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  1. Sep 2025
  2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Maspin/SerpinB5 is a cytoskeleton-binding protein that regulates epithelial cell shape
    1
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    pmc.ncbi.nlm.nih.gov/articles/PMC12373898/
  3. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Ankyrin-B modulates mitochondrial fission in skeletal muscle and is required for optimal endurance exercise capacity
    4
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    pmc.ncbi.nlm.nih.gov/articles/PMC12379235/
  4. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Targeting of IRAK4 and GSPT1 enhances therapeutic efficacy in AML via c-Myc destabilization
    3
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    pmc.ncbi.nlm.nih.gov/articles/PMC12380595/
  5. link.springer.com link.springer.com
    RACK1 attenuates pancreatic tumorigenesis by suppressing acinar-to-ductal metaplasia through inflammatory signaling modulation
    26
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    link.springer.com/article/10.1007/s13402-025-01084-3
  6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    A conformational change in α-catenin’s actin-binding domain governs adherens junction maturation
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    pmc.ncbi.nlm.nih.gov/articles/PMC12402239/
  7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Domain coupling in activation of a family C GPCR
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    pmc.ncbi.nlm.nih.gov/articles/PMC12394075/
  8. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Sensitive Fluorescent Biosensor Reveals Differential Subcellular Regulation of PKC
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    pmc.ncbi.nlm.nih.gov/articles/PMC12392915/
  9. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Morphodynamics of human early brain organoid development
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    pmc.ncbi.nlm.nih.gov/articles/PMC12390842/
  10. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Hypoxic stress incites HIF1α-driven ribosome biogenesis that can be exploited by targeting RNA Polymerase I
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    pmc.ncbi.nlm.nih.gov/articles/PMC12391393/
  11. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Coordinated active repression operates via transcription factor cooperativity and multiple inactive promoter states in a developing organism
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    pmc.ncbi.nlm.nih.gov/articles/PMC12402238/
  12. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Targeting a pathogenic cryptic exon that drives HLRCC to induce exon skipping
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    pmc.ncbi.nlm.nih.gov/articles/PMC12396411/
  13. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Molecular basis for shifted receptor recognition by an encephalitic arbovirus
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    pmc.ncbi.nlm.nih.gov/articles/PMC12406711/
  14. link.springer.com link.springer.com
    Scratch Behavior of Micro-Patterned Polymeric Surfaces
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    link.springer.com/article/10.1007/s11249-025-02063-3
  15. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov
    Extracellular matrix reorganization during endometrial decidualization
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    pmc.ncbi.nlm.nih.gov/articles/PMC12394361/
  16. www.youtube.com www.youtube.com
    120 personnes jouent à 'Qui veut gagner des millions'… EN VOTANT ! [Feat.@patrick_baud ]
    1
    1. tanemika 06 Sep 2025

      Briefing Document: Prise de Décision Collective et Intelligence de la Foule

      Ce document récapitule les thèmes principaux et les faits marquants des extraits de la vidéo "120 personnes jouent à 'Qui veut gagner des millions'… EN VOTANT ! [Feat.@patrick_baud ]", qui explore différentes méthodes de décision collective et l'efficacité de l'intelligence de la foule à travers une série de quiz.

      I. Introduction à l'Expérience et aux Règles

      Objectif Scientifique: L'expérience vise à comprendre comment un groupe de 120 personnes peut s'organiser pour prendre la meilleure décision possible face à des questions de culture générale de difficulté croissante. C'est "un problème de décision collective".

      Participants: 120 participants.

      • Format du Quiz: 12 questions de culture générale de plus en plus difficiles.
      • Méthodes de Vote Testées:Vote Simple (Majorité): Première série.
      • Vote Pondéré par Degré de Confiance: Deuxième série.
      • Vote Majoritaