsated females. However, when sugar-starved, a higher proportion of females consumed both blood and sugar, while fewer fed on blood alone.

For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data as it does not capture the variability in the data.

(4) Figure 3

In the neurotranscriptome analysis of the (central) brain involving the two types of comparisons, can the authors clarify what "excluded in males" refers to? Does this imply that only genes not expressed in males were considered in the analysis? If so, what about co-expressed genes that have a specific function in female feeding behaviour?

This is indeed correct. We reasoned that since blood feeding is exclusive to females, we should focus our analysis on genes that were specifically upregulated in them. As the reviewer points out, it is very likely that genes commonly upregulated in males and females may also promote blood feeding and we will miss out on any such candidates based on our selection criteria.

(5) Figure 4

The authors state that there is more efficient knockdown in the head of unfed females; however, this is not accurate since they only get knockdown in unfed animals, and no evidence of any knockdown in fed animals (panel D). This point should be revised in the results test as well.

Perhaps we do not understand the reviewer’s point or there has been a misunderstanding. In figure 4D, we show that while there is more robust gene knockdown in unfed females, blood-fed females also showed modest but measurable knockdowns ranging from 5-40% for RYamide and 2-21% for sNPF.

Relatedly, blood-feeding is decreased when both neuropeptide transcripts are targeted compared to uninjected (panel C) but not compared to dsGFP injected (panel E). Why is this the case if authors showed earlier in this figure (panel B) that dsGFP does not impact blood feeding?

We realise this concern stems from our representation of the data. Since we had earlier determined that dsGFP-injected females fed similarly to uninjected females (fig 4B), we used these controls interchangeably in subsequent experiments. To avoid confusion, we have now only used the label ‘control’ in figure 4 (and supplementary figure S9) and specified which control was used for each experiment in the legend.

In addition to this, we wanted to clarify that fig 4C and 4E are independent experiments. 4C is the behaviour corresponding to when the neuropeptides were knocked down in both heads and abdomens.

4E is the behaviour corresponding to when the neuropeptides were knocked down in only the abdomens. We have now added a schematic in the plots to make this clearer.

In addition, do the uninjected and dsGFP-injected relative mRNA expression data reflect combined RYa and sNPF levels? Why is there no variation in these data,…

In these qPCRs, we calculated relative mRNA expression using the delta-delta Ct method (see line 975). For each neuropeptide its respective control was used. For simplicity, we combined the RYa and sNPF control data into a single representation. The value of this control is invariant because this method sets the control baseline to a value of 1.

…and how do transcript levels of RYa and sNPF compare in the brain versus the abdomen (the presentation of data doesn't make this relationship clear).

The reviewer is correct in pointing out that we have not clarified this relationship in our current presentation. While we have not performed absolute mRNA quantifications, we extracted relative mRNA levels from qPCR data of 96h old unmanipulated control females. We observed that both sNPF and RYa transcripts are expressed at much lower levels in the abdomens, as compared to those in the heads, as shown in the graphs inserted below.

Author response image 1.

(6) As an overall comment, the figure captions are far too long and include redundant text presented in the methods and results sections.

We thank the reviewer for flagging this and have now edited the legends to remove redundancy.

(7) Criteria used for identifying neuropeptides promoting blood-feeding: statement that reads "all neuropeptides, since these are known to regulate feeding behaviours". This is not accurate since not all neuropeptides govern feeding behaviors, while certainly a subset do play a role.

We agree with the reviewer that not all neuropeptides regulate feeding behaviours. Our statement refers to the screening approach we used: in our shortlist of candidates, we chose to validate all neuropeptides.

(8) In the section beginning with "Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels...", the authors state that there was no change in blood-feeding and later state the opposite. The wording should be clarified as it is unclear.

Thank you for pointing this out. We were referring to an unchanged proportion of the blood fed females. We have now edited the text to the following:

“Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels in the heads but the proportion of females that took blood meals remained unchanged”. See lines 338-340.

(9) Just before the conclusions section, the statement that "neuropeptide receptors are often ligand promiscuous" is unjustified. Indeed, many studies have shown in heterologous systems that high concentrations of structurally related peptides, which are not physiologically relevant, might cross-react and activate a receptor belonging to a different peptide family; however, the natural ligand is often many times more potent (in most cases, orders of magnitude) than structurally related peptides. This is certainly the case for various RYamide and sNPF receptors characterized in various insect species.

We agree with the reviewer and apologise for the mistake. We have now removed the statement.

(10) Methods

In the dsRNA-mediated gene knockdown section, the authors could more clearly describe how much dsRNA was injected per target. At the moment, the reader must carry out calculations based on the concentrations provided and the injected volume range provided later in this section.

We have now edited the section to reflect the amount of dsRNA injected per target. Please see lines 921-931.

It is also unclear how tissue-specific knockdown was achieved by performing injection on different days/times. The authors need to explain/support, and justify how temporal differences in injection lead to changes in tissue-specific expression. Does the blood-brain barrier limit knockdown in the brain instead, while leaving expression in the peripheral organs susceptible?

To achieve tissue-specific knockdowns of sNPF and RYa, we optimised both the time of injection as well as the dsRNA concentration to be injected. Injecting dsRNA into 0-10h females produced abdomen specific knockdowns without affecting head expression, whereas injections into 96h old females resulted in knockdowns in both tissues. Head knockdowns in older females required higher dsRNA concentrations, with knockdown efficiency correlating with the amount injected. In contrast, abdominal knockdowns in younger females could be achieved even with lower dsRNA amounts, reflecting the lower baseline expression of sNPF in abdomens compared to heads and the age-dependent increase in head expression (as confirmed by qPCR). It is possible that the blood-brain barrier also limits the dsRNA entering the brain, thereby requiring higher amounts to be injected for head knockdowns.

We have now edited this section to state our methodology more clearly (see lines 932-948).

For example, in Figure 4, the data support that knockdown in the head/brain is only effective in unfed animals compared to uninjected animals, while there is no evidence of knockdown in the brain relative to dsGFP-injected animals. Comparatively, evidence appears to show stronger evidence of abdominal knockdown mostly for the RYa transcript (>90%) while still significantly for the sNPF transcript (>60%).

As we explained earlier, this concern likely stems from our representation of the data. Since we had earlier determined that dsGFP-injected females fed similarly to uninjected females (fig 4B), we used these controls interchangeably in subsequent experiments. To avoid confusion, we have now only used the label ‘control’ in figure 4 (and supplementary figure S9) and specified which control was used for each experiment in the legend.

In addition to this, we wanted to clarify that fig 4C and 4E are independent experiments. 4C is the behaviour corresponding to when the neuropeptides were knocked down in both heads and abdomens. 4E is the behaviour corresponding to when the neuropeptides were knocked down in only the abdomen. We have now added a schematic in the plots to make this clearer.

Reviewer #3 (Public review):

Summary:

This manuscript investigates the regulation of host-seeking behavior in Anopheles stephensi females across different life stages and mating states. Through transcriptomic profiling, the authors identify differential gene expression between "blood-hungry" and "blood-sated" states. Two neuropeptides, sNPF and RYamide, are highlighted as potential mediators of host-seeking behavior. RNAi knockdown of these peptides alters host-seeking activity, and their expression is anatomically mapped in the mosquito brain (sNPF and RYamide) and midgut (sNPF only).

Strengths:

(1) The study addresses an important question in mosquito biology, with relevance to vector control and disease transmission.

(2) Transcriptomic profiling is used to uncover gene expression changes linked to behavioral states.

(3) The identification of sNPF and RYamide as candidate regulators provides a clear focus for downstream mechanistic work.

(3) RNAi experiments demonstrate that these neuropeptides are necessary for normal host-seeking behavior.

(4) Anatomical localization of neuropeptide expression adds depth to the functional findings.

Weaknesses:

(1) The title implies that the neuropeptides promote host-seeking, but sufficiency is not demonstrated (for example, with peptide injection or overexpression experiments).

Demonstrating sufficiency would require injecting sNPF peptide or its agonist. To date, no small-molecule agonists (or antagonists) that selectively mimic sNPF or RYa neuropeptides have been identified in insects. An NPY analogue, TM30335, has been reported to activate the Aedes aegypti NPY-like receptor 7 (NPYLR7; Duvall et al., 2019), which is also activated by sNPF peptides at higher doses (Liesch et al., 2013). Unfortunately, the compound is no longer available because its manufacturer, 7TM Pharma, has ceased operations. Synthesising the peptides is a possibility that we will explore in the future.

(2) The proposed model regarding central versus peripheral (gut) peptide action is inconsistently presented and lacks strong experimental support.

The best way to address this would be to conduct tissue-specific manipulations, the tools for which are not available in this species. Our approach to achieve head+abdomen and abdomen only knockdown was the closest we could get to achieving tissue specificity and allowed us to confirm that knockdown in the head was necessary for the phenotype. However, as the reviewer points out, this did not allow us to rule out any involvement of the abdomen. This point has been addressed in lines 364-371.

(3) Some conclusions appear premature based on the current data and would benefit from additional functional validation.

The most definitive way of demonstrating necessity of sNPF and RYa in blood feeding would be to generate mutant lines. While we are pursuing this line of experiments, they lie beyond the scope of a revision. In its absence, we relied on the knockdown of the genes using dsRNA. We would like to posit that despite only partial knockdown, mosquitoes do display defects in blood-feeding behaviour, without affecting sugar-feeding. We think this reflects the importance of sNPF in promoting blood feeding.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Overall, I found this manuscript to be well-prepared, visually the figures are great and clearly were carefully thought out and curated, and the research is impacwul. It was a wonderful read from start to finish. I have the following recommendations:

Thank you very much, we are very pleased to hear that you enjoyed reading our manuscript!

(1) For future manuscripts, it would make things significantly easier on the reviewer side to submit a format that uses line numbers.

We sincerely apologise for the oversight. We have now incorporated line numbers in the revised manuscript.

(2) There are a few statements in the text that I think may need clarification or might be outside the bounds of what was actually studied here. For example, in the introduction "However, mating is dispensable in Anophelines even under conditions of nutritional satiety". I am uncertain what is meant by this statement - please clarify.

We apologise for the lack of clarity in the statement and have now deleted it since we felt it was not necessary.

(3) Typo/Grammatical minutiae:

a) A small idiosyncrasy of using hyphens in compound words should also be fixed throughout. Typically, you don't hyphenate if the words are being used as a noun, as in the case: e.g. "Age affects blood feeding.". However, you would hyphenate if the two words are used as a compound adjective "Age affects blood-feeding behavior". This may not be an all-inclusive list, but here are some examples where hyphens need to either be removed or added. Some examples:

"Nutritional state also influences other internal state outputs on blood-feeding": blood-feeding -> blood feeding

"... the modulation of blood-feeding": blood-feeding -> blood feeding

"For example, whether virgin females take blood-meals...": blood-meals -> blood meals

".... how internal and external cues shape meal-choice"-> meal choice

"blood-meal" is often used throughout the text, but is correctly "blood meal" in the figures.

There are many more examples throughout.

We apologise for these errors and appreciate the reviewer’s keen eye. We have now fixed them throughout the manuscript.

b) Figure 1 Caption has a typo: "co-housed males were accessed for sugar-feeding" should be "co-housed males were assessed for sugar feeding"

We apologise for the typo and thank the reviewer for spotting it. We have now corrected this.

c) It would be helpful in some other figure captions to more clearly label which statement is relevant to which part of the text. For example, in Figure 4's caption.

"C,D. Blood-feeding and sugar-feeding behaviour of females when both RYa and sNPF are knocked down in the head (C). Relative mRNA expressions of RYa and sNPF in the heads of dsRYa+dssNPF - injected blood-fed and unfed females, as compared to that in uninjected females, analysed via qPCR (D)."

I found re-referencing C and D at the end of their statements makes it look as thought C precedes the "Relative mRNA expression" and on a first read through, I thought the figure captions were backwards. I'd recommend reformating here and throughout consistently to only have the figure letter precede its relevant caption information, e.g.:

"C. Blood-feeding and sugar-feeding behaviour of females when both RYa and sNPF are knocked down in the head. D. Relative mRNA expressions of RYa and sNPF in the heads of dsRYa+dssNPF - injected bloodfed and unfed females, as compared to that in uninjected females, analysed via qPCR."

We have now edited the legends as suggested.

Reviewer #2 (Recommendations for the authors):

Separately from the clarifications and limitations listed above, the authors could strengthen their study and the conclusions drawn if they could rescue the behavioural phenotype observed following knockdown of sNPF and RYamide. This could be achieved by injection of either sNPF or RYa peptide independently or combined following knockdown to validate the role of these peptides in promoting blood-feeding in An. stephensi. Additionally, the apparent (but unclear) regionalized (or tissue-specific) knockdown of sNPF and RYamide transcripts could be visualized and verified by implementing HCR in situ hyb in knockdown animals (or immunohistochemistry using antibodies specific for these two neuropeptides).

In a follow up of this work, we are generating mutants and peptides for these candidates and are planning to conduct exactly the experiments the reviewer suggests.

Reviewer #3 (Recommendations for the authors):

The loss-of-function data suggest necessity but not sufficiency. Synthetic peptide injection in non-host seeking (blood-fed mated or juvenile) mosquitoes would provide direct evidence for peptide-induced behavioral activation. The lack of these experiments weakens the central claim of the paper that these neuropeptides directly promote blood feeding.

As noted above, we plan to synthesise the peptide to test rescue in a mutant background and sufficiency.

Some of the claims about knockdown efficiency and interpretation are conflicting; the authors dismiss Hairy and Prp as candidates due to 30-35% knockdown, yet base major conclusions on sNPF and RYamide knockdowns with comparable efficiencies (25-40%). This inconsistency should be addressed, or the justification for different thresholds should be clearly stated.

We have not defined any specific knockdown efficacy thresholds in the manuscript, as these can vary considerably between genes, and in some cases, even modest reductions can be sufficient to produce detectable phenotypes. For example, knockdown efficiencies of even as low as about 25% - 40% gave us observable phenotypes for sNPF and RYa RNAi (Figure S9B-G).

No such phenotypes were observed for Hairy (30%) or Prp (35%) knockdowns. Either these genes are not involved in blood feeding, or the knockdown was not sufficient for these specific genes to induce phenotypes. We cannot distinguish between these scenarios.

The observation that knockdown animals take smaller blood meals is interesting and could reflect a downstream effect of altered host-seeking or an independent physiological change. The relationship between meal size and host-seeking behavior should be clarified.

We agree with the reviewer that the reduced meal size observed in sNPF and RYa knockdown animals could result from their inability to seek a host or due to an independent effect on blood meal intake. Unfortunately, we did not measure host-seeking in these animals. We plan to distinguish between these possibilities using mutants in future work.

Several figures are difficult to interpret due to cluttered labeling and poorly distinguishable color schemes. Simplifying these and improving contrast (especially for co-housed vs. virgin conditions) would enhance readability.

We regret that the reviewer found the figures difficult to follow. We have now revised our annotations throughout the manuscript for enhanced readability. For example, “D1^B” is now “D1^PBM” (post-bloodmeal) and “D1^O” is now “D1^PO” (post-oviposition). Wherever mated females were used, we have now appended “(m)” to the annotations and consistently depicted these females with striped abdomens in all the schematics. We believe these changes will improve clarity and readability.

The manuscript does not clearly justify the use of whole-brain RNA sequencing to identify peptides involved in metabolic or peripheral processes. Given that anticipatory feeding signals are often peripheral, the logic for brain transcriptomics should be explained.

The reviewer is correct in pointing out that feeding signals could also emerge from peripheral tissues. Signals from these tissues – in response to both changing nutritional and reproductive states – are then integrated by the central brain to modulate feeding choices. For example, in Drosophila, increased protein intake is mediated by central brain circuitry including those in the SEZ and central complex (Munch et al., 2022; Liu et al., 2017; Goldschmidt et al., 2023). In the context of mating, male-derived sex peptide further increases protein feeding by acting on a dedicated central brain circuitry (Walker et al., 2015). We, therefore focused on the central brain for our studies.

The proposed model suggests brain-derived peptides initiate feeding, while gut peptides provide feedback. However, gut-specific knockdowns had no effect, undermining this hypothesis. Conversely, the authors also suggest abdominal involvement based on RNAi results. These contradictions need to be resolved into a consistent model.

We thank the reviewer for raising this point and recognise their concern. Our reasons for invoking an involvement of the gut were two-fold:

(1) We find increased sNPF transcript expression in the entero-endocrine cells of the midgut in blood-hungry females, which returns to baseline  after a blood-meal (Fig. 4L, M).

(2) While the abdomen-only knockdowns did not affect blood feeding, every effective head knockdown that affected blood feeding also abolished abdominal transcript levels (Fig. S9C, F). (Achieving a head-only reduction proved impossible because (i) systemic dsRNA delivery inevitably reaches the abdomen and (ii) abdominal expression of both peptides is low, leaving little dynamic range for selective manipulation.) Consequently, we can only conclude the following: 1) that brain expression is required for the behaviour, 2) that we cannot exclude a contributory role for gut-derived sNPF. We have discussed this in lines 364-371.

The identification of candidate receptors is promising, but the manuscript would be significantly strengthened by testing whether receptor knockdowns phenocopy peptide knockdowns. Without this, it is difficult to conclude that the identified receptors mediate the behavioral effects.

We agree that functional validation of the receptors would strengthen the evidence for sNPF and RYa_mediated control of blood feeding in _An. stephensi. We selected these receptors based on sequence homology. A possibility remains that sNPF neuropeptides activate more than one receptor, each modulating a distinct circuit, as shown in the case of Drosophila Tachykinin (https://pmc.ncbi.nlm.nih.gov/articles/PMC10184743/). This will mean a systematic characterisation and knockdown of each of them to confirm their role. We are planning these experiments in the future.

The authors compared the percentage changes in sugar-fed and blood-fed animals under sugar-sated or sugar-starved conditions. Figure 1F should reflect what was discussed in the results.

Perhaps this concern stems from our representation of the data in figure 1F? We have now edited the xaxis and revised its label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose to take.

For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data because it does not capture the variability in the data.

Minor issues:

(1) The authors used mosquitoes with belly stripes to indicate mated females. To be consistent, the post-oviposition females should also have belly stripes.

We thank the reviewer for pointing this out. We have now edited all the figures as suggested.

(2) In the first paragraph on the right column of the second page, the authors state, "Since females took blood-meals regardless of their prior sugar-feeding status and only sugar-feeding was selectively suppressed by prior sugar access." Just because the well-fed animals ate less than the starved animals does not mean their feeding behavior was suppressed.

Perhaps there has been a misunderstanding in the experimental setup of figure 1F, probably stemming from our data representation. The experiment is a choice assay in which sugar-starved or sugar-sated females, co-housed with males, were provided simultaneous access to both blood and sugar, and were assessed for the choice made (indicated on the x-axis): both blood and sugar, blood only, sugar only, or neither. We scored females only for the presence or absence of each meal type (blood or sugar) and did not quantify the amount consumed.

(3) The figure legend for Figure 1A and the naming convention for different experimental groups are difficult to follow. A simplified or consistently abbreviated scheme would help readers navigate the figures and text.

We regret that the reviewer found the figure difficult to follow. We have now revised our annotations throughout the manuscript for enhanced readability. For example, “D1^B” is now “D1^PBM” (post-bloodmeal) and “D1^O” is now “D1^PO” (post-oviposition).

(4) In the last paragraph of the Y-maze olfactory assay for host-seeking behaviour in An. stephensi in Methods, the authors state, "When testing blood-fed females, aged-matched sugar-fed females (bloodhungry) were included as positive controls where ever possible, with satisfactory results." The authors should explicitly describe what the criteria are for "satisfactory results".

We apologise for the lack of clarity. We have now edited the statement to read:

“When testing blood-fed females, age-matched sugar-fed females (blood-hungry) were included wherever possible as positive controls. These females consistently showed attraction to host cues, as expected.” See lines 786-790.

(5) In the first paragraph of the dsRNA-mediated gene knockdown section in Methods, dsRNA against GFP is used as a negative control for the injection itself, but not for the potential off-target effect.

We agree with the reviewer that dsGFP injections act as controls only for injection-related behavioural changes, and not for off-target effects of RNAi. We have now corrected the statement. See lines 919-920.

To control for off-target effects, we could have designed multiple dsRNAs targeting different parts of a given gene. We regret not including these controls for potential off-target effects of dsRNAs injected.

(6) References numbers 48, 89, and 90 are not complete citations.

We thank the reviewer for spotting these. We have now corrected these citations.

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Reply to the reviewers

We appreciated the positive, detailed and helpful feedback from all three reviewers.

Reviewer 1.

Minor comments.

1. In the introduction, on page 2, the authors seem a little confused about the Plk1 Polo-box domain - text as written: "...kinase domain linked to tandem Polo-box domains (PBD)", and cite a review paper. Actually, there is only a single Polo-box domain in these kinases, which contains both Polo-boxes and a bit of the upstream linker region. The "PBD" terminology denotes his 2-Polo-box +linker structure. Perhaps it would be better here to cite the PBD structure (Elia et al., Cell, 2002) as a primary citation here.

Response: Thank you for finding this error, the text has been updated and the new citation included within the text on line 65.

1. Similarly, the line "...during the G2/M transition following successful DNA damage repair" cites the Seki et al paper, but those findings are shown in the Macurek et al paper, not the Seki et al paper.

_Response: _Thank you for finding this error, the new citation included within the text on line 69.

1. Using the model of the ternary complex as shown in Figure 1B, deletion constructs of Bora missing regions within the disordered loops, but still retaining the residues that bind the PBD, FW pocket and Aurora A, can be modeled and tested to see if such deletions can improve the ipTM scores and binding affinity.

Response: ____AlphaFold3 modelling was attempted with shorter regions of Bora to see the effect on the ipTM scores. Unfortunately, when Bora was reduced to shorter sequences, such as 18-88 or 18-45 modelled with 68-120, the models became inconsistent and of a low quality. Models were also created including the short region of Bora surrounding Ser252 that interacts with the polo box domain as well as Bora 18-120, but this had minimal effect on the calculated iPTM scores.

1. On page 5, "S112A" within the sentence "Unexpectedly, the F56A/W58A Bora was less efficiently phosphorylated on S112A (Supplementary Figure S11, F compared to H and Supplementary Table S4)." This should be "S112".

Response: ____Thank you for spotting this, the error has been corrected.

1. In the assays shown in Figure 2D, the presence of excess F56AW58A Bora that remained unphosphorylated on S112 may complicate the interpretation of the results. Can the authors show that the S112-phosphorylated F56AW68A Bora is predominantly bound to Aurora A in such a mixture, perhaps by NMR using labelled pS112 F56AW58A Bora and unlabeled S112 F56AW58A Bora?

_Response: _15N13C labelled of Bora 18-120 F56A W58A was produced and assigned. We then phosphorylated a sample using ERK2, tracking with NMR, and when the reaction had progressed to a 50:50 mixture of pSer112 and Ser112 (based on peak intensities) the kinase activity was quenched by addition of EDTA to sequester Mg2+. This produced a solution containing both pS112 and unphosphorylated S112 Bora species with marker peaks in HSQC spectra that could be used to directly compare Aurora-binding to the two species. Aurora-A was introduced to the sample and the peak intensities were monitored. Although both species are affected, there is much greater peak loss from the pS112 related peaks than those for unphosphorylated S112. This indicates that Aurora-A still preferentially binds pS112 Bora over S112 Bora when the F56A W58A mutation is present. This data has been included in Supplementary Figure S11.

1. Please expand Figure 3A to better show the FW pocket-forming residues on Plk1.

Response: ____Figure 3 has been amended to reduce the size of the sequence alignments so that 3A could be made slightly larger.

1. It would be helpful to label the peaks in the mass spectra in Fig. S11 with the phospho-species that they correspond to.

Response: ____This information has been added to the mass spectra in Fig. S11 (now supplementary Figure S14) to make them easier to view.

1. In the last paragraph on page 7, "see we" in the sentence "As well as a decrease in intensity around pSer112 in Bora, see we an overall effect with decreased intensity across most of the Bora sequence." Should be corrected to "we see".

Response: ____Thank you for spotting this, the error has been corrected.

1. While not required, it would be helpful if binding or Bora to Aurora A after Erk2 phosphorylation could be shown using fluorescence polarization or ITC to lend additional support to the NMR data for S112 and S59 phosphorylation and for CEP192 and TPX2 competition.

Response: ____This question has been partially answered in previous work by Tavernier et al. (2021), who showed improved binding of Aurora-A to Bora after Erk phosphorylation (by SPR), and they used labelled-TPX2 for a series of competition FP assays in that and the recent parallel study (Pillan et al. 2025).

We made initial efforts to perform additional FP assays using longer sections of Bora with different phosphorylation states but without success (perhaps due to the multisite-binding nature of the Bora–Aurora interaction, and difficulties with directly expressing phosphorylated Bora). The revised manuscript now includes some additional NMR data to show improved Bora–Aurora-A interaction after phosphorylation at Ser59 (Supplementary Figure S12).

1. The Aurora A phosphorylation motif has been further defined beyond that reported by the Pinna lab in 2005. Notably, the Ser-59 sequence on Bora (F-R-W-S-I), has, in addition to dominant selection for AR in the -2 position, both favorable -1 (W) and +1 (I) positions based on peptide library measurements (Alexander et al., Science Signaling 2011), further arguing that it may be an excellent Aurora A phosphorylation site.

Response: ____Thank you for highlighting this publication and how it further reinforces the likelihood of Ser59 being an effective substrate for Aurora-A, this should have been included in the original manuscript. This citation has now been included.

1. Have the authors tried to model the Drosophila melanogaster Aurora A-Bora-Polo complex to see if the Asn substitution of Bora Ser59, and the expected loss of the interactions between Bora pSer59 and Plk1 Arg59 and Aurora A Arg205 are compensated by other features?

Response: ____A ternary complex between the Drosophila melanogaster orthologues was modelled using AlphaFold3 (Uniprot code PLK1 (Q9VVR2 72-165), Aurora-A kinase (Q9VGF9) 151-411 and PLK1 (P52304 21-280)). This model was analysed using PDBe PISA to identify potential interactions between the three proteins, focusing on residues that are not conserved between the human and Drosophila sequences. From this model a potential salt bridge was identified between Drosophila Bora Lys120 and PLK1 Glu93 that would not occur in the human ternary complex given Lys120 is replaced with an asparagine. This could be an alternative (kinase-independent) method for improved Bora-PLK1 interaction. When comparing the Bora:Aurora-A side of the predicted interface and focusing on the short region of Bora in between Aurora-A and PLK1, there were no clear differences seen in the residues predicted to bind to Aurora-A. This modelling has been included in Supplementary Figure S10 C and D.

1. Given the relevance of the recent publication from Zhu et al. to this study, the authors may want to comment on, or test, the relative importance of PKA and Aurora A as a potential kinase for Bora S59. While those authors argue that PKA phosphorylates Bora on Ser-59, one could easily imagine a model in which either PKA or Aurora A could initially phosphorylate that site followed by a propagation step after initial Aurora A activation, in which Aurora A phosphorylation of Bora Ser-59 is the dominant process.

Response: ____A brief discussion of this recent publication has been added to the discussion, highlighting the similarities between the two publications and the importance of pSer59, as well as suggesting that in cellulo this modification could be achieved via more than one pathway. We also include some additional NMR data to show improved Bora–Aurora-A interaction after phosphorylation at Ser59 (Supplementary Figure S12).

Reviewer 2.

Minor comments.

Page 5: '... a K82R PLK1 mutant was used to increase the stability of the protein' - It is not clear how this mutation confers increased stability of the protein. The authors do not show any data to support this. Isn't the PLK1 K82R an ATP-binding-deficient, kinase-inactive mutant?

Response: ____Thank you for spotting this, the text has been updated to clarify that this version of PLK1 was used as it is acting as a substrate in the in vitro assay as we didn’t want to see any PLK1 activity within this assay.

All panels showing the Alphabridge diagram - it would be helpful if pictorial definitions of the colour codes were provided with corresponding score ranges (in addition to the description in the figure legend).

Response:____The AlphaBridge images have been updated to include details about the plDDT scores each of the different colours refer to.

Fig 2B - The Fluorescence anisotropy assay curves do not reach a plateau. Though the effect of mutation on binding affinity is pretty clear, if possible, I suggest including more data points at higher concentrations and estimating apparent Kd values.

__Response:____The direct binding assay was repeated with a higher concentration of PLK1 in order to try and see a top plateau. This was successful and has been included in Figure 2B (shown in black). The measured Kd was 24 ± 3 µM. __

The cartoon representation of the structures and molecular interfaces - better to avoid shadows, as they compromise the clarity of the figures, particularly the ones where side chains are shown in stick representation.

Response:____The structural images have been remade to remove the shadows and improve the clarity of the images.

It is important to discuss how the parallel studies by Verza et al. and Pillan et al. complement this study, highlighting similarities and differences.

Response:____References to these two publications and details on the similarities and differences seen are now included in the discussion.

Reviewer 3.

Major comments

It would be helpful to measure the level of pThr210 PLK1 in some experiments and graph the data. The current presentation is Fig. 2D-E is qualitative rather than quantitative.

Response:____Graphs displaying the levels of pThr210 produced in the assay are now shown in Supplementary Figure S4.

Have the authors measured the binding affinity of the F/W mutant Bora for PLK1 using the assay in Fig. 2B? Likewise, for Fig. 7 the S59 mutant could be tested to see if it affects PLK1 binding or activation.

Response:____The direct binding assay has been repeated with the use of a FAM-Bora peptide that incorporates the F56A W58A mutation which shows reduced binding (Figure 2B, shown in blue). A version of the Bora peptide phosphorylated on Ser59 was also tested in the direct binding assay and this shows a similar affinity for PLK1 to the wild-type sequence (Figure 2B, shown in red compared to the wild-type shown in black).

It would be helpful if measurements of pThr210 PLK1 for all conditions were shown in the graph Fig. 7F.

Response:____This graph has been updated to include the levels of phosphorylation seen for PLK1 in all of the conditions tested.

Minor comments

I found Figure S1B easier to understand than Fig S1A and Fig 1A-B. Some of the supplemental data Fig. S1C-E could be moved to a revised Figure 1, dropping the current Fig. 1A-B. Can the interaction plots (Fig. S1C-D) be rotated to have the same original at the top and order of proteins (i.e. Bora > Aurora A > {plus minus} PLK1 depending on the plot).

Response:____Figure 1 and S1 have been rearranged to hopefully make them easier to understand, with all AlphaFold3 models of the full-length sequences kept in the supplementary figure and the focus in 1B just on the truncated model. The AlphaBridge plots have been rotated as suggested.

Figure 3F. Typo "Strongyl" not "Strongly".

Response:____Thank you for spotting this, this has been corrected in the updated manuscript.

Figure 3 could be supplemental material.

Response:__Thank you for your suggestion, but we have decided to keep this as a main figure.

Fig. 7E. Run a positive control reaction +ERK2 on the second gel to allow direct comparison of pThr210 across all the conditions tested.

Response:____These samples have been rerun on the same membrane and the levels of phosphorylation have been quantified and included in Figure 7F.

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Referee #3

Evidence, reproducibility and clarity

Summary.

Miles and co-workers have carried out a careful and high-quality study of the activation mechanisms of the mitotic kinase PLK1. Multiple proteins have been implicated in PLK1 activation and localisation as cell enter and pass through mitosis. Initial activation of PLK1 is promoted by a complex of Bora with another kinase Aurora A. Later in mitosis, this activated PLK1 associates with mitotic spindle and centrosome proteins regulating different aspects of mitosis and cytokinesis. In this study, Miles et al. extend previous work on this question by proposing and testing detailed models for Bora/Aurora A-mediated activation of PLK1 to elucidate the mechanism of this reaction.

Using the latest Alphafold they generate a series of models of the PLK1/Bora/Aurora A complex to home in on the key regions mediating interactions of the three proteins. This approach suggests an arrangement where the first ~120 amino acids of Bora wrap Aurora A and create an interaction surface for the N-terminal kinase domain of PLK1. This orients Thr210 in PLK1 towards Aurora A creating a situation likely favourable for phosphorylation, although has the authors discuss there are some caveats to this. A further prediction of the modelling helps explain the requirement for Bora phosphorylation to promote the interaction with Aurora A. This data is presented in Fig. 1 and Fig. S1-S3.

In the subsequent figures the details of this model are tested using biochemical assays and structural biology methods to validate key predictions. First the PLK1 interaction with Bora was shown to require the conserved F/W motif of Bora and a conserved pocket close to R106 on PLK1 (Fig. 2 and 3). In reconstituted PLK1 activation assays the F/W motif mutant Bora showed greatly attenuated pThr210 phosphorylation. This reaction also required phosphorylation of Bora at S112, presumably due to the interaction with Aurora A. An R106A mutant PLK1 showed reduced binding to Bora and reduced kinase activation. This data is clear and provides compelling support for the model.

Using NMR the authors then investigate the interaction between Bora and Aurora A, and more specifically the requirement for Bora phosphorylation at Ser112. The NMR data in Fig. 4 and Fig. 6 provide good support for the Alphafold model. A helpful comparison with known Aurora A binding proteins is also shown to highlight the way CEP192, TPX2 and TACC3 contact a series of conserved pockets on the surface of Aurora A which are common to the Bora interaction. S59 phosphorylation by Aurora A is also shown to play an important role in contacting PLK1 and is required for pThr210 phosphorylation.

In summary, the authors have made valuable progress in working out details of the PLK1 activation mechanism, that extends previous work in the field.

Major comments.

It would be helpful to measure the level of pThr210 PLK1 in some experiments and graph the data. The current presentation is Fig. 2D-E is qualitative rather than quantitative.

Have the authors measured the binding affinity of the F/W mutant Bora for PLK1 using the assay in Fig. 2B? Likewise, for Fig. 7 the S59 mutant could be tested to see if it affects PLK1 binding or activation.

It would be helpful if measurements of pThr210 PLK1 for all conditions were shown in the graph Fig. 7F.

Minor comments.

I found Figure S1B easier to understand than Fig S1A and Fig 1A-B. Some of the supplemental data Fig. S1C-E could be moved to a revised Figure 1, dropping the current Fig. 1A-B. Can the interaction plots (Fig. S1C-D) be rotated to have the same original at the top and order of proteins (i.e. Bora > Aurora A > {plus minus} PLK1 depending on the plot). Figure 3F. Typo "Strongyl" not "Strongly". Figure 3 could be supplemental material. Fig. 7E. Run a positive control reaction +ERK2 on the second gel to allow direct comparison of pThr210 across all the conditions tested.

Significance

Timely and orchestrated activation of multiple mitotic protein kinases is crucial for the alignment and segregation of chromosomes, and for the process of cell division. In this study the authors explore how activation of the mitotic kinase PLK1 is triggered by another mitotic kinase Aurora A, and the role played by a scaffold protein Bora.

Strengths: Detailed analysis of mechanism using biochemical and structural approaches.

Limitations: The study is focussed on the biochemical and structural mechanisms rather than the cellular outcomes. Some data would benefit from additional quantitative measurement.

Relevance: Cancer and cell biology due to the role of Aurora A in many cancers.

Reviewer expertise: Biochemistry, molecular and cell biology.

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Referee #1

Evidence, reproducibility and clarity

Miles et al. used a combination of AlphaFold modeling, biochemical assays of mutant constructs and NMR spectroscopy to model the ternary complex of Aurora A, Bora and Plk1, and elucidate how Bora can act as a molecular bridge that facilitates the phosphorylation of the activation loop Thr210 within Plk1 by Aurora A. Their studies identified an interaction between residues 52-73 within Bora and the 'FW' pocket on the N-terminal lobe of Plk1, which binds Phe56 and Trp58 of Bora. Additionally, Ser59 of Bora was identified as a good Aurora A substrate using a Bora peptide array, and pSer59 was predicted to form bridging interactions with Aurora Arg205 and Plk1 Arg59. This was supported by NMR and biochemical assays. In addition, the authors validate that phosphorylation of Ser-112 on Bora enhances stabilization of the Aurora A-Bora complex Overall, the model revealed novel details of the interactions within the Aurora A-Bora-Plk1 ternary complex that are supported by the biochemical and NMR data. The work will be of significant interest to basic scientists whose work involves protein kinase signaling, cell division/mitosis, signal transduction, and cancer biology. We recommend publication of this manuscript with the following minor changes and additions.

1. In the introduction, on page 2, the authors seem a little confused about the Plk1 Polo-box domain - text as written: "...kinase domain linked to tandem Polo-box domains (PBD)", and cite a review paper. Actually, there is only a single Polo-box domain in these kinases, which contains both Polo-boxes and a bit of the upstream linker region. The "PBD" terminology denotes his 2-Polo-box +linker structure. Perhaps it would be better here to cite the PBD structure (Elia et al., Cell, 2002) as a primary citation here.
2. Similarly, the line "...during the G2/M transition following successful DNA damage repair" cites the Seki et al paper, but those findings are shown in the Macurek et al paper, not the Seki et al paper.
3. Using the model of the ternary complex as shown in Figure 1B, deletion constructs of Bora missing regions within the disordered loops, but still retaining the residues that bind the PBD, FW pocket and Aurora A, can be modeled and tested to see if such deletions can improve the ipTM scores and binding affinity.
4. On page 5, "S112A" within the sentence "Unexpectedly, the F56A/W58A Bora was less efficiently phosphorylated on S112A (Supplementary Figure S11, F compared to H and Supplementary Table S4)." This should be "S112".
5. In the assays shown in Figure 2D, the presence of excess F56AW58A Bora that remained unphosphorylated on S112 may complicate the interpretation of the results. Can the authors show that the S112-phosphorylated F56AW68A Bora is predominantly bound to Aurora A in such a mixture, perhaps by NMR using labelled pS112 F56AW58A Bora and unlabeled S112 F56AW58A Bora?
6. Please expand Figure 3A to better show the FW pocket-forming residues on Plk1.
7. It would be helpful to label the peaks in the mass spectra in Fig. S11 with the phospho-species that they correspond to.
8. In the last paragraph on page 7, "see we" in the sentence "As well as a decrease in intensity around pSer112 in Bora, see we an overall effect with decreased intensity across most of the Bora sequence." Should be corrected to "we see".
9. While not required, it would be helpful if binding or Bora to Aurora A after Erk2 phosphorylation could be shown using fluorescence polarization or ITC to lend additional support to the NMR data for S112 and S59 phosphorylation and for CEP192 and TPX2 competition.
10. The Aurora A phosphorylation motif has been further defined beyond that reported by the Pinna lab in 2005. Notably, the Ser-59 sequence on Bora (F-R-W-S-I), has, in addition to dominant selection for AR in the -2 position, both favorable -1 (W) and +1 (I) positions based on peptide library measurements (Alexander et al., Science Signaling 2011), further arguing that it may be an excellent Aurora A phosphorylation site.
11. Have the authors tried to model the Drosophila melanogaster Aurora A-Bora-Polo complex to see if the Asn substitution of Bora Ser59, and the expected loss of the interactions between Bora pSer59 and Plk1 Arg59 and Aurora A Arg205 are compensated by other features?
12. Given the relevance of the recent publication from Zhu et al. in https://doi.org/10.1038/s41467-025-63352-y to this study, the authors may want to comment on, or test, the relative importance of PKA and Aurora A as a potential kinase for Bora S59. While those authors argue that PKA phosphorylates Bora on Ser-59, one could easily imagine a model in which either PKA or Aurora A could initially phosphorylate that site followed by a propagation step after initial Aurora A activation, in which Aurora A phosphorylation of Bora Ser-59 is the dominant process.

-Dan Lim and Michael Yaffe

Significance

The work is well done and clearly presented.

Reviewer #1 (Public review):

In this study, the authors aim to elucidate both how Pavlovian biases affect instrumental learning from childhood to adulthood, as well as how reward outcomes during learning influence incidental memory. While prior work has investigated both of these questions, findings have been mixed. The authors aim to contribute additional evidence to clarify the nature of developmental changes in these processes. Through a well-validated affective learning task and a large age-continuous sample of participants, the authors reveal that adolescents outperform children and adults when Pavlovian biases and instrumental learning are aligned, but that learning performance does not vary by age when they are misaligned. They also show that younger participants show greater memory sensitivity for images presented alongside rewards.

The manuscript has notable strengths. The task was carefully designed and modified with a clever, developmentally appropriate cover story, and the large sample size (N = 174) means their study was better powered than many comparable developmental learning studies. The addition of the memory measure adds a novel component to the design. The authors transparently report their somewhat confusing findings.

The manuscript also has weaknesses, which I describe in detail below.

It was not entirely clear to me what central question the researchers aimed to address. They note that prior studies using a very similar learning task design have reported inconsistent findings, but they do not propose a reason for why these inconsistent findings may emerge nor do they test a plausible cause of them (in contrast, for example, Raab et al. 2024 explicitly tested the idea that developmental changes in inferences about controllability may explain age-related change in Pavlovian influences on learning). While the authors test a sample of participants that is very large compared to many developmental studies of reinforcement learning, this sample is much smaller than two prior developmental studies that have used the same learning task (and which the authors cite - Betts et al., 2020; Moutoussis et al., 2018). Thus, the overall goal seems to be to add an additional ~170 subjects of data to the existing literature, which isn't problematic per se, but doesn't do much to advance our theoretical understanding of learning across development. They happen to find a pattern of results that differs from all three prior studies, and it is not clear how to interpret this.

Along those lines, the authors extend prior work by adding a memory manipulation to the task, in which trial-unique images were presented alongside reward outcomes. It was not clear to me whether the authors see the learning and memory questions as fundamentally connected or as two separate research questions that this paradigm allows them to address. The manuscript would potentially be more impactful if the authors integrated their discussion of these two ideas more. Did they have any a priori hypotheses about how Pavlovian biases may affect the encoding of incidentally presented images? Could heightened reward sensitivity explain both changes in learning and changes in memory? It was also not clear to me why the authors hypothesized that younger participants would demonstrate the greatest effects of reward on memory, when most of the introduction seems to suggest they might hypothesize an adolescent peak in both learning and memory.

As stated above, while the task methods seemed sound, some of the analytic decisions are potentially problematic and/or require greater justification for the results of the study to be interpretable.

Firstly, it is problematic not to include random participant slopes in the regression models. Not accounting for individual variation in the effects of interest may inflate Type I errors. I would suggest that the authors start with the maximal model, or follow the same model selection procedure they did to select the fixed effects to include for the random effects as well.

Secondly, the central learning finding - that adolescents demonstrate enhanced learning in Pavlovian-congruent conditions only - is interesting, but it is unclear why this is the case or how much should be made of this finding. The authors show that adolescents outperform others in the Pavlovian-congruent conditions but not the Pavlovian-incongruent conditions. However, this conclusion is made by analyzing the two conditions separately; they do not directly compare the strength of the adolescent peak across these conditions, which would be needed to draw this strong conclusion. Given that no prior study using the same learning design has found this, the authors should ensure that their evidence for it is strong before drawing firm conclusions.

It was also not clear to me whether any of the RL models that the authors fit could potentially explain this pattern. Presumably, they need an algorithmic mechanism in which the Pavlovian bias is enhanced when it is rewarded. This seems potentially feasible to implement and could help explain the condition-specific performance boosts.

I also have major concerns about the computational model-fitting results. While the authors seemingly follow a sound approach, the majority of the fitted lapse rates (Figure S10) are near 1. This suggests that for most participants, the best-fitting model is one in which choices are random. This may be why the authors do not observe age-related change in model parameters: for these subjects, the other parameter values are essentially meaningless since they contribute to the learned value estimate, which gets multiplied by a near-0 weight in the choice function. It is important that the authors clarify what is going on here. Is it the case that most of these subjects truly choose at random? It does seem from Figure 2A that there is extensive variability in performance. It might be helpful if the authors re-analyze their data, excluding participants who show no evidence of learning or of reward-seeking behavior. Alternatively, are there other biases that are not being accounted for (e.g., choice perseveration) that may contribute to the high lapse rates?

Parameter recovery also looks poor, particularly for gain & loss sensitivity, the lapse rate, and the Pavlovian bias - several parameters of interest. As noted above, this may be due to the fact that many of the simulations were conducted with lapse rates sampled from the empirical distribution. It would be helpful for the authors to a.) plot separately parameter recoverability for high and low lapse rates and b.) report the recoverability correlation for each parameter separately.

Finally, many of the analytic decisions made regarding the memory analyses were confusing and merit further justification.

(1) First, it seems as though the authors only analyze memory data from trials where participants "could gain a reward". Does this mean only half of the memory trials were included in the analyses? What about memory as a function of whether participants made a "correct" response? Or a correct x reward interaction effect?

(2) The RPE analysis overcomes this issue by including all trials, but the trial-wise RPEs are potentially not informative given the lapse rate issue described above.

(3) The authors exclude correct guesses but include incorrect guesses. Is this common practice in the memory literature? It seems like this could introduce some bias into the results, especially if there are age-related changes in meta-memory.

(4) Participants provided a continuum of confidence ratings, but the authors computed d' by discretizing memory into 'correct' or 'incorrect'. A more sensitive approach could compute memory ROC curves taking into account the full confidence data (e.g., Brady et al., 2020).

(5) The learning and memory tradeoff idea is interesting, but it was not clear to me what variables went into that regression model.

Reviewer #1 (Public review):

Summary:

This study examines how different parts of the brain's reward system regulate eating behavior. The authors focus on the medial shell of the nucleus accumbens, a region known to influence pleasure and motivation. They find that nerve cells in the front (rostral) portion of this region are inhibited during eating, and when artificially activated, they reduce food intake. In contrast, similar cells at the back (caudal) are excited during eating but do not suppress feeding. The team also identifies a molecular marker, Stard5, that selectively labels the rostral hotspot and enables new genetic tools to study it. These findings clarify how specific circuits in the brain control hedonic feeding, providing new entry points to understand and potentially treat conditions such as overeating and obesity.

Strengths:

(1) Conceptual advance: The work convincingly establishes a rostro-caudal gradient within the medNAcSh, clarifying earlier pharmacological studies with modern circuit-level and genetic approaches.

(2) Methodological rigor: The combination of fiber photometry, optogenetics, CRISPR-Cas9 genetic engineering, histology, FISH, scRNA-seq, and novel mouse genetics adds robustness, with complementary approaches converging on the central claim.

(3) Innovation: The generation of a Stard5-Flp line is a valuable resource that will enable precise interrogation of the rostral hotspot in future studies.

(4) Specificity of findings: The dissociation between appetitive and aversive conditions strengthens the interpretation that the observed gradient is restricted to feeding.

Weaknesses and points for clarification

(1) Role of D2-SPNs: Since D1 and D2 pathways often show opposing roles in feeding, testing, or discussing D2-SPN contributions would provide an important control and context. Since the claim is that Stard5 is expressed in both D1- and D2MSNs, it seems to contradict the exclusive role of D1R MSNs in authorizing food intake.

(2) Behavioral analyses:

a) In Figure 2, group differences in consumption appear uneven; additional analyses (e.g., lick counts across blocks and session totals) would strengthen interpretation.

b) The design and contribution of aversive assays to the main conclusions remain somewhat unclear and could be better justified.

c) The scope of behavior is mainly limited to consumption; testing related domains (motivation, reward valuation, and extinction) could broaden the significance.

(3) Molecular profiling:

a) Stard5 expression is present in both D1- and D2-SPNs; comparisons to bulk calcium signals and quantification of percentages across rostral and caudal cells would be helpful. The authors should establish whether these cells also express SerpinB2, an established marker of LH projecting neurons.

b) Verification of the Stard5-2A-Flp line (specificity, overlap with immunomarkers) should be documented more thoroughly.

c) The molecular analysis is restricted to a small set of genes; broader spatial transcriptomics could uncover additional candidate markers. See also above.

Reviewer #2 (Public review):

Summary:

This manuscript tackles an important and often neglected aspect of time-series analysis in ecology - the multitude of "small" methodological choices that can alter outcomes. The findings are solid, though they may be limited in terms of generalizability, due to the simple use case tested.

Strengths:

(1) Comprehensive Methodological Benchmarking:

The study systematically evaluates 30 test variants (5 correlation statistics × 6 surrogate methods), which is commendable and provides a broad view of methodological behavior.

(2) Important Practical Recommendations:

The manuscript provides valuable real-world guidance, such as the superiority of tailored lags over fixed lags, the risks of using shuffling-based nulls, and the importance of selecting appropriate surrogate templates for directional tests.

(3) Novel Insights into System Dependence:

A key contribution is the demonstration that test results can vary dramatically with system state (e.g., initial conditions or abundance asymmetries), even when interaction parameters remain constant. This highlights a real-world issue for ecological inference.

(4) Clarification of Surrogate Template Effects:

The study uncovers a rarely discussed but critical issue: that the choice of which variable to surrogate in directional tests (e.g., convergent cross mapping) can drastically affect false-positive rates.

(5) Lag Selection Analysis:

The comparison of lag selection methods is a valuable addition, offering a clear takeaway that fixed-lag strategies can severely inflate false positives and that tailored-lag approaches are preferred.

(6) Transparency and Reproducibility Focus:

The authors advocate for full methodological transparency, encouraging researchers to report all analytical choices and test multiple methods.

Weaknesses / Areas for Improvement:

(1) Limited Model Generality:

The study relies solely on two-species systems and two types of competitive dynamics. This limits the ecological realism and generalizability of the findings. It's unclear how well the results would transfer to more complex ecosystems or interaction types (e.g., predator-prey, mutualism, or chaotic systems).

(2) Method Description Clarity:

Some method descriptions are too terse, and table references are mislabeled (e.g., Table 1 vs. Table 2 confusion). This reduces reproducibility and clarity for readers unfamiliar with the specific tests.

(3) Insufficient Discussion of Broader Applicability:

While the pairwise test setup justifies two-species models, the authors should more explicitly address whether the observed test sensitivities (e.g., effect of system state, template choice) are expected to hold in multi-species or networked settings.

(4) Lack of Practical Summary:

The paper offers great insights, but currently spreads recommendations throughout the text. A dedicated section or table summarizing "Best Practices" would increase accessibility and application by practitioners.

(5) No Real-World Validation:

The work is based entirely on simulation. Including or referencing an empirical case study would help illustrate how these methodological choices play out in actual ecological datasets.

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Hi, I have a question about it.

Q1. pseudoinverse notebook uses JacobianWrtVariable.kQDot in the PseudoInverseController. However, JacobianWrtVariable.kV is used in the Pick notebook.

It means that qDot = v ?? So, is it okay to use either one ? - One confused thing : iiwa robot is 7 DOF robot. So, refer to the chapter 3 contents, position has 7 DOF (perhaps using quaternion), velocity has 6 DOF. How do we determine that qDot is not equal to v ? (Because I'm a beginner of the Drake, so I don't understand why qDot is equal to v in this example.)

Thanks.

Reviewer #2 (Public review):

Summary:

This study analyzes muscle interactions in post-stroke patients undergoing rehabilitation, using information-theoretic and network analysis tools applied to sEMG signals with task performance measurements. The authors identified patterns of muscle interaction that correlate well with therapeutic measures and could potentially be used to stratify patients and better evaluate the effectiveness of rehabilitation.

However, I found that the Methods and Materials section, as it stands, lacks sufficient detail and clarity for me to fully understand and evaluate the quality of the method. Below, I outline my main points of concern, which I hope the authors will address in a revision to improve the quality of the Methods section. I would also like to note that the methods appear to be largely based on a previous paper by the authors (O'Reilly & Delis, 2024), but I was unable to resolve my questions after consulting that work.

I understand the general procedure of the method to be: (1) defining a connectivity matrix, (2) refining that matrix using network analysis methods, and (3) applying a lower-dimensional decomposition to the refined matrix, which defines the sub-component of muscle interaction. However, there are a few steps not fully explained in the text.

(1) The muscle network is defined as the connectivity matrix A. Is each entry in A defined by the co-information? Is this quantity estimated for each time point of the sEMG signal and task variable? Given that there are only 10 repetitions of the measurement for each task, I do not fully understand how this is sufficient for estimating a quantity involving mutual information.

In the previous paper (O'Reilly & Delis, 2024), the authors initially defined the co-information (Equation 1.3) but then referred to mutual information (MI) in the subsequent text, which I found confusing. In addition, while the matrix A is symmetrical, it should not be orthogonal (the authors wrote AᵀA = I) unless some additional constraint was imposed?

(2) The authors should clarify what the following statement means: "Where a muscle interaction was determined to be net redundant/synergistic, their corresponding network edge in the other muscle network was set to zero."

(3) It should be clarified what the 'm' values are in Equation 1.1. Are these the co-information values after the sparsification and applying the Louvain algorithm to the matrix 'A'? Furthermore, since each task will yield a different co-information value, how is the information from different tasks (r) being combined here?

(4) In general, I recommend improving the clarity of the Methods section, particularly by being more precise in defining the quantities that are being calculated. For example, the adjacency matrix should be defined clearly using co-information at the beginning, and explain how it is changed/used throughout the rest of the section.

(5) In the previous paper (O'Reilly & Delis, 2024), the authors applied a tensor decomposition to the interaction matrix and extracted both the spatial and temporal factors. In the current work, the authors simply concatenated the temporal signals and only chose to extract the spatial mode instead. The authors should clarify this choice.

it is important to remember the location of where the information that you are citing and what paragraphs there in and the sentence to the period

components of works cited entry

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Annotation 3 - Historical Setting Context: This image was created in the late 18th century (around 1788) in Britian during the transatlantic slave trade. It was used by abolitionists to expose the brutality od the slave trade and convince the public to support its end. The diagram is based on an actual ship called the Brookes that sailed from Liverpool to Africa and then to the Americans.

It’s interesting how quickly the leadership became more centralized and male-dominated. Did persecution push the church to become more organized, or was this part of a wider social trend?

That’s quite impressive for the ancient world as it almost sounds like Spartan women had more influence than Athenian women. I assuming this made the Spartan society more stable while the men were away at war, which would make sense.

It actually kind of scary to think about how young and early the men had to go into military training, but also interesting because their culture was so closely connected with their military and fighting.

I thought Amazon would have a much higher profit margin since used. This shows that even big companies in retail have lower profit percentage compared to technology or publishing industry

This chart shows how much profit Amazon is making and what industry they are in.

Author response:

The following is the authors’ response to the original reviews

Public Reviews

Reviewer #1 (Public review):

Summary:

The authors performed genome assemblies for two Fagaceae species and collected transcriptome data from four natural tree species every month over two years. They identified seasonal gene expression patterns and further analyzed species-specific differences.

Strengths:

The study of gene expression patterns in natural environments, as opposed to controlled chambers, is gaining increasing attention. The authors collected RNA-seq data monthly for two years from four tree species and analyzed seasonal expression patterns. The data are novel. The authors could revise the manuscript to emphasize seasonal expression patterns in three species (with one additional species having more limited data). Furthermore, the chromosome-scale genome assemblies for the two Fagaceae species represent valuable resources, although the authors did not cite existing assemblies from closely related species.

Thank you for your careful assessment of our manuscript.

Weaknesses:

Comment; The study design has a fundamental flaw regarding the evaluation of genetic or evolutionary effects. As a basic principle in biology, phenotypes, including gene expression levels, are influenced by genetics, environmental factors, and their interaction. This principle is well-established in quantitative genetics.

In this study, the four species were sampled from three different sites (see Materials and Methods, lines 543-546), and additionally, two species were sampled from 2019-2021, while the other two were sampled from 2021-2023 (see Figure S2). This critical detail should be clearly described in the Results and Materials and Methods. Due to these variations in sampling sites and periods, environmental conditions are not uniform across species.

Even in studies conducted in natural environments, there are ways to design experiments that allow genetic effects to be evaluated. For example, by studying co-occurring species, or through transplant experiments, or in common gardens. To illustrate the issue, imagine an experiment where clones of a single species were sampled from three sites and two time periods, similar to the current design. RNA-seq analysis would likely detect differences that could qualitatively resemble those reported in this manuscript.

One example is in line 197, where genus-specific expression patterns are mentioned. While it may be true that the authors' conclusions (e.g., winter synchronization, phylogenetic constraints) reflect real biological trends, these conclusions are also predictable even without empirical data, and the current dataset does not provide quantitative support.

If the authors can present a valid method to disentangle genetic and environmental effects from their dataset, that would significantly strengthen the manuscript. However, I do not believe the current study design is suitable for this purpose.

Unless these issues are addressed, the use of the term "evolution" is inappropriate in this context. The title should be revised, and the result sections starting from "Peak months distribution..." should be either removed or fundamentally revised. The entire Discussion section, which is based on evolutionary interpretation, should be deleted in its current form.

If the authors still wish to explore genetic or evolutionary analyses, the pair of L. edulis and L. glaber, which were sampled at the same site and over the same period, might be used to analyze "seasonal gene expression divergence in relation to sequence divergence." Nevertheless, the manuscript would benefit from focusing on seasonal expression patterns without framing the study in evolutionary terms.

We sincerely thank the reviewer for the detailed and thoughtful comments. We fully recognize the importance of carefully distinguishing genetic and environmental contributions in transcriptomic studies, particularly when addressing evolutionary questions. The reviewer identified two major concerns regarding our study design: (1) the use of different monitoring periods across species, and (2) the use of samples collected from different study sites. We addressed both concerns with additional analyses using 112 new samples and now present new evidence that supports the robustness of our conclusions.

(1) Monitoring period variation does not bias our conclusions<br /> To address concerns about the differing monitoring periods, we added new RNA-seq data (42 samples each for bud and leaf samples for L. glaber and 14 samples each for bud and leaf samples for _L. eduli_s) collected from November 2021 to November 2022, enabling direct comparison across species within a consistent timeframe. Hierarchical clustering of this expanded dataset (Fig. S6) yielded results consistent with our original findings: winter-collected samples cluster together regardless of species identity. This strongly supports our conclusion that the seasonal synchrony observed in winter is not an artifact of the monitoring period and demonstrates the robustness of our conclusions across datasets.

(2) Site variation is limited and does not confound our findings<br /> Although the study included three sites, two of them (Imajuku and Ito Campus) are only 7.3 km apart, share nearly identical temperature profiles (see Fig. S2), and are located at the edge of similar evergreen broadleaf forests. Only Q. acuta was sampled from a higher-altitude, cooler site. To assess whether the higher elevation site of Q. acuta introduced confounding environmental effects, we reanalyzed the data after excluding this species. Hierarchical clustering still revealed that winter bud samples formed a distinct cluster regardless of species identity (Fig. S7), consistent with our original finding.

Furthermore, we recalculated the molecular phenology divergence index D (Fig. 4C) and the interspecific Pearson’s correlation coefficients (Fig. 5A) without including Q. acuta. These analyses produced results that were similar to those obtained from the full dataset (Fig. S12; Fig. S14), indicating that the observed patterns are not driven by environmental differences associated with elevation.

(3) Justification for our approach in natural systems<br /> We agree with the reviewer that experimental approaches such as common gardens, reciprocal transplants, and the use of co-occurring species are valuable for disentangling genetic and environmental effects. In fact, we have previously implemented such designs in studies using the perennial herb Arabidopsis halleri (Komoto et al., 2022, https://doi.org/10.1111/pce.14716) and clonal Someiyoshino cherry trees (Miyawaki-Kuwakado et al., 2024, https://doi.org/10.1002/ppp3.10548) to examine environmental effects on gene expression. However, extending these approaches to long-lived tree species in diverse natural ecosystems poses significant logistical and biological challenges. In this study, we addressed this limitation by including three co-occurring species at the same site, which allowed us to evaluate interspecific differences under comparable environmental conditions. Importantly, even when we limited our analyses to these co-occurring species, the results remained consistent, indicating that the observed variation in transcriptomic profiles cannot be attributed to environmental factors alone and likely reflects underlying genetic influences.

Accordingly, we added four new figures (Fig. S6, Fig. S7, Fig. S12 and Fig. S14) and revised the manuscript to clarify the limitations and strengths of our design, to tone down the evolutionary claims where appropriate, and to more explicitly define the scope of our conclusions in light of the data. We hope that these efforts sufficiently address the reviewer’s concerns and strengthen the manuscript.

To better support the seasonal expression analysis, the early RNA-seq analysis sections should be strengthened. There is little discussion of biological replicate variation or variation among branches of the same individual. These could be important factors to analyze. In line 137, the mapping rate for two species is mentioned, but the rates for each species should be clearly reported. One RNA-seq dataset is based on a species different from the reference genome, so a lower mapping rate is expected. While this likely does not hinder downstream analysis, quantification is important.

We thank the reviewer 1 for the helpful comment. To evaluate the variation among biological replicates, we compared the expression level of each gene across different individuals. We observed high correlation between each pair of individuals (Q. glauca (n=3): an average correlation coefficient r = 0.947; Q. acuta (n=3): r = 0.948; L. glaber (n=3): r = 0.948)). This result suggests that the seasonal gene expression pattern is highly synchronized across individuals within the same species. We mentioned this point in the Result section in the revised manuscript. We also calculated the mean mapping rates for each species. As the reviewer expected, the mapping rate was slightly lower in Q. acuta (88.6 ± 2.3%) and L. glaber (84.3 ± 5.4%), whose RNA-Seq data were mapped to reference genomes of related but different species, compared to that in Q. glauca (92.6 ± 2.2%) and L. edulis (89.3 ± 2.7%). However, we minimized the impact of these differences on downstream analysis. These details have been included in the revised main text.

In Figures 2A and 2B, clustering is used to support several points discussed in the Results section (e.g., lines 175-177). However, clustering is primarily a visualization method or a hypothesis-generating tool; it cannot serve as a statistical test. Stronger conclusions would require further statistical testing.

We thank the reviewer for the helpful comment. As noted, we acknowledge that hierarchical clustering (Fig. 2A) is primarily a visualization and hypothesis-generating method. To assess the biological relevance of the clusters identified, we conducted a Mann-Whitney U test or the Steel-Dwass test to evaluate whether the environmental temperatures at the time of sample collection differed significantly among the clusters. This analysis (Fig. 2B) revealed statistically significant differences in temperature in the cluster B3 (p < 0.01), indicating that the gene expression clusters are associated with seasonal thermal variation. These results support the interpretation that the clusters reflect coordinated transcriptional responses to environmental temperature. We revised the Results section to clarify this point.

The quality of the genome assemblies appears adequate, but related assemblies should be cited and discussed. Several assemblies of Fagaceae species already exist, including Quercus mongolica (Ai et al., Mol Ecol Res, 2022), Q. gilva (Front Plant Sci, 2022), and Fagus sylvatica (GigaScience, 2018), among others. Is there any novelty here? Can you compare your results with these existing assemblies?

We agree that genome assemblies of Fagaceae species are becoming increasing available. However, our study does not aim to emphasize the novelty of the genome assemblies per se. Rather, with the increasing availability of chromosome-level genomes, we regard genome assembly as a necessary foundation for more advanced analyses. The main objective of our study is to investigate how each gene is expressed in response to seasonal environmental changes, and to link genome information with seasonal transcriptomic dynamics. To address the reviewer’s comment in line with this objective, we added a discussion on the syntenic structure of eight genome assemblies spanning four genera within the Fagaceae, including a species from the genus Fagus (Ikezaki et al. 2025, https://doi.org/10.1101/2025.07.31.667835). This addition helps to position our work more clearly within the context of existing genomic resources.

Most importantly, Figure 1B-D shows synteny between the two genera but also indicates homology between different chromosomes. Does this suggest paleopolyploidy or another novel feature? These chromosome connections should be interpreted in the main text-even if they could be methodological artifacts.

A previous study on genome size variation in Fagaceae suggested that, given the consistent ploidy level across the family, genome expansion likely occurred through relatively small segmental duplications rather than whole-genome duplications. Because Figure 1B-D supports this view, we cited the following reference in the revised version of the manuscript. Chen et al. (2014) https://doi.org/10.1007/s11295-014-0736-y

In both the Results and Materials and Methods sections, descriptions of genome and RNA-seq data are unclear. In line 128, a paragraph on genome assembly suddenly introduces expression levels. RNA-seq data should be described before this. Similarly, in line 238, the sentence "we assembled high-quality reference genomes" seems disconnected from the surrounding discussion of expression studies. In line 632, Illumina short-read DNA sequencing is mentioned, but it's unclear how these data were used.

We relocated the explanation regarding the expression levels of single-copy and multi-copy genes to the section titled “Seasonal gene expression dynamics.” Additionally, we clarified in the Materials and Methods section that short-read sequencing data were used for both genome size estimation and phylogenetic reconstruction.

Reviewer #2 (Public review):

Summary:

This study explores how gene expression evolves in response to seasonal environments, using four evergreen Fagaceae species growing in similar habitats in Japan. By combining chromosome-scale genome assemblies with a two-year RNA-seq time series in leaves and buds, the authors identify seasonal rhythms in gene expression and examine both conserved and divergent patterns. A central result is that winter bud expression is highly conserved across species, likely due to shared physiological demands under cold conditions. One of the intriguing implications of this study is that seasonal cycles might play a role similar to ontogenetic stages in animals. The authors touch on this by comparing their findings to the developmental hourglass model, and indeed, the recurrence of phenological states such as winter dormancy may act as a cyclic form of developmental canalization, shaping expression evolution in a way analogous to embryogenesis in animals.

Strengths:

(1) The evolutionary effects of seasonal environments on gene expression are rarely studied at this scale. This paper fills that gap.

(2) The dataset is extensive, covering two years, two tissues, and four tree species, and is well suited to the questions being asked.

(3) Transcriptome clustering across species (Figure 2) shows strong grouping by season and tissue rather than species, suggesting that the authors effectively controlled for technical confounders such as batch effects and mapping bias.

(4) The idea that winter imposes a shared constraint on gene expression, especially in buds, is well argued and supported by the data.

(5) The discussion links the findings to known concepts like phenological synchrony and the developmental hourglass model, which helps frame the results.

We are grateful for the reviewer for the detailed and thoughtful review of our manuscript.

Weaknesses:

(1) While the hierarchical clustering shown in Figure 2A largely supports separation by tissue type and season, one issue worth noting is that some leaf samples appear to cluster closely with bud samples. The authors do not comment on this pattern, which raises questions about possible biological overlap between tissues during certain seasonal transitions or technical artifacts such as sample contamination. Clarifying this point would improve confidence in the interpretation of tissue-specific seasonal expression patterns.

Leaf samples clustered into the bud are newly flushed leaves collected in April for Q. glauca, May for Q. acuta, May and June for L. edulis, and August and September for L. glaber. To clarify this point, we highlighted these newly flushed leaf samples as asterisk in the revised figure (Fig. 2A).

(2) While the study provides compelling evidence of conserved and divergent seasonal gene expression, it does not directly examine the role of cis-regulatory elements or chromatin-level regulatory architecture. Including regulatory genomic or epigenomic data would considerably strengthen the mechanistic understanding of expression divergence.

We thank the reviewer for this insightful comment. As noted in the Discussion section, we hypothesize that such genome-wide seasonal expression patterns—and their divergence across species—are likely mediated by cis-regulatory elements and chromatin-level mechanisms. While a direct investigation of regulatory architecture was beyond the scope of the present study, we fully agree that incorporating regulatory genomic and epigenomic data would significantly deepen the mechanistic understanding of expression divergence. In this regard, we are currently working to identify putative cis-regulatory elements in non-coding regions and are collecting epigenetic data from the same tree species using ChIP-seq. We believe the current study provide a foundation for these future investigations into the regulatory basis of seasonal transcriptome variation. We made a minor revision to the Discussion to note that an important future direction is to investigate the evolution of non-coding sequences that regulate gene expression in response to seasonal environmental changes.

(3) The manuscript includes a thoughtful analysis of flowering-related genes and seasonal GO enrichment (e.g., Figure 3C-D), providing an initial link between gene expression timing and phenological functions. However, the analysis remains largely gene-centric, and the study does not incorporate direct measurements of phenological traits (e.g., flowering or bud break dates). As a result, the connection between molecular divergence and phenotypic variation, while suggestive, remains indirect.

We would like to note that phenological traits have been observed in the field on a monthly basis throughout the sampling period and the phenological data were plotted together with molecular phenology (e.g. Fig. 2A, C; Fig. 3C, D). Although the temporal resolution is limited, these observations captured species-specific differences in key phenological events such as leaf flushing and flowering times. We revised the manuscript to clarify this point.

(4) Although species were sampled from similar habitats, one species (Q. acuta) was collected at a higher elevation, and factors such as microclimate or local photoperiod conditions could influence expression patterns. These potential confounding variables are not fully accounted for, and their effects should be more thoroughly discussed or controlled in future analyses.

We fully agree with the reviewer that local environmental conditions, including microclimate and photoperiod differences, could potentially influence gene expression patterns. To assess whether the higher elevation site of Q. acuta introduced confounding environmental effects, we reanalyzed the data after excluding this species. Hierarchical clustering still revealed that winter bud samples formed a distinct cluster regardless of species identity (Fig. S7), consistent with our original finding.

Furthermore, we recalculated the molecular phenology divergence index D (Fig. 4C) and the interspecific Pearson’s correlation coefficients (Fig. 5A) without including Q. acuta. These analyses produced results that were qualitatively similar to those obtained from the full dataset (Fig. S12; Fig. S14), indicating that the observed patterns are not driven by environmental differences associated with elevation.

We believe these additional analyses help to decouple the effects of environment and genetics, and support our conclusion that both seasonal synchrony and phylogenetic constraints play key roles in shaping transcriptome dynamics. We added four new figures (Fig. S6, Fig. S7, Fig. S12 and Fig. S14) and revised the text accordingly to clarify this point and to acknowledge the potential impact of site-specific environmental variation.

(5) Statistical and Interpretive Concerns Regarding Δφ and dN/dS Correlation (Figures 5E and 5F):

a) Statistical Inappropriateness: Δφ is a discrete ordinal variable (likely 1-11), making it unsuitable for Pearson correlation, which assumes continuous, normally distributed variables. This undermines the statistical validity of the analysis.

We thank the reviewer for the insightful comment. We would like to clarify that the analysis presented in Figures 5E and 5F was based on linear regression, not Pearson’s correlation. Although Δ_φ_ is a discrete variable, it takes values from 0 to 6 in 0.5 increments, resulting in 13 levels. We treated it as a quasi-continuous variable for the purposes of linear regression analysis. This approach is commonly adopted in practice when a discrete variable has sufficient resolution and ordering to approximate continuity. To enhance clarity, we revised the manuscript to explicitly state that linear regression was used, and we now reported the regression coefficient and associated p-value to support the interpretation of the observed trend.

b) Biological Interpretability: Even with the substantial statistical power afforded by genome-wide analysis, the observed correlations are extremely weak. This suggests that the relationship, if any, between temporal divergence in expression and protein-coding evolution is negligible.

Taken together, these issues weaken the case for any biologically meaningful association between Δφ and dN/dS. I recommend either omitting these panels or clearly reframing them as exploratory and statistically limited observations.

We agree with the reviewer’s comment. While we retained the original panels, we reframed our interpretation to emphasize that, despite statistical significance, the observed correlation is very weak—suggesting that coding region variation is unlikely to be the primary driver of seasonal gene expression patterns. Accordingly, we revised the “Relating seasonal gene expression divergence to sequence divergence” section in the Results, as well as the relevant part of the Discussion.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Sentences around lines 250-251 are incomplete and need revision.

We thank the reviewer for pointing this out. We revised the sentences in the subsection “Peak month distribution of rhythmic genes and intra-genus and inter-genera comparison” in the Results section to ensure clarity and completeness. In addition, to improve the interpretability of the peak month distribution, we added arrows to indicate the major peaks in the circular histograms shown in Fig. 3C and 3D.

Reviewer #2 (Recommendations for the authors):

(1) In Figure 1E-G, the term Copy number or Copy number variation could be misleading, as it is commonly associated with inter-individual gene copy number variation in a population. Since the analysis here refers to orthology relationships rather than population-level variation, a more precise term, such as orthogroup classification, may be preferable.

We thank the reviewer for this helpful suggestion. We agree that the term “copy number” could be misleading in this context. Accordingly, we updated the labeling in Fig. 1 to reflect the more precise term “orthogroup classification.”

(2) In Figure 3A, the x-axis label Period (month) may be misleading, as it could be mistaken for calendar months rather than referring to the periodicity of gene expression cycles. A more explicit label, such as Expression periodicity (months), might improve clarity for the reader.

We thank the reviewer for this valuable suggestion. In the original version of Fig. 3A, we used the label “Period (month),” which could indeed be misinterpreted as referring to calendar months. To clarify that this axis represents the length of gene expression cycles, we revised the label to “Period length (months).” This change also aligns with the terminology used throughout the manuscript, where “Period” refers specifically to cycle length, and “Periodicity” denotes the presence or absence of rhythmic expression.

Other minor revisions

We also made minor revisions for the reference list and the grant number details, and included the accession numbers for all DNA and RNA sequence data deposited in the DNA Data Bank of Japan (DDBJ) in the Data deposition and code availability section, in addition to the BioProject ID.

Reviewer #3 (Public review):

Summary:

The goal of this paper is to characterize an anti-diuretic signaling system in insects using Drosophila melanogaster as a model. Specifically, the authors wished to characterize a role for ion transport peptide (ITP) and its isoforms in regulating diverse aspects of physiology and metabolism. The authors combined genetic and comparative genomic approaches with classical physiological techniques and biochemical assays to provide a comprehensive analysis of ITP and its role in regulating fluid balance and metabolic homeostasis in Drosophila. The authors further characterized a previously unrecognized role for Gyc76C as a receptor for ITPa, an amidated isoform of ITP, and in mediating the effects of ITPa on fluid balance and metabolism. The evidence presented in favor of this model is very strong as it combines multiple approaches and employs ideal controls. Taken together, these findings represent an important contribution to the field of insect neuropeptides and neurohormones and has strong relevance for other animals. The authors have addressed all weaknesses raised in my previous review.

Author Response:

The following is the authors’ response to the current reviews.

Reviewer #1 (Public review):

The scale bar for fly and ovary images should be included in Figures 9, 10, and 12.

We agree with this comment and apologize for the oversight. We have now modified Figures 9, 10, and 12 to include the scale bars for the ovary images. The fly images were acquired using a stereo microscope where scale bar calculation was not possible. However, all images were acquired at the same magnification for consistency.

Reviewer #2 (Public review):

A weakness of this paper is the phylogenetic analysis to investigate if there is correspondence in the phylogenetic distribution of ITP-type and Gyc76C-type genes/proteins. Unfortunately, the evidence presented is rather limited in scope. Essentially, the authors report that they only found ITP-type and Gyc76C-type genes/proteins in protostomes, but not in deuterostomes. What is needed is a more fine-grained analysis at the species level within the protostomes. However, I recognise that such a detailed analysis may extend beyond the scope of this paper, which is already rich in data.

We thank the reviewer for their comment and the suggestion to perform a fine-grained species level comparison of ITP and Gyc76C genes across protostomes. We are unsure of the utility of this analysis for the present study given that we have now shown that ITPa can activate Gyc76C using both an ex vivo and a heterologous assay, the latter being the gold standard in GPCR and guanylate cyclase discovery (see Huang et al 2025 https://doi.org/10.1073/pnas.2420966122; Beets et al 2023 https://doi.org/10.1016/j.celrep.2023.113058); Chang et al 2009 https://doi.org/10.1073/pnas.0812593106.

Additionally, absence of a gene in a genome/proteome is hard to prove especially when many/most of the protostomian datasets are not as high-quality as those of model systems (e.g. Drosophila melanogaster and Caenorhabditis elegans). Secondly, based on previous findings in Bombyx mori (Nagai et al. 2014 https://doi.org/10.1074/jbc.m114.590646 and Nagai et al. 2016 https://doi.org/10.1371/journal.pone.0156501) and Drosophila (Xu et al. 2023 https://doi.org/10.1038/s41586-023-06833-8 and our study) it is evident that different products of the ITP gene (ITPa and ITPL) could signal via different receptor types depending on the species. Hence, we would need to explore the presence of several genes (ITP, tachykinin, pyrokinin, tachykinin receptor, pyrokinin receptor, CG30340 orphan receptor and Gyc76C) to fully understand which components of these diverse signaling systems are present in a given species to decipher the potential for cross-talk.

While this species-level comparison will certainly be useful in the context of ITP-Gyc76C evolution, it will not alter the conclusions of the present study – ITPa acts via Gyc76C in Drosophila. We therefore agree with the reviewer that these analyses are beyond the scope of this paper.

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):  

Summary:  

In Drosophila melanogaster, ITP has functions on feeding, drinking, metabolism, excretion, and circadian rhythm. In the current study, the authors characterized and compared the expression of all three ITP isoforms (ITPa and ITPL1&2) in the CNS and peripheral tissues of Drosophila. An important finding is that they functionally characterized and identified Gyc76C as an ITPa receptor in Drosophila using both in vitro and in vivo approaches. In vitro, the authors nicely confirmed that the inhibitory function of recombinant Drosophila ITPa on MT secretion is Gyc76C-dependent (knockdown Gyc76C specifically in two types of cells abolished the anti-diuretic action of Drosophila ITPa on renal tubules). They also used a combination of multiple approaches to investigate the roles of ITPa and Gyc76C on osmotic and metabolic homeostasis modulation in vivo. They revealed that ITPa signaling to renal tubules and fat body modulates osmotic and metabolic homeostasis via Gyc76C.  

Furthermore, they tried to identify the upstream and downstream of ITP neurons in the nervous system by using connectomics and single-cell transcriptomic analysis. I found this interesting manuscript to be well-written and described. The findings in this study are valuable to help understand how ITP signals work on systemic homeostasis regulation. Both anatomical and single-cell transcriptome analysis here should be useful to many in the field. 

We thank this reviewer for the positive and thorough assessment of our manuscript.  

Strengths:  

The question (what receptors of ITPa in Drosophila) that this study tries to address is important. The authors ruled out the Bombyx ITPa receptor orthologs as potential candidates. They identified a novel ITP receptor by using phylogenetic, anatomical analysis, and both in vitro and in vivo approaches. 

The authors exhibited detailed anatomical data of both ITP isoforms and Gyc76C (in the main and supplementary figures), which helped audiences understand the expression of the neurons studied in the manuscript.  

They also performed connectomes and single-cell transcriptomics analysis to study the synaptic and peptidergic connectivity of ITP-expressing neurons. This provided more information for better understanding and further study on systemic homeostasis modulation.  

Weaknesses:  

In the discussion section, the authors raised the limitations of the current study, which I mostly agree with, such as the lack of verification of direct binding between ITPa and Gyc76C, even though they provided different data to support that ITPa-Gyc76C signaling pathway regulates systemic homeostasis in adult flies. 

We now provide evidence of Gyc76C activation by ITPa in a heterologous system (new Figure 7 and Figure 7 Supplement 1).

Reviewer #2 (Public Review):  

Summary:  

The physiology and behaviour of animals are regulated by a huge variety of neuropeptide signalling systems. In this paper, the authors focus on the neuropeptide ion transport peptide (ITP), which was first identified and named on account of its effects on the locust hindgut (Audsley et al. 1992). Using Drosophila as an experimental model, the authors have mapped the expression of three different isoforms of ITP (Figures 1, S1, and S2), all of which are encoded by the same gene.  

The authors then investigated candidate receptors for isoforms of ITP. Firstly, Drosophila orthologs of G-protein coupled receptors (GPCRs) that have been reported to act as receptors for ITPa or ITPL in the insect Bombyx mori were investigated. Importantly, the authors report that ITPa does not act as a ligand for the GPCRs TkR99D and PK2-R1 (Figure S3). Therefore, the authors investigated other putative receptors for ITPs. Informed by a previously reported finding that ITP-type peptides cause an increase in cGMP levels in cells/tissues (Dircksen, 2009, Nagai et al., 2014), the authors investigated guanylyl cyclases as candidate receptors for ITPs. In particular, the authors suggest that Gyc76C may act as an ITP receptor in Drosophila.  

Evidence that Gyc76C may be involved in mediating effects of ITP in Bombyx was first reported by Nagai et al. (2014) and here the authors present further evidence, based on a proposed concordance in the phylogenetic distribution ITP-type neuropeptides and Gyc76C (Figure 2). Having performed detailed mapping of the expression of Gyc76C in Drosophila (Figures 3, S4, S5, S6), the authors then investigated if Gyc76C knockdown affects the bioactivity of ITPa in Drosophila. The inhibitory effect of ITPa on leucokinin- and diuretic hormone-31-stimulated fluid secretion from Malpighian tubules was found to be abolished when expression of Gyc76C was knocked down in stellate cells and principal cells, respectively (Figure 4). However, as discussed below, this does not provide proof that Gyc76C directly mediates the effect of ITPa by acting as its receptor. The effect of Gyc76C knockdown on the action of ITPa could be an indirect consequence of an alteration in cGMP signalling.  

Having investigated the proposed mechanism of ITPa in Drosophila, the authors then investigated its physiological roles at a systemic level. In Figure 5 the authors present evidence that ITPa is released during desiccation and accordingly, overexpression of ITPa increases survival when animals are subjected to desiccation. Furthermore, knockdown of Gyc76C in stellate or principal cells of Malphigian tubules decreases survival when animals are subject to desiccation. However, whilst this is correlative, it does not prove that Gyc76C mediates the effects of ITPa. The authors investigated the effects of knockdown of Gyc76C in stellate or principal cells of Malphigian tubules on i). survival when animals are subject to salt stress and ii). time taken to recover from of chill coma. It is not clear, however, why animals overexpressing ITPa were also not tested for its effect on i). survival when animals are subject to salt stress and ii). time taken to recover from of chill coma. In Figures 6 and S8, the authors show the effects of Gyc76C knockdown in the female fat body on metabolism, feeding-associated behaviours and locomotor activity, which are interesting. Furthermore, the relevance of the phenotypes observed to potential in vivo actions of ITPa is explored in Figure 7. The authors conclude that "increased ITPa signaling results in phenotypes that largely mirror those seen following Gyc76C knockdown in the fat body, providing further support that ITPa mediates its effects via Gyc76C." Use of the term "largely mirror" seems inappropriate here because there are opposing effects- e.g. decreased starvation resistance in Figure 6A versus increased starvation resistance in Figure 7A. Furthermore, as discussed above, the results of these experiments do not prove that the effects of ITPa are mediated by Gyc76C because the effects reported here could be correlative, rather than causative. 

We thank this reviewer for an extremely thorough and fair assessment of our manuscript. 

We have now performed salt stress tolerance and chill coma recovery assays using flies over-expressing ITPa (new Figure 10 Supplement 1).

We agree that the use of the term “largely mirrors” to describe the effects of ITPa overexpression and Gyc76C knockdown is not appropriate and have changed this sentence. We also agree that the experiments did not provide direct evidence that the effects of ITPa are mediated by Gyc76C. To address this, we now provide evidence of Gyc76C activation by ITPa in a heterologous system (new Figure 7 and Figure 7 Supplement 1).

Lastly, in Figures 8, S9, and S10 the authors analyse publicly available connectomic data and single-cell transcriptomic data to identify putative inputs and outputs of ITPa-expressing neurons. These data are a valuable addition to our knowledge ITPa expressing neurons; but they do not address the core hypothesis of this paper - namely that Gyc76C acts as an ITPa receptor.  

The goal of our study was to comprehensively characterize an anti-diuretic system in Drosophila. Hence, in addition to identifying the receptor via which ITPa exerts its effects, we also wanted to understand how ITPa-producing neurons are regulated. Connectomic and single-cell transcriptomic analyses are highly appropriate for this purpose. We have now updated the connectomic analyses using an improved connectome dataset that was released during the revision of this manuscript. Our new analysis shows that lNSC^ITP are connected to other endocrine cells that produce other homeostatic hormones (new Figure 13F). We also identify a pathway through which other ITP-producing neurons (LNd^ITP) receive hygrosensory inputs to regulate water seeking behavior (new Figure 13E). Moreover, we now include results which showcase that ITPa-producing neurons (l-NSC^ITP) are active (new Figure 8A and B) and release ITPa under desiccation. Together with other analyses, these data provide a comprehensive outlook on the when, what and how ITPa regulates systemic homeostasis.  

Strengths:  

(1) The main strengths of this paper are i) the detailed analysis of the expression and actions of ITP and the phenotypic consequences of overexpression of ITPa in Drosophila. ii). the detailed analysis of the expression of Gyc76C and the phenotypic consequences of knockdown of Gyc76C expression in Drosophila.  

(2) Furthermore, the paper is generally well-written and the figures are of good quality. 

We thank this reviewer for highlighting the strengths of this manuscript.

Weaknesses:  

(1) The main weakness of this paper is that the data obtained do not prove that Gyc76C acts as a receptor for ITPa. Therefore, the following statement in the abstract is premature: "Using a phylogenetic-driven approach and the ex vivo secretion assay, we identified and functionally characterized Gyc76C, a membrane guanylate cyclase, as an elusive Drosophila ITPa receptor." Further experimental studies are needed to determine if Gyc76C acts as a receptor for ITPa. In the section of the paper headed "Limitations of the study", the authors recognise this weakness. They state "While our phylogenetic analysis, anatomical mapping, and ex vivo and in vivo functional studies all indicate that Gyc76C functions as an ITPa receptor in Drosophila, we were unable to verify that ITPa directly binds to Gyc76C. This was largely due to the lack of a robust and sensitive reporter system to monitor mGC activation." It is not clear what the authors mean by "the lack of a robust and sensitive reporter system to monitor mGC activation". The discovery of mGCs as receptors for ANP in mammals was dependent on the use of assays that measure GC activity in cells (e.g. by measuring cGMP levels in cells). Furthermore, more recently cGMP reporters have been developed. The use of such assays is needed here to investigate directly whether Gyc76C acts as a receptor for ITPa. In summary, insufficient evidence has been obtained to conclude that Gyc76C acts as a receptor for ITPa. Therefore, I think there are two ways forward, either:  

(a) The authors obtain additional biochemical evidence that ITPa is a ligand for Gyc76C.  

or  

(b) The authors substantially revise the conclusions of the paper (in the title, abstract, and throughout the paper) to state that Gyc76C MAY act as a receptor for ITPa, but that additional experiments are needed to prove this. 

We thank the reviewer for this comment and agree with the two options they propose. We had previously tried different a cGMP reporter (Promega GloSensor cGMP assay) to monitor activation of Gyc76C by ITPa in a heterologous system. Unfortunately, we were not successful in monitoring Gyc76C activation by ITPa. We now utilized another cGMP sensor, Green cGull, to show that ITPa can indeed activate Gyc76C heterologously expressed in HEK cells (new Figure 7 and Figure 7 Supplement 1). However, we still cannot rule out the possibility that ITPa can act on additional receptors in vivo. This is based on our ex vivo Malpighian tubule assays (new Figure 6E and F). ITPa inhibits DH31- and LK-stimulated secretion and we show that this effect is abolished in Gyc76C knockdown specifically in principal and stellate cells, respectively. Interestingly, application of ITPa alone can stimulate secretion when Gyc76C is knocked down in principal cells (new Figure 6E). This could be explained by: 1) presence of another receptor for ITPa which results in diuretic actions and/or 2) low Gyc76C signaling activity (RNAi based knockdown lowers signaling but does not abolish it completely) could alter other intracellular messenger pathways that promote secretion. We have added text to indicate the possibility of other ITPa receptors. Nonetheless, our conclusions are supported by the heterologous assay results which indicate that ITPa can activate Gyc76C. Therefore, we do not alter the title. 

(2) The authors state in the abstract that a phylogenetic-driven approach led to their identification of Gyc76C as a candidate receptor for ITPa. However, there are weaknesses in this claim. Firstly, because the hypothesis that Gyc76C may be involved in mediating effects of ITPa was first proposed ten years ago by Nagai et al. 2014, so this surely was the primary basis for investigating this protein. Nevertheless, investigating if there is correspondence in the phylogenetic distribution of ITP-type and Gyc76C-type genes/proteins is a valuable approach to addressing this issue. Unfortunately, the evidence presented is rather limited in scope. Essentially, the authors report that they only found ITP-type and Gyc76C-type genes/proteins in protostomes, but not in deuterostomes. What is needed is a more fine-grained analysis at the species level within the protostomes. Thus, are there protostome species in which both ITP-type and Gyc76C-type genes/proteins have been lost? Furthermore, are there any protostome species in which an ITP-type gene is present but an Gyc76C-type gene is absent, or vice versa? If there are protostome species in which an ITP-type gene is present but a Gyc76C-type gene is absent or vice versa, this would argue against Gyc76C being a receptor for ITPa. In this regard, it is noteworthy that in Figure 2A there are two ITP-type precursors in C. elegans, but there are no Gyc76Ctype proteins shown in the tree in Figure 2B. Thus, what is needed is a more detailed analysis of protostomes to investigate if there really is correspondence in the phylogenetic distribution of Gyc76C-type and ITP-type genes at the species level. 

We thank the reviewer for this comment. While the previous study by Nagai et al had implicated Gyc76C in the ITP signaling pathway, how they narrowed down Gyc76C as a candidate was not reported. Therefore, our unbiased phylogenetic approach was necessary to ensure that we identified all suitable candidate receptors. Indeed, our phylogenetic analysis also identified Gyc32E as another candidate ITP receptor. However, we did not pursue this receptor further as our expression data (new Figure 4 Supplement 2) indicated that Gyc32E is not expressed in osmoregulatory tissues and therefore likely does not mediate the osmotic effects of ITPa. 

We also appreciate the suggestion to perform a more detailed phylogenetic analysis for the peptide and receptor. We did not include C. elegans receptors in the phylogenetic analysis because they tend to be highly evolved and routinely cause long-branch attraction (see: Guerra and Zandawala 2024: https://doi.org/10.1093/gbe/evad108). We (specifically the senior author) have previously excluded C. elegans receptors in the phylogenetic analysis of GnRH and Corazonin receptors for similar reasons (see: Tian and Zandawala et al. 2016: 10.1038/srep28788). 

Unfortunately, absence of a gene in a genome is hard to prove especially when they are not as high-quality as the genomes of model systems (e.g. Drosophila and mice). Moreover, given the concern of this reviewer that our physiological and behavioral data on ITPa and Gyc76C only provide correlative evidence, we decided against performing additional phylogenetic analysis which also provides correlative evidence. Our only goal with this analysis was to identify a candidate ITPa receptor. Since we have now functionally characterized this receptor using a heterologous system, we feel that the current phylogenetic analysis was able to successfully serve its purpose.  

(3) The manuscript would benefit from a more comprehensive overview and discussion of published literature on Gyc76C in Drosophila, both as a basis for this study and for interpretation of the findings of this study.  

We thank the reviewer for this comment. We have now included a broader discussion of Gyc76C based on published literature.  

Reviewer #3 (Public Review):  

Summary:  

The goal of this paper is to characterize an anti-diuretic signaling system in insects using Drosophila melanogaster as a model. Specifically, the authors wished to characterize a role of ion transport peptide (ITP) and its isoforms in regulating diverse aspects of physiology and metabolism. The authors combined genetic and comparative genomic approaches with classical physiological techniques and biochemical assays to provide a comprehensive analysis of ITP and its role in regulating fluid balance and metabolic homeostasis in Drosophila. The authors further characterized a previously unrecognized role for Gyc76C as a receptor for ITPa, an amidated isoform of ITP, and in mediating the effects of ITPa on fluid balance and metabolism. The evidence presented in favor of this model is very strong as it combines multiple approaches and employs ideal controls. Taken together, these findings represent an important contribution to the field of insect neuropeptides and neurohormones and have strong relevance for other animals. 

We thank this reviewer for the positive and thorough assessment of our manuscript.

Strengths:  

Many approaches are used to support their model. Experiments were wellcontrolled, used appropriate statistical analyses, and were interpreted properly and without exaggeration.  

Weaknesses:  

No major weaknesses were identified by this reviewer. More evidence to support their model would be gained by using a loss-of-function approach with ITPa, and by providing more direct evidence that Gyc76C is the receptor that mediates the effects of ITPa on fat metabolism. However, these weaknesses do not detract from the overall quality of the evidence presented in this manuscript, which is very strong.  

We agree with this reviewer regarding the need to provide additional evidence using a loss-of-function approach with ITPa. We now characterize the phenotypes following knockdown of ITP in ITP-producing cells (new Figure 9). Our results are in agreement with phenotypes observed following Gyc76C knockdown, lending further support that ITPa mediates its effects via Gyc76C. Unfortunately, we are not able to provide evidence that ITPa acts on Gyc76C in the fat body using the assay suggested by this reviewer (explained in detail below). Instead, we now provide direct evidence of Gyc76C activation by ITPa in a heterologous system (new Figure 7 and Figure 7 Supplement 1).

Reviewer #1 (Recommendations For The Authors):  

Here, I have several extra concerns about the work as below:  

(1) The authors confirmed the function of ITPa in regulating both osmotic and metabolic homeostasis by specifically overexpressing ITPa driven by ITP-RCGal4 in adult flies (Figures. 5 and 7). Have authors ever tried to knock down ITP in ITP-RC-Gal4 neurons? What was the phenotype? Especially regarding the impact on metabolic homeostasis, does knocking down ITP in ITP neurons mimic the phenotypes of Gyc76C fat body knockdown flies? 

We thank the reviewer for this suggestion. We now characterize the phenotypes following knockdown of ITP using ITP-RC-Gal4 (new Figure 9). Our results are in agreement with phenotypes observed following Gyc76C knockdown, lending further support that ITPa mediates its effects via Gyc76C.

The authors mentioned that the existing ITP RNAi lines target all three isoforms. It would be interesting if the authors could overexpress ITPa in ITPRC-Gal4>ITP-RNAi flies and confirm whether any phenotypes induced by ITP knockdown could be rescued. It will further confirm the role of ITPa in homeostasis regulation.  

We thank the reviewer for this suggestion. Unfortunately, this experiment is not straightforward because knockdown with ITP RNAi does not completely abolish ITP expression (see Figure 9A). Hence, the rescue experiment needs to be ideally performed in an ITP mutant background. However, ITP mutation leads to developmental lethality (unpublished observation) so we cannot generate all the flies necessary for this experiment. Therefore, we cannot perform the rescue experiments at this time. In future studies, we hope to perform knockdown of specific ITP isoforms using the transgenes generated here (Xu et al 2023: 10.1038/s41586-023-06833-8).   

(2) In Figures 5A and B, the authors nicely show the increased release of ITPa under desiccation by quantifying the ITPa immunolabelling intensity in different neuronal populations. It may be induced by the increased neuronal activity of ITPa neurons under the desiccated condition. Have the authors confirmed whether the activity of ITPa-expressing neurons is impacted by desiccation?  

The TRIC system may be able to detect the different activity of those neurons before and after desiccation. This may further explain the reduced ITPa peptide levels during desiccation.  

We thank the reviewer for this suggestion. We have now monitored the activity of ITPa-expressing neurons using the CaLexA system (Masuyama et al 2012: 10.3109/01677063.2011.642910). Our results indicate that ITPa neurons are indeed active under desiccation (new Figure 8A and B). These results are also in agreement with ITPa immunolabelling showing increased peptide release during desiccation (new Figure 8C and D). Together, these results show that ITPa neurons are activated and release ITPa under desiccation.  

(3) What about the intensity of ITPa immunolabelling in other ITPa-positive neurons (e.g., VNC) under desiccation? If there is no change in other ITPa neurons, it will be a good control. 

We thank the reviewer for this suggestion. Unfortunately, ITPa immunostaining in VNC neurons is extremely weak preventing accurate quantification of ITPa levels under different conditions. We did hypothesize that ITPa immunolabelling in clock neurons (5^th-LN_v and LN<Sub>d</sub>^ITP) would not change depending on the osmotic state of the animal. However, our results (Figure 8C and D) indicate that ITPa from these neurons is also released under desiccation. Interestingly, LNd^ITP, which also coexpress Neuropeptide F (NPF) have recently been implicated in water seeking during thirst (Ramirez et al, 2025: 10.1101/2025.07.03.662850). Our new connectomic-driven analysis shows that these neurons can receive thermo/hygrosensory inputs (new Figure 13E). Hence, it is conceivable that other ITPa-expressing neurons also release ITPa during thirst/desiccation.

(4) The adult stage, specifically overexpression of ITPa in ITP neurons, does show significant phenotypes compared to controls in both osmotic and metabolic homeostasis-related assays. It would be helpful if authors could show how much ITPa mRNA levels are increased in the fly heads with ITPa overexpression (under desiccation & starvation or not). 

We thank the reviewer for this suggestion. We have now included immunohistochemical evidence showing increase in ITPa peptide levels in flies with ITPa overexpression (new Figure 10A). We feel that this is a better indicator of ITPa signaling level instead of ITPa mRNA levels.   

(5) Another question concerns the bloated abdomens of ITPa-overexpressing flies. Are the bloated abdomens of ITPa OE female flies (Figure 5E) due to increased ovary size (Figure 7G)? Have the authors also detected similar bloated abdomens in male flies with ITPa overexpression? Since both male and female flies show more release of ITPa during the desiccation.  

We thank the reviewer for this comment. The bloated abdomen phenotype seen in females can be attributed to increased water content since we see a similar phenotype in males (see Author response image 1 below).

Author response image 1.

Reviewer #2 (Recommendations For The Authors):  

(1) Page 1 - change "Homeostasis is obtained by" to "Homeostasis is achieved by".  

Changed

(2) Page 1 - change "Physiological responses" to "Physiological processes". 

Changed

(3) Page 2 - Change "Recently, ITPL2 was also shown to mediate anti-diuretic effects via the tachykinin receptor" to "Recently, ITPL2 was also shown to exert anti-diuretic effects via the tachykinin receptor". 

Changed

(4) Page 9 - "(C) Adult-specific overexpression of ITPa using ITP- RC-GAL4TS (ITP-RC-T2A-GAL4 combined with temperature-sensitive tubulinGAL80) increases desiccation" Unless I am misunderstanding Fig 5C, I think what is shown is that overexpression of ITPa prolongs survival during a period of desiccation. I am not sure what the authors mean by "increases desiccation". In the text (page 9) the authors state "ITPa overexpression improves desiccation tolerance, which is a much clearer statement than what is in the figure legend. 

We thank the reviewer for identifying this oversight. We have now changed the caption to “increases desiccation tolerance”.  

(5) Page 11 - The authors conclude that "increased ITPa signaling results in phenotypes that largely mirror those seen following Gyc76C knockdown in the fat body, providing further support that ITPa mediates its effects via Gyc76C." Use of the term "largely mirror" seems inappropriate here because there are opposing effects- e.g. decreased starvation resistance in Figure 6A versus increased starvation resistance in Figure 7A.  

Perhaps there is a misunderstanding of what is meant by "mirroring" - it means the same, not the opposite. 

We thank the reviewer for this comment. We agree that the use of the term “largely mirrors” to describe the effects of ITPa overexpression and Gyc76C knockdown is not appropriate and have changed this sentence as follows: “Taken together, the phenotypes seen following Gyc76C knockdown in the fat body largely mirror those seen following ITP knockdown in ITP-RC neurons, providing further support that ITPa mediates its effects via Gyc76C.”

(6) Page 12 - There appear to be words missing between "neurons during desiccation, as well as their downstream" and "the recently completed FlyWire adult brain connectome" 

We thank the reviewer for highlighting this mistake. We have changed the sentence as following: “Having characterized the functions of ITP signaling to the renal tubules and the fat body, we wanted to identify the factors and mechanisms regulating the activity of ITP neurons during desiccation, as well as their downstream neuronal pathways. To address this, we took advantage of the recently completed FlyWire adult brain connectome (Dorkenwald et al., 2024, Schlegel et al., 2024) to identify pre- and post-synaptic partners of ITP neurons.”

(7) Page 15 - "can release up to a staggering 8 neuropeptides" - I suggest that the word "staggering" is removed. The notion that individual neurons release many neuropeptides is now widely recognised (both in vertebrates and invertebrates) based on analysis of single-cell transcriptomic data. 

Removed staggering.

(8) Page 16 - "(Farwa and Jean-Paul, 2024)" - this citation needs to be added to the reference list and I think it needs to be changed to "Sajadi and Paluzzi, 2024". 

We thank the reviewer for highlighting this oversight. The correct citation has now been added.

(9) It is noteworthy that, based on a PubMed search, there are at least thirteen published papers that report on Gyc76C in Drosophila (PMIDs: 34988396, 32063902, 27642749, 26440503, 24284209, 23862019, 23213443,  21893139, 21350862, 16341244, 15485853, 15282266, 7706258). However, none of these papers are discussed/cited by the authors. This is surprising because the authors' hypothesis that Gyc76C acts as a receptor for ITPa surely needs to be evaluated and discussed with reference to all the published insights into the developmental/physiological roles of this protein. 

We thank the reviewer for this comment. Some of the references mentioned above (21350862, 16341244, 15485853) mainly report on soluble guanylyl cyclases and not membrane guanylyl cyclase like Gyc76C. Based on other studies on Gyc76C and its role in immunity and development, we have now expanded the discussion on additional roles of ITPa.

Reviewer #3 (Recommendations For The Authors):  

I have only a few comments that will help the authors strengthen a couple of aspects of their model.  

(1) The case for Gyc76C as a receptor for ITPa in regulating fluid homeostasis is clear, given the experiments the authors carried out where they applied ITPa to tubules and showed that the effects of ITPa on tubule secretion were blocked if Gyc76C was absent in tubules. This approach, or something similar, should be used to provide conclusive proof that ITPa's metabolic effects on the fat body go through Gyc76C.  

At present (unless I missed it) the authors only show that gain of ITPa has the opposite phenotype to fat body-specific loss of Gyc76C. While this would be the expected result if ITPa/Gyc76C is a ligand-receptor pair, it is not quite sufficient to conclusively demonstrate that Gyc76C is definitely the fat body receptor. Ex vivo experiments such as soaking the adult fat body carcasses with and without Gyc76C in ITPa and monitoring fat content via Nile Red could be one way to address this lack of direct evidence. The authors could also make text changes to explicitly mention this lack of conclusive evidence and suggest it as a future direction.

We thank the reviewer for this comment. We have now conclusively demonstrated that Gyc76C is activated by ITPa in a heterologous assay (new Figure 7 and Figure 7 Supplement 1). With this evidence, we can confidently claim that ITPa can mediate its actions via Gyc76C in various tissues including the Malpighian tubules and fat body. Nonetheless, we liked the suggestion by this reviewer to perform the ex vivo assay and test the effect of ITPa on the fat body. Unfortunately, it is challenging to do this because increased ITPa signaling (chronically using ITPa overexpression) results in increased lipid accumulation in the fat body in vivo. Therefore, we would likely not see the effect of ITPa addition in an ex vivo fat body preparation since lipogenesis will not occur in the absence of glucose. However, ITPa could counteract the effects of other lipolytic factors such as adipokinetic hormone (AKH). To test this hypothesis, we monitored fat content in the fat body incubated with and without AKH (see Author response image 2 below showing representative images from this experiment). Since we did not observe any differences in fat levels between these two conditions, we were unable to test the effects of ITPa on AKH-activity using this assay.

Author response image 2.

(2) I did not see any loss of function data for ITPa - is this possible? If so this would strengthen the case for a 1:1 relationship between loss of ligand and loss of receptor. Alternatively, the authors could suggest this as an important future direction. 

We agree with this reviewer regarding the need to provide additional evidence using a loss-of-function approach with ITPa. We have now characterized the phenotypes following knockdown of ITP in ITP-producing cells (new Figure 9). Our results are in agreement with phenotypes observed following Gyc76C knockdown, lending further support that ITPa mediates its effects via Gyc76C.

(3) For clarity, please include the sex of all animals in the figure legend. Even though the methods say 'females used unless otherwise indicated' it is still better for the reader to know within the figure legend what sex is displayed. 

We thank the reviewer for this suggestion and have now included sex of the animals in the figure legends.  

(4) Please state whether females are mated or not, as this is relevant for taste preferences and food intake. 

We apologize for this oversight. We used mated females for all experiments. This has now been included in the methods.  

(5) More discussion on the previous study on metabolic effects of ITP in this study compared with past studies would help readers appreciate any similarities and/or differences between this study and past work (Galikova 2018, 2022) 

We thank the reviewer for this suggestion. Unfortunately, it is difficult to directly compare our phenotypes with the metabolic effects of ITP reported in Galikova and Klepsatel 2022 because the previous study used a ubiquitous driver (Da-GAL4) to manipulate ITP levels. Ectopically overexpressing ITPa in non-ITP producing cells can result in non-physiological phenotypes. This is evident in their metabolic measurements where both global overexpression and knockdown of ITP results in reduced glycogen and fat levels, and starvation tolerance. Moreover, ITP-RC-GAL4 used in our study to overexpress and knockdown ITPa is more specific than the Da-GAL4 used previously. Da-GAL4 would include other ITP cells (e.g. ITP-RD producing cells). Since ITP is broadly expressed across the animal, it is difficult to parse out the phenotypes of ITPa and other isoforms using manipulations performed with Da-GAL4. We have mentioned this limitation in the results for ITP knockdown as follows: “A previous study employing ubiquitous ITP knockdown and overexpression suggests that Drosophila ITP also regulates feeding and metabolic homeostasis (Galikova and Klepsatel, 2022) in addition to osmotic homeostais (Galikova et al., 2018). However, given the nature of the genetic manipulations (ectopic ITPa overexpression and knockdown of ITP in all tissues) utilized in those studies, it is difficult to parse the effects of ITP signaling from ITPa-producing neurons.”

eLife Assessment

This study provides convincing evidence that homologous recombination can occur in telophase-arrested cells, independently of cohesin subunits Smc 1-3. These findings are valuable as they point to investigate the role of cohesins re-association with chromatin in the allelic inter-sister repair by homologous recombination.

Reviewer #2 (Public review):

Cohesin drive inter-sister repair of DNA breaks by homologous recombination (HR) in G2/M. Cohesion is lost at the metaphase to anaphase transition upon digestion of the Scc1 subunit of cohesin by Esp1, raising the question as to whether and how break repair by HR could occur in late mitosis (late-M).

Here the author investigate the behavior of cohesin in cells arrested in telophase and experiencing a DNA break at the mating-type locus on chr. III (a specialized recombination process required for mating-type switching) or upon random DNA break formation with the drug phleomycin.

The revised version of the manuscript now convincingly establishes three facts:

- The cohesin subunit Scc1 can re-associate with chromatin and the other Smc1-3 subunits upon formation of an unrepairable DSB at MAT in telophase.<br /> - HR can occur in telophase-arrested cells<br /> - Cohesin (an a fortiori cohesin that reassociated with chromatin) plays no role in non-allelic HR in telophase in the specific context of MAT switching.

Unfortunately, the role of cohesin re-association with chromatin for the allelic inter-sister repair by HR is not addressed. In the absence of such evidence, the main claims of the paper making up the title (cohesin re-association and HR repair) appear disconnected. Even if the very last sentence of the abstract corrects the false sense from the title and the rest of the abstract that cohesin reconstitution has somehow something to do with efficient HR in late mitosis, I think a general rewriting of the abstract and a different title would better lift any ambiguity about the conclusions of the paper.

Author response:

The following is the authors’ response to the original reviews

We would like to thank the reviewers for taking the time to thoroughly revise our work. We have considered their suggestions carefully and tried our best to respond to them point by point. Based on their recommendations, two major issues came forward: (1) the strength of our claims about the involvement of cohesin in HR-driven repair in late mitosis; and (2) the underlying mechanism that reconstitutes cohesin in late mitosis after DNA damage. In this revision, we focused on the former and left the latter out (yet it is discussed). We considered that the question of how cohesin returns in late mitosis after DNA damage is important and worthy of further research, but it is beyond the scope of this study (as it is the putative role of condensin). Thus, we have focused on buttressing our main claims, as otherwise pointed out by the reviewers. What have we done to strengthen the role of cohesin in late mitotic DSB repair?

(1) We have biologically replicated and quantified the reappearance of Scc1 after DSB generation (new Figure 1e). We have also quantified changes for the other core subunits (new Figure 1c-e).

(2) We now show that the newly synthetized Scc1 serves to assemble back the cohesin complex (new Figure 2a and S1).

(3) We have performed chromatin fractionation and show that cohesin binding to chromatin increases after the HO-induced DSB (new Figure 2b and S2).

(4) We have performed ChIP assays and show that, despite the increase in the chromatin-bound fraction, the HOcs DSB does not recruit new cohesin to the locus (new Figure 2c and S3).

(5) A key assertion in the preprint version was that depleting cohesin using the auxin degron system impairs HR-driven MAT switching. This claim was based on a direct comparison of cultures treated or not with auxin (-/+ IAA). However, during the revision process, we realized that auxin treatment itself could interfere with MAT switching. Firstly, we noticed a diminished HOcs cutting efficiency by HO in +IAA cultures (Figure S6). Secondly, the apparently dramatic delay in gene conversion to MAT_α could actually be related to other undesirable effects of IAA downstream in the repair process. Thus, we decided to repeat this experiment with strains that differ in their response to auxin, so that we could compare all strains in the presence of auxin. We compared four isogenic strains: _SMC3; SMC3-aid*; SMC3 + OsTIR1; and SMC3-aid* + OsTIR1. As a result, we can now show that cohesin depletion does not affect MAT switching (see new Figure 4b-d).

(6) We recently reported a negative chemical interaction between auxin and phleomycin. Auxin appears to diminish the ability of phleomycin to generate DSBs (Comm Biol 2025, doi: 10.1038/s42003-025-08416-x; see Figures S14 and S15 in that paper). While the underlying nature of this interaction is unknown to us (we are working on it), this leads us to omit the coalescence assay included in the preprint version (old Figure 4c), as the diminished coalescence upon IAA addition is actually due to this effect rather than cohesin depletion. This is also in agreement with the new data we include in the revised version, in which we observed only minor changes in cohesin reconstitution and chromatin binding after phleomycin (Figure 2a,b; S1 and S2).  

(7) In addition to addressing these reviewers’ requests, we have better characterized the MAT switching in late mitosis by incorporating the kinetics of _rad9_Δ (deficient in the DNA damage checkpoint), _yku70_Δ (deficient in non-homologous end joining) and _mre11_Δ (deficient in DSB end tethering). The effect of _rad52_Δ (deficient in HR) has been described elsewhere (our iScience 2024, 10.1016/j.isci.2024.110250).

As a result of these new experiments, new figure panels have been added in the main figures and as supplementary figures. To make room for the these panels in the main figures and keep the short report format, the following changes have been made: (i) old figures and new panels have been combined into four main figures, (ii) some panels from the old figures have been moved to supplementary figures, and (iii) some panels have been reordered for the sake of simplicity and fluidity in the main text. 

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The cohesin complex maintains sister chromatid cohesion from S phase to anaphase. Beyond that, DSBs trigger cohesin recruitment and post-replication cohesion at both damage sites and globally, which was originally reported in 2004. In their recent study, Ayra-Plasencia et al reported in telophase, DSBs are repaired via HR with re-coalesced sister chromatids (Ayra-Plasencia & Machín, 2019). In this study, they show that HR occurs in a Smc3-dependent way in late mitosis.

Strengths:

The authors take great advantage of the yeast system, they check the DSB processing and repair of a single DSB generated by HO endonuclease, which cuts the MAT locus in chromosome III. In combination with cell synchronization, they detect the HR repair during G2/M or late mitosis. and the cohesin subunit SMC3 is critical for this repair. Beyond that, full-length Scc1 protein can be recovered upon DSBs.

Weaknesses:

These new results basically support their proposal although with a very limited molecular mechanistic progression, especially compared with their recent work.

Reviewer #2 (Public Review):

Summary:

The manuscript "Cohesin still drives homologous recombination repair of DNA double-strand breaks in late mitosis" by Ayra-Plasencia et al. investigates regulations of HR repair in conditional cdc15 mutants, which arrests the cell cycle in late anaphase/telophase. Using a non-competitive MAT switching system of S. cerevisiae, they show that a DSB in telophase-arrested cells elicits a delayed DNA damage checkpoint response and resection. Using a degron allele of SMC3 they show that MATa-to-alpha switching requires cohesin in this context. The presence of a DSB in telophase-arrested cells leads to an increase in the kleisin subunit Scc1 and a partial rejoining of sister chromatids after they have separated in a subset of cells.

Strengths:

The experiments presented are well-controlled. The induction systems are clean and well thought-out.

Weaknesses:

The manuscript is very preliminary, and I have reservations about its physiological relevance. I also have reservations regarding the usage of MAT to make the point that inter-sister repair can occur in late mitosis.

Regarding these two weaknesses:

- Physiological relevance: This is something we already addressed in our previous research work (Nat Commun. 2019; 10(1):2862. doi: 10.1038/s41467-019-10742-8), and which was further discussed in a follow-up theoretical paper (Bioessays. 2020 ;42(7):e2000021. doi: 10.1002/bies.202000021). In summary, this is physiologically relevant because a DSB in anaphase activates a late-mitotic checkpoint so the DSB can be repaired before cytokinesis. The fact that anaphase is quick and only a minor fraction of cells get a DSB in this cell cycle stage in an asynchronous population does not preclude its importance since it is enough a single mis-repaired DSB in hundreds of cells to mutate a population in an health- or evolution-relevant way.

- MAT system in late mitosis: It was not our intention to use the MAT switching assay to state that inter-sister repair can occur in late-M. The purpose was to address whether HR was fully functional in this non-G2/M non-G1 stage. Having said that, it is very challenging to design a strategy based on sequence-specific DSB to tackle the inter-sister repair in late-M. Any endonuclease-generated DSB is going to cut in both sisters. This is something we also deeply discussed in our previous works (Nat Commun & Bioessays).    

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points:

(1) Smc3 degradation affects Rad53 activation upon DSBs, and this may directly lead to HR repair deficiency. Smc3 also could be phosphorylated by ATM and functions in DNA damage checkpoint activation, these alternative possibilities should also be tested before addressing the bona fide role of Smc3 in this context.

Our previous data already suggested that Rad53 hyperphosphorylation still occurs after Smc3 degradation (Figure S6). Regardless, the question of whether the DNA damage checkpoint (DDC) may play a distinct role in the MAT switching has been addressed in this revision by comparing RAD9 versus rad9_Δ. Rad9 is a mediator in the DDC required for the activation of Rad53. We have seen that MAT switching in _rad9_Δ is as efficient as in _RAD9 (new Figure S5d-f).

On the other hand, our new results, in which we have compared four different strains with all auxin system combinations in the presence of auxin, show that cohesin depletion does not affect MAT switching. Previously, we compared minus versus plus auxin and noticed diminished HO cutting efficiency. Thus, we repeated this experiment with four isogenic strains (SMC3; SMC3-aid*; SMC3 + OsTIR1; and SMC3-aid* + OsTIR1) that differ in their response to auxin and ability to degrade cohesin, so that we could compare all strains in the presence of auxin. As a result, we can now affirm that cohesin depletion does not affect MAT switching (see new Figure 4b-d). Therefore, HR appears efficient after cohesin depletion.

(2) The requirement of cohesin subunit Smc3 and "coincidently" recovery of Scc1 are not sufficient to claim they act as a cohesin complex in this scenario. CoIP in the chromatin fraction after DSBs to prove the cohesin complex formation is recommended. If they act as a complex, are cohesin loader Scc2/4 required?

We have constructed a SMC3-HA SCC1-myc strain. We have purified the chromatin-bound fraction as well as performing the co-IP. We have found Smc1-acSmc3-Scc1 forms a complex after Scc1 returns, and that at least a fraction of this complex binds to the chromatin in our HO model of DSBs in late anaphase (the cdc15-2 arrest). This is now shown in the new Figures 2a,b and S1,S2.

As for the requirement of Scc2/4, we consider that the mechanisms underlying how Scc1 comes back, how a new cohesin complex is reassembled, and how it can partly bind to the chromatin in late anaphase are beyond the scope of this study and worth pursuing in a follow-up story.

(3) Figure 3b. acetylated SMC3 was prominently detected in the absence of DSBs. During the cohesion cycle, the cohesin was released from chromatin in a separase-dependent manner at the anaphase onset. Released Smc3 was deacetylated by Hos1 subsequently. In principle, the acSMC3 level could be very low in late mitosis.

In that figure (now renumbered as Fig S6), we did detect acetylated Smc3 for the remnant Smc3 still found in late mitosis, however, a direct comparison between the acetylated versus non-acetylated pools was not performed, and would require more sophisticated approaches. Note that blots are distinctly exposed until the band is detected, and that signal intensity is antibody-specific. The presence of an acSmc3 pool in the cdc15-2 arrest is now further confirmed by the new blots in Figures 2a, S1 and S2b.

On the other hand, previous time course experiments from G1 and G2/M releases point out that Smc3 deacetylation is incomplete in anaphase, with up to 30% of acetylated Smc3 remaining (Beckouët et al, 2010 doi:10.1016/j.molcel.2010.08.008). This is consistent with the presence of acSmc3 in the cdc15-2 arrest.   

(4) Did the author examine the acSMC3 levels returning after DSB, as Scc1's levels? If so, how about the Eco1's protein level? Chromatin fractionation could be conducted to check the chromatin-bound SMC3, acSMC3/Eco1, SCC1, SCC1 phosphorylation, and SMC1. These results will tell us whether cohesin functions in DSB repair in late M in a cohesion state.

As stated above, we have now determined that cohesin depletion does not affect HR-driven MAT switching. As for the other questions, yes, we have performed both an assessment of acSmc3 in the pull down and chromatin fractionation, before and after DSBs (new Figures 2a, S1 and S2b). Interestingly, we have noticed a difference between the HO-generated and the phle-generated DSBs. It appears that the former leads to a better reconstituted Smc1-acSmc3-Scc1 complex and more chromatin-bound cohesin. The overall acSmc3 levels do not appear to significantly change in the whole cell extracts, although there could be further posttranslational modifications in telophase (see the changes in intensity between the two acSmc3 bands in Figure S1).

The role of Eco1 has not been directly addressed but is discussed. The main point here is that Eco1 levels may be low after G2/M (e.g., Lyons and Morgan, 2011), but there is still a significant acSmc3 pool in anaphase as Hof1 does not deacetylate all Smc3 (Beckouët et al., 2010). 

(5) Figure 4a, the return of full-length Scc1 is based on a single experiment. What's the mechanism? Inhibition of cleavage or re-expression? How about its mRNA levels?

We have repeated the full-length Scc1 experiment two more times. Now, an expression graph is included as a new Figure 1e. The two other subunits, Smc1 and Smc3, have been assessed as well, with no major changes in abundance (new Figure 1c and d).

We feel that the exact molecular mechanism of how Scc1 returns is beyond the scope of this study, but we discuss that the DDC may either inactivate separase or protect Scc1 against it. Indeed, there is literature that supports both mechanisms (e.g., Heidinger-Pauli et al., 2008 doi:10.1016/j.molcel.2008.06.005; Yam et al., 2020 doi:10.1093/nar/gkaa355).   

Minor points:

(6) FACS data should be shown for all cell synchronization experiments.

From our previous own works, FACS profiles add little to late-M experiments. To properly confirm late-M, microscopy is a must. FACS cannot differentiate between G2/M (metaphase-like), anaphase, telophase and the ensuing G1 (as cdc15-2 cells do not immediately split apart after re-entering G1). In all experiments, Tel samples (late-M cdc15-2 arrest) were characterized by >95% large budded binucleated cells.

(7) Figure 1d, A loading control of Rad53-P in is missing. The "Arrest" samples should be loaded again on the right to confirm the shift of Rad53, but not due to "smiling gels".

It is true that the blot on the right has a right-handed smile; however, it is very clear the presence of the Rad53/Rad53-P partner. Because there is not a full shift from Rad53 to Rad53-P, the concern of misidentifying Rad53-P as a result of a blot smile is unfounded.

(8) Figure 1c, After the HO cut, the resected DNA at the 726 bp site reaches to platform at about 4 hrs, while it still increases at the 5.6 kb site. Thus, it is difficult to conclude that "The time to reach half of the maximum possible resection (t1/2) was ~1 h at 0.7 Kb and ~2.5 h at 5.7 Kb from the DSB, respectively".

We assumed that both loci reach the plateau at 0.8 (which is consistent with other studies), so the t1/2 was calculated when the resected intersected 0.4.

(9) Figure 2b and 2c are wrongly labeled.

We have fixed this (now Fig. 3d and e).

(10) Figure 2d, Double check and make sure the quantitative data reflects the representative result. E.g. in Figure 2b (in fact should be 2c). For instance, in Figure 2b, the MATα signals seem to remain stable from 60' to 180', but they keep increasing in Figure 2d. In Yamaguchi & James E. Haber's paper, the signals and changes of MATa and MATα over time are way stronger compared to this study.

We have double checked this. It is true that the sum of MATα, MATalpha and cut HOcs bands throughout the assay does not have the intensity seen for MATa before the HO induction (Tel), but MATalpha and HOcs signals cannot be established based on the equimolarity of the reaction as all band signals are probe-specific (the best indication of this can be seen in the signal comparison between MAT_α and _MAT distal at Tel). Alternatively, some resected HOcs may remain unrepaired.

As for the referred example (now Figure 3e), note that they are double normalized to ACT1 and MAT_α (Tel), and the _ACT1 band gets fainter after 60’. This explains the increase in the MATalpha quantification in spite of what is apparently seen in the blot.

(11) Typos and fonts: e.g. lines 111-112; line 76 "his link".

We have fixed this. Thanks.

Reviewer #2 (Recommendations For The Authors):

Major concerns:

(1) Physiological relevance. The authors show that HR can happen in the anaphase to telophase interval, yet does it outside of an hours-long artificial arrest upon inactivation of Cdc15? It is this reviewer's understanding that the duration of the anaphase to telophase transition is short, in the order of minutes. In fact, break signaling and resection are delayed by ~1 hour (Fig. 1), which suggests that cells avoid dealing with the damage and engaging in HR in the anaphase-telophase interval. Is there any described physiological context or checkpoint that blocks this transition for extended periods, that would make any of the findings in this paper relevant?

This concern about the physiological relevance was addressed in our previous study (Nat Commun. 2019; 10(1):2862. doi: 10.1038/s41467-019-10742-8). In that paper’s Figure 1, we showed that G1 re-entry after a cdc15-2 release was delayed by several hours when DSBs had been previously generated at the cdc15-2 arrest. We also showed that such a delay depended on Rad9 (i.e., the DNA damage checkpoint). In addition, synchronized (not arrested) cells transiting through anaphase responded to DSB generation by slowing anaphase transition while partly regressing chromosome segregation (Figure S7 in that paper).

(2) Methodological caveats. It is unclear why the authors chose to study DSB-repair in the context of MATa-to-alpha switching (which uses an ectopic donor on the other chromosome arm) as a model for inter-sister repair. It creates a disconnect in the claims of the paper, which means to study inter-sister repair. Studying the kinetics of DSB repair by cytology following low-dose irradiation or radiomimetic drugs would have been a better option. Phleomycin is used in Fig. 4, but the repair kinetics (e.g. Rad52 foci) is not studied.

The MAT switching assay was used here to address how much HR was functional in late-M compared to G2/M (metaphase-like). Then, it was employed to check how cohesin depletion hampers HR in late-M. Even though this is something we already deeply discussed previously (Nat Commun. 2019; 10(1):2862. doi: 10.1038/s41467-019-10742-8; Bioessays. 2020 ;42(7):e2000021. doi: 10.1002/bies.202000021), it is worth recapitulating the methodological challenges that the study of inter-sister repair has in late-M: (i) endonuclease-based DSBs are going to generate two DSBs, one per sister chromatid; (ii) the use of a homologous chromosome without the cutting site as a template is pointless because a sister of the homolog is always going to co-segregate with the broken chromatid, and the same caveat applies for any other ectopic sequence. In this context, the MATa with the HML ectopic intrachromosomal sequence is as valid as any other option, with the advantage that it is a very well-known system.

On the other hand, most of the reviewer’s concerns about the inter-sister repair by cytology and the role of Rad52 was addressed in our previous paper (Nat Commun). Note that our new results about the cohesin role on MAT switching show that this HR-mediated DSB repair does not depend on cohesin (new Figure 4b-d).

(3) Preliminary work. The requirement of cohesin for MAT switching in cdc15 mutants would have warranted several additional experiments. Indeed, Cohesin has been shown to regulate homology search in multiple ways upon DNA damage checkpoint-induced metaphase-arrest (see Piazza et al. Nat Cell Biol 2021 (10.1038/s41556-021-00783-x), not cited in the current manuscript). Consequently, is the effect of cohesin observed in the MAT system specific to telophase or is it true in other cell-cycle phases? What is the mechanism behind this requirement (one may expect it not to depend on the sister since the HML donor is available within the damaged chromatid)? Does cohesin re-accumulate around the DSB site or genome-wide? How does the Esp1 activity decay from anaphase onset? Is cohesin required for the horseshoe folding of chr. III involved in MATa-to-alpha switching? Furthermore, condensin is involved in MATa-specific switching (Li et al. PLoS Genet 2019, 10.1371/journal.pgen.1008339), and condensin remains active on chromatin in cdc15 arrested cells, as shown on chr. XII (Lazar-Stefanita et al. EMBO J. 2017 10.15252/embj.201797342), which calls for determining the impact contribution of condensin in the recoil of the right ch.XII arm (Fig 4c) and on MAT switching.

There are several points here:

- Is the effect of cohesin observed in the MAT system specific to telophase or is it true in other cell-cycle phases?

Our new results show that cohesin depletion does not affect MAT switching when four different strains with all auxin system combinations are compared in the presence of auxin. Previously, when we compared minus versus plus auxin, we noticed diminished HO cutting efficiency. Therefore, we repeated the experiment using four isogenic strains (SMC3, SMC3-aid*, SMC3 + OsTIR1, and SMC3-aid* + OsTIR1), which differ in their response to auxin and ability to degrade cohesin. This allowed us to compare all strains in the presence of auxin. As a result, we can now confirm that cohesin depletion does not affect MAT switching (see the new Figures 4b–d). Therefore, HR appears efficient after cohesin depletion. In agreement, the new ChIPs we have performed do not detect an increment in local cohesin after the HO DSB in telophase (but it does in cells arrested in G2/M).

- What is the mechanism behind this requirement (one may expect it not to depend on the sister since the HML donor is available within the damaged chromatid)?

As just said, we have changed our previous conclusion on cohesin and MAT switching. It was an effect of auxin addition rather than cohesin depletion.

- Does cohesin re-accumulate around the DSB site or genome-wide?

We have performed ChIP around the HOcs. We have found that it does accumulate in G2/M after HO induction, but it does not in telophase (new Figures 2c and S3). As for the global binding of cohesin, our chromatin fractionation data suggest there is ~2-fold increase in Smc1-Smc3, which also binds to the newly formed Scc1, rendering an overall increase in the chromatin-bound canonical complex (new Figures 2b and S2). Altogether, this suggests a genome-wide binding but with little role in the repair of HO DSBs.

- How does the Esp1 activity decay from anaphase onset?

We have not checked this here but it is an interesting question for a follow-up story.

- Is cohesin required for the horseshoe folding of chr. III involved in MATa-to-alpha switching?

Probably not in view of our new data in Figures 2c and 4b-d. The Piazza papers are cited and discussed.

- Contribution of condensin in the recoil of the right ch.XII arm (Fig 4c) and on MAT switching.

The role of condensin, which overtakes some cohesin function in late-M as the reviewer reminds, is worth studying indeed. However, we feel this deserves a separate and focus-on study. We does discuss, though, that condensin loading onto the arms in anaphase may prevent Smc1-Smc3 from loading after DSBs.

Other points:

(4) Is the retrograde behavior in Fig. 4c dependent on recombination?

No, this is something we addressed in our previous paper (see Figure 4 in Nat Commun. 2019; 10(1):2862. doi: 10.1038/s41467-019-10742-8).

(5) Fig 3c: add a scheme of the system.

A scheme was already shown in the old Figure 2a (note that the old Fig 3c is now Fig S6).

(6) Fig 3b: annotate as in Fig 2b.

We have fixed this (now the referred figures are S6a and 3d, respectively).

(7) Authors used IAA concentrations 4- to 8-fold higher than commonly used. Given the solubility of IAA in DMSO (the most commonly used solvent), it is likely that authors treated their cells with >2% DMSO. This is expected to have broad transcriptional and physiological effects on yeast. A comparison of +IAA samples with a mock (DMSO) treatment would be more appropriate than a lack of treatment.

The IAA stock solution was 500 mM in DMSO, so the final DMSO concentration for an 8 mM IAA solution was 1.6% (v/v). Although the stock concentration was high and some precipitation was observed during preparation, we always heated, sonicated, and vigorously vortexed the stock tube before adding IAA to the cultures. Thus, we kept the uncertainty in the final IAA concentration to a minimum.

onderstreepte dingen zijn echt belangrijk

Reviewer #1 (Public review):

Summary:

What are the overarching principles by which prokaryotic genomes evolve? This fundamental question motivates the investigations in this excellent piece of work. While it is still very common in this field to simply assume that prokaryotic genome evolution can be described by a standard model from mathematical population genetics, and fit the genomic data to such a model, a smaller group of researchers rightly insists that we should not have such preconceived ideas and instead try to carefully look at what the genomic data tell us about how prokaryotic genomes evolve. This is the approach taken by the authors of this work. Lacking a tight theoretical framework, the challenge of such approaches is to device analysis methods that are robust to all our uncertainties about what the underlying evolutionary dynamics might be.

The authors here focus on a collection of ~300 single-cell genomes from a relatively well-isolated habitat with a relatively simple species composition, i.e. cyanobacteria living in hot springs in Yellowstone National Park. They convincingly demonstrate that the relative simplicity of this habitat increases our ability to interpret what the genomic data tells us about the evolutionary dynamics.

Using a very thorough and multi-faceted analysis of these data, the authors convincingly show that there are three main species of Synechococcus cyanobacteria living in this habitat, and that apart from very frequent recombination within each species (which is in line with insights from other recent studies) there is also a remarkably frequent occurrence of hybridization events between the different species, and with as of yet unindentified other genomes. Moreover, these hybridization events drive much of the diversity within each species. The authors also show convincing evidence that many of these hybridization events are not neutral but are driven by natural selection.

Strengths:

The great strength of this paper is that, by not making any preconceived assumptions about what the evolutionary dynamics is expected to look like, but instead devicing careful analysis methods to tease apart what the data tells us about what has happened in the evolution in these genomes, highly novel and unexpected results are obtained, i.e. the major role of hybridization across the 3 main species living in this habitat.

The analysis is very thorough and reading the detailed descriptions in the appendices it is clear that these authors took a lot of care in using these methods and avoiding the pitfalls that unfortunately affect many other studies in this research area.

The picture of the evolutionary dynamics of these three Synechococcus species that emerges from this analysis is quite novel and surprising. I think this study is a major stepping stone toward development of more realistic quantitative theories of genome evolution in prokaryotes.

The analysis methods that the authors employ are also partially quite novel and will no doubt by very valuable for analysis of many other datasets.

Weaknesses:

The main text is tight and concise, but this sort of hides the very large amount of careful complementary analyses that went into the conclusions presented in the main text. The appendices are quite well written but they are substantial, so that really understanding the paper is not an easy read. However, I do not really think the authors can be faulted for this. The topic is complex and a lot of care is required to make sure conclusions are valid.

A very interesting observation is that a lot of hybridization events (i.e. about half) originate from species other than the alpha, beta, and gamma Synechococcus species from which the genomes that are analyzed here derive. For this to occur, these other species must presumably also be living in the same habitat and must be relatively abundant. But if they are, why are they not being captured by the sampling? I did not see a clear explanation for this very common occurrence of hybridization events from outside of these Synechococcus species. The authors raise the possibility that these other species used to live in these hot springs but are now extinct or that the occur in other pools. I guess this is possible but I still find it puzzling and wonder if these donors could have been filtered out at some step of the experimental and/or analysis procedures.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

What are the overarching principles by which prokaryotic genomes evolve? This fundamental question motivates the investigations in this excellent piece of work. While it is still very common in this field to simply assume that prokaryotic genome evolution can be described by a standard model from mathematical population genetics, and fit the genomic data to such a model, a smaller group of researchers rightly insists that we should not have such preconceived ideas and instead try to carefully look at what the genomic data tell us about how prokaryotic genomes evolve. This is the approach taken by the authors of this work. Lacking a tight theoretical framework, the challenge of such approaches is to devise analysis methods that are robust to all our uncertainties about what the underlying evolutionary dynamics might be.

The authors here focus on a collection of ~300 single-cell genomes from a relatively well-isolated habitat with relatively simple species composition, i.e. cyanobacteria living in hotsprings in Yellowstone National Park, and convincingly demonstrate that the relative simplicity of this habitat increases our ability to interpret what the genomic data tells us about the evolutionary dynamics.

Using a very thorough and multi-faceted analysis of these data, the authors convincingly show that there are three main species of Synechococcus cyanobacteria living in this habitat, and that apart from very frequent recombination within each species (which is in line with insights from other recent studies) there is also a remarkably frequent occurrence of hybridization events between the different species, and with as of yet unidentified other genomes. Moreover, these hybridization events drive much of the diversity within each species. The authors also show convincing evidence that these hybridization events are not neutral but are driven by selected by natural selection.

Strengths:

The great strength of this paper is that, by not making any preconceived assumptions about what the evolutionary dynamics is expected to look like, but instead devising careful analysis methods to tease apart what the data tells us about what has happened in the evolution in these genomes, highly novel and unexpected results are obtained, i.e. the major role of hybridization across the 3 main species living in this habitat.

The analysis is very thorough and reading the detailed supplementary material it is clear that these authors took a lot of care in devising these methods and avoiding the pitfalls that unfortunately affect many other studies in this research area.

The picture of the evolutionary dynamics of these three Synechococcus species that emerge from this analysis is highly novel and surprising. I think this study is a major stepping stone toward the development of more realistic quantitative theories of genome evolution in prokaryotes.

The analysis methods that the authors employ are also partially novel and will no doubt be very valuable for analysis of many other datasets.

We thank the reviewer for their appreciation of our work.

Weaknesses:

I feel the main weakness of this paper is that the presentation is structured such that it is extremely difficult to read. I feel readers have essentially no chance to understand the main text without first fully reading the 50-page supplement with methods and 31 supplementary materials. I think this will unfortunately strongly narrow the audience for this paper and below in the recommendations for the authors I make some suggestions as to how this might be improved.<br /> A very interesting observation is that a lot of hybridization events (i.e. about half) originate from species other than the alpha, beta, and gamma Synechococcus species from which the genomes that are analyzed here derive. For this to occur, these other species must presumably also be living in the same habitat and must be relatively abundant. But if they are, why are they not being captured by the sampling? I did not see a clear explanation for this very common occurrence of hybridization events from outside of these Synechococcus species. The authors raise the possibility that these other species used to live in these hot springs but are now extinct. I'm not sure how plausible this is and wonder if there would be some way to find support for this in the data (e.g that one does not observe recent events of import from one of these unknown other species). This was one major finding that I believe went without a clear interpretation.

We agree with the reviewer that the extent of hybridization with other species is surprising. While we do feel that our metagenome data provide convincing evidence that “X” species are not present in MS or OS, we cannot currently rule out the presence of X in other springs. In the revision we explicitly mention the alternative hypothesis (Lines 239-242).

The core entities in the paper are groups of orthologous genes that show clear evidence of hybridization. It is thus very frustating that exactly the methods for identifying and classifying these hybridization events were really difficult to understand (sections I and V of the supplement). Even after several readings, I was unsure of exactly how orthogroups were classified, i.e. what the difference between M and X clusters is, what a `simple hybrid' corresponds to (as opposed to complex hybrids?), what precisely the definitions of singlet and non-singlet hybrids are, etcetera. It also seems that some numbers reported in the main text do not match what is shown in the supplement. For example, the main text talks about "around 80 genes with more than three clusters (SM, Sec. V; fig. S17).", but there is no group with around 80 genes shown in Fig S17! And similarly, it says "We found several dozen (100 in α and 84 in β) simple hybrid loci" and I also cannot match those numbers to what is shown in the supplement. I am convinced that what the authors did probably made sense. But as a reader, it is frustrating that when one tries to understand the results in detail, it is very difficult to understand what exactly is going on. I mention this example in detail because the hybrid classification is the core of this paper, but I had similar problems in other sections.

We thank the reviewer for pointing out these issues with our original presentation. In the revision, we have redone most of the analysis to simplify the methods and check the consistency of the results. We did not find any qualitative differences in our results after reanalysis, but some of the numbers for different hybridization patterns have changed. The most notable difference is an increase in the number of alpha-gamma simple hybrids and a corresponding decrease in mixed-species clusters (now labeled mosaic hybrids). These transfers are difficult to assign because we only have access to a single gamma genome. We have added a short explanation of this point in Lines 219-222.

To improve the presentation, we significantly expanded the “Results” section to better explain our analysis and the different steps we take. We included two additional figures (Figs. 3 and 4) that illustrate the different types of hybrids and the heterogeneity in the diversity of alpha which is discussed in the main text and is important for interpreting our results. We also included two additional figures (Figs. 2 and 6) that were previously in the Appendix but were mentioned in the main text. We believe these changes should address most of the issues raised by the reviewer and hopefully make the manuscript easier to read.

Although I generally was quite convinced by the methods and it was clear that the authors were doing a very thorough job, there were some instances where I did not understand the analysis. For example, the way orthogroups were built is very much along the lines used by many in the field (i.e. orthoMCL on the graph of pairwise matchings, building phylogenies of connected components of the graph, splitting the phylogenies along long branches). But then to subdivide orthogroups into clusters of different species, the authors did not use the phylogenetic tree already built but instead used an ad hoc pairwise hierarchical average linkage clustering algorithm.

The reviewer is correct that there is an unexplained discrepancy between the clustering methods we used at different steps in our pipeline. We followed previous work by using phylogenetic distances for the initial clustering of orthogroups. On these scales we expect hybridization to play a minor role and phylogenetic distances to correlate reasonably well with evolutionary divergence. However, because of the extensive hybridization we observed, the use of phylogenetic models for species clustering is more difficult to justify. We therefore chose to simply use pairwise nucleotide distances, which make fewer assumptions about the underlying evolutionary processes and should be more robust. We have briefly explained our reasoning and the details of our clustering method in the revision (Lines 182-190).

Reviewer #2 (Public Review):

Summary:

Birzu et al. describe two sympatric hotspring cyanobacterial species ("alpha" and "beta") and infer recombination across the genome, including inter-species recombination events (hybridization) based on single-cell genome sequencing. The evidence for hybridization is strong and the authors took care to control for artefacts such as contamination during sequencing library preparation. Despite hybridization, the species remain genetically distinct from each other. The authors also present evidence for selective sweeps of genes across both species - a phenomenon which is widely observed for antibiotic resistance genes in pathogens, but rarely documented in environmental bacteria.

Strengths:

This manuscript describes some of the most thorough and convincing evidence to date of recombination happening within and between cohabitating bacteria in nature. Their single-cell sequencing approach allows them to sample the genetic diversity from two dominant species. Although single-cell genome sequences are incomplete, they contain much more information about genetic linkage than typical short-read shotgun metagenomes, enabling a reliable analysis of recombination. The authors also go to great lengths to quality-filter the single-cell sequencing data and to exclude contamination and read mismapping as major drivers of the signal of recombination.

We thank the reviewer for their appreciation of our work.

Weaknesses:

Despite the very thorough and extensive analyses, many of the methods are bespoke and rely on reasonable but often arbitrary cutoffs (e.g. for defining gene sequence clusters etc.). Much of this is warranted, given the unique challenges of working with single-cell genome sequences, which are often quite fragmented and incomplete (30-70% of the genome covered). I think the challenges of working with this single-cell data should be addressed up-front in the main text, which would help justify the choices made for the analysis.

We have significantly expanded the “Results” section to better justify and explain the choices we made during our analysis. We hope these changes address the reviewer’s concerns and make the manuscript more accessible to readers.

The conclusions could also be strengthened by an analysis restricted to only a subset of the highest quality (>70% complete) genomes. Even if this results in a much smaller sample size, it could enable more standard phylogenetic methods to be applied, which could give meaningful support to the conclusions even if applied to just ~10 genomes or so from each species. By building phylogenetic trees, recombination events could be supported using bootstraps, which would add confidence to the gene sequence clustering-based analyses which rely on arbitrary cutoffs without explicit measures of support.

It seems to us that the reviewer’s suggestion presupposes that the recombination events we find can be described as discrete events on an asexual phylogeny, similar to how rare mutations are treated in standard phylogenetic inference. Popular tools, such as ClonalFrame and its offshoots, have attempted to identify individual recombination events starting from these assumptions. But the main conclusion of both our linkage and SNP block analysis is that the ClonalFrame assumptions do not hold for our data. Under a clonal frame, the SNP blocks we observe should be perfectly linked, similar to mutations on an asexual tree. But our results in Fig. 7D show the opposite. Part of the issue may have been that in our original presentation, we only briefly discuss the results of our linkage analysis and refer readers to the Appendix for more details. To fix this issue we have added an extra figure (Fig. 2), showing rapid linkage decrease in both species and that at long distances the linkage values are essentially identical to the unlinked case, similar to sexual populations. We hope that this change will help clarify this point.

The manuscript closes without a cartoon (Figure 4) which outlines the broad evolutionary scenario supported by the data and analysis. I agree with the overall picture, but I do think that some of the temporal ordering of events, especially the timing of recombination events could be better supported by data. In particular, is there evidence that inter-species recombination events are increasing or decreasing over time? Are they currently at steady-state? This would help clarify whether a newly arrived species into the caldera experiences an initial burst of accepting DNA from already-present species (perhaps involving locally adaptive alleles), or whether recombination events are relatively constant over time.

The reviewer raises some very interesting questions about the dynamics of recombination in the population, which we hope to pursue in future work. We have added this as an open question in the Discussion (Lines 365-382).

These questions could be answered by counting recombination events that occur deeper or more recently in a phylogenetic tree.

The reviewer here seems to presuppose that recombination is rare enough that a phylogenetic tree can reliably be inferred, which is contrary to our linkage analysis (see the response to an earlier comment). Perhaps the reviewer missed this point in our original manuscript since it was discussed primarily in the Appendix. See also our response to a previous comment by the reviewer.

The cartoon also shows a 'purple' species that is initially present, then donates some DNA to the 'blue' species before going extinct. In this model, 'purple' DNA should also be donated to the more recently arrived 'orange' species, in proportion to its frequency in the 'blue' genome. This is a relatively subtle detail, but it could be tested in the real data, and this may actually help discern the order of the inferred recombination events.

We have included an extra figure in the main text (Fig. 6) that addresses the question of timing of events. A quantitative test of our cartoon model along the lines the reviewer suggested would certainly be worthwhile and we hope to do that in future work.  

The abstract also makes a bold claim that is not well-supported by the data: "This widespread mixing is contrary to the prevailing view that ecological barriers can maintain cohesive bacterial species..." In fact, the two species are cohesive in the sense that they are identifiable based on clustering of genome-wide genetic diversity (as shown in Fig 1A). I agree that the mixing is 'widespread' in the sense that it occurs across the genome (as shown in Figure 2A) but it is clearly not sufficient to erode species boundaries. So I believe the data is consistent with a Biological Species Concept (sensu Bobay & Ochman, Genome Biology & Evolution 2017) that remains 'fuzzy' - such that there are still inter-species recombination events, just not sufficient to erode the cohesion of genomic clusters. Therefore, I think the data supports the emerging picture of most bacteria abiding by some version of a BSC, and is not particularly 'contrary' to the prevailing view.

We have revised the phrase mentioned by the reviewer to “prevent genetic mixture between bacterial species,” which more accurately represents our conclusions. 

The final Results paragraph begins by posing a question about epistatic interactions, but fails to provide a definitive answer to the extent of epistasis in these genomes. Quantifying epistatic effects in bacterial genomes is certainly of interest, but might be beyond the scope of this paper. This could be a Discussion point rather than an underdeveloped section of the Results.

We agree with the reviewer that an exhaustive analysis of epistasis in the population is beyond the scope of the manuscript. Our original intention was to answer whether SNP blocks we discovered showed evidence of strong linkage, as might be expected if only a small number of strains are present in the population. In light of the previous comments by the reviewer regarding the consistency with the clonal frame hypothesis, we believe this is especially relevant for our results. Moreover, the results we found‑especially for the beta population‑were quite conclusive: SNP block linkages in beta are indistinguishable from an unlinked model. To avoid misdirecting the reader about the significance of our results, we have revised the relevant paragraph (Lines 316-319).

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

Although I am entirely convinced of the validity of the results, methodology, and interpretations presented in this work, I must say I found the paper very hard to read. And I think I am really quite familiar with these kinds of approaches. I fear that for people other than experts on these kinds of comparative genomic analyses, this paper will be almost impossible to read. With the aim of expanding the audience for this compelling work, I think the authors might want to consider ways to improve the presentation.

At the end of a long project, the obtained results typically form a web of mutual interconnections and dependencies and one of the key challenges in presenting the results in a paper is having to untangle this web of connected results and analysis into a linear ordered narrative so that, at any point in the narrative, understanding the next point only depends on previous points in the narrative. I frankly feel that this paper fails at this.

The paper reads to me as if one author put together the supplement by essentially writing a report of all the analyses that were done together with supplementary figures summarizing all those analyses, and that another author then wrote the main text by using the materials in the supplement almost in the way a cook uses ingredients for a dish. Almost every other sentence in the main text refers to results in the (31!) supplementary figures and can only be understood by reading the appropriate corresponding sections in the supplementary materials. I found it essentially impossible to read the main text without having first read the entire 50-page supplement.

I think the paper could be hugely improved by trying to restructure the presentation so as to make it more linear. The main text can be expanded to include a summary of the crucial methods and analysis results from the supplement needed to understand the narrative in the main text. For example, as it currently stands it is really challenging to understand what is shown in figures 2 and 3 of the main text without having to first read a very substantial part of the supplement. Figure 3, even after having read the relevant sections in the supplement, took me quite a while to understand and almost felt like a puzzle to decypher. Rethinking which parts of the supplement are really necessary would also help. Finally, it would also help if the terminology was kept as simple, transparent, and consistent as possible.

I understand that my suggestion to thoroughly reorganize the presentation may feel like a big hassle, but I am afraid that in its current form, these important results are essentially rendered inaccessible to all but a small group of experts in this area. This paper deserves a wider readership.

We thank the reviewer for these valuable suggestions. In the revision, we have significantly expanded and restructured the “Results” section to make the presentation more linear, as the reviewer suggested (see our reply to the public comment by the reviewer for details). We hope these changes will make the manuscript easier to read.

Reviewer #2 (Recommendations For The Authors):

I found this paper challenging to follow since the main text was so condensed and the supplementary material so extensive. Given that eLife does not impose strong limits on the length of the main text, I suggest moving some key sections from the supplement into the main text to make it easier for the reader to follow rather than flipping back and forth. Adding to the confusion, supplementary figures were referenced out of order in the main text (e.g. S23 is referenced before S1). Please check the numbering and ensure figures are mentioned in the main text in the correct order.

We thank the reviewer for their feedback on the presentation of the results. In response to similar comments from Reviewer #1, we have significantly expanded and restructured the “Results” section to make it easier to read (see also our responses to Reviewer #1).

Page 2: The term 'coevolution' is typically reserved for two species that mutually impose selective pressures on one another (e.g. predator-prey interactions; see Janzen, Evolution 1980). In the context of these two cyanobacterial species, it's not clear that this is the case so I would simply refer to them 'cohabitating' or being sympatric in the same environment.

It is true that the term "coevolution” has become associated with predator-prey interactions, as the reviewer said. However, we feel that in our case “coevolution” fairly accurately describes the continual hybridization over long time scales we observe. We have therefore chosen to keep the term.

Page 3: The authors mention that the gamma SAG is ~70% complete, which turns out to be quite high. It would be useful to mention early in the Results the mean/median completeness across SAGs, and how this leads to some challenges in analysing the data. Some of the material from the Supplement could be moved into the Results here.

We have added a short note on the completeness in the Results (Lines 153-154). We have also added an extra figure in Appendix 1 with the completeness of all the SAGs for interested readers.

I was left puzzled by the sentence: "Alternatively, high rates of recombination could generate different genotypes within each genome cluster that are adapted to different temperatures, with the relative frequencies of each cluster being only a correlated and not a causal driver of temperature adaptation." This is suggesting that individual genes or alleles, rather than entire genomes, could be adapted to temperature. But figure 1B seems to imply that the entire genome is adapted to different temperatures. Anyway, this does not seem to be a key point and could probably be removed (or clarified if the authors deem this an important point, which I failed to understand).

We have revised this section to clarify the alternative hypothesis mentioned by the reviewer (Lines 100-103).

Page 4. 'Several dozen' hybrid genes were found, but please also specify how many genes were tested. In general, it would be good to briefly outline the sample size (SAGs or genes) considered for each analysis.

We have added the total numbers of genes we analyzed at each step of our analysis.

'Mosaic hybrid loci' are mentioned alongside the issue of poor alignment. Presumably, the mosaic hybrid loci are first filtered to remove the poor alignments? This should be specified, and please mention how many loci are retained before/after this filter.

We thank the reviewer for highlighting this important point. In the revision, we have implemented a more aggressive filtering of genes with poor alignments. We have added an extra paragraph to Appendix 1 (step 5 in the pipeline analysis) briefly explaining the issue.

Page 5. "By contrast, the diversity of mosaic loci was typical of other loci within beta, suggesting most of the beta genome has undergone hybridization." Please point to the data (figure) to support this statement.

We have restructured our discussion of the different hybrid loci so this comment is no longer relevant. In case the reviewer is interested, the synonymous diversity within beta was 0.047, while in mosaic hybrids it was 0.064.

Page 6. "The largest diversity trough contained 28 genes." Since this trough is discussed in detail and seems to be of interest, it would be nice to illustrate it, perhaps as an inset in Figure 2 or as a separate figure. If I understood correctly, this trough includes genes (in a nitrogen-fixation pathway) that are present in all genomes, but are exchanged by homologous recombination. So I don't think it's correct to say that the "ancestors acquired the ability to fix nitrogen." Rather, the different alleles of these same genes were present in the ancestor. So perhaps there was a selective sweep involving alleles in this region that provided adaptation to local nitrogen sources or concentrations, but not a gain of new genes. Perhaps I misunderstood, in which case clarification would be appreciated.

The reviewer raises an interesting possibility. We agree that it is in principle possible that the ancestor contained the nitrogen fixation genes and the selective sweep simply replaced the ancestral alleles. In this particular case, there is additional evidence that the entire pathway was acquired around roughly the same time from gene order. The gene order between alpha and beta is almost entirely different, with only a few segments containing more than 2-3 genes in the same order, as shown by Bhaya et al. 2007 and confirmed by additional unpublished analysis of the SAGs. One of the few exceptions is the nitrogen fixation pathway, which has essentially the same gene order over more than 20 kbp. Thus, if the ancestor of both alpha and beta contained the nitrogen-fixation pathway, we would expect these genes to be scatter across the genome. We have revised the sentences in question to clarify this point (Lines 260-271).

Page 6. Last paragraph on epistasis references Fig 3C, but I believe it should be Fig 3D.

Fixed.

Page 7. Figure 3 legend. "Note that alpha-2 is identical to gamma here." I believe it should be beta, not gamma.

The reviewer is correct. We have fixed this error.

Page 8. What is the evidence for "at least six independent colonizers"? I could not find the data supporting this claim.

The statement mentioned by the reviewer was based on the maximum number of species clusters we identified in different core genes. However, during the revision, we found that only a handful of genes contained five or more clusters. We did find several tens of genes with four clusters. In addition, Rosen et al. (2018) also found additional 16S clusters at low frequency in the same springs. Based on these results we conservatively estimate that at least four independent strains colonized the caldera, but the number could be much greater. We have revised the text in question accordingly (Lines 336-339) and added Fig. 2 in Appendix 1 to support the conclusion.

Page 9. Line 200: "acting to homogenize the population." It should be specified that the population is only homogenized at these introgressed loci, not genome-wide. Otherwise, the genome-wide species clusters seen in Fig 1 would not be maintained.

It is true that the selective sweeps that lead to diversity throughs only homogenize the introgressed loci. But other hybrid segments could also rise to high frequency in the population during the sweep through hitchhiking. The fact that we observe SNP blocks generated through secondary recombination events of introgressed segments throughout the genome supports this view. While we do not fully understand the dynamics of this process currently, we do feel that the current evidence supports the statement that mixing is occurring throughout the genome and not just at a few loci so we have kept the original statement.

The final sentence (lines 221-222) is vague and uninformative. On the one hand, "investigating whether hybridization plays a major role" is what the current manuscript has already done - depending on what is meant by 'major' (how much of the genome? Or whether there are ecological implications?). It is also not clear what is meant by a predictive theory and 'possible evolutionary scenarios. This should be elaborated upon, otherwise, it is not clear what the authors mean. Otherwise, this sentence could be cut.

We thank the reviewer for their feedback. One possible source of confusion could be that in this sentence we were referring to detecting hybridization in other communities. We have changed “these communities” to “other communities” to make this clearer.

Supplement.

Broadly speaking, I appreciate the thorough and careful analysis of the single cell data. On the other hand, it is hard to evaluate whether these custom analyses are doing what is intended in many cases. Would it be possible to consider an analysis using more established methods, e.g. taking a subset of genomes with 'good' completeness and using Panaroo to find the core and accessory genome, then ClonalFrameML or Gubbins to infer a phylogeny and recombination events? Such analyses could probably be applied to a subset of the sample with relatively complete genomes. I don't want to suggest an overly time-consuming analysis, but the authors could consider what would be feasible.

We have added a comparison between our analysis and that from two other methods, including ClonalFrameML mentioned by the author. One important point that we feel might have been lost in the first version is that our linkage results imply that recombination is not rare such that it can be mapped onto an asexual tree as assumed by ClonalFrameML. Note that this is not simply due to technical limitations due to incomplete coverage and is instead a consequence of the evolutionary dynamics of the population. Consistent with this, we found several inconsistencies in how recombination events were assigned by ClonalFrameML. We have summarized these conclusions in Appendix 7 of the revised manuscript.

Page 8. Line 190. What is meant by 'minimal compositional bias'?

We mean that the sample is not biased towards strains that grow in the lab. We have revised the sentence to clarify.

Page 25. Figure S14 is not referenced in the text.

We have added part of this figure to the main text since it illustrates one of our main results, namely that sites at long genomic distances are essentially unlinked.

Page 26. The 'unlinked controls' (line 530) are very useful, but it would be even more informative to see if these controls also show the same decline in linkage with distance in the genome as observed in the real data. In particular, it would be good to know if the observed rapid decline in linkage with distance in the low-diversity regions is also observed in controls. Currently, it is unclear if this observation might be due to higher uncertainty in inferring linkage in low-diversity regions, which by definition have less polymorphism to include in the linkage calculation.

We thank the reviewer for the suggestion. After further consideration, we have decided to remove the subsection on linkage decrease in the low-diversity regions. We feel such detailed quantitative analysis would be better suited for a more technical paper, which we hope to do at a later time.

Page 26. There are some sections with missing identifiers (Sec ??).

Fixed.

Page 27. The information about the typical breadth of SAG coverage (~30%) would be better to include earlier in the Supplement, and also mentioned in the main text so the reader can more easily understand the nature of the dataset.

We have added an extra figure with the SAG coverages to Appendix 1.

Page 29. Any sensitivity analysis around the S = 0.9 value? Even if arbitrary, could the authors provide justification why they think this value is reasonable?

We have significantly revised this section in response to earlier comments by one of the reviewers. We hope that this would clarify the details of our methods to interested readers. To answer the reviewer’s specific question, we chose this heuristic after examining the fraction of cells of each species in different species clusters. For the clusters assigned to alpha and beta, we found a sharp peak near one and that a cutoff of 0.9 captured most clusters while still being high enough to inconsistent with a mixed cluster.

Page 30. I could not see where Fig. S17 was mentioned in the text. Also, how are 'simple hybrid genes' defined?

We have removed this figure in the revision. The definition of the different types of hybrid genes have been added to the main text in response to a comment from the other reviewer.

Page 36. It is hard to see that divergence is 'high' relative to what reference. Would it be possible to include the expected value (from ref. 12) in the plot, or at least explicitly mentioned in the text?

We have added the mean synonymous and non-synonymous divergences between alpha and beta to the figures for reference.

Page 38. Line 770 "would be comparable to that of beta." This is not necessarily the case since beta could have a different time to its most recent common ancestor. It could have a different time to the last bottleneck or selective sweep, etc.

We thank the reviewer for pointing out this misleading statement. Our point here was that in the first scenario the TMRCA of alpha and beta would be similar since the diversity in the high-diversity alpha genes is similar to beta. We have clarified this statement in the revision.

Page 39. Line 793. The use of the term 'genomic backbone' implies the presence of a clonal frame, which is not what the data seems to support. Perhaps another term such as 'genetic diversity' would more appropriately capture the intended meaning here.

We agree with the reviewer that the low-diversity regions may not be asexual. We used “genomic backbone” to distinguish from the “clonal frame,” which is usually used to mean that the backbone is asexual. We have added a note in the revision to clarify this point.

Page 39. Lines 802-805. I found this explanation hard to follow. Could the logic be clarified?

We simply meant that although the beta distribution is unimodal, it is not consistent with a simple Poisson distribution, unlike in alpha. We have added an extra sentence to clarify this.

Reviewer #2 (Public review):

In this valuable manuscript, Lin et al attempt to examine the role of long non coding RNAs (lncRNAs) in human evolution, through a set of population genetics and functional genomics analyses that leverage existing datasets and tools. Although the methods are incomplete and at times inadequate, the results nonetheless point towards a possible contribution of long non coding RNAs to shaping humans, and suggest clear directions for future, more rigorous study.

Comments on revisions:

I thank the authors for their revision and changes in response to previous rounds of comments. As before, I appreciate the changes made in response to my comments, and I think everyone is approaching this in the spirit of arriving at the best possible manuscript, but we still have some deep disagreements on the nature of the relevant statistical approach and defining adequate controls. I highlight a couple of places that I think are particularly relevant, but note that given the authors disagree with my interpretation, they should feel free to not respond!

(1) On the subject of the 0.034 threshold, I had previously stated:<br /> "I do not agree with the rationale for this claim, and do not agree that it supports the cutoff of 0.034 used below."

In their reply to me, the authors state:<br /> "What we need is a gene number, which (a) indicates genes that effectively differentiate humans from chimpanzees, (b) can be used to set a DBS sequence distance cutoff. Since this study is the first to systematically examine DBSs in humans and chimpanzees, we must estimate this gene number based on studies that identify differentially expressed genes in humans and chimpanzees. We choose Song et al. 2021 (Song et al. Genetic studies of human-chimpanzee divergence using stem cell fusions. PNAS 2021), which identified 5984 differentially expressed genes, including 4377 genes whose differential expression is due to trans-acting differences between humans and chimpanzeees. To the best of our knowledge, this is the only published data on trans-acting differences between humans and chimpanzeees, and most HS lncRNAs and their DBSs/targets have trans-acting relationships (see Supplementary Table 2). Based on these numbers, we chose a DBS sequence distance cutoff of 0.034, which corresponds to 4248 genes (the top 20%), slightly fewer than 4377."

I have some notes here. First, Agoglia et al, Nature, 2021, also examined the nature of cis vs trans regulatory differences between human and chimps using a very similar set up to Song et al; their Supplementary Table 4 enables the discovery of genes with cis vs trans effects although admittedly this is less straightforward than the Song et al data. Second, I can't actually tell how the 4377 number is arrived at. From Song et al, "Of 4,671 genes with regulatory changes between human-only and chimpanzee-only iPSC lines, 44.4% (2,073 genes) were regulated primarily in cis, 31.4% (1,465 genes) were regulated primarily in trans, and the remaining 1,133 genes were regulated both in cis and in trans (Fig. 2C). This final category was further broken down into a cis+trans category (cis- and trans-regulatory changes acting in the same direction) and a cis-trans category (cis- and trans-regulatory changes acting in opposite directions)." Even when combining trans-only and cis&trans genes that gives 2,598 genes with evidence for some trans regulation. I cannot find 4,377 in the main text of the Song et al paper.

Elsewhere in their response, the authors respond to my comment that 0.034 is an arbitrary threshold by repeating the analyses using a cutoff of 0.035. I appreciate the sentiment here, but I would not expect this to make any great difference, given how similar those numbers are! A better approach, and what I had in mind when I mentioned this, would be to test multiple thresholds, ranging from, eg, 0.05 to 0.01 at some well-defined step size.

(2) The authors have introduced a new TFBS section, as a control for their lncRNAs - this is welcome, though again I would ask for caution when interpreting results. For instance, in their reply to me the authors state:<br /> "The number of HS TFs and HS lncRNAs (5 vs 66) alone lends strong evidence suggesting that HS lncRNAs have contributed more significantly to human evolution than HS TFs (note that 5 is the union of three intersections between and the three )."

But this assumes the denominator is the same! There are 35899 lncRNAs according to the current GENCOVE build; 66/35899 = 0.0018, so, 0.18% of lncRNAs are HS. The authors compare this to 5 TFs. There are 19433 protein coding genes in the current GENCOVE build, which naively (5/19433) gives a big depletion (0.026%) relative to the lnc number. However, this assumes all protein coding genes are TFs, which is not the case. A quick search suggests that ~2000 protein coding genes are TFs (see, eg, https://pubmed.ncbi.nlm.nih.gov/34755879/); which gives an enrichment (although I doubt it is a statistically significant one!) of HS TFs over HS lncRNAs (5/2000 = 0.0025). Hence my emphasis on needing to be sure the controls are robust and valid throughout!

(3) In my original review I said:<br /> line 187: "Notably, 97.81% of the 105141 strong DBSs have counterparts in chimpanzees, suggesting that these DBSs are similar to HARs in evolution and have undergone human-specific evolution." I do not see any support for the inference here. Identifying HARs and acceleration relies on a far more thorough methodology than what's being presented here. Even generously, pairwise comparison between two taxa only cannot polarise the direction of differences; inferring human-specific change requires outgroups beyond chimpanzee.

In their reply to me, the authors state:<br /> Here, we actually made an analogy but not an inference; therefore, we used such words as "suggesting" and "similar" instead of using more confirmatory words. We have revised the latter half sentence, saying "raising the possibility that these sequences have evolved considerably during human evolution".

Is the aim here to draw attention to the ~2.2% of DBS that do not have a counterpart? In that case, it would be better to rewrite the sentence to emphasise those, not the ones that are shared between the two species? I do appreciate the revised wording, though.

(4) Finally, Line 408: "Ensembl-annotated transcripts (release 79)" Release 79 is dated to March 2015, which is quite a few releases and genome builds ago. Is this a typo? Both the human and the chimpanzee genome have been significantly improved since then!

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #2 (Public review):

In this valuable manuscript, Lin et al attempt to examine the role of long non coding RNAs (lncRNAs) in human evolution, through a set of population genetics and functional genomics analyses that leverage existing datasets and tools. Although the methods are incomplete and at times inadequate, the results nonetheless point towards a possible contribution of long non coding RNAs to shaping humans, and suggest clear directions for future, more rigorous study.

Comments on revisions:

I thank the authors for their revision and changes in response to previous rounds of comments. As it had been nearly two years since I last saw the manuscript, I reread the full text to familiarise myself again with the findings presented. While I appreciate the changes made and think they have strengthened the manuscript, I still find parts of it a bit too speculative or hyperbolic. In particular, I think claims of evolutionary acceleration and adaptation require more careful integration with existing human/chimpanzee genetics and functional genomics literature.

We thank the reviewer heartfully for the great patience and valuable comments, which have helped us further improve the manuscript. Before responding to comments point by point, we provide a summary here.

(1) On parameters and cutoffs.

Parameters and cutoffs influence data analysis. The large number of Supplementary Notes, Supplementary Figures, and Supplementary Tables indicates that we paid great attention to the influence of parameters and robustness of analyses. Specifically, here we explain the DBS sequence distance cutoff of 0.034, which determines the top 20% genes that most differentiate humans from chimpanzees and influences the gene set enrichment analysis (Figure 2). As described in the revised manuscript, we estimated this cutoff based on Song et al., verified its rationality based on Prufer et al. (Song et al. 2021; Prufer et al. 2017), and measured its influence by examining slightly different cutoff values (e.g., 0.035).

(2) Analyses of HS TFs and HS TF DBSs.

It is desirable to compare the contribution of HS lncRNAs and HS TFs to human evolution. Identifying HS TFs faces the challenges that different institutions (e.g., NCBI and Ensembl) annotate orthologous genes using different criteria, and that multiple human TF lists have been published by different research groups. Recently, Kirilenko et al. identified orthologous genes in hundreds of placental mammals and birds and organized different types of genes into datasets of parewise comparison (e.g., hg38-panTro6) using humans and mice as references (Kirilenko et al. Integrating gene annotation with orthology inference at scale. Science 2023). Based on (a) the many2zero and one2zero gene lists in the “hg38-panTro6” dataset, (b) three human TF lists reported by two studies (Bahram et al. 2015; Lambert et al. 2018) and used in the SCENIC package, we identified HS TFs. The number of HS TFs and HS lncRNAs (5 vs 66) alone lends strong evidence suggesting that HS lncRNAs have contributed more significantly to human evolution than HS TFs (note that 5 is the union of three intersections between <many2zero + one2zero> and the three <human TF list>).

TF DBS (i.e., TFBS) prediction has also been challenging because they are very short (mostly about 10 bp) and TF-DNA binding involves many cofactors (Bianchi et al. Zincore, an atypical coregulator, binds zinc finger transcription factors to control gene expression. Science 2025). We used two TF DBS prediction programs to predict HS TF DBSs, including the well-established FIMO program (whose results have been incorporated into the JASPAR database) (Rauluseviciute et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles Open Access. NAR 2023) and the recently reported CellOracle program (Kamimoto et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 2023). Then, we performed downstream analyses and obtained two major results. One is that on average (per base), fewer selection signals are detected in HS TF DBSs (anyway, caution is needed because TF DBSs are very short); the other is that HS TFs and HS lncRNAs contribute to human evolution in quite different ways (Supplementary Figs. 25 and 26).

(3) On genes with more transcripts may appear as spurious targets of HS lncRNAs.

Now, the results of HS TF DBSs allow us to address the question of whether genes with more transcripts may appear as spurious targets of HS lncRNAs. We note that (a) we predicted HS lncRNA DBSs and HS TF DBSs in the same promoter regions before the same 179128 Ensembl-annotated transcripts (release 79), (b) we used the same GTEx transcript expression matrices in the analyses of HS TF DBSs and HS lncRNA DBSs (the GTEx database includes gene expression matrices and transcript expression matrices, the latter includes multiple transcripts of a gene). Thus, the analyses of HS TF DBSs provide an effective control for examining the question of whether genes with more transcripts may appear as spurious targets of HS lncRNAs, and consequently, cause the high percentages of HS lncRNA-target transcript pairs that show correlated expression in the brain (Figure 3). We find that the percentages of HS TF-target transcript pairs that show correlated expression are also high in the brain, but the whole profile in GTEx tissues is significantly different from that of HS lncRNA DBSs (Figure 3A; Supplementary Figure 25). On the other hand, on the distribution of significantly changed DBSs in GTEx tissues, the difference between HS lncRNA DBSs and HS TF DBSs is more apparent (Figure 3B; Supplementary Figure 26). Together, these suggest that the brain-enriched distribution of co-expressed HS lncRNA-target transcript pairs must arise from HS lncRNA-mediated transcriptional regulation rather than from the transcript number difference.

(4) Additional notes on HS TFs and HS TF DBSs.

First, the “many2zero” and “one2zero” gene lists in the “hg38-panTro6” dataset of Kirilenko et al. provide the most update, but not most complete, data on human-specific genes because “hg38-panTro6” is a pairwise comparison. On the other hand, the Ensembl database also annotates orthologous genes, but lacks such pairwise comparisons as “hg38-panTro6”. Therefore, not all HS genes based on “hg38-panTro6” agree with orthologous genes in the Ensembl database. Second, if HS genes are identified based on both Ensembl and Kirilenko et al., HS TFs will be fewer.

(5) On speculative or hyperbolic claims.

First, the title “Human-specific lncRNAs contributed critically to human evolution by distinctly regulating gene expression” is now further supported by HS TF DBSs analyses. Second, we have carefully revised the entire manuscript, trying to make it more readable, accurate, logically reasonable, and biologically acceptable. Third, specifically, in the revision, we avoid speculative or hyperbolic claims in results, interpretations, and discussions as possible as we can. This includes the tone-down of statements and claims, for example, using “reshape” to replace “rewire” and using “suggest” to replace “indicate”. Since the revisions are pervasive, we do not mark all of them, except those that are directly relevant to the reviewer’s comments.

(1) Line 155: "About 5% of genes have significant sequence differences in humans and chimpanzees," This statement needs a citation, and a definition of what is meant by 'significant', especially as multiple lines below instead mention how it's not clear how many differences matter, or which of them, etc.

Different studies give different estimates, from 1.24% (Ebersberger et al. Genomewide Comparison of DNA Sequences between Humans and Chimpanzees. Am J Hum Genet. 2002) to 5% (Britten RJ. Divergence between samples of chimpanzee and human DNA sequences is 5%, counting indels. PNAS 2002). The 5% for significant gene sequence differences arises when considering a broader range of genetic variations, particularly insertions and deletions of genetic material (indels). To provide more accurate information, we have replaced this simple statement with a more comprehensive one and cited the above two papers.

(2) line 187: "Notably, 97.81% of the 105141 strong DBSs have counterparts in chimpanzees, suggesting that these DBSs are similar to HARs in evolution and have undergone human-specific evolution." I do not see any support for the inference here. Identifying HARs and acceleration relies on a far more thorough methodology than what's being presented here. Even generously, pairwise comparison between two taxa only cannot polarise the direction of differences; inferring human-specific change requires outgroups beyond chimpanzee.

Here, we actually made an analogy but not an inference; therefore, we used such words as “suggesting” and “similar” instead of using more confirmatory words. We have revised the latter half sentence, saying “raising the possibility that these sequences have evolved considerably during human evolution”.

(3) line 210: "Based on a recent study that identified 5,984 genes differentially expressed between human-only and chimpanzee-only iPSC lines (Song et al., 2021), we estimated that the top 20% (4248) genes in chimpanzees may well characterize the human-chimpanzee differences". I do not agree with the rationale for this claim, and do not agree that it supports the cutoff of 0.034 used below. I also find that my previous concerns with the very disparate numbers of results across the three archaics have not been suitably addressed.

(1) Indeed, “we estimated that the top 20% (4248) genes in chimpanzees may well characterize the human-chimpanzee differences” is an improper claim; we made this mistake due to the flawed use of English.

(2) What we need is a gene number, which (a) indicates genes that effectively differentiate humans from chimpanzees, (b) can be used to set a DBS sequence distance cutoff. Since this study is the first to systematically examine DBSs in humans and chimpanzees, we must estimate this gene number based on studies that identify differentially expressed genes in humans and chimpanzees. We choose Song et al. 2021 (Song et al. Genetic studies of human–chimpanzee divergence using stem cell fusions. PNAS 2021), which identified 5984 differentially expressed genes, including 4377 genes whose differential expression is due to trans-acting differences between humans and chimpanzeees. To the best of our knowledge, this is the only published data on trans-acting differences between humans and chimpanzeees, and most HS lncRNAs and their DBSs/targets have trans-acting relationships (see Supplementary Table 2). Based on these numbers, we chose a DBS sequence distance cutoff of 0.034, which corresponds to 4248 genes (the top 20%), slightly fewer than 4377.

(3) If we chose DBS sequence distance cutoff=0.033 or 0.035, slightly more or fewer genes would be determined, raising the question of whether they would significantly influence the downstream gene set enrichment analysis (Figure 2). We found that 91 genes have a DBS sequence distance of 0.034. Thus, if cutoff=0.035, 4248-91=4157 genes were determined, and the influence on gene set enrichment analysis was very limited.

(4) On the disparate numbers of results across the three archaics. Figure 1A is based on Figure 2 in Prufer et al. 2017. At first glance, our Figure 1A indicates that Altai Neanderthal is older than Denisovan (upon kya), making our result “identified 1256, 2514, and 134 genes in Altai Neanderthals, Denisovans, and Vindija Neanderthals” unreasonable. However, Prufer et al. (2017) reported that “It has been suggested that Denisovans received gene flow from a hominin lineage that diverged prior to the common ancestor of modern humans, Neandertals, and Denisovans……In agreement with these studies, we find that the Denisovan genome carries fewer derived alleles that are fixed in Africans, and thus tend to be older, than the Altai Neandertal genome”. This note by Prufer et al. provides an explanation for our result, which is that more genes with large DBS sequence distances were identified in Denisovans than in Altai Neanderthals. Of course, the 1256, 2514, and 134 depend on the cutoff of 0.034. If cutoff=0.035, these numbers change slightly, but their relationships remain (i.e., more genes in Denisovans). We examined multiple cutoff values and found that more genes in Denisovans have large DBS sequence distances than in Altai Neanderthals.

(4) I also think that there is still too much of a tendency to assume that adaptive evolutionary change is the only driving force behind the observed results in the results. As I've stated before, I do not doubt that lncRNAs contribute in some way to evolutionary divergence between these species, as do other gene regulatory mechanisms; the manuscript leans down on it being the sole, or primary force, however, and that requires much stronger supporting evidence. Examples include, but are not limited to:

(1) Indeed, the observed results are also caused by other genomic elements and mechanisms (but it is hardly feasible to identify and differentiate them in a single study), and we do not assume that adaptive evolutionary change is the only driving force. Careful revisions have been made to avoid leaving readers the impression that we have this tendency or hold the simple assumption.

(2) Comparing HS lncRNAs to HS TFs is critical, and we have done this.

(5) line 230: "These results reveal when and how HS lncRNA-mediated epigenetic regulation influences human evolution." This statement is too speculative.

We have toned down the statement, just saying “These results provide valuable insights into when and how HS lncRNA-mediated epigenetic regulation impacts human evolution”.

Line 268: "yet the overall results agree well with features of human evolution." What does this mean? This section is too short and unclear.

(1) First, the sentence “Selection signals in YRI may be underestimated due to fewer samples and smaller sample sizes (than CEU and CHB), yet the overall results agree well with features of human evolution” has been deleted, because CEU, CHB, and YRI samples are comparable (100, 99, and 97, respectively).

(2) Now the sentence has been changed to “These results agree well with findings reported in previous studies, including that fewer selection signals are detected in YRI (Sabeti et al., 2007; Voight et al., 2006)”.

(3) On “This section is too short and unclear” - To make the manuscript more readable, we adopt short sections instead of long ones. This section expresses that (a) our finding that more selection signals were detected in CEU and CHB than in YRI agrees with well-established findings (Voight et al. A Map of Recent Positive Selection in the Human Genome. PLoS Biology 2006; Sabeti et al. Genome-wide detection and characterization of positive selection in human populations. Nature 2007), (b) in considerable DBSs, selection signals were detected by multiple tests.

Line 325: "and form 198876 HS lncRNA-DBS pairs with target transcripts in all tissues." This has not been shown in this paper - sequence based analyses simply identify the “potential” to form pairs.

This section describes transcriptomic analysis using the GTEx data. Indeed, target transcripts of HS lncRNAs are results of sequence-based analysis, and a predicted target is not necessarily regulated by the HS lncRNA in a tissue. Here, “pair” means a pair of HS lncRNA-target transcript whose expression shows significant Pearson correlation in a GTEx tissue (by the way, we do not mean correlation equals regulation; actually, we identified HS lncRNA-mediated transcriptional regulation upon both DBS-targeting relationship and correlation relationship).

Line 423: "Our analyses of these lncRNAs, DBSs, and target genes, including their evolution and interaction, indicate that HS lncRNAs have greatly promoted human evolution by distinctly rewiring gene expression." I do not agree that this conclusion is supported by the findings presented - this would require significant additional evidence in the form of orthogonal datasets.

(1) As mentioned above, we have used “reshape” to replace “rewire” and used “suggest” to replace “indicate”. In addition, we have substantially revised the Discussion, in which this sentence is replaced by “our results suggest that HS lncRNAs have greatly reshaped (or even rewired) gene expression in humans”.

(2) Multiple citations have been added, including Voight et al. 2006 (Voight et al. A Map of Recent Positive Selection in the Human Genome. PLoS Biology 2006) and Sabeti et al. 2007 (Sabeti et al. Genome-wide detection and characterization of positive selection in human populations. Nature 2007).

(3) We have analyzed HS TF DBSs, and the obtained results also support the critical contribution of HS lncRNAs.

I also return briefly to some of my comments before, in particular on the confounding effects of gene length and transcript/isoform number. In their rebuttal the authors argued that there was no need to control for this, but this does in fact matter. A gene with 10 transcripts that differ in the 5' end has 10 times as many chances of having a DBS than a gene with only 1 transcript, or a gene with 10 transcripts but a single annotated TSS. When the analyses are then performed at the gene level, without taking into account the number of transcripts, this could introduce a bias towards genes with more annotated isoforms. Similarly, line 246 focuses on genes with "SNP numbers in CEU, CHB, YRI are 5 times larger than the average." Is this controlled for length of the DBS? All else being equal a longer DBS will have more SNPs than a shorter one. It is therefore not surprising that the same genes that were highlighted above as having 'strong' DBS, where strength is impacted by length, show up here too.

(1) In gene set enrichment analysis (Figure 2, which is a gene-level analysis), when determining genes differentiating humans from chimpanzees based on DBS sequence distance, if a gene has multiple transcripts/DBSs, we choose the DBS with the largest distance. That is, the input to g:Profiler is a non-redundant gene list.

(2) In GTEx data analysis (Figure 3, which is a transcriptome-level analysis), the analyses of HS TF DBSs using the GTEx data provide evidence suggesting that different DBS/transcript numbers of genes are unlikely to cause confounding effects. As explained above, we predicted HS TF DBSs in the same promoter regions of 179128 Ensembl-annotated transcripts (release 79), but Supplementary Figures 25 and 26 are distinctly different from Figure 3AB.

(3) In evolutionary analysis, a gene with 10 DBSs has a higher chance of having selection signals than a gene with 1 DBS. This is biologically plausible, because many conserved genes have novel transcripts whose expression is species-, tissue-, or developmental period-specific, and DBSs before these novel transcripts may differ from DBSs before conserved transcripts.

(4) “line 246 focuses on genes with "SNP numbers in CEU, CHB, YRI are 5 times larger than the average." Is this controlled for the length of the DBS?” - This is a defect. We have now computed SNP numbers per base and used the new table to replace the old Supplementary Table 8. After examining the new table, we find that the major results of SNP analysis remain.

(5) On “Is this controlled for length of the DBS? All else being equal a longer DBS will have more SNPs than a shorter one” - We do not think there are reasons to control for the length of DBSs; also, what “All else being equal” means matters. First, DBS sequences have specific features; thus, the feature of a long DBS is stronger than the feature of a short one, making a long DBS less likely to be generated by chance in the genome and less likely to be predicted wrongly than a short one. This means that longer DBSs are less likely to be false ones (note our explanation that the chance of a DBS of 147 bp, the mean length of DBSs, to be wrongly predicted is extremely low, p<8.2e-19 to 1.5e-48). Second, the difference in length suggests a difference in binding affinity, which in turn influences the regulation of the specific transcripts and influences the analysis of GTEx data. Third, it cannot be excluded that some SNPs may be selection signals (detecting selection signal is challenging, and many selection signals cannot be detected by statistical tests, see Grossman et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 2010).

(6) On “It is therefore not surprising that the same genes that were highlighted above as having 'strong' DBS, where strength is impacted by length” - Indeed, strength is influenced by length, see the above response.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Finally, figure 1 panels D and F are not legible - the font is tiny! There's also a typo in panel A, where "Homo Sapien" should be "Homo sapiens".

(1) “Homo sapien” is changed to “Homo sapiens”.

(2) Even if we double the font size, they are still too small. Inserting a very large panel D into Figure 1 will make Figure 1 ugly, and converting Figure 1D into an independent figure is unnecessary. Actually, panels 1D and F are illustrative figures; the full Fig.1D is Supplementary Figure 6, and the full Fig.1F is Figure 3. We have revised Fig.1’s legend to explain these.

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Reviewer #1 (Public review):

This valuable study explores the regulatory mechanisms underlying the regional distribution of enteroendocrine cell subtypes in the Drosophila midgut. The regional distribution of EE cell subtypes is carefully documented, and the data convincingly show that each EE cell subtype has a unique spatial pattern. The study aims at determining how the spatial distribution of EE cell subtypes is established and maintained, and explores the roles of three pathways: Notch, WNT, and BMP. The data show evidence that Notch signaling regulates the subtype specificity, being necessary for the specification of Type II, but not Type I and III EE cell subtype specification. The immunofluorescence data in Figure 3 are convincing, but the analysis is incomplete due to a lack of quantification. How Notch signaling activity relates to the emergence of the regional EE cell patterns remains unclear.

As WNT and BMP are known as morphogens, the study explores their expression patterns and their roles in establishing and maintaining the subtype identities. The observed patterns of WNT and BMP are consistent with earlier studies. Manipulation of WNT and BMP pathway activities in intestinal stem cells is shown to have some region-specific effects on specific EE cell subtypes. The overall conclusion that both WNT and BMP have local effects on EE cell subtypes is based on solid evidence. However, the study falls short in achieving its main objective, i.e., to explain the regional subtype patterns by the action of WNT and BMP gradients. Despite displaying a large volume of phenotypic data in Figures 4-7, the study remains incomplete in providing sufficient evidence to support the models shown in Figures 7 M and N. The main challenge is that the reader is provided with a large volume of individual data fragments of selected regions (e.g., Figures 4 and 5) or images of whole midgut without proper quantification (Figure 7). There is not sufficient effort made to display the data in a way that allows observing changes in the global patterns of EE cell subtypes throughout the midgut and compare these patterns with the observed WNT and BMP gradients.

Reviewer #3 (Public review):

Summary:

The authors aimed to elucidate the mechanisms underlying the regional patterning of enteroendocrine cell (EE) subtypes along the Drosophila midgut. Through detailed immunohistochemical mapping and genetic perturbation of Notch, WNT, and BMP signaling pathways, they sought to determine how extrinsic morphogen gradients and intrinsic stem cell identity contribute to EE diversity.

Strengths:

A major strength of this work is the meticulous regional analysis of EE pairs and the use of multiple genetic tools to manipulate signaling pathways in a spatiotemporally controlled manner. The data robustly demonstrate that WNT and BMP signaling gradients play key roles in specifying EE subtypes and division modes across different gut regions.

Weaknesses:

However, the study does not fully explore the mechanistic basis for the region-specific dependence on Notch signaling. Additionally, while the authors propose that symmetric divisions occur in R1a and R4b, the observed heterogeneity in CCHa2 expression within AstC+ pairs in R4b suggests that asymmetric mechanisms may still be at play, possibly involving apical-basal polarity as previously reported.

Appraisal of achievements:

The authors successfully achieve their aims by providing a compelling model in which intercalated WNT and BMP gradients regulate EE subtype specification and EEP division modes. The genetic data strongly support the conclusion that these pathways are central to establishing regional EE diversity during pupal development.

Author response:

We would like to express our gratitude to all three reviewers for their time and valuable feedback on the manuscript. Below, we provide our point-by-point responses to their comments. Additionally, we summarize here the experiments we plan to conduct in accordance with the reviewers' suggestions:

Revision plan 1. To further explore the mechanisms of Notch signaling in the decision of regional EE pattern.

Our observation of EE subtype changes in Notch mutant clones revealed that Notch is required for the specification of Type II EEs, but whether it promotes the generation of Type III EEs is not quite clear. In this revision, we will complete the quantification of Type I and Type III EEs in Notch mutant clones to demonstrate whether Notch signaling participate the determination of these two EE subtypes. Further, we will attempt to combine Notch mutant with different manipulation of WNT and BMP gradients to investigate their interplays.

Revision plan 2. To supplement the global pattern of WNT and BMP gradient along the whole gut.

The levels of WNT and BMP gradients are variable in different gut regions both under normal condition and genetic manipulation, leading to different outcomes of EE subtype composition. To further support our model, we will supply the changes of WNT and BMP gradients along the whole gut after genetic manipulation, and perform semi-quantification of their levels to correlate with EE subtype compositions. Additionally, we will also test the gradient levels at different time point during pupal stage to interpret the establishment of regional identity during the development.

Revision plan 3. To investigate the involvement of apical-basal polarity in the determination of regional EE diversity.

Although we have demonstrated WNT and BMP gradients orchestrate the regional EE identity, but some observations cannot be fully explained by their roles, such as asymmetric expression of CCHa2 in EE pairs from R4b. A potential mechanism is apical-basal polarity, which has been reported to determine cell fate of ISC progenies at pupal stage. We will specifically knockdown or overexpress key genes related to apical-basal polarity in ISCs or EEs to test whether they are involved preliminarily.

Please find our detailed point-by-point responses below.

Public Reviews:

Reviewer #1 (Public review):

This valuable study explores the regulatory mechanisms underlying the regional distribution of enteroendocrine cell subtypes in the Drosophila midgut. The regional distribution of EE cell subtypes is carefully documented, and the data convincingly show that each EE cell subtype has a unique spatial pattern. The study aims at determining how the spatial distribution of EE cell subtypes is established and maintained, and explores the roles of three pathways: Notch, WNT, and BMP. The data show evidence that Notch signaling regulates the subtype specificity, being necessary for the specification of Type II, but not Type I and III EE cell subtype specification. The immunofluorescence data in Figure 3 are convincing, but the analysis is incomplete due to a lack of quantification. How Notch signaling activity relates to the emergence of the regional EE cell patterns remains unclear.

Indeed, the role of Notch signaling in regional EE determination was not fully characterized in this work. As the requirement of Notch activation for the differentiation of enterocytes, introduction of Notch or Delta mutant led to rapid accumulation of ISCs and EEs, making it being a challenge to dive into the details of how EE subtypes were generated. We will try to complete the quantification of Type I and Type III EEs in the Notch mutant clones from different gut regions to figure out whether Notch could influence the specification of these two EE subtypes. Additionally, different from WNT and BMP gradients, Notch signaling can only function locally and is not significantly changed along the whole gut, including Type II EE-enriched R1a and Type I EE-enriched R4b, which implies that function of Notch signaling may can be overridden by the impact of specific combination of WNT and BMP gradients. To test this hypothesis, we will attempt to combine Notch mutant with the activation or inhibition of WNT and BMP signaling since pupal stage, and further examine whether the regional EE identity could be altered, especially in R1a and R4b regions.

As WNT and BMP are known as morphogens, the study explores their expression patterns and their roles in establishing and maintaining the subtype identities. The observed patterns of WNT and BMP are consistent with earlier studies. Manipulation of WNT and BMP pathway activities in intestinal stem cells is shown to have some region-specific effects on specific EE cell subtypes. The overall conclusion that both WNT and BMP have local effects on EE cell subtypes is based on solid evidence. However, the study falls short in achieving its main objective, i.e., to explain the regional subtype patterns by the action of WNT and BMP gradients. Despite displaying a large volume of phenotypic data in Figures 4-7, the study remains incomplete in providing sufficient evidence to support the models shown in Figures 7 M and N. The main challenge is that the reader is provided with a large volume of individual data fragments of selected regions (e.g., Figures 4 and 5) or images of whole midgut without proper quantification (Figure 7). There is not sufficient effort made to display the data in a way that allows observing changes in the global patterns of EE cell subtypes throughout the midgut and compare these patterns with the observed WNT and BMP gradients.

As the variation of WNT and BMP gradients along the whole gut, manipulating these two pathways is not able to align their activation levels in different gut regions. This forced us to analyze the change of each region separately, making it to be a challenge to provide a comprehensive global overview. We will supplement the comprehensive profile of WNT and BMP activity under the manipulation of these two signaling pathways to correlated with the change of EE identity, and also try to perform a semi-quantitative interpretation to further support the model in Figure 7M and 7N.

Reviewer #2 (Public review):

Summary:

By labeling the three major enteroendocrine cell markers - AstC, Tk, and CCHa2-the authors systematically investigated the distribution of distinct EE subtypes along the Drosophila midgut, as well as their emergence via symmetric and asymmetric divisions of enteroendocrine progenitor cells. Moreover, they dissected the molecular mechanisms underlying regional patterning by modulating Wnt and BMP signaling pathways, revealing that these compartment boundary signals play key roles in regulating EE subtype diversity.

Strengths:

This work establishes a solid methodological and conceptual foundation for future studies on how stem cells acquire positional identity and modulate region-specific behaviors.

Weaknesses:

Given that the transcriptional profiles of intestinal stem cells across different regions are highly similar, it is reasonable to hypothesize that the behavior of ISCs and enteroendocrine precursor cells may be regulated non-autonomously, potentially through interactions with enterocytes, which exhibit more distinct region-specific characteristics.

This is a quite complicated point to discuss. Drosophila adult midgut is established by pISCs (pupal ISCs), which arise from AMPs (adult midgut progenitors) in larval midgut. AMPs are encased by PCs (peripheral cells) to be islands, scattered throughout the entire larval midgut by mid L3 stage (Mathur D. et al. Science. 2010). After pupariation, larval midgut is delaminated to become the yellow body and finally meconium in the pupal midgut. Simultaneously, PCs break down and die, allowing AMPs to give rise to the presumptive adult epithelium (generating enterocyte precursors) and the specification of ISCs in the adult midgut (Jiang H, Edgar BA. Development. 2009; Micchelli CA. et al. Gene Expr Patterns. 2011). During the pupal stage, pISCs only proliferate to generate new ISCs and EE lineages, while adult enterocytes start to appear after eclosion (Takashima S. et al. Dev Biol. 2011). This rules out the possibility that the interaction with enterocytes regulates regional ISC identity during pupal stage.

However, whether AMPs already acquire the regional identity during larval stage, and whether pISCs interact with enterocyte precursors at pupal stage, are not quite clear. Our study revealed that pISCs can be influenced by WNT and BMP gradients to acquire regional identity, and further establish regional EE diversity. The change of WNT and BMP gradients during the metamorphosis will be supplemented in revision. While WNT and BMP signaling ligands are provided by muscles and adult enterocytes, and even other surrounding tissues, to regulate regional ISC identity, which indicates that non-autonomous mechanisms indeed exist.

Reviewer #3 (Public review):

Summary:

The authors aimed to elucidate the mechanisms underlying the regional patterning of enteroendocrine cell (EE) subtypes along the Drosophila midgut. Through detailed immunohistochemical mapping and genetic perturbation of Notch, WNT, and BMP signaling pathways, they sought to determine how extrinsic morphogen gradients and intrinsic stem cell identity contribute to EE diversity.

Strengths:

A major strength of this work is the meticulous regional analysis of EE pairs and the use of multiple genetic tools to manipulate signaling pathways in a spatiotemporally controlled manner. The data robustly demonstrate that WNT and BMP signaling gradients play key roles in specifying EE subtypes and division modes across different gut regions.

Weaknesses:

However, the study does not fully explore the mechanistic basis for the region-specific dependence on Notch signaling. Additionally, while the authors propose that symmetric divisions occur in R1a and R4b, the observed heterogeneity in CCHa2 expression within AstC+ pairs in R4b suggests that asymmetric mechanisms may still be at play, possibly involving apical-basal polarity as previously reported.

As previously mentioned, we acknowledge that the role of Notch signaling in regional EE determination remains further exploration. We will supplement the quantification of Type I and Type III EEs in Figure 3 and Figure S4, and further combine Notch mutant with activation or inhibition of WNT and BMP signaling to test whether they have any interplays, especially in R1a and R4b.

Apical-basal polarity has been reported to determine the precise segregation of Pros to control ISC number and cell fate at the pupal stage (Wu S. et al. Cell Rep. 2023). During this time, generation of regional EEs are completed and may also be affected except for the influence of Notch, WNT and BMP pathways. Therefore, the apical-basal polarity is quite a potential mechanism to induce asymmetric cell division in R4b, which we will perform experiments to test.

Appraisal of achievements:

The authors successfully achieve their aims by providing a compelling model in which intercalated WNT and BMP gradients regulate EE subtype specification and EEP division modes. The genetic data strongly support the conclusion that these pathways are central to establishing regional EE diversity during pupal development.

Reviewer #1 (Public review):

Summary:

This study asks how selection for male aggressiveness affects life-history and reproductive fitness traits in Drosophila melanogaster males.

Strengths:

Multiple comprehensive assays are used to address the question.

Weaknesses:

(1) The flies used for comparisons are inadequate. Behavioral assays compare Bully males mated to non-coevolved Cs females with Cs males mated to coevolved Cs females.

(2) Lifespan analysis is done on male progeny of Cs females mated to either genetically more distant Bully or co-evolved Cs males; the longer lifespan and performance on the former is interpreted as a trade-off with aggressiveness, rather than a simple explanation of hybrid vigor.

(3) Differences in CHCs between Bully and Cs males and Cs females mated to those males are not shown to cause differences in measured behavioral outcomes.

Author response:

eLife Assessment

This valuable study addresses the effects of selection on aggression on fitness and life-history trade-offs in Drosophila melanogaster. However, the evidence presented is incomplete and does not support the claims proposed in the study of increased survival of highly aggressive males at the expense of reproductive success and shorter mating duration. The main limitation of the study is the choice to use males from only one aggressive Drosophila line in combination with CantonS females, that do not allow disambiguation between nonaggression-related factors, such as hybrid vigor and aggression-related factors influencing mating and lifespan.

We would like to clarify the points raised in the eLife assessment.

The report states that we relied on a single line of hyper-aggressive males tested with CantonS females, and implies that Bully and Cs have not co-evolved. This is a misunderstanding: Bully flies were derived from Cs population. Thus, Bully and Cs have co-evolved. In addition to the Bully A line presented in the main figures of the manuscript, we replicated several of our findings with a second independent selected line, Bully B. Results from courtship assays involving both Bully A and Bully B couples males and females were presented in Figure Supp1. We apologies for not having made this more explicit in the original manuscript, which we will correct. These experiments should alleviate the concerns from the reviewers; they demonstrate that our conclusions are supported by two independent hyper-aggressive lines, and these include assays with selected male and female flies.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study asks how selection for male aggressiveness affects life-history and reproductive fitness traits in Drosophila melanogaster males.

Strengths:

Multiple comprehensive assays are used to address the question.

We thank the reviewer for recognizing these strengths.

Weaknesses:

(1) The flies used for comparisons are inadequate. Behavioral assays compare Bully males mated to non-coevolved Cs females with Cs males mated to coevolved Cs females.

We thank the reviewer for this comment, which made us realize that we had not sufficiently highlighted some of our experiments. The Bully lines used in our work were derived from Canton-S flies and thus did co-evolve with Cs. As originally described by Penn et al. (2010), highly aggressive “Bully” lines were generated through selective breeding from Canton-S males that consistently won aggressive encounters. After 34–37 generations, stable Bully lines were established. Thus, Bully and Cs flies have co-evolved and 2) the selection applied was male-specific. Independent selection replicates produced distinct lines, including Bully A and Bully B. Previous studies only characterized Bully A (Penn et al., 2010; Chowdhury et al., 2017), but our work includes both Bully A and Bully B (Fig. S1).

The rationale for pairing Bully or Cs males with Cs females (with which both male types co-evolved) follows the approach used by Dierick et al. (2006), who investigated how the male-specific selection for aggression affected courtship and mating behaviors by testing them with standard Canton-S females. This design allows to isolate the effects of male genotype and behavior on courtship and mating outcomes, avoiding confounding effects from female behavioral changes.

We initially compared selected Bully pairs (Bully males × Bully females) (Fig. S1) with Cs pairs and observed similarly shortened mating durations in both Bully × Bully and Bully × Cs matings (Fig. S1, Fig. 1F and G). Thus, the reduction in mating duration arises specifically from Bully males. We therefore chose to use Cs females as a standard background to assess the consequences of male-specific selection for aggression on reproductive behaviors.

(2) Lifespan analysis is done on male progeny of Cs females mated to either genetically more distant Bully or co-evolved Cs males; the longer lifespan and performance on the former is interpreted as a trade-off with aggressiveness, rather than a simple explanation of hybrid vigor.

We appreciate this comment, which again stems from a poor explanation from our part about the origin of the Bully line in the original manuscript. The Bully flies were derived from the same original population as the Cs line. Hybrid vigor typically arises when crossing individuals from distinct populations, which is not the case here as both Bully and CS come from the same population.

To further support our conclusions, we conducted additional experiments using progeny from within-line crosses (Bully males × Bully females) and results revealed the same phenotype: the progeny of these flies also exhibited significantly longer lifespans than Cs males x Cs females progeny. This finding argues against hybrid vigor as the main explanation for the observed phenotype, since both the Bully and Cs crosses result in inbreeding, yet give longer lifespan in Bully. We will include these additional longevity data (currently not included in the manuscript) to strengthen our results and reinforce our interpretation.

(3) Differences in CHCs between Bully and Cs males and Cs females mated to those males are not shown to cause differences in measured behavioral outcomes.

We thank the reviewer for raising this important point regarding causality. One way to establish a causal link between differences in CHCs observed in Bully and Cs flies and the corresponding behavioral outcomes would be to experimentally manipulate CHC profiles. For instance, one could perfume oenocyte-less males with the compounds found in higher abundance in Bully flies, then perform behavioral assays to assess causality. We agree that such experiments would be highly informative in determining the functional roles of specific CHCs elevated in Bully males. However, this approach is technically challenging, as the perfuming technique must be optimized to transfer precise amounts of each compound. For example, this method can be used to gradually perfume flies to assess dose–response behavioral effects, whereas matching exactly the natural concentrations found in individuals, especially given inter-individual variability, remains difficult.

We considered conducting such experiments during our study but did not pursue them for these technical reasons. Nevertheless, we can include a statement in the Discussion acknowledging this as an important future direction to test the causal relationship between CHC variation and behavior.

Reviewer #2 (Public review):

Summary:

The authors compare "Bully" lines, selected for male aggression, to Canton-S controls and find that Bully males have lower mating success, shorter mating durations, and remate sooner. Chemical analyses show Bully males have distinct cuticular hydrocarbons (CHC) signatures and transfer markedly less cVA to females, offering a plausible mechanistic link to weaker mate-guarding.

Paradoxically, Bully males live longer and remain fertile at older ages when CS males no longer mate, indicating a shift in the reproduction-survival trade-off in aggression-selected populations.

Importantly, the work sheds light on proximate mechanisms, demonstrating that shifts in CHCs and pheromone transfer co-occur with changes in fitness traits, thus offering new entry points for understanding life-history evolution.

We thank the reviewer for this positive summary of our work.

Strengths:

The manuscript's strengths lie in its comprehensive and integrative approach framed within an evolutionary context. By combining behavioral assays, chemical profiling, and lifespan measurements, the authors reveal a coherent pattern linking aggression selection to life-history trade-offs. The direct quantification of cVA in female reproductive tracts after mating provides a particularly compelling mechanistic correlate, strengthening the link between behavior and chemical signaling. Findings on altered 5-T and 5-P levels further highlight how chemical communication shapes mating and mate-guarding strategies. Analytical approaches are largely rigorous, and the results provide valuable insights into the pleiotropic effects of selection on socially relevant traits. The study will be of interest to Drosophila biologists working on sexual selection, behavioral evolution, and aging.

We thank the reviewer for recognizing the integrative design and mechanistic contributions of our study.

Weaknesses:

The weaknesses are primarily conceptual rather than procedural. The generality of the findings is uncertain, as selection appears to be represented by only one (and a second closely related) Bully line, limiting conclusions about selection responses versus line-specific drift or founder effects. The causal link between aggression selection and increased longevity is not established: the data show a correlated shift but do not identify mechanisms underlying lifespan extension. In several places, the manuscript uses causal language (e.g., that selection 'influences' longevity or mating strategy) where association would be more accurate; this should be toned down to avoid overstatement. Ecological relevance is also not addressed, since laboratory conditions may bias the balance between costs and benefits of aggression compared with variable natural environments. Addressing these points would strengthen both the impact and clarity of the study.

(1) Generality of findings and potential line effects

We agree that our results presented in the main figures of the manuscript relied mainly on one Bully line (Bully A). To address potential line-specific effects, we replicated key courtship experiments with another independent line, Bully B, selected in parallel from the same Canton-S stock but through distinct selection replicates. The results obtained from Bully B closely matched those from Bully A, suggesting that the observed phenotypes are consistent consequences of aggression selection rather than random drift or founder effects.

(2) Causality versus correlation

We concur that some sentences in the manuscript could overstate causal interpretations. We will revise the text to clearly distinguish correlation from causation and to avoid implying direct causal relationships where data only support association.

(3) Ecological relevance

We appreciate this point. Our experiments were performed under controlled laboratory conditions, which may not fully capture the ecological contexts shaping the costs and benefits of aggression. We will acknowledge this limitation and expand the Discussion to consider how environmental variability could modulate the fitness trade-offs associated with aggression in natural populations.

We thank both reviewers for their constructive feedback, which will help us strengthen the rigor and clarity of the manuscript. We believe that the additional results and revisions will satisfactorily address their concerns.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This unique study reports original and extensive behavioral data collected by the authors on 21 living mammal taxa in zoo conditions (primates, tree shrew, rodents, carnivorans, and marsupials) on how descent along a vertical substrate can be done effectively and securely using gait variables. Ten morphological variables reflecting head size and limb proportions are examined in relationship to vertical descent strategies and then applied to reconstruct modes of vertical descent in fossil mammals.

Strengths:

This is a broad and data-rich comparative study, which requires a good understanding of the mammal groups being compared and how they are interrelated, the kinematic variables that underlie the locomotion used by the animals during vertical descent, and the morphological variables that are associated with vertical descent styles. Thankfully, the study presents data in a cogent way with clear hypotheses at the beginning, followed by results and a discussion that addresses each of those hypotheses using the relevant behavioral and morphological variables, always keeping in mind the relationships of the mammal groups under investigation. As pointed out in the study, there is a clear phylogenetic signal associated with vertical descent style. Strepsirrhine primates much prefer descending tail first, platyrrhine primates descend sideways when given a choice, whereas all other mammals (with the exception of the raccoon) descend head first. Not surprisingly, all mammals descending a vertical substrate do so in a more deliberate way, by reducing speed, and by keeping the limbs in contact for a longer period (i.e., higher duty factors).

Weaknesses:

The different gait patterns used by mammals during vertical descent are a bit more difficult to interpret. It is somewhat paradoxical that asymmetrical gaits such as bounds, half bounds, and gallops are more common during descent since they are associated with higher speeds and lower duty factors. Also, the arguments about the limb support polygons provided by DSDC vs. LSDC gaits apply for horizontal substrates, but perhaps not as much for vertical substrates.

We analyzed gait patterns using methods commonly found in the literature and discussed our results accordingly. However, the study of limbs support polygons was indeed developed specifically for studying locomotion on horizontal supports, and may not be applicable for studying vertical locomotion, which is in fact a type of locomotion shared by all arboreal species. In the future, it would be interesting to consider new methods for analyzing vertical gaits.

The importance of body mass cannot be overemphasized as it affects all aspects of an animal's biology. In this case, larger mammals with larger heads avoid descending head-first. Variation in trunk/tail and limb proportions also covaries with different vertical descent strategies. For example, a lower intermembral index is associated with tail-first descent. That said, the authors are quick to acknowledge that the five lemur species of their sample are driving this correlation. There is a wide range of intermembral indices among primates, and this simple measure of forelimb over hindlimb has vital functional implications for locomotion: primates with relatively long hindlimbs tend to emphasize leaping, primates with more even limb proportions are typically pronograde quadrupeds, and primates with relatively long forelimbs tend to emphasize suspensory locomotion and brachiation. Equally important is the fact that the intermembral index has been shown to increase with body mass in many primate families as a way to keep functional equivalence for (ascending) climbing behavior (see Jungers, 1985). Therefore, the manner in which a primate descends a vertical substrate may just be a by-product of limb proportions that evolved for different locomotor purposes. Clearly, more vertical descent data within a wider array of primate intermembral indices would clarify these relationships. Similarly, vertical descent data for other primate groups with longer tails, such as arboreal cercopithecoids, and particularly atelines with very long and prehensile tails, should provide more insights into the relationship between longer tail length and tail-first descent observed in the five lemurs. The relatively longer hallux of lemurs correlates with tail-first descent, whereas the more evenly grasping autopods of platyrrhines allow for all four limbs to be used for sideways descent. In that context, the pygmy loris offers a striking contrast. Here is a small primate equipped with four pincer-like, highly grasping autopods and a tail reduced to a short stub. Interestingly, this primate is unique within the sample in showing the strongest preference for head-first descent, just like other non-primate mammals. Again, a wider sample of primates should go a long way in clarifying the morphological and behavioral relationships reported in this study.

We agree with this statement. In the future, we plan to study other species, particularly large-bodied ones with varied intermembral indexes.

Reconstruction of the ancient lifestyles, including preferred locomotor behaviors, is a formidable task that requires careful documentation of strong form-function relationships from extant species that can be used as analogs to infer behavior in extinct species. The fossil record offers challenges of its own, as complete and undistorted skulls and postcranial skeletons are rare occurrences. When more complete remains are available, the entire evidence should be considered to reconstruct the adaptive profile of a fossil species rather than a single ("magic") trait.

We completely agree with this, and we would like to emphasize that our intention here was simply to conduct a modest inference test, the purpose of which is to provide food for thought for future studies, and whose results should be considered in light of a comprehensive evolutionary model.

Reviewer #2 (Public review):

Summary:

This paper contains kinematic analyses of a large comparative sample of small to medium-sized arboreal mammals (n = 21 species) traveling on near-vertical arboreal supports of varying diameter. This data is paired with morphological measures from the extant sample to reconstruct potential behaviors in a selection of fossil euarchontaglires. This research is valuable to anyone working in mammal locomotion and primate evolution.

Strengths:

The experimental data collection methods align with best research practices in this field and are presented with enough detail to allow for reproducibility of the study as well as comparison with similar datasets. The four predictions in the introduction are well aligned with the design of the study to allow for hypothesis testing. Behaviors are well described and documented, and Figure 1 does an excellent job in conveying the variety of locomotor behaviors observed in this sample. I think the authors took an interesting and unique angle by considering the influence of encephalization quotient on descent and the experience of forward pitch in animals with very large heads.

Weaknesses:

The authors acknowledge the challenges that are inherent with working with captive animals in enclosures and how that might influence observed behaviors compared to these species' wild counterparts. The number of individuals per species in this sample is low; however, this is consistent with the majority of experimental papers in this area of research because of the difficulties in attaining larger sample sizes.

Yes, that is indeed the main cost/benefit trade-off with this type of study. Working with captive animals allows for large comparative studies, but there is a risk of variations in locomotor behavior among individuals in the natural environment, as well as few individuals per species in the dataset. That is why we plan and encourage colleagues to conduct studies in the natural environment to compare with these results. However, this type of study is very time-consuming and requires focusing on a single species at a time, which limits the comparative aspect.

Figure 2 is difficult to interpret because of the large amount of information it is trying to convey.

We agree that this figure is dense. One possible solution would be to combine species by phylogenetic groups to reduce the amount of information, as we did with Fig. 3 on the dataset relating to gaits. However, we believe that this would be unfortunate in the case of speed and duty factor because we would have to provide the complete figure in SI anyway, as the species-level information is valuable. We therefore prefer to keep this comprehensive figure here and we will enlarge the data points to improve their visibility, and provide the figure with a sufficiently high resolution to allow zooming in on the details.

Reviewer #3 (Public review):

Summary:

Different types of retinal ganglion cell (RGC) have different temporal properties - most prominently a distinction between sustained vs. transient responses to contrast. This has been well established in multiple species, including mouse. In general, RGCs with dendrites that stratify close to the ganglion cell layer (GCL) are sustained; whereas those that stratify near the middle of the inner plexiform layer (IPL) are transient. This difference in RGC spiking responses aligns with similar differences in excitatory synaptic currents as well as with differences in glutamate release in the respective layers - shown previously and here, with a glutamate sensor (iGluSnFR) expressed in the RGCs of interest. Differences in glutamate release were not explained by differences in the distinct presynaptic bipolar cells' voltage responses, which were quite similar to one another. Rather, the difference in transient vs. sustained responses seems to emerge at the bipolar cell axon terminals in the form of glutamate release. This difference in the temporal pattern of glutamate release was correlated with differences in the size of synaptic ribbons (larger in the bipolar cells with more sustained responses), which also correlated with a greater number of vesicles in the vicinity of the larger ribbons.

The main conclusion of the study relates to a correlation (because it is difficult to manipulate ribbon size or vesicle density experimentally): the bipolar cells with increased ribbon size/vesicle number would have a greater possibility of sustained release, which would be reflected in the postsynaptic RGC synaptic currents and RGC firing rates. This model proposes a mechanism for temporal channels that is independent of synaptic inhibition. Indeed, some experiments in the paper suggest that inhibition cannot explain the transient nature of glutamate release onto one of the RGC types. Still, it is surprising that such a diverse set of inhibitory interneurons in the retina would not play some role in diversifying the temporal properties of RGC responses.

Strengths:

(1) The study uses a systematic approach to evaluating temporal properties of retinal ganglion cell (RGC) spiking outputs, excitatory synaptic inputs, presynaptic voltage responses, and presynaptic glutamate release. The combination of these experiments demonstrates an important step in the conversion from voltage to glutamate release in shaping response dynamics in RGCs.

(2) The study uses a combination of electrophysiology, two-photon imaging and scanning block face EM to build a quantitative and coherent story about specific retinal circuits and their functional properties.

Weaknesses:

(1) There were some interesting aspects of the study that were not completely resolved, and resolving some of these issues may go beyond the current study. For example, it was interesting that different extracellular media (Ames medium vs. ACSF) generated different degrees of transient vs. sustained responses in RGCs, but it was unclear how these media might have impacted ion channels at different levels of the circuit that could explain the effects on temporal tuning.

(2) It was surprising that inhibition played such a small role in generating temporal tuning. The authors explored this further in the revision, which supported the original claim that inhibition plays a minor role in glutamate release dynamics from the bipolar cells under study.

Author response:

The following is the authors’ response to the current reviews.

Reviewer #2 had several remaining suggestions:

In some instances, the authors face well-known limitations. For example, bath application of drugs. Blockers of Gly and Gaba receptors are likely problematic when studying a network that includes a diverse set of inhibitory interneurons. Likewise, the results derived from application of AMPAR and KAR blockers should impact HC cell fxn, and presumably inner retina interneuron networks. In the Discussion the authors are encouraged to address more of these concerns (e.g., Discussion line 709).

Rather than concluding that the bath application of drugs is without complications, they can conclude that under the experimental conditions, glutamate release from these On-bipolars continues to exhibit Transient and Sustained release. This is really the key point of their study.

This is a good suggestion.  We have added a discussion of the complications of the pharmacology starting on line 754.  

If indeed sustained release is a reflection of higher release rates, ribbon size is what point to but, there are many other possibilities, such as SV recycling, or recruitment of reserve pools of SVs, fusion machinery, Cav channel behavior. The authors could cite more literature in the Discussion.

We added a sentence to this effect in the discussion, starting on line 866.

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review): 

Summary: 

In the retina, parallel processing of cone photoreceptor output under bright light conditions dissects critical features of our visual environment and is fundamental to visual function. Cone photoreceptor signals are sampled by several types of bipolar cells and passed onto the ganglion cells. At the output of retinal processing, retinal ganglion cells send about 40 different codes of the visual scene to the brain for further processing. In this study, the authors focus on whether subtype-specific differences in the size of synaptic ribbon-associated vesicle pools of bipolar cells contribute to different retinal ganglion cell (RGC) responses. Specifically, inputs to ON alpha RGCs producing transient versus sustained kinetics (ON-S vs. ON-T, respectively) are compared. The authors first demonstrate that ON-S vs. ON-T RGCs are readily identifiable in a whole mount preparation and respond differently to both static and to a spatially uniform, randomly fluctuating (Gaussian noise) light stimulus. Liner-nonlinear (LN) models were used to estimate the transformation between visual input and excitatory synaptic input for each RGCs; these models suggested the presence of transient versus sustained kinetics already in the excitatory inputs to ON-T and ON-S RGCs. Indeed, the authors show that (glutamatergic) excitatory inputs to ON-S vs. ON-T RGCs are of distinct kinetics. The subtypes of bipolar cells providing input to ON-S are known (i.e., type 6 and 7), but the source of excitatory bipolar inputs to ON-T RGCs needed to be determined. In a tedious process, it is elegantly shown here that ON-T RGCs receive most of their excitatory inputs from type 5 and 6 bipolars. Interestingly, the temporal properties of light-evoked responses of type 5, 6, and 7 bipolars recorded from the somas were indistinguishable and rather sustained, suggesting that the origin of transient kinetics of excitatory inputs to ON-T RGCs suggested by the LN model might be found in the processing of visual signals at the bipolar cell axon terminal. Blocking GABA- or glycinergic inhibitory inputs did not alter the light-evoked excitatory input kinetics to ON-T and ON-S RGCs. Twophoton glutamate sensor imaging revealed significantly faster kinetics of light-evoked glutamate signals at ON-T versus ON-S RGCs. Detailed EM analysis of bipolar cell ribbon synapses onto ON-T and ON-S RGCs revealed fewer ribbon-associated vesicles at ON-T synapses, which is consistent with stronger paired-flash depression of lightevoked excitatory currents in ON-T RGCS versus ON-S RGCs. This study suggests that bipolar subtype-specific differences in the size of synaptic ribbon-associated vesicle pools contribute to transient versus sustained kinetics in RGCs. 

Strengths: 

The use of multiple, state-of-the-art tools and approaches to address the kinetics of bipolar to ganglion cell synapse in an identified circuit. 

Weaknesses: 

For the most part, the data in the paper support the conclusions, and the authors were careful to try to address questions in multiple ways. Two-photon glutamate sensor imaging experiment showing that blocking GABA- and glycinergic inhibition does not change the kinetics of light-evoked glutamate signals at ON-T RGCs would strengthen the conclusion that bipolar subtype-specific differences in the size of synaptic ribbon-associated vesicle pools contribute to transient versus sustained kinetics in RGCs. 

Thank you for this suggestion. We have revised the text throughout to be careful not to imply that amacrine cells have no role in shaping EPSCs and spike output, but instead that the transience of the On-T responses persists without amacrine cells (see for example lines 91, 450-453, 514-518, 696-714). We have also added additional iGluSnFR experiments to the paper to further test this conclusion (new Figure 7). The new data shows that the transience of glutamate release from the On-T cells is retained when 1) spiking amacrine cell activity is suppressed by blocking voltage-gated Na⁺ channels with TTX or 2) all amacrine cell activity is suppressed by blocking AMPA receptors with NBQX. This does provide nice additional evidence that amacrine cells are not necessary for the sustained/transient distinction.

Reviewer #2 (Public Review): 

Summary: 

Goal of the study. The authors tried to pinpoint the origins of transient and sustained responses measured at retinal ganglion cells (rgcs), which is the output layer of the retina. Response characteristics of rgcs are used to group them into different types. The diversity of rgc types represents the ability of the retina to transform visual inputs into distinct output channels. They find that the physical dimensions of bipolar cell's synaptic ribbons (specialized release sites/active zones) vary across the different types of cone on-bpcs, in ways that they argue could facilitate transient or sustained release. This diversity of release output is what they argue underlies the differences in on-rgcs response characteristics, and ultimately represents a mechanism for creating parallel cone-driven channels. 

Strengths: 

The major strengths of the study are the anatomical approaches employed and the use of the "glutamate sniffer" to assay synaptic glutamate levels. The outline of the study is elegant and reflects the strengths of the authors. 

Weaknesses: 

The major weakness is that the ambitious outline is not matched with a complete set of results, and the set of physiological protocols is disjointed, not sufficient to bridge the systems-level question with the presynaptic release question. 

Thank you for this comment as it provides an opportunity (here and in the paper) for us to clarify our main goal. We wanted to link the well-established distinction between transient and sustained retinal responses to anatomy. This required locating where this difference arises within the circuitry – which we show to be at least largely the bipolar output synapse – and then examining the structure of this synapse in detail. While we would certainly be interested in connecting our results to a biophysical description of the synapse, that was not the primary focus of our study and was not something we could add without substantial additional work.  

Major comments on the results and suggestions. 

The ribbon model of release has been explored for decades and needs to be further adapted to systems-level work. The study under consideration by Kuo et al. takes on this task. Unfortunately, the experimental design does not permit a level of control over presynaptic/bpc behavior that is comparable to earlier studies, nor do they manipulate release in ways that test the ribbon model (i.e., paired recordings or Ribeye-ko). Furthermore, the data needs additional evaluation, and the presentation and interpretations should draw on published biophysical and molecular studies. 

As described above, our goal was to test several possible explanations for the difference between transient and sustained responses in OnT and OnS ganglion cells: (1) differences in the light responses of the bipolar cells that convey photoreceptor signals to the relevant ganglion cells; (2) shaping of bipolar transmitter release by presynaptic inhibition; (3) shaping of ganglion cell responses by postsynaptic inhibition or spike generation; (4) differences in feedforward bipolar synapses. We were surprised to find that the feedforward bipolar synapses play a central role in this difference, and your comment nicely prompts us to relate this to the large literature on biophysical studies of release from ribbon synapses. We have made substantial revisions in the text to do this. This includes anticipating the importance of feedforward synaptic properties in the abstract and introduction (lines 36-37 and 61-64), pointers in the results (lines 539-548), and several new paragraphs in the discussion (starting on lines 751, 773 and 787). By showing that the transient/sustained differences originates largely at feedforward bipolar synapses, we set the stage for future work that shows how biophysical properties of the synapse shape physiological signals that traverse it.

To build a ribbon-centric context, consider recent literature that supports the assertion that ribbons play a role in forming AZ release sites and facilitating exocytosis. Reference Ribeye-ko studies. For example, ribbonless bpcs show an 80% reduction in release (Maxeiner et al EMBO J 2016), the ribbonless retina exhibits signaling deficits at the output layer (Okawa et al ...Rieke, ..Wong Nat Comm 2019), and ribbonless rods show an 80% reduction the readily releasable pool (RRP) of SVs (Grabner Moser, elife 2021). In addition, the authors could refer to whole-cell membrane capacitance studies on mammalian rods, cones, and bpcs, because the size of the RRP of SVs scales with the dimensions and numbers of ribbons (total ribbon footprint). For comparison, bipolars see the review by Wan and Heidelberger 2011. For a comparison of mammalian rods and cones, see, rods: Grabner and Moser (2021 eLife), Mueller.. Regus Leidig et al. (2019; J Neurosci) and cones Grabner ...DeVries (Nat Comm 2023). A comparison of cell types shows that the extent of release is (1) proportional to the total size of the ribbon footprint, and (2) less release is witnessed when ribbons are deleted (also see photo ablation studies by Snellman.... And Mehta..Zenisek, Nat Neurosci and Neuron).

Thank you for these pointers into the literature.  We have included much of this work in the revised Discussion (see three paragraphs starting on line 751). The revised text focuses on the evidence that larger and more numerous ribbons lead to increased release. The direct evidence from previous work for this relationship supports our (indirect) conclusions in the current paper about the role of ribbon size and associated vesicle pools in transient vs sustained responses.  

Ribbon morphology may change in an activity-dependent manner. The rod ribbon AZ has been reported to lengthen in the dark (Dembla et al 2020), and deletion of the ribbon shortens the length of the AZ (defined by Cav1,4 or RIM2); in addition, the Ribeye-ko AZs fail to change in size with light and dark conditioning. Furthermore, EM studies on rod and cone AZs in light and dark argue that the number of SVs at the base of the ribbon increases in the dark, when PRs are depolarized (see Figure 10, Babai et al 2016 JNeurosci). Lastly, using goldfish Mb1 on-bipolars, Hull et al (2006, J Neurophysio) correlated an increase in release efficiency with an increase in ribbon numbers, which accompanied daylight. >> When release activity is high, ribbon AZ length increases (Dembla, rods), the number of docked SVs increases (Babai, rods cones), and the number of ribbons increases (Hull, diurnal Mb1s). 

We have extensively revised the discussion section to include more discussion of ribbons, particularly emphasizing evidence supporting the general argument that larger ribbons support higher release rates. We focused on studies that provided direct links between release rates and ribbon size or number of ribbon-associated vesicles.  This includes studies that pair electrophysiology and anatomy and those that measure the consequences of ablating ribbons,

The results under review, Kuo et al., were attained with SBF-SEM, which has the benefit of addressing large-volume questions as required here, yet it achieves lower spatial resolution than what is attained with TEM tomography and FIB-EM. Ideally, the EM description would include SV size, and the density of ribbon-tethered SVs that are docked at the plasma membrane, because this is where the SVs fuse (additional non-ribbon release sites may also exist? Mehta ... Singer 2014 J Neurosci). Studies by Graydon et al 2011 and 2014 (both in J Neurosci), and Jean ... Moser et al 2018 (eLife) are good examples of quantitative estimates of SVs docking sites at ribbons. SBF-SEM does not allow for an assessment of SVs within 5 nm of the PM, but if the authors can identify the number of SVs that appear within the limit of resolution (10 to 15 nm) from the PM, then this data would be useful. Also, what dimension(s) of the large ribbons make them larger? Typically, ribbons are fixed in height (at least in the outer retina, 200 to 250 nm), but their length varies and the number ribbons per terminal varies. Is the larger ribbon size observed in type 6 bpcs do to longer ribbons, or taller ribbons? A longer ribbon likely has more docked SVs. An additional possibility is that more SVs are about the ribbon-PM footprint, either more densely packed and/or expanding laterally (see definitions in Jean....Moser, elife 2018). 

We have included an additional analysis of ribbon surface area from our 3D SBFSEM reconstructions. As with the volume measurements included in the original submission, ribbon surface areas are distinct between type 5i and type 6 bipolar cells (Fig. S10A), ON-T RGCs on average receive input from ribbons with smaller surface area than ON-S RGCs (Fig. S10B), and ribbon surface area predicts the number of adjacent vesicles across bipolar cell types (Fig. S10C).  We agree that a higher resolution view of presynaptic structures would be very helpful, but the resolution of our SBF-SEM data is limited (e.g. each pixel is 40 nm on a side).  This resolution does not allow us to distinguish between vesicles at vs near the membrane. 

In our observations, both length and height of the ribbons showed variability across individual bipolar cells. And ribbons in type 6 bipolar cells tended to be either longer and/or taller compared to those in type 5 cells. We agree that a longer ribbon may accommodate more docked SVs. A more definitive analysis would benefit from higher-resolution, isotropic 3D reconstructions of ribbons, which would allow more precise shape analysis and ,together with a detailed assessment of docked SVs at the ribbons.

The ribbon literature given above makes the argument that ribbons increase exocytotic output, and morphological studies suggest that release activity enhances 1) ribbon length (Dembla) and 2) the density of SVs near the PM (Babai). These findings could lead one to propose that type 6 bpcs (inputs to On-sustained) are more active than type 5i (feed into On-transient). Here Kuo et al. show that the bpcs have similar Vm (measured from the soma) in response to light stimulation. Does Vm predict release? Not entirely as the authors acknowledge, because: Cav channel properties, SV availability, and negative feedback are all downstream of bpc Vm. The only experiment performed to test downstream factors focused on negative feedback from amacrines. The data presented in Figures 5C-F led me to conclude the opposite of what the authors concluded. My impression is that the T-ON rgc exhibits strong disinhibition when GABA-blockers are applied (the initial phase is greatly increased in amplitude and broadened with the drug), which contrasts with the S-On rgc responses that show a change in the amplitude of the initial phase but not its width (taus would be nice). Here and in many places the authors refer to changes in release kinetics, without implementing a useful description of kinetics. For instance, take the cumulative current (charge) in Figure 5C and fit the control and drug traces to arrive at taus, and their respective amplitudes, and use these values to describe kinetic phases. One final point, the summary in Figure 5D has a p: 0.06, very close to the cutoff for significance, which begs for more than an n = 5. Given that previous studies have shown that bpc output is shaped by immediate msec GABA feedback, in ways that influence kinetic phases of release (..Mb1 bipolars, see Vigh et al 2005 Neuron), more attention to this matter is needed before the authors rule out feedback inhibition in favor of ribbon size. If by chance, type 5i bpcs are under uniquely strong feedback inhibition, then ribbon size may result from less activity, not less output resulting from smaller ribbons.

The text surrounding Figure 5 led to some confusion, and we have revised that text and the figure for clarity.  First, the data in that figure is entirely from On-T cells (the upper and lower panels show block of GABA and glycine receptors separately).  Second, the observation that we make there is that block of inhibitory receptors increases the transience of the On-T excitatory input, rather than decreasing it as would be expected if the transience is created by presynaptic inhibition. We have added additional data and that increase in transience is now significant. Inhibitory block does substantially increase the amplitude of the postsynaptic response, and a likely origin of this change in response is inhibitory feedback to the bipolar synaptic terminal. We now indicate this in the text on page 13, lines 438-453. 

The key result of this figure for our purposes here is that the transience of the excitatory input to the OffT cell remains with inhibitory input blocked. We have clarified throughout the text that our results indicate that inhibitory feedback is not necessary for the difference between transient release into On-T and sustained release onto On-S. This does not mean that inhibitory feedback does not shape the responses in other ways or contribute to the transient/sustained difference - just that for the specific stimuli we use that difference is retained without presynaptic inhibition. We have also added citations to past work showing that activity of amacrine cells can modulate bipolar transmitter release. 

Whether strong feedback inhibition limits activity and therefore limits ribbon size in an activity-dependent way is an intriguing possibility. Indeed, addressing why ribbons are larger in type 6 bipolar cells vs. other bipolar types will be an interesting avenue of further study. However, it would be surprising if ribbon sizes changed during the acute pharmacological block conditions (~10-15 minutes) we employed in our study. Our point here is that there is an interesting correlation between presynaptic ribbon size and the kinetics of glutamate release. We do not think that the two possibilities stated in the last sentence (“…ribbon size may result from less activity, not less output resulting from smaller ribbons”) are mutually exclusive.

We have not further quantified the response kinetics in the experiments of Figure 5 as the large changes induced by the pharmacology (especially GABA receptor block) make it unclear how to interpret quantitative differences.  In other places we have quantified kinetics through the STA or specified that our focus was more qualitative (i.e. transient vs sustained kinetics). 

As mentioned above, the behavior of Cav channels is important here. This is difficult to address with voltage clamps from the soma, especially in the Vm range relevant to this study. Given that it has previously been modeled that the rod bpc to AII pathway adapts to prolonged depolarization of rbcs through downregulating Cav channel-mediated Ca²⁺ influx (Grimes ....Rieke 2014 Neuron), it seems important for Kou et al to test if there is a difference in Cav regulation between type 6 and 5i bpcs. Ca²⁺  imaging with a GCaMP strategy (Baden....Lagnado Current Biology, 2011) or filling the presynapse with Ca dyes (see inner hair cells: Ozcete and Moser, EMBO J 2020) would allow for the correlation of [Ca]intra with GluSnf signals (both local readouts).

This is a good suggestion but is outside the scope of our current paper. Our focus was on the circuit origin of the difference in response of the OnT and OnS responses rather than the specific biophysical mechanism.  We are of course interested in the mechanism, but the additional experiments needed to pin that down would need to be a part of future experiments. The work here represents an important step in that direction as it greatly reduces the number of possible locations and mechanisms for the sustained/transient difference and hence serves to focus any future mechanistic investigations.

Stimulation protocol and presentation of Glutamate Sniffer data in Figure 6. In all of your figures where you state steady st as a % of pk amplitude, please indicate in the figure where you estimate steady state. Alternatively, if you take the cumulative dF/F signal, then you can fit the different kinetic phases. From the appearance of the data, the Sustained Glu signals look like square waves (Figure 6B ROI1-4), without a transient at onset, which is not predicted in your ribbon model that assumes different kinetic phases (1. depletion of docked SVs, and 2. refilling and repriming). The Transient responses (Figure 6B ROI5-8) are transient and more compatible with a depressing ribbon scheme. If you take the cumulative, for all of the On-S and compare it to all of the On-T responses, my guess is the cumulative dF/F will be 10 to 20 larger for the S-On. Would you conclude that bpc inputs to On-S (type 6) release 20fold more SVs per 4 seconds on a per ribbon basis, and does the surface area of the type 6 bpcs account for this difference? From Figures 8B and D, the volume of the ribbon is ~2 fold greater for type 6 vs 5i, but the Surface Area (both faces of ribbon) is more relevant to your model that claims ribbon size is the pivotal factor. If making cumulative traces, and comparisons on an absolute scale is unfounded, then we need to know how to compare different observations. The classic ribbon models always have a conversion factor such as the capacitance of an SV or q size that is used to derive SV numbers from total dCm or Qcontent. See Kim ....et al von Gersdorff, 2023, Cell Reports. Why not use the Gaussian noise stimulus in Fig 6 as in Figure 1 and 2? 

For iGluSnFR recordings, steady-state responses were measured from the mean fluorescence over the last 1 sec of the light step (2 sec duration) response. We have included this information in the figure caption and in the Methods. 

There is a good deal of variability in the iGluSnR responses from one ROI to another, and the ROIs shown in the original submission had a less prominent transient component than many other ROIs. We have replaced this figure with another that is more representative of the average behavior across ROIs. The full range of behavior is captured in Figure 6C; it is clear across ROIs that glutamate release near ON-S dendrites shows both sustained and transient components. The new experiments in which we block amacrine cell activity also include a few more example ROIs from ON-S cells, and those also show both transient and sustained components.

Your suggestion to integrate the iGluSnFR signals to compare to our structural analysis of ribbons is interesting. However, we are hesitant to make a quantitative comparison between the two without further experiments to validate how the iGluSnFR signals we measure relate to release of single vesicles. For example, a quantitative measure of release based on the iGluSnR experiments would require accounting for possible differences in the expression of the indicator - which could differ both in overall level and/or location relative to release sites. 

This comment and one above highlight the importance of measures of ribbon surface area, which we now provide (Figure S10).

Figure 7. What is the recovery time for mammalian cones derived from ribbon-based models? There are estimates from membrane capacitance studies. Ground squirrel cones take 0.7 to 1 sec to recover the ultrafast, primed pool of SVs when probed with a paired-pulse protocol (Grabner ...DeVries 2016, Neuron). Their off-bpcs take anywhere from under 0.2 sec to a second to recover, which is a combination of many synaptic factors (Grabner ...DeVries Nat Comm 2023). Rod On bpcs take over a second (Singer Diamond 2006, reviewed Wan and Heidelberger 2011). In Figure 7B, the recovery time is ~150 ms for the responses measured at rgcs. This brief recovery time is incompatible with existing ribbon models of release. Whole-cell membrane capacitance measurements would be helpful here.

Thanks for drawing our attention to this issue. Indeed, we see a relatively rapid recovery in the paired-flash experiments. We now discuss this recovery time in the context of past measurements of recovery of responses in cones and bipolar cells (paragraph starting on line 773). There are many factors that could contribute to the relatively rapid recovery we observe - including synaptic factors such as those highlighted by Grabner et al., (2016) either at the cone-to-bipolar synapses or the bipolar-to-RGC synapses. We are certainly interested in a more detailed understanding of this issue, but the additional experiments are outside the scope of this paper.  

Experimental Suggestion: Add GABA blockers and see if type 5i bpc responds with more release (GluSniff) and prolonged [Ca2+] intra (GCaMP). Compare this to type 6 bpc behavior with GABA/gly blockers. This will rule in or out whether feedback inhibition is involved. 

Figure 7 in the revised manuscript includes two new experiments examining glutamate release (without the simultaneous measurement of bipolar cell intracellular calcium) while blocking (1) all/most amacrine cell-mediated inhibition via inclusion of NBQX in the bath solution, and (2) blocking spiking amacrine cells via inclusion of TTX in the bath solution. The transient vs sustained difference in light-evoked glutamate release around ON-T and ON-S RGC dendrites remained with amacrine activity suppressed. These new results are consistent with the anatomical and pharmacological data that were included in the initial submission of the manuscript (Fig. 5) that indicate presynaptic inhibition does not have a major role in shaping release kinetics at these synapses. 

Reviewer #3 (Public Review): 

Summary: 

Different types of retinal ganglion cell (RGC) have different temporal properties - most prominently a distinction between sustained vs. transient responses to contrast. This has been well established in multiple species, including mice. In general, RGCs with dendrites that stratify close to the ganglion cell layer (GCL) are sustained; whereas those that stratify near the middle of the inner plexiform layer (IPL) are transient. This difference in RGC spiking responses aligns with similar differences in excitatory synaptic currents as well as with differences in glutamate release in the respective layers - shown previously and here, with a glutamate sensor (iGluSnFR) expressed in the RGCs of interest. Differences in glutamate release were not explained by differences in the distinct presynaptic bipolar cells' voltage responses, which were quite similar to one another. Rather, the difference in transient vs. sustained responses seems to emerge at the bipolar cell axon terminals in the form of glutamate release. This difference in the temporal pattern of glutamate release was correlated with differences in the size of synaptic ribbons (larger in the bipolar cells with more sustained responses), which also correlated with a greater number of vesicles in the vicinity of the larger ribbons. 

The main conclusion of the study relates to a correlation (because it is difficult to manipulate ribbon size or vesicle density experimentally): the bipolar cells with increased ribbon size/vesicle number would have a greater possibility of sustained release, which would be reflected in the postsynaptic RGC synaptic currents and RGC firing rates. This model proposes a mechanism for temporal channels that is independent of synaptic inhibition. Indeed, some experiments in the paper suggest that inhibition cannot explain the transient nature of glutamate release onto one of the RGC types. Still, it is surprising that such a diverse set of inhibitory interneurons in the retina would not play some role in diversifying the temporal properties of RGC responses. 

Strengths: 

(1) The study uses a systematic approach to evaluating temporal properties of retinal ganglion cell (RGC) spiking outputs, excitatory synaptic inputs, presynaptic voltage responses, and presynaptic glutamate release. The combination of these experiments demonstrates an important step in the conversion from voltage to glutamate release in shaping response dynamics in RGCs. 

(2) The study uses a combination of electrophysiology, two-photon imaging, and scanning block-face EM to build a quantitative and coherent story about specific retinal circuits and their functional properties. 

Weaknesses: 

(1) There were some interesting aspects of the study that were not completely resolved, and resolving some of these issues may go beyond the current study. For example, it was interesting that different extracellular media (Ames medium vs. ACSF) generated different degrees of transient vs. sustained responses in RGCs, but it was unclear how these media might have impacted ion channels at different levels of the circuit that could explain the effects on temporal tuning.

We do not have an explanation for the quantitative differences in response kinetics we observed in Ames’ medium vs. ACSF. There are modest differences in calcium and magnesium concentration and a larger difference in potassium (2.5 mM in ACSF vs 3.6 mM in Ames). It would be interesting to test which of these (or other) differences accounts for the difference in response kinetics.

(2) It was surprising that inhibition played such a small role in generating temporal tuning. At the same time, there were some gaps in the investigation of inhibition (e.g., IPSCs were not measured in either of the RGC types; pharmacology was used to investigate responses only in the transient RGCs).

We were also surprised at this result. We have included additional data on inhibition in the revised manuscript. Figure S3 shows light-evoked IPSC data from both RGC types (Fig. S3) and Fig. 7 shows additional iGluSnFR measurements around both ON-T and ON-S RGC dendrites with inhibition blocked via bath application of NBQX (Fig. 7) and separately with inhibition from spiking amacrine cells blocked with TTX. These experiments provide additional evidence for the small role of inhibition. We attempted to measure the kinetics of excitatory input to ON-S cells with inhibition blocked, but we found that the excitatory input showed strong spontaneous oscillations under these conditions and the light responses were changed so drastically that we did not feel we could make a clear comparison with control conditions.

(3) There could be additional discussion and references to the literature describing several topics, including: temporal dynamics of glutamate release at different levels of the IPL; previous evidence that release sites from a single presynaptic neuron can differ in their temporal properties depending on the postsynaptic target; previous investigations of the role of inhibition in temporal tuning within retinal circuitry. 

Thanks, we have included more discussion and references to the relevant literature as you have suggested in the recommendations to authors.

Reviewer #1 (Recommendations For The Authors): 

The presented raw data of the pharmacological experiments show that SR95531 and TPMPA robustly increased both the amplitude and duration of the transient component of the light step-evoked excitatory currents, with slight, if any enhancement of the sustained component in ON-T RGCs Figure 5C. Statistical analysis of the population data (n=5) with Wilcoxon signed rank test yielded no significant difference (ln 363). However, reanalyzing the data extracted from the graph (Figure 5D) revealed that the difference between the paired observations is normally distributed (Shapiro-Wilk normality test, P=0.48) allowing parametric statistics to be used, which provides higher statistical power. Accordingly, reanalyzing the presented data with paired Student's t-test data revealed significant differences (P=0.01) in the steady-state amplitude normalized to that of the peak, recorded in the presence of SR95531 and TPMPA. In other words, based on the (rough) analysis of the presented pharmacology data GABAergic feedback inhibition significantly contributes to shaping the transient portion of the light-evoked excitatory currents in ON-T RGCs, by making it more transient. I believe a similar analysis based on the actual data is necessary, and the results should be communicated either way. However, if warranted, two-photon glutamate sensor imaging experiments showing that blocking GABA- and glycinergic inhibition does not change the kinetics of light-evoked glutamate signals at ON-T RGCs should also be performed, as these would be critical in drawing a conclusion regarding the effect of feedback inhibition on glutamate release from bipolar cells.

Thanks for this feedback. We have added another cell to the data set in Fig. 5D. With this addition, SR95531/TPMPA application significantly increases the response transience of excitatory currents measured in ON-T RGCs compared to control. This enhanced transience in GABA_A/C receptor blockers is due to an increase in the amplitude of the initial peak component of the response (control peak amplitude: -833.7±103.3 pA; SR95531+TPMPA peak amplitude: 2023±372.7pA; p=0.03, Wilcoxon signed rank test), with no change to the later sustained component (control plateau amplitude: -200.7±14.71pA; SR95531+TPMPA plateau amplitude: -290.9±43.69pA; p=0.15, Wilcoxon signed rank test).

We should clarify that this result indicates that GABAergic inhibition makes the excitatory inputs to ON-T RGCs less transient. Block of GABA receptors increased transience, thus intact GABAergic transmission appears to limit the initial peak of the response and therefore make excitatory currents more sustained. We unfortunately were not able to examine whether sustained excitatory currents in ON-S RGCs would become more transient using the same approach. In our hands, bath application of SR95531+TPMPA led to the generation of large-amplitude (>1nA) oscillatory bursts of excitatory input that developed within 5 minutes and persisted for the duration of the incubation (up to ~30 min) in drugs. Further, presentation of light steps tended to induce variable amplitude responses, likely dependent on the presence of spontaneous bursts; when large amplitude responses were evoked, these typically oscillated for several seconds after the step.

To examine a potential role for presynaptic inhibition in transient vs. sustained bipolar cell output, we therefore chose to eliminate amacrine cell-mediated inhibition by bath application of the AMPA/kainate receptor antagonist NBQX in additional iGluSnFR measurements. This manipulation should leave ON bipolar cell responses intact while eliminating most amacrine cell-mediated responses (and OFF bipolar cell driven responses). In separate experiments, we also eliminated inhibition from spiking amacrine cells by bath application of TTX. As shown in new Fig. 7, sustained and transient responses persisted in distal versus proximal RGC dendrites, respectively. Compared to SR95531/TPMPA, bath application of NBQX was not associated with spontaneous bursts of glutamate release around ON-S dendrites. These results show that amacrine cell-mediated inhibition is not required for either sustained or transient glutamate release from bipolar cells that provide input to ON-S and ON-T RGCs.

Small points: 

(1) The legend of Figure 1 (D) refers to shaded areas to show {plus minus} SEM, but no shade is visible (at least in my printout).

The SEM shading is there in Fig. 1D but is mostly obscured by the mean lines for the respective RGC types. We have added this to the figure caption.

(2) I found the reported Vrest for the ON bipolar cells somewhat depolarized. Perhaps due to the uncompensated junction potentials? 

These measurements are indeed not corrected for the liquid junction potential (which is approximately -10.8 mV between K-gluconate internal and Ames’ solution). We did not apply this correction since the appropriate value is not clear in perforated patch recordings as the intracellular chloride concentration is unknown (and can differ from that in the pipette solution). We have clarified this in the results text where we describe the Vrest values (lines 335-338).

(3) It is Wilcoxon signed rank test, not Wilcoxan. 

Thanks for catching this. This has been corrected in the revised manuscript.

Reviewer #2 (Recommendations For The Authors): 

Some amacrines express vesicular Glut-3 transporter and are reported to release glutamate (Marshak, Vis Neurosci 2016). Are Amacrine vGlut3 signals postsynaptic (within ~0.5 um) to cone bpc ribbons?

We did not characterize VgluT3-expressing amacrine cells in our SEM datasets. A recent study by Friedrichson et al. (Nat. Comm. 2024; PMID 38580652) using 3D SEM reconstructions found that Vglut3-amacrines are postsynaptic to both type 5i and type 6 bipolar cells, as well as other type 5/xbc bipolar cells (and receive >50% of their input from type 3a OFF bipolar cells).

How far apart are the postsynaptic targets from the ribbon release sites? The ribbons at type 5i bpc/On-T input appear separated from the dendrites of On-T rgcs (Figure 8C). At least further away than the type 6 bpc ribbons are from On-S rgc dendrites (Figure 8C). Distance may create a thresholding phenomenon, whereby only multivesicular bouts at the onset of depolarization are able to elevate synaptic Glu to levels needed to activate On-T GluRs. See Grabner et al Nat Comm 2023 for such scenarios in the outer retina.

This is an intriguing possibility, but we should point out that the presynaptic ribbons in Fig. 9C (former Fig. 8C) are similar distances (within the resolution of our reconstructions) from the ON-T and ON-S dendrites. We have increased the brightness of the dendrite segments for both RGC types in the resubmission figure; note that ON-T RGCs have spine-like protrusions that may not have been as apparent in the previously submitted version of our manuscript.

In Figures 1 and 2, Sustained responses look like the derivative of Transient responses, minus the negative going inflection. In addition, the sustained responses appear to have a lower threshold of activation than the transient On rgcs, because there are more bouts of action potentials (and membrane depol in V-clamp) with earlier onset in sustained than transients traces. It would be great if the GLuSniff data captured these differences. Take cumulative dF/F and see what the onset time is, or an initial tau if possible.

This is a good suggestion. However, we are reluctant to make detailed quantitative comparisons such as this without further validation of how the kinetics of the iGluSnFR signals relate to kinetics of glutamate release.  A specific concern is that differences in the location and amount of iGluSnFR expression could impact any such comparisons.

A recent study by Kim et al von Gersdorff (Cell Reports, 2023) presents interesting phases of release in response to light flashes, measured from AIIs, and complementary results from pairs of rbcs-AIIs. The findings highlight the complexity of SV pools under well-controlled experiments. Could their results be explained as variations in rbc ribbon size through development, and possibly between rbcs or within an rbc? 

This certainly seems possible and would be consistent with the dependence of release on ribbon size that our results support.  It would be interesting to see if there are clear anatomical correlates of that change in release properties.  

Figure 5 is a pivotal point in the study, but my review has identified numerous weaknesses. The feedback inhibition onto bipolar cell terminals is likely to sculpt glutamate release, and the results do not convincingly rule out this possibility. The suggestions for improvements range from the data needing to be reanalyzed with regard to statistical tests, and/or adding a few more data points (n = 5) before concluding a p: 0.06 is insignificant. 

We have added an additional recording to this data set. With n= 6 cells, there is now a statistically significant difference between ON-T RGC excitatory currents measured in control conditions versus during GABA_A/C receptor blockade. Please note that all the recordings shown in Figure 5C-F are from ON-T RGCs (the two panels show separately block of GABergic and glycinergic receptors). We did not make it sufficiently clear that the original trend (now statistically significant) is opposite of that expected if presynaptic GABAergic inhibition contributes to response transience in ON-T RGCs.  What we see is that excitatory synaptic inputs to ON-T RGCs become more transient (rather than mpre sustained) during GABA_A/C receptor blockade. We have revised the text in that section to make this point more clearly.

We have also included new data from iGluSnFR measurements showing that bath application of NBQX does not affect light step-evoked glutamate release kinetics at proximal (sustained) or distal (transient) RGC dendrites (control: steady-state amp. as % of peak amp. 13 ± 10; mean ± S.D.; n = 189 ROIs/4 FOVs for ON-T dendrites vs 40 ± 12; mean ± S.D.; n = 287 ROIs/8 FOVs for ON-S dendrites; NBQX: 6 ± 3; mean ± S.D.; n = 112 ROIs/1 FOV for ON-T dendrites vs 23 ± 9; mean ± S.D.; n = 97 ROIs/2 FOVs for ON-S dendrites; *p<0.001). By blocking glutamate receptors on amacrine cells, NBQX (AMPA/KAR antagonist) eliminates all/most amacrine cell-mediated signaling in the retina and should therefore abolish presynaptic inhibitory input to bipolar cell terminals across the IPL. Taken together, our results indicate that presynaptic inhibition does not play a critical role in establishing transient versus sustained kinetics for the stimulus conditions we employed in our study.

There is a need to cite more recent literature on bipolar cell ribbons (e.g. see Wakeham et al., Front. Cell. Neurosci., 2023), in order to support experimental design and interpretation of the results. The authors should discuss their Ribeye-KO data from Okawa et al 2019 Nat Comm, Figure 7, in the context of their new iGluSnFR results. 

Thank you for prompting us on this issue. We have expanded the discussion regarding ribbons and included more citations to the ribbon literature. That is largely in the three paragraphs starting on line 727.

One point deserves emphasis because it is central to the authors' ribbon model but not consistent with their data. The ribbon model as they put it, and as commonly stated, holds that a transient phase of release at the onset of depolarization indicates the depletion of the primed SVs, and the subsequent slower rate of release (steady state release in the authors' terms) reflects recruiting, priming, and release of new SVs. The On-transient dendrite GluSnf responses agree with this multiphasic process, but the sustained responses show only an elevation in glutamate without a pronounced initial peak, creating a square-wave-shaped response (Figure 6B). This does not agree with the simple ribbon-based release model. I would expect the signals from the T- and S-on dendrites to have a comparable initial phase, while the sustained phase should be greater in amplitude for the S-on dendrites. More discussion may clarify possible mechanisms.

Thanks for pointing this out. The example iGluSnFR traces we originally included in the manuscript were not entirely representative in that they did not show much initial transient phase. Note there is a distribution of steady-state amplitudes for proximal dendrites in Fig. 6C; the examples are from ROIs from the upper end of the distribution. In the new Figure 7, we have included some additional examples that show both a clear transient and sustained component. The summary data in Figure 6C shows the distribution of sustained/transient ratios across ROIs.  

Reviewer #3 (Recommendations For The Authors): 

(1) It would be interesting to understand the differences in IPSCs in the two RGC types. Perhaps they are small in both types, which would explain their apparent lack of impact on temporal tuning. The authors may already have these data.

We did make measurements of noise-evoked IPSCs (as well as EPSCs) in a subset of ON-T and ON-S recordings. We have now included this data as Figure S3. There are slight differences in the kinetics of inhibition between RGC types (Fig. S3C) and there is a trend towards stronger inhibition (relative to excitation) in ON-T RGCs compared to ON-S RGCs (Fig. S3E), although there is not a statistically significant difference. In both cases excitatory synaptic currents are as large or larger than inhibitory currents, and this does not include the difference in driving force near spike threshold which will favor excitatory input by a factor of 2-3.  Hence our data suggests that postsynaptic inhibition does not play a major role in generating the differential temporal spiking responses of ON-T and ON-S RGCs. However, additional experiments examining the relative contribution of excitation and inhibition to spiking output in these RGCs would be needed to reach a firm conclusion.

The pharmacological experiments in which we blocked inhibition (Fig. 5C-F, new Fig. 7) were designed to test the effect of presynaptic inhibition on bipolar cell output (voltage-clamp isolation of excitatory currents in Fig. 5; iGluSnFR measurements of glutamate release in Fig. 7). We do not mean to suggest that postsynaptic inhibition does not have any role in shaping the spiking behavior of these RGC types, but that transient vs. sustained kinetics are already present in the bipolar cell output and that presynaptic inhibition of bipolar cell terminals does not appear to account for this difference.  We have revised the text throughout to be clearer on this point.

(2) It could be convincing to show transient/sustained differences between RGC types in dim light, where the response would depend on the rod bipolar/AII circuit. In this case, any difference in temporal properties would presumably be explained by differences that localize to the cone bipolar cell axon terminals. Indeed, is that the result in Figure 1B? This seems to be a dim stimulus presented on darkness, which may be driven through the rod bipolar pathway. The authors could then discuss the interpretation of this data in terms of the rod bipolar circuit. 

Yes, Figure 1B is a dim light step (~30R*/rod/s) presented from darkness and the distinction between cells is clear down at still lower light levels that more effectively isolate signaling through the rod bipolar pathway. Thanks for making this point that observation of distinct temporal responses under scotopic conditions where signals suggests these differences must arise at and/or downstream of cone bipolar cell output. We have included additional text (lines 361-365) in the results describing bipolar cell responses that raise this point.

(3) Glutamate release was already measured across the full IPL depth by Borghuis et al. (2013) and Franke et al. (2017). It would be appropriate to better motivate the current study based on these existing measurements.

We have clarified that these important studies provided important motivation for measuring excitatory synaptic input to ON-T vs. ON-S RGCs (lines 165-169).   

(4) Line 212/213. It would be appropriate to add to the list of papers showing the different stratification of transient vs. sustained responses: Borghuis et al. (2013) and Beaudoin et al. (2019).

Thank you - these references have been added.  

(5) Line 635-638. It would be useful to discuss papers by Pottackal et al. (2020, 2021), which suggested that a single presynaptic cell (starburst) can signal with different temporal properties depending on the postsynaptic target (other starburst vs. DSGCs). The mechanism was not completely resolved (i.e., it was not explained by differences in presynaptic Ca channels at the two synapse types), but it at least shows that neurotransmitter release can show different filtering depending on the postsynaptic target from the same presynaptic neuron. (This could also be at play for the type 6 bipolar cell inputs to ON-S vs. ON-T RGCs in the present study.)

We have added a reference to Pottackal et al 2021 in this section.

(6) Line 714. Should describe the procedure for embedding the tissue in agarose. 

We have added more detail regarding agarose embedding for preparation of retinal slices in the methods.

(7) Line 775. Need a better description of the virus (not the construct), what serotype? Provide the Addgene number if available. 

This has been added to the methods.

(8) Line 808. Was the SD for the gaussian really 50%? That would cut off a lot of the distribution, i.e., it would get clipped at 0. 

Yes, the SD for Gaussian noise was 50%. This high contrast stimulus was used in part to achieve measurable signals from bipolar cells. You are correct that some of the distribution was clipped at 0 (it was also clipped at twice the mean to make sure that the distribution remained symmetrical). The clipping was accounted for during our LN analyses.

(9) The paper should discuss Swygart et al. (2024) results showing different spatial surround properties of neighboring synapses from a type 6 bipolar cell. Based on this result, it would seem very likely that amacrine cells could play a role in shaping the temporal processing of bipolar cell glutamate release as well. Indeed, spatial and temporal processing will not be completely independent in a typical experiment. For example, with the spot stimulus used in the present study, bipolar cells within the center versus the edge of the spot will have different balances of center/surround activation, which could potentially influence their temporal processing.

We have included discussion of results from Swygart et al 2024 in the section of the Discussion in which we point out differences in surround inhibition between ON-S and ON-T RGCs (lines 710-714). We agree that spatial and temporal processing are not completely independent. Our results with SR95531/TPMPA indicate ON-T RGCs receive stronger GABAergic surround inhibition than ON-S RGCs (Fig. S8). However, our results in Fig. 5C-D show GABAergic surround inhibition makes ON-T excitation more sustained rather than more transient. So even though bipolar cells presynaptic to ON-T RGCs receive stronger surround inhibition (Fig. S8), this inhibition does not establish the transient kinetics of glutamate release from these bipolar cells (in fact, it works to make release more sustained). Additional iGluSnFR experiments where we used NBQX to block all/most amacrine cell-mediated responses also suggest presynaptic inhibition does not have an important role in establishing differential glutamate release kinetics onto ON-S vs. ON-T RGC dendrites (Fig. 7).

(10) Cui et al. 2016 described ON-S Alpha as having a divisive suppression mechanism that explained the temporal properties of white-noise response better than a standard LN model. Do the authors think the divisive suppression reflects a property of the excitatory synapses independent of inhibition?

This is an interesting question, but one for which we don’t have a good answer for now. As mentioned in some of the above responses and as we have tried to clarify in the manuscript, we do not mean to imply that there is no role for presynaptic inhibition in modulating bipolar cell output, including for the divisive suppression described by Cui et al. Rather, our point is that the distinction between transient and sustained excitatory input to ON-T and ON-S RGCs does not require presynaptic inhibition and is more likely an intrinsic property of the bipolar cell synapses. 

(11) Do the authors mean to imply that the pool size at bipolar cell ribbon synapses could depend on the use of Ames vs. ACSF? 

For now, we do not have a good answer as to why there are quantitative differences in response kinetics between Ames and ACSF. We have not done any experiments to investigate whether ribbon sizes or ribbon pools are different in the different solutions.

(12) More generally, different mean luminance levels could drive different levels of baseline glutamate release, which could alter the available pool of vesicles at bipolar cell ribbon synapses. Can we explain varying degrees of transient/sustained in the same cell at different levels of mean luminance based on this mechanism (e.g., Grimes et al., 2014)?

Yes, the emergence of a transient component of excitatory input to ON-S RGCs at ~100 R*/rod/s versus at scotopic levels (0.5 R*/rod/s) in Grimes et al. (2014) could be due to differences in the number of releasable vesicles (due to different type 6 bipolar cell axon terminal membrane potentials and hence differences in spontaneous release rates) at the different light levels.

We should note that although ON-T and ON-S RGCs exhibit some changes in transient/sustained kinetics across different light levels, the relative differences between these RGC types are preserved across light levels. We have included a statement about this in the text (lines 361-367).

(13) Figure 1. Have the authors considered performing the LN analysis of the firing responses, to compare the degree of rectification between the two RGC types?

This is a good suggestions. From an LN analysis of spiking responses, we do not observe a clear difference between the static nonlinearity component of the model for ON-T and ON-S RGCs. Both RGC types are strongly rectified under our experimental conditions.  

(14) Figure 5. Do the authors have the pharmacology data for the ON-S cells? There are examples of sustained EPSCs in amacrine cells that become more transient after blocking inhibition, which at least suggests that inhibition can play some role in the transient/sustained nature of glutamate release (Park et al., 2015, Figure 3). Perhaps ON-S cells likewise become more transient with inhibition blocked. 

(The colored symbols in A were not visible in a printout. It would be useful to indicate the cell type (ON-T) in C and E). 

As described above in the response to reviewer 1’s recommendation for authors, we were not able to use SR95531/TPMPA for recordings from ON-S RGCs. Bath application of these drugs led to oscillatory bursts of excitatory input to ON-S RGCs. However, the lack of effect of bath-applied NBQX on the kinetics of glutamate release around either ON-T or ON-S RGC dendrites (new Fig. 7) suggests that presynaptic inhibition does not contribute to generating sustained excitation to ON-S RGCs (or transient excitation to ON-T RGCs).  

We have corrected Fig. 5A to include the referenced colored symbols and have also edited Fig 5C and E to clarify that measurements in Fig. 5C-F are from ON-T RGCs.

(15) Figure 6 legend. Should be Kcng4-Cre, not KCNG-Cre. Also, it should make clear that this is cre-dependent expression of iGluSnFR. For C, were the statistics based on the number of FOVs? 

Thanks for catching this, we have corrected Figure 6 legend. The methods section includes a description of how we achieved iGluSnFR expression on alpha RGC dendrites via a cre-dependent viral strategy in Kcng4-Cre mice.  We have also clarified that the statistics are based on ROIs in Figure 6C.

(16) Figure 7, Flashes were apparently 400% contrast on a dim background. What was the background? Is there a rod component to the response in this case? 

In Figure 7 (now Figure 8), the same background (~3300 R*/rod/s; 2000 P*/Scone/s) was used as in the Gaussian noise and step response experiments. At this light level, the response should be primarily be mediated by cones.

(17) Figure S1. The colors here differ from those in previous figures (Here, ON-T, magenta; ON-S, cyan). Is something mislabeled? 

Thanks for catching this. We mistakenly swapped the labels in the legend for Fig. S1. The figure colors were correct, but we have corrected the legend in the revised manuscript.

(18) Figure S2. For the LN model for RGC synaptic currents, the ON-S are more rectified than some previous recordings (Cui et al., 2016). Is this perhaps explained by different light levels?

We aren’t sure why ON-S excitatory currents are more strongly rectified in our recordings compared to Cui et al., 2016. Cui et al. used an ~20-fold higher background light intensity (~40,000 P*/cone/s vs. ~2000 P*/cone/s in our study), so different light levels may be a factor (although we should point out that rectification increases in these RGCs between scotopic to low photopic light levels (see Grimes et al., 2014 and Kuo et al., 2016).

(19) The study is apparently comparing PV1 and PV2 described in Farrow et al. (2013; see Supplementary information for stratification analysis), which should be cited.

Thanks, we have corrected this oversight in the revised manuscript. We now cite Farrow et al and mention the connection to PV1 and PV2 in the first paragraph of Results (lines 104-108).

• ARE 1018459 ED
• Órgão julgador: Tribunal Pleno
• Relator(a): Min. GILMAR MENDES
• Julgamento: 12/09/2023

• Publicação: 30/10/2023

• Embargos de declaração em processo paradigma da sistemática da repercussão geral.

• Direito do Trabalho. Tema 935.

• Alegação de omissão, contradição ou obscuridade.

• Efeitos infringentes. Admissão da cobrança da contribuição assistencial prevista no art. 513 da Consolidação das Leis do Trabalho, inclusive aos não filiados ao sistema sindical, assegurado ao trabalhador o direito de oposição.

• A constitucionalidade das contribuições assistenciais, respeitado o direito de oposição, faculta a trabalhadores e sindicatos instrumento capaz de, ao mesmo tempo, recompor a autonomia financeira do sistema sindical e concretizar o direito à representação sindical sem ferir a liberdade de associação dos trabalhadores.

• Embargos de declaração conhecidos e providos em parte para retificar a tese da repercussão geral, que passa a ter a seguinte redação: “É constitucional a instituição, por acordo ou convenção coletivos, de contribuições assistenciais a serem impostas a todos os empregados da categoria, <u>ainda que não sindicalizados</u>, desde que <u>assegurado o direito de oposição</u>.”

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, but in its current form, the study feels incomplete and requires additional work.

My major concerns/suggestions are as follows:

(1) Context and literature

The introduction provides only limited background on prior attempts to use heparinized plasma for cfDNA work. It is well known that heparin can inhibit PCR and sequencing library preparation, which has historically discouraged its use. The authors should summarize the relevant literature more comprehensively and explain clearly why this approach has not been widely adopted until now, and how their work differs from or overcomes these earlier challenges.

(2) Genome-wide coverage

The analyses focus on correlations in methylation patterns and fragmentation metrics, but there is no evaluation of sequencing coverage across the genome. For both WGS and WMS, it would be important to demonstrate whether cfDNA from heparin plasma provides unbiased coverage, or whether certain genomic regions are systematically under-represented. A comparison against coverage profiles from cell-derived DNA (e.g., PBMC genomic DNA) would help to put the results in context and assess whether the material is suitable for whole-genome applications.

(3) Viral detection sensitivity

The study shows strong concordance in viral detection between EDTA and heparin samples, but the sensitivity analysis is lacking. For clinical relevance, it is critical to demonstrate how well heparin-derived plasma performs in low viral load cases. A quantitative comparison of viral read counts and genome coverage across tube types would strengthen the conclusions.

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.

Author response:

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, but in its current form, the study feels incomplete and requires additional work.

We thank the reviewer for the encouragement and for recognizing the potential of clinical chemistry plasma as an accessible source for cfDNA-based analyses. We look forward to addressing the gaps described below.

My major concerns/suggestions are as follows:

(1) Context and literature

The introduction provides only limited background on prior attempts to use heparinized plasma for cfDNA work. It is well known that heparin can inhibit PCR and sequencing library preparation, which has historically discouraged its use. The authors should summarize the relevant literature more comprehensively and explain clearly why this approach has not been widely adopted until now, and how their work differs from or overcomes these earlier challenges.

We thank the reviewer for their valuable comments and agree that the review of prior work needs to be more thorough, with the gaps clearly identified. In the revised manuscript, we will expand the introduction to include a more comprehensive summary of prior studies. Some of the material was in the Discussion, but we will move it to the introduction in the revision. In general, we will comment briefly here about the novelty of this work and the previous gap in the literature:

(1) Previous pre-analytical studies use DNA fluorometry and qPCR, which cannot distinguish between genomic DNA contamination (from cells) and cfDNA. In contrast, our study uses adapter-based NGS with DNA spike-ins, which can exclude genomic DNA contamination and enable precise quantification of cfDNA input and measurement of their lengths. In Figure 5b-c, we demonstrate that we were able to match our paired sample results only under the measurements of our NGS study, not in previous attempts. Note the current Fig. 5 captions b&c should be swapped and will be corrected in the revision.

(2) As the reviewer has astutely mentioned, heparin is a well-recognized inhibitor of PCR, and heparinized specimens are historically contraindicated for molecular testing. However, most modern cfDNA assays now use NGS, which includes multiple purification steps before PCR amplification, minimizing the impact of heparin interference.

(3) Previous clinical chemistry tests used serum tubes, which are known to generate background gDNA during clotting and are therefore unsuitable for cfDNA-based analyses. In recent years, modern hospital chemistry laboratories, especially those supporting emergency departments, have gradually transitioned to heparin separator tubes for faster turnaround. Hence, residual plasma from heparin separator tubes is a more recent option, one that was not widely available when key pre-analytical studies on cfDNA were performed.

(2) Genome-wide coverage

The analyses focus on correlations in methylation patterns and fragmentation metrics, but there is no evaluation of sequencing coverage across the genome. For both WGS and WMS, it would be important to demonstrate whether cfDNA from heparin plasma provides unbiased coverage, or whether certain genomic regions are systematically under-represented. A comparison against coverage profiles from cell-derived DNA (e.g., PBMC genomic DNA) would help to put the results in context and assess whether the material is suitable for whole-genome applications.

Thank you for the insightful comment. We agree that evaluating sequencing coverage across the genome is important for assessing the suitability of cfDNA from heparin separators. In response, we are performing additional, in-depth runs to compare genome-wide coverage profiles in the Hospital Cohort. The results of these analyses will be included in the revised version of the manuscript.

(3) Viral detection sensitivity

The study shows strong concordance in viral detection between EDTA and heparin samples, but the sensitivity analysis is lacking. For clinical relevance, it is critical to demonstrate how well heparin-derived plasma performs in low viral load cases. A quantitative comparison of viral read counts and genome coverage across tube types would strengthen the conclusions.

We agree that evaluating analytical sensitivity in cases with low viral loads is important for understanding clinical performance. To address this point, we plan to include additional paired cases with viral loads below 1,000 IU/mL and examine the correlation of viral read counts between EDTA and heparin separators in this subset.

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.

We thank the reviewer for the insightful comments and will work to clarify and address the mentioned issues. We do not find the residual plasma from the heparin separator to be a replacement for gold standard methods. Instead, we take it as a practical and complementary resource that may help broaden the accessibility of samples. Comparable cfDNA metrics highlight its potential to serve as an additional source for biobanking and research applications.

Strengths:

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

We thank the reviewer for the encouraging comment. While cost-effectiveness is a practical advantage, we believe the greater strength of this approach lies in the accessibility of sampling. Residual plasma from routine clinical tests offers an opportunity to include patients or time points that would otherwise be difficult to capture, such as those with severe illness or those sampled before treatment.

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.

We thank both reviewers for this comment. See above under Reviewer 1’s responses for our provisional perspective on the background literature and gap. We will expand the Introduction to provide a more comprehensive summary of prior studies.

(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.

We appreciate this thoughtful comment, which highlights reasonable concerns about cfDNA degradation in heparin. We would like to clarify that the Hospital Cohort, which only used leftover plasma in the clinical lab, was designed to reflect real-world clinical workflows, where unavoidable delays before plasma processing are already incorporated. In the Healthy Cohort, a subset of samples is also processed after controlled delays, as shown in Supplementary Figure 2.

Regarding the differing results in Barra et al. (2025, LabMed), where heparin tubes showed 85% cfDNA degradation, it is important to note that samples were incubated at 37 °C for 24 hours. We anticipate that endogenous nuclease would be active under 37 °C and would cause cfDNA degradation. However, this condition differs markedly from the relevant clinical workflows we describe here. In the routine hospital settings, blood samples are typically kept at room temperature for up to 60 minutes during transport and waiting. The outpatient setting can be more variable, but samples here are supposed to be refrigerated during transportation. They are then processed in high-throughput, fully automated systems that comply with nationally standardized quality regulations in the United States (CLIA). The resultant plasma will be physically separated from cellular components because of the gel in the heparin separators. The processed tubes are subsequently transferred to refrigerated storage at 4 °C. Under these conditions, samples do not experience prolonged exposure to elevated temperatures such as 37 °C, and refrigeration usually occurs within two hours of collection. We will incorporate these details in the revised manuscript.

Also, as we mentioned in our reply to Reviewer 1, Barra et al. used qPCR like most cfDNA pre-analytical studies, but qPCR is not a perfect DNA quantification method for NGS-based downstream analyses because it measures both cfDNA and contaminating genomic DNA. The latter can be excluded by most NGS assays. By using constant spike-in internal controls, our approach directly quantifies the amount of sequenceable cfDNA, providing a more accurate estimate of input DNA (Figure 5c). In one possible future experiment, the same sample in the Healthy Cohort can be delayed by 1-2 hours prior to processing (centrifugation and refrigeration) and kept at room temperature rather than 4 °C to mimic real-world delays. Outputs would be cfDNA yields and fragment sizes, and we would use constant spike-ins to quantify the amount of sequenceable DNA.

(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.

We appreciate the reasonable concerns regarding cfDNA degradation and agree that the methylation profile is not an adequate metric for degradation. To evaluate for degradation, we will focus on NGS-derived length profiles (WGS data) and constant spike-in DNA. We appreciate the reviewer’s suggestion to use the Spearman correlation, and this will be incorporated.

(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.

We appreciate the reviewer’s constructive comments regarding the CNV analysis. We agree that the use of low-coverage WGS (~5×) limits the reliable detection of small CNVs, and we will acknowledge this as a limitation in the revised manuscript. To address this point, we will perform additional analyses using 50kb as bin sizes. To reduce potential bias from single-sample representation, we will show the aggregated CNV plots for all CNA-positive cases along with their log₂ copy ratio correlations, and Spearman’s correlation will be applied as suggested.

(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.

Thank you for this suggestion. We will evaluate CNV profiles using alternative informatics methods.

(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.

We thank the reviewer for emphasizing SNVs as an important application of cfDNA. We agree that the limited volume of residual plasma is a constraint. Routine chemistry tests leave ~1–2 mL of plasma, and this limited volume places an upper limit on performing SNV analysis. We will expand the discussion of this limitation in the paper. Our approach is not intended to replace specialized tubes for large-volume cfDNA collection but rather to complement them by enabling the use of residual material.

utilizing

Clue/Trail/Plex Mark Atomic Terms used for naming

info-morphic units of information that are high-resolution addressable high fildeilty meaning/intentfully deeply intertwingled named info-morphic-colab-orative interpersonal nterplanetary structures amenable to muassive multiplayer interplays that plays nicely with other structures

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Reply to the reviewers

Reviewer #1

Major comments:

(comment #1)- It is interesting that TRF2 loss not only fails to increase γH2AX/53BP1 levels but may even slightly reduce them (e.g., Fig. S2c and the IF images). While the main hypothesis is that TRF2 loss does not trigger telomere dysfunction in NSCs, this observation raises the possibility that TRF2 itself contributes to DDR signaling (ATM-P, γH2AX, 53BP1) in these cells and that in its absence, cells are not able to form those foci. To exclude the possibility that telomere-specific DDR is being missed due to an overall dampened DDR response in the absence of TRF2, it would be informative to induce exogenous DSBs in TRF2-depleted cells and test DDR competence (e.g., IF for γH2AX/53BP1). In other words, are those NSC lacking TRF2 even able to form H2AX/53BP1 foci when damaged? In addition, it would be interesting to perform telomere fusion analysis in TRF2 silenced cells (and TRF1 silenced cells as a positive control).

We acknowledge a slight reduction; however, this difference is not statistically significant (Fig S2c,e). We will quantify the levels of DDR markers upon TRF2 loss and exogenous DSBs and include it in the subsequent revision.

(comment #2)-A TRF2 ChIP-seq should be performed in NSC as this list of genes (named TAN genes in the text) was determined using a ChIP performed in another cell line (HT1080). For the ChIP-qPCR in the various conditions, primers for negative control regions should be included to show the specific binding of TRF2 to the promoter of the genes associated with neuronal differentiation. For example, an intergenic region and/or promoters of genes that are not associated with neuronal differentiation (or don't contain a potential G4). The same comment goes true for the gene expression analysis: a few genes that are not bound by TRF2 should be included as negative controls to exclude a potential global effect of TRF2 loss on gene expression (ideally a RNA-seq would be performed instead). We have performed NSC-specific TRF2 ChIP-seq for an upcoming manuscript, which confirms TRF2 occupancy at multiple promoters of differentiation-associated genes. These data are provided solely for confidential evaluation by the designated reviewers.

Regarding the ChIP-qPCR control experiments: We thank reviewer for pointing this out, indeed we included controls in our PCR assays as positive (telomeric) and TRF2-nonbinding loci (GAPDH, RPS18, and ACTB, based on HT1080 TRF2 ChIP-seq data) as negative controls. These results were not included earlier for clarity given that we were presenting several ChIP-PCR figures - in response to the comment we have included this now in the revised version (Fig. S3d,e). Gene expression analyses show selective upregulation of the TAN genes upon TRF2 loss (data normalised to GAPDH); whereas negative control genes lacking TRF2 binding (RPS18, ACTB) remain unchanged, ruling out non-specific effects. (Fig S3f,g,j,k).

-(comment #3) A co-IP should be performed between the TRF2 PTM mutant K176R or WT TRF2 and REST and PRC2 components to directly show a defect of interaction between them when TRF2 is mutated (a co-IP with DNase/RNase treatment to exclude nucleic-acid bridging). The TRF2 PTM mutant T188N also seems to lead to an increased differentiation (Fig. S5a). Could the author repeat the measure of gene expression and co-IP with REST upon the overexpression of this mutant too?

We confirm that DNase/RNase is routinely included in our pull-down experiments to exclude nucleic-acid bridging, with detailed methodology now elaborated in the Methods section. Not including this in the manuscript Methods was an oversight from our side. Our data demonstrate that only REST directly interacts with TRF2, while TRF2 engages PRC2 indirectly via REST, as also previously shown by us and others (page 6; ref. [62]; Sharma et al., ref. [15]).

We thank the reviewer for noting the apparent differentiation in Fig. S5a. However, this observation represents rare spontaneous differentiation event and is not statistically significant (as shown in Fig S5b). Consistently, gene expression analysis of the TRF2-T188N mutant shows no significant change in TRF2-associated neuronal differentiation (TAN) genes. Therefore, Co-IP for TRF2-T188N with REST was not done.

(comment #4) - The authors show that the G4 ligands SMH14.6 and Bis-indole carboxamide upregulate TAN genes and promote neuronal differentiation, but the underlying mechanism remains unclear. Bis-indole carboxamide is generally considered a G4 stabilizer, while SMH14.6 is less characterized and should be better introduced. The authors should clarify how G4 stabilization would interfere with TRF2 binding, it seems that it would likely be by blocking access. A more detailed discussion, and ideally TRF2 ChIP after ligand treatment and/or G4 helicase treatment, would strengthen the model.

We clarify that Bis-indole carboxamide acts as a G4 stabilizer, while SMH14.6 is also a noted G4-binding ligand that stabilizes G4s (ref. [15]). The exclusion of TRF2 from G4 motifs in gene promoters by G4-binding ligands has also been documented previously (ref. [18]). In line with these findings, ChIP experiments performed following ligand treatment revealed a decreased occupancy of TRF2 at TAN gene promoters, supporting the proposed mechanism (added Fig. 6h).

Minor comments:

• Supp Figures related to the scRNA-seq are difficult to read (blurry).

Corrected

• Fig S1h: The red box mentioned in the legend is not visible

Corrected

• In the text, the Figures 1 f-g are misannotated as Fig 1m and l

Corrected

• The symbol γ of γH2AX is missing in the text

Corrected

• Fig.3d, please indicate in the legend that it is done in SH-SY5Y.

Added SH-SY5Y in the legend of Fig. 3d.

• Fig. S3b: Please consider replotting this panel with an increased y-axis scale. As currently presented, the TRF2 ChIP-seq peaks at several promoters appear truncated by the scaling.

Corrected

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

1. For most of the data graphs in the manuscript, there is no indication of the number of independent biological replicates carried out (which should ideally be plotted as individual dots overlaying the column graphs), or what the error bars represent, or what statistical test was used. All the figure legends and methods have now been updated with the corresponding biological replicates per experiment, with error bars as SD/SEM and the corresponding statistical test along with p values.

Figure S1.1a: needs a marker to show that the tissue is dentate gyrus.

We acknowledge the reviewers' concern that high-magnification images alone make it difficult to verify whether the fields are taken from the correct anatomical location. The dentate gyrus (DG) of the hippocampus is a well-defined structure. In the revised figure (Fig S1.1a), we now include a low-magnification image showing the entire hippocampus, including the CA fields, along with two high-magnification fields specifically from the DG region. Consistent with our claim, the co-immunostaining demonstrates that Sox2-positive neural stem cells in the DG are also positive for TRF2.

Figure 1c (and all other flow cytometry panels throughout the manuscript): it is not clear if the expression of any of these proteins, except maybe MAP2, are significantly different in the presence or absence of TRF2. These differences need to be presented more quantitatively, with the results compiled from multiple biological replicates and analysed statistically. I am not sure that flow cytometry is the best way to determine differences in protein expression levels for non-surface proteins, because many of the reported differences are not at all convincing.

To detect intracellular/nuclear proteins by flow cytometry, cells were permeabilized using pre-chilled 0.2% Triton X-100 for 10 minutes, as described in the Methods section.

We have revised the figures (Fig 1c,e) and now included statistical analysis from three independent biological replicates for these experiments.(Fig S1.4h-j, S2e, S6d)

Fig 1d: has TRF2 been effectively silenced in this experiment? There appears to be just as many TRF2+ nuclei in the "TRF2 silenced" panel vs the control, including in the cells with neurite outgrowths.

Quantification of nuclear levels of TRF2 showing decrease in nuclear TRF2 has been included in supplementary Fig S1g.

Fig 2a-c: these experiments need a positive control, showing increased expression of these proteins in mNSC and SH-SY5Y cells in response to a DNA damaging agent. Again, flow cytometry may not be the best method for this; immunofluorescence combined with telomere FISH would be more convincing.

We confirm that doxorubicin induces 53BP1 foci (IF-FISH Sup Fig. S2b) and TRF1 silencing elevates γH2AX (Sup Fig. S2c) validating DDR sensitivity. Unlike TRF2 loss (Fig. 2a-c), no TIFs appear with IF and telomere probes (Fig. 2d, Sup Fig. 2a), and without TIFs, there is no telomeric fusion. Flow cytometry was performed with Triton X- 100 to target nuclear protein. These findings adequately address the concern; therefore, further IF-FISH experiments were not included in the present study.

To conclude that telomere damage is not occurring, an independent marker of such damage, such as telomere fusions, should also be measured.

In response to uncapped telomeres, ATM kinase activates the DNA damage response (DDR), recruiting γH2AX and 53BP1 to telomeres, which precedes the end-to-end fusions (Takai et al., 2003; Maciejowski & de Lange, 2015; Takai et al., 2003; d'Adda di Fagagna et al., 2003; Cesare & Reddel, 2010; Hayashi et al., 2012; Sarek et al., 2015). We observe no DDR activation or foci (Fig. 2; Sup. Fig. 2). This absence of a DDR response and TIFs indicates no telomere uncapping, negating the need for direct telomere fusion analysis.

Figure S2b is lacking a no-doxorubicin control.

Untreated control has been included Fig. S2b.

Figures 3a and 3b need a positive control (e.g. TRF2 binding to telomeric DNA) and a negative control (e.g. a promoter that did not show any TRF2 binding in the HT1080 ChiP-seq experiment in Fig S3).

We have included positive (telomere) and negative (GAPDH) controls (based on HT1080 TRF2 ChIP-seq data) for the TRF2 ChIP assay in Supplementary Fig. S3d,e. Additionally, positive and negative controls for all ChIP experiments conducted in this study are presented in Supplementary Figs. S3d, S3e, S3h, S3i, S4c-h, and S5c-e

The data in Figure 3 would be more compelling if all experiments were also performed in fibroblasts to confirm the cell-type specificity of the effect.

Our HT1080 fibrosarcoma ChIP-seq data (ref. [18]; Sup. Fig. 3a,b) show TRF2 binding to TAN gene promoters in a fibroblast-derived model, with enrichment in neurogenesis-related genes (refs. [19,20]). In fibroblasts TRF2 depletion, as expected, induce telomere dysfunction and DDR (Fig. 2d; Sup. Fig. 2a), and eventually cell-cycle arrest and cell death as also reported earlier (van Steensel et al., 1998; Smogorzewska & de Lange, 2002). Therefore, the suggested experiments which would require sustained TRF2-depletion are not possible to perform in fibroblasts. TRF2 occupancy on the promoter of the genes in question in cells other than NSC was noted in HT1080 cells (ref. [18]; Sup. Fig. 3a,b).

No references are provided for the TRF2 posttranslational modifications on R17, K176, K190 and T188. What is the evidence for these modifications, and is it known if they participate in the telomeric role of TRF2?

These lines with references have been included in the manuscript (highlighted in blue).

R17 methylation enhances telomere stability (66). K176/K190 acetylation stabilizes telomeres and is deacetylated by SIRT6 (67). T188 phosphorylation facilitates telomere repair after DSBs(68). These PTMs primarily support telomeric roles.

The experiments in Fig 5 should also be performed with WT TRF2, to confirm that effects are not due to the overexpression of TRF2.

WT TRF2 shows no differentiation phenotype and change in TAN gene expression (Fig. 1f,g; 3h, Sup Fig. 5a). Confirming effects are not due to TRF2 overexpression.

Fig 5c has not been described in the text, and there are multiple technical problems with the TRF2 WT experiment: i) There appears to be significant background binding of REST to the IgG beads, though this blot has such high background it is hard to tell (the REST blot in Fig S4b is also of poor quality), ii) TRF2 is migrating at two different positions in the Input and IP lanes, and the TRF2 band in the K176R blot is at a different position to either, and iii) the relative loading of the Input and IP lanes is not indicated, so it's not clear why K176R appears to be so enriched in the IP.

We acknowledge the oversight in not citing Fig 5c in the manuscript. This has been corrected, and, highlighted in blue in the revised manuscript.

i) Multiple optimization attempts were made for the Co-IP experiments, and the presented figure reflects the best achievable result despite REST blot smearing, a pattern also reported previously (Ref. 65). The TRF2-REST interaction is well established, and a similar background was also observed in the cited study

ii)Variable migration patterns of TRF2 were also noted in the cited study (Ref. 65), consistent with our observations. Our primary emphasis, however, is on the TRF2 K176R mutant, which clearly disrupts its interaction with REST.

iii)The input loading corresponds to 10% of the total lysate. As the experiments were conducted independently, variations in transfection and pull-down efficiencies may account for observed differences.

To rule out indirect effects of the G4 ligands on the results in Fig 6g, the binding of BG4 and TRF2 at the promoters of these genes should be measured by ChIP.

To confirm that G4 ligand effects on TAN gene promoters are direct, TRF2 occupancy was assessed using ChIP. Significantly decreased occupancy of TRF2 was noted at TAN gene promoters, (added Fig. 6h). This implies that ligand-induced changes in TRF2 binding are directly linked to promoter-level G4 stabilization.

Minor comments:

1. The size of all the size markers in western blots should be added to the figures. Size has been included in all the western blots

2. There are several figure panels that are incorrectly referenced in the text, e.g. Fig S1.1 (e-f) should be Fig S1.1 (e-h); Fig. 1m should be Fig. 1f; Figs 5e and 5f have been swapped.

Corrected.

1. Fig S1.4 is not referred to in the text. It is not clear what the purpose of Fig S1.4a is.

The following line has been included in the manuscript highlighted in blue.

Neurospheres were characterized using PAX6, a NSC marker (Fig S1.4a).

Are the experiments in Figs 3e, 4a, 4c and 4e using 4-OHT treatment, or siRNA? If the latter, I don't think a control for the effectiveness of the knockdown in this cell type has been included anywhere in the manuscript.

It is using siRNA, a western blot showing the effectiveness of knockdown is presented in supplementary figure S4c (now S4a).

The lanes of the western blots in Fig S4c are not labelled.

Corrected.

1. Given that the experiments in Fig 5 were carried out on a background of endogenous WT TRF2 expression, presumably the K176R mutant is having a dominant negative effect. To understand the mechanism of this effect (e.g, is it simply due to replacement of endogenous WT TRF2 at its genomic binding sites by a large excess of exogenous K176R, or is dimerisation with WT TRF2 needed?) it would be helpful to know the relative expression levels of endogenous and K176R TRF2.

To address the query, qRT-PCR with 3′ UTR-specific primers showed no change in endogenous TRF2 mRNA upon K176R expression in SH-SY5Y cells, while primers detecting total TRF2 revealed ~10-fold higher expression of K176R compared to control (Figure below). This indicates the absence of suppression of endogenous TRF2 mRNA. Given that the mutant's DNA binding is intact (Fig. 5f), the dominant-negative effect of K176R likely arises from overexpression of the exogenous mutant.

For the sentence "...and critical for transcription factor binding including epigenetic functions that are G4 dependent" (bottom of page 3 of the PDF), the authors cite only their own prior papers, but there are examples from others that could be cited.

We have incorporated citations from other research groups, now included as references 23-26.

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Referee #2

Evidence, reproducibility and clarity

This manuscript examines the effects of depletion of the telomeric protein TRF2 in mouse neural stem cells, using mice carrying a floxed allele of TRF2 and inducible Cre recombinase under the control of the stem cell-specific Nestin promoter. The results are also backed up in a human neuroblastoma cell line that has progenitor-like properties. There is no apparent induction of telomere damage in either of these cell types, but there is an increase in expression of neurogenesis genes. This is accompanied by an increase in binding of TRF2 to the relevant promoters, and evidence is provided that this binding involves G-quadruplexes in the promoters.

On the whole, these core findings of this study are interesting, and reasonably robust. However, the study as a whole is marred by a large number of technical issues and missing controls which should be addressed prior to publication:

1. For most of the data graphs in the manuscript, there is no indication of the number of independent biological replicates carried out (which should ideally be plotted as individual dots overlaying the column graphs), or what the error bars represent, or what statistical test was used.
2. Figure S1.1a: needs a marker to show that the tissue is dentate gyrus.
3. Figure 1c (and all other flow cytometry panels throughout the manuscript): it is not clear if the expression of any of these proteins, except maybe MAP2, are significantly different in the presence or absence of TRF2. These differences need to be presented more quantitatively, with the results compiled from multiple biological replicates and analysed statistically. I am not sure that flow cytometry is the best way to determine differences in protein expression levels for non-surface proteins, because many of the reported differences are not at all convincing.
4. Fig 1d: has TRF2 been effectively silenced in this experiment? There appears to be just as many TRF2+ nuclei in the "TRF2 silenced" panel vs the control, including in the cells with neurite outgrowths.
5. Fig 2a-c: these experiments need a positive control, showing increased expression of these proteins in mNSC and SH-SY5Y cells in response to a DNA damaging agent. Again, flow cytometry may not be the best method for this; immunofluorescence combined with telomere FISH would be more convincing.
6. To conclude that telomere damage is not occurring, an independent marker of such damage, such as telomere fusions, should also be measured.
7. Figure S2b is lacking a no-doxorubicin control.
8. Figures 3a and 3b need a positive control (e.g. TRF2 binding to telomeric DNA) and a negative control (e.g. a promoter that did not show any TRF2 binding in the HT1080 ChiP-seq experiment in Fig S3).
9. The data in Figure 3 would be more compelling if all experiments were also performed in fibroblasts to confirm the cell-type specificity of the effect.
10. No references are provided for the TRF2 postranslational modifications on R17, K176, K190 and T188. What is the evidence for these modifications, and is it known if they participate in the telomeric role of TRF2?
11. The experiments in Fig 5 should also be performed with WT TRF2, to confirm that effects are not due to the overexpression of TRF2.
12. Fig 5c has not been described in the text, and there are multiple technical problems with the TRF2 WT experiment: i) There appears to be significant background binding of REST to the IgG beads, though this blot has such high background it is hard to tell (the REST blot in Fig S4b is also of poor quality), ii) TRF2 is migrating at two different positions in the Input and IP lanes, and the TRF2 band in the K176R blot is at a different position to either, and iii) the relative loading of the Input and IP lanes is not indicated, so it's not clear why K176R appears to be so enriched in the IP.
13. To rule out indirect effects of the G4 ligands on the results in Fig 6g, the binding of BG4 and TRF2 at the promoters of these genes should be measured by ChIP.

Minor comments:

1. The size of all the size markers in western blots should be added to the figures.
2. There are several figure panels that are incorrectly referenced in the text, e.g. Fig S1.1 (e-f) should be Fig S1.1 (e-h); Fig. 1m should be Fig. 1f; Figs 5e and 5f have been swapped.
3. Fig S1.4 is not referred to in the text. It is not clear what the purpose of Fig S1.4a is.
4. Are the experiments in Figs 3e, 4a, 4c and 4e using 4-OHT treatment, or siRNA? If the latter, I don't think a control for the effectiveness of the knockdown in this cell type has been included anywhere in the manuscript.
5. The lanes of the western blots in Fig S4c are not labelled.
6. Given that the experiments in Fig 5 were carried out on a background of endogenous WT TRF2 expression, presumably the K176R mutant is having a dominant negative effect. To understand the mechanism of this effect (e.g is it simply due to replacement of endogenous WT TRF2 at its genomic binding sites by a large excess of exogenous K176R, or is dimerisation with WT TRF2 needed?) it would be helpful to know the relative expression levels of endogenous and K176R TRF2.
7. For the sentence "...and critical for transcription factor binding including epigenetic functions that are G4 dependent" (bottom of page 3 of the PDF), the authors cite only their own prior papers, but there are examples from others that could be cited.

Significance

The protein TRF2 was first identified as one of the core proteins that bind to the double-stranded region of telomeric DNA, and its many-faceted role in telomere protection has been well studied over the last 3 decades. More recent data from several labs indicate that TRF2 has additional roles outside the telomere, including in regulating gene expression, but these roles are so far much less characterised. Also, it has recently been shown that mouse ES cells, unexpectedly, do not require TRF2 for telomere protection (references 3 and 4 in this paper).

The findings of the current findings expand the type of stem cells in which TRF2 is likely to be playing more of a role elsewhere in the genome, and not at telomeres, and hence is likely to be of high interest to both researchers of telomere biology, and those interested in the regulation of stem cell biology and neurogenesis.

The strengths of the study are its novelty, its use of an inducible system to knock out TRF2 in the mouse neural stem cells of interest, and a thorough analysis of changes in gene expression and promoter occupancy across a range of genes of relevance to neurogenesis. The major weakness of the study, as descibed above, is the large number of technical problems, missing controls and missing indications of biological reproducibility.

Reviewer #1 (Public Review):

Summary:

Characterization of a dissociable Mediator subunit implicated in cellular pathways, particularly lung alveolar function, and HIV latency is conceptually interesting.

Strengths:

The strengths of this study are:

(1) Demonstration of MED16 dissociation from the core Mediator complex and formation of a subcomplex containing MED16, upstream-binding protein 1 (UBP1), and transcription factor cellular promoter 2 (TFCP2) by elegant biochemical fractionation and immunoblotting analysis.

(2) Defining nine N-terminal WD-40 repeats (WDRs) of MED16 as a Mediator-incorporating module and the C-terminal ⍺β-domain (157 amino acids) important for interaction with the UBP1-TFCP2 heterodimeric complex.

(3) Illustration of a weak hydrophobic interaction between MED16 and the Mediator core that could be disrupted by 1,6-hexanediol, but not by its 2,5-hexanediol isomer nor by high salt (500 mM NaCl) disruption.

(4) Classification of UBP1-upregulated cellular genes typically containing binding sites flanking the transcription start site (TSS) in contrast to UBP1-downregulated genes often containing a TSS-overlapping UBP1-binding site

(5) Presenting evidence for Mediator complex-dissociated free MED16-repressed HIV promoter activity through functional association with UBP1 and showing bromodomain-containing protein 4 (BRD4) inhibitor JQ1 that potentially disrupts BRD4-inhibited HIV-1 transcription elongation could lead to reversal of HIV-1 latency.

Weaknesses:

Nevertheless, foreseeable weaknesses include:

(1) No clear demonstration of MED16-UBP1-TFCP2 indeed forming a trimeric core subcomplex in regulating cellular gene transcription and HIV-1 promoter inhibition

(2) No validation of transcriptomic datasets and pathways identified.

(3) Use of mostly artificial reporter gene constructs and non-HIV host cells (e.g., human 293T embryonic kidney cells, human HeLa cervical cancer cells, and mouse HT pancreatic cancer cells) for examining MED16/UBP1-regulated HIV transcription.

(4) Inconsistent use of 293T and HeLa cells in the characterization of dissociated MED16 interaction with UBP1 and TFCP2.

(5) In vitro transcription using immobilized DNA templates was not performed to a high standard, thus failing to convincingly show MED16/UBP1-inhibited HIV-1 transcription preinitiation complex formation.

Reviewer #2 (Public Review):

Summary:

The article from Zheng et al. proposes an interesting hypothesis that the Med16 subunit of Mediator detaches from the complex, associates with transcription factor UBP1, and this complex activates or represses specific sets of genes in human cells. Despite my excitement upon reading the abstract, I was concerned by the lack of rigor in the experimental design. The only statement in the abstract that has some experimental support is the finding that Med16 dissociates from the Mediator and forms a subcomplex, but the data shown remain incomplete.

Strengths:

The authors have preliminary evidence that a stable Med16 complex may exist and that it may regulate specific sets of genes.

Weaknesses:

The experiments are poorly designed and can only infer possible roles for Med16 or UBP1 at this point. Furthermore, the data are often of poor quality and lack replication and quantitation. In other cases, key data such as MS results aren't even shown. Instead, we are given a curated list of only about 6 proteins (Figure S1), a subset of which the authors chose to pursue with follow-up experiments. This is not the expected level of scientific process.

(1) The data supporting the Med16 dissociation and co-association with UBP1 are incomplete and not convincing at this stage. According to the Methods and text, the gel filtration column was run with "un-dialyzed HeLa cell nuclear extract" and eluted in 300mM KCl buffer. The extracts were generated with the Dignam/Roeder method according to the text. Undialyzed, that means the extract would be between 0.4 - 0.5M NaCl. Under these high salt conditions (not physiological), it's possible and even plausible that Mediator subunits could separate over time. This caveat is not mentioned or controlled for by the authors. Because a putative Med16 subcomplex is a foundational point of the article, this is concerning.

The data are incomplete because a potential Med16 complex is not defined biochemically. The current state suggests a smaller Med16-containing complex that may also contain UBP1 and other factors, but its composition is not determined. This is important because if you're going to conclude a new and biologically relevant Med16 complex, which is a point of the article, then readers will expect you to do that.

Equally concerning are the IP-western results shown in Figure 1. In my opinion, these experiments do nothing to support the claims of the authors. The authors use hexanediols at 5% or 10% in an effort to disrupt the Mediator complex. Assuming this was weight/volume, that means ~400 to 800mM hexanediol solution, which is fairly high and can be expected to disrupt protein complexes, but the effects haven't been carefully assessed as far as I'm aware. The 2,5 HD (Figure 1B) experiments appear to simply contain greater protein loading, and this may contribute to the apparent differential results. In fact, in looking at the data, it seems that all MED subunits probed show the same trend as Med16. They are all reduced in the 1,6HD experiment relative to the 2,5 HD experiment. But it's hard to know, because replicates weren't completed and quantitation was not done. There aren't even loading controls. Other concerns about the IP-Western experiments are outlined in point 2.

(2) At no point do the authors apply rigorous methods to test their hypothesis. Instead, methods are applied that have been largely discredited over time and can only serve as preliminary data for pilot studies, and cannot be used to draw definitive conclusions about protein function.

a) IP-westerns are fraught with caveats, especially the way they were performed here, in which the beads were washed at relatively low salt and then eluted by boiling the beads in loading buffer. This will "elute" bound proteins, but also proteins that non-specifically interact with or precipitate on the beads. And because Westerns are so sensitive, it is easy to generate positive results. It's just not a rigorous experiment.

b) Many conclusions relied on transient transfection experiments, which are problematic because they require long timeframes, during which secondary/indirect effects from expression/overexpression will result. This is especially true if the proteins being artificially expressed/overexpressed are major transcription regulators, which is the case here. It is simply impossible to separate direct from indirect effects with these types of experiments. Another concern is that there was no effort to assess whether the induced protein levels were near physiological levels. Protein overexpression, especially if the protein is a known regulator of pol2 transcription (e.g., UBP1 or Med16), will create many unintended consequences.

c) Many conclusions were made based upon shRNA knockdown experiments, which are problematic because they require long timeframes (see above point), which makes it nearly impossible to identify effects that are direct vs. indirect/secondary/tertiary effects. Also, shRNA experiments will have off-target effects, which have been widely reported for well over a decade. An advantage of shRNA knockdowns is that they prevent genetic adaptation (a caveat with KO cell lines). A minimal test would be to show phenotypic rescue of the knockdown by expressing a knockdown-resistant Med16 (for example), but these types of experiments were not done.

d) Many experiments used reporter assays, which involved artificial, non-native promoters. Reporters are good for pilot studies, but they aren't a rigorous test of direct regulatory roles for Med16 or other proteins. Reporters don't even measure transcription directly. In fact, no experiment in this study directly measures transcription. An RNA-seq experiment was done with overexpressed or Med16 knockdown cells, but these required long timeframes and RNA-seq measures steady-state mRNA, which doesn't test the potential direct effects of these proteins on nascent transcription.

e) The MS experiments show promise, but the data were not shown, so it's hard to judge. The reader cannot compare/contrast the experiments, and we have no indication of the statistical confidence of the proteins identified. How many biological replicate MS experiments were performed?

(3) The data are over-interpreted, and alternative (and more plausible) hypotheses are ignored. Many examples of this, some of which are alluded to in the points above. For example, Med16 loss or overexpression will cause compensatory responses in cells. An expected result is that Mediator composition will be disrupted, since Med16 directly interacts with several other subunits. Also in yeast, the Robert, Gross, and Morse labs showed that loss of Med16/Sin4 causes loss of other tail module subunits, and this would be expected to cause major changes in the transcriptome. The authors also mention that yeast Med16/Sin4 "alters chromatin accessibility globally" and this would be expected to cause major changes in the transcriptome, leading to unintended consequences that will make data analysis and identification of direct Med16 effects impossible. The unintended consequences will be magnified with prolonged disruption of MED16 levels in cells (e.g., longer than 4h). These unintended consequences are hard to predict or define, and are likely to be widespread given the pivotal role of Mediator in gene expression. One unintended consequence appears to be loss of pol2 upon Med16 over-expression, as suggested by the western blot in Figure 8B. I point this out as just one example of the caveats/pitfalls associated with long-term knockdowns or over-expression.

Reviewer #3 (Public Review):

Summary:

There are two major flaws that fundamentally undermine the value of the study. First, nearly all the central conclusions drawn here rely on the unfounded assumption that the effects observed are direct. No rigorous cause-and-effect relationships are established to support the claims. Second, the quality of the experimental data is substandard. Collectively, these concerns significantly limit any advances that might be gained in our understanding of the UBP1 pathway or Mediator function.

Weaknesses:

(1) The decrease in 1,6-hexanediol-treated cells of MED16 is modest, variable, not quantified, and internally inconsistent. For example, in Figure 1A, 1,6-hexanediol treatment should not have an impact on the level of the protein being directly IP. For MED12 (and CDK8 and MED1 to a lesser extent), 1,6-hexanediol treatment alters the level of the target protein in the IP. Along these lines, Figure 1A shows a no 1,6H-D dependent decrease in MED1 or MED12 levels in the CDK8 IP, whereas Figure 1B does show a decrease. Figure 1A shows no 1,6H-D dependent decrease in CDK8 levels in the MED1 IP, whereas Figure 1B shows a dramatic decrease. MED24 levels in the MED12 IP increase upon 1,6H-D in Figure 1A, but decrease in Figure 1B. Internal inconsistencies of this nature persist in the other Figures.

(2) Undermining the value of Figure 1E/F, UBP1 and TFCP2 may also associate with the small amount of MED16 in the 2MDa fractions. This is not tested, and therefore, the conclusion that they just associate with the dissociable form of MED16 is not supported.

(3) Domain mapping studies in Figure 2 are overinterpreted. Since the interactions could be indirect, it is not accurate to conclude "Therefore, the N-terminal WDR domain of MED16 is crucial for its integration into the Mediator complex, while the C-terminal αβ-domain is essential for interacting with UBP1-TFCP2. "

(4) A close examination of Figure 2C undermines confidence in the association studies. The bait protein in lanes 5-8 should be equal. Also, there is significant binding of GST to UBP1 and TFCP2, in roughly the same patterns as they bind to GST-MED16 αβ. The absence of input samples makes the results even more difficult to interpret.

(5) The domain deletion mutants are utilized throughout the manuscript as evidence of the importance of the UBP1-MED16 interaction. However, in Figure 2F lanes 7 and 8, the delta-S mutant binds MED16 as well as full-length UBP1. This undermines much of the subsequent data and conclusions about specificity.

(6) Even if the delta-S mutant were defective for MED16 binding, the result in Figure 3B does not "confirm that MED16 is required for the transcriptional activity of UBP1,". Removal of that domain may have other effects.

(7) As Mediator is critical for the activation of many genes, it is not accurate to assume that the impact of its deletion in Figure 3E/F demonstrates a direct requirement in UBP1-driven transcription. This could easily be an indirect effect.

(8) Without documenting the relative protein expression levels in Figure 3G/H, conclusions cannot be drawn about the titration experiments, nor the co-expression experiments. These findings are likely the result of squelching or some form of competition that is not directly related to the UBP1-mediated transcription. A great deal of validation would be required in order to support the model that these effects are a result of MED16 overexpression sequestering UBP1 away from holo-Mediator.

(9) The lack of any documentation of expression levels for the various ectopic proteins in the majority of Figures, renders mechanistic claims meaningless (Figures 3, 4, 5, 6, 7, S2, S3). This is particularly relevant since the model presented for many of the results invokes concentration-dependent competition.

I'll have to look further into how phones versus televisions and other devices affect total screen time.

This will be discussed in my paper, it goes to show the reach of phones between classes.

The "TLDR" for this section. Understanding the formula and this statement may help solving questions related to this.

3 ways we can improve child education is providing more funding for the schools, repair decaying school buildings, and finally expand early childhood.

Not standard in my experience, most of these seem like academic standards

broken link, https://digitalcommons.kennesaw.edu/cgi/viewcontent.cgi?article=1016&context=oertechcomm

3 things that could help reduce gender inequality are 1. confronting stereotypes towards others in media or news. 2. Have the government fund rape crisis centers to spread awareness and to help those who have been victims. Finally, 3. Use political support to help push efforts to downsize the issue altogether.

An example would be from the text are how schools in low-income areas are underfunded and lack materials to support their students.

3 example sources of social change can be 1. political interference, 2. social constructs, and 3. the right of free speech

dus voor een quadratic waar 3 richtingsveranderingen inzitten moet je minimaal 3 groepen

zijn liniare combinaties van groepsgemiddelden die getest worden wanneer er een natuurlijke orde is van laag naar medium naar hoog. deze contrasten hebben minimaal 3 groepen nodig en er zijn vormen

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Reply to the reviewers

We thank the reviewers for their thoughtful and constructive feedback, which helped us strengthen the study on both the computational and biological side. In response, we added substantial new analyses and results in a total of 26 new supplementary figures and a new supplementary note. Importantly, we demonstrated that our approach generalizes beyond tissue outcomes by predicting final-timepoint morphology clusters from early frames with good accuracy as new Figure 4C. Furthermore, we completely restructured and expanded the human expert panel: six experts now provided >30,000 annotations across evenly spaced time intervals, allowing us to benchmark human predictions against CNNs and classical models under comparable conditions. We verified that morphometric trajectories are robust: PCA-based reductions and nearest-neighbor checks confirmed that patterns seen in t-SNE/UMAP are genuine, not projection artifacts. To test whether z-stacks are required, we re-did all analyses with sum- and maximum-intensity projections across five slices; results were unchanged, showing that single-slice imaging is sufficient. From a bioinformatics perspective, we performed negative-label baselines, downsampling analyses to quantify dataset needs, and statistical tests confirming CNNs significantly outperform classical models. Biologically, we clarified that each well contains one organoid, further introduced the Latent Determination Horizon concept tied to expert visibility thresholds, and discussed limits in cross-experiment transfer alongside strategies for domain adaptation and adaptive interventions. Finally, we clarified methods, corrected terminology and a scaler leak, and made all code and raw data publicly available.

Together, these revisions in our opinion provide an even clearer, more reproducible, and stronger case for the utility of predictive modeling in retinal organoid development.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This study presents predictive modeling for developmental outcome in retinal organoids based on high-content imaging. Specifically, it compares the predictive performance of an ensemble of deep learning models with classical machine learning based on morphometric image features and predictions from human experts for four different task: prediction of RPE presence and lense presence (at the end of development) as well as the respective sizes. It finds that the DL model outperforms the other approaches and is predictive from early timepoints on, strongly indicating a time-frame for important decision steps in the developmental trajectory.

Response: We thank the reviewer for the constructive and thoughtful feedback. In response to the review as found below, we have made substantial revisions and additions to the manuscript. Specifically, we clarified key aspects of the experimental setup, changed terminology regarding training/validation/test sets, and restructured our human expert baseline analysis by collecting and integrating a substantially larger dataset of expert annotations according to suggestion. We introduced the Latent Determination Horizon concept with clearer rationale and grounding. Most importantly, we significantly expanded our interpretability analyses across three CNN architectures and eight attribution methods, providing comprehensive quantitative evaluations and supplementary figures that extend beyond the initial DenseNet121 examples (new Supplementary Figures S29-S37). We also ensured full reproducibility by making both code and raw data publicly available with documentation. While certain advanced interpretability methods (e.g., Discover) could not be integrated despite considerable effort, we believe the revised manuscript presents a robust, well-documented, and carefully qualified analysis of CNN predictions in retinal organoid development.

Major comments: I find the paper over-all well written and easy to understand. The findings are relevant (see significance statement for details) and well supported. However, I have some remarks on the description and details of the experimental set-up, the data availability and reproducibility / re-usability of the data.

1. Some details about the experimental set-up are unclear to me. In particular, it seems like there is a single organoid per well, as the manuscript does not mention any need for instance segmentation or tracking to distinguish organoids in the images and associate them over time. Is that correct? If yes, it should be explicitly stated so. Are there any specific steps in the organoid preparation necessary to avoid multiple organoids per well? Having multiple organoids per well would require the aforementioned image analysis steps (instance segmentation and tracking) and potentially add significant complexity to the analysis procedure, so this information is important to estimate the effort for setting up a similar approach in other organoid cultures (for example cancer organoids, where multiple organoids per well are common / may not be preventable in certain experimental settings).

Response: We thank the reviewer for this question. We agree that these preprocessing steps would add more complexity to our presented preprocessing steps and would definitely be required in some organoid systems. In our experimental setup, there is only one organoid per well which forms spontaneously after cell seeding from (almost) all seeded cells. There are no additional steps necessary in order to ensure this behaviour in our setup. We amended the Methods section to now explicitly state this accordingly (paragraph ‘Organoid timelapse imaging’).

The terminology used with respect to the test and validation set is contrary to the field, and reporting the results on the test set (should be called validation set), should be avoided since it is used to select models. In more detail: the terms "test set" and "validation set" (introduced in 213-221) are used with the opposite meaning to their typical use in the deep learning literature. Typically, the validation set refers to a separate split that is used to monitor convergence / avoid overfitting during training, and the test set refers to an external set that is used to evaluate the performance of trained models. The study uses these terms in an opposite manner, which becomes apparent from line 624: "best performing model ... judged by the loss of the test set.". Please exchange this terminology, it is confusing to a machine learning domain expert. Furthermore, the performance on the test set (should be called validation set) is typically not reported in graphs, as this data was used for model selection, and thus does not provide an unbiased estimate of model performance. I would remove the respective curves from Figures 3 and 4.

Response: We are thankful for the reviewers comments on this matter. Indeed, we were using an opposite terminology compared to what is commonly used within the field. We have adjusted the Results, Discussion and Methods sections as well as the figures accordingly. Further, we added a corresponding disclaimer for the code base in the github repository. However, we prefer to not remove the respective curves from the figures. We think that this information is crucial to interpret the variability in accuracy between organoids from the same experiments and organoids acquired from a different, independent experiment. The results suggest that the accuracy for organoids within the same experiments is still higher, indicating to users the potential accuracy drop resulting from independent experiments. As we think that this is crucial information for the interpretability of our results, we would like to still include it side-by-side with the test data in the figures.

The experimental set-up for the human expert baseline is quite different to the evaluation of the machine learning models. The former is based on the annotation of 4,000 images by seven expert, the latter based on a cross-validation experiments on a larger dataset. First of all, the details on the human expert labeling procedure is very sparse, I could only find a very short description in the paragraph 136-144, but did not find any further details in the methods section. Please add a methods section paragraph that explains in more detail how the images were chosen, how they were assigned to annotators, and if there was any redundancy in annotation, and if yes how this was resolved / evaluated. Second, the fact that the set-up for human experts and ML models is quite different means that these values are not quite comparable in a statistical sense. Ideally, human estimators would follow the same set-up as in ML (as in, evaluate the same test sets). However, this would likely prohibitive in the required effort, so I think it's enough to state this fact clearly, for example by adding a comment on this to the captions of Figure 3 and 4.

Response: We thank the reviewer for this constructive suggestion. We agree that the curves for human evaluations in the original draft were calculated differently compared to the curves for the classification algorithms, mostly stemming from feasibility of data set annotation at the time. In order to still address this suggestion, we went on to repeat and substantially expand the number of images annotated and thus revised the full human expert annotation. Each one of 6 human experts was asked to predict/interpret 6 images of each organoid within the full dataset. In order to select the images, we divided the time course (0-72h) into 6 evenly spaced intervals of 12 hours. For each interval, one image per organoid and human expert was randomly selected and assigned. This resulted in a total of 31,626 classified images (up from 4000 in the original version of the manuscript), from which the assigned images were overlapping between experts for each source interval but not for the individual images. We then changed the calculation of the curves to be the same as for the classification analysis: F1 data were calculated for each experiment over 6 timeframes and all experts, and plotted within the respective figure. We have amended the Methods section accordingly and replaced the respective curves within Figures 3 and 4 and Supplementary Figures S1, S8 and S19.

It is unclear to me where the theoretical time window for the Latent Determination Horizon in Figure 5 (also mentioned in line 350) comes from? Please explain this in more detail and provide a citation for it.

Response: We thank the reviewer for this important point. The Latent Determination Horizon (LDH) is a conceptual framework we introduced in this study to describe the theoretical period during which the eventual presence of a tissue outcome of interest (TOI) is being determined but not yet detectable. It is derived from two main observations in our dataset: (i) the inherent intra- and inter-experimental heterogeneity of organoid outcomes despite standardized protocols, and (ii) the progressive increase in predictive performance of our deep learning models over time, which suggests that informative morphological features only emerge gradually. We have now clarified this rationale in the manuscript (Discussion section) further and explicitly stated that the LDH is a concept we introduce here, rather than a previously described or cited term.

The timewindow is defined by the TOI visibility, which is defined empirically as indicated by the results of our human expert panel (compare also Supplementary Figure S1).

The intepretability analysis (Figure 4, 634-639) based on relevance backpropagation was performed based on DenseNet121 only. Why did you choose this model and not the ResNet / MobileNet? I think it is quite crucial to see if there are any differences between these model, as this would show how much weight can be put on the evidence from this analysis and I would suggest to add an additional experiment and supplementary figure on this.

Response: We thank the reviewer for this important comment regarding the interpretability analysis and the choice of model. In the original submission, we restricted the attribution analyses shown in originial Figure 4C to DenseNet121, which served as our main reference model throughout the study. This choice was made primarily for clarity and to avoid redundancy in the main figures, as all three convolutional neural network (CNN) architectures (DenseNet121, ResNet50, MobileNetV3_Large) achieved comparable classification performance on our tasks.

In response to the reviewer’s concern, we have now extended the interpretability analyses to include all three CNN architectures and a total of eight attribution methods (new Supplementary Note 1). Specifically, we generated saliency maps for DenseNet121, ResNet50, and MobileNetV3_Large across multiple time points and evaluated them using a systematic set of metrics: pairwise method agreement within each model (new Supplementary Figure S29), cross-model consistency per method (new Supplementary Figure S34), entropy and diffusion of saliencies over time (new Supplementary Figure S35), regional voting overlap across methods (new Supplementary Figure S36), and spatial drift of saliency centers of mass (new Supplementary Figure S37).

These pooled analyses consistently showed that attribution methods differ markedly in the regions they prioritize, but that their relative behaviors were mostly stable across the three CNN architectures. For example, Grad-CAM and Guided Grad-CAM exhibited strong internal agreement and progressively focused relevance into smaller regions, while gradient-based methods such as DeepLiftSHAP and Integrated Gradients maintained broader and more diffuse relevance patterns but were the most consistent across models. Perturbation-based methods like Feature Ablation and Kernel SHAP often showed decreasing entropy and higher spatial drift, again similarly across architectures.

To further address the reviewer’s point, we visualized the organoid depicted in original Figure 4C across all three CNNs and all eight attribution methods (new Supplementary Figures S30-S33). These comparisons confirm and extend analysis of the qualitative patterns described in original Figure 4C and show that they are not specific to DenseNet121, but are representative of the general behavior across architectures.

In sum, we observed notable differences in how relevance was assigned and how consistently these assignments aligned. Highlighted organoid patterns were not consistent enough across attribution methods for us to be comfortable to base unequivocal biological interpretation on them. Nevertheless we believe that the analyses in response to the reviewer’s suggestions (new Supplementary Note 1 and new Supplementary Figures S29-S37) add valuable context to what can be expected from machine learning models in an organoid research setting.

As we did not base further unequivocal biological claims on the relevance backpropagation, we decided to move the analyses to the Supporting Information and now show a new model predicting organoid morphology by morphometrics clustering at the final imaging timepoint in new Figure 4C in line with suggestions by Reviewer #3.

The code referenced in the code availability statement is not yet present. Please make it available and ensure a good documentation for reproducibility. Similarly, it is unclear to me what is meant by "The data that supports the findings will be made available on HeiDoc". Does this only refer to the intermediate results used for statistical analysis? I would also recommend to make the image data of this study available. This could for example be done through a dedicated data deposition service such as BioImageArchive or BioStudies, or with less effort via zenodo. This would ensure both reproducibility as well as potential re-use of the data. I think the latter point is quite interesting in this context; as the authors state themselves it is unclear if prediction of the TOIs isn't even possible at an earlier point that could be achieved through model advances, which could be studied by making this data available.

Response: We thank the reviewer for this comment. We have now made the repository and raw data public on the suggested platform (Zenodo) and apologize for this oversight. The links are contained within the github repository which is stated in the manuscript under “Data availability”.

Minor comments:

Line 315: Please add a citation for relevance backpropagation here.

Response: We have included citations for all relevance backpropagation methods used in the paper.

Line 591: There seems to be typo: "[...] classification of binary classification [...]"

Response: Corrected as suggested.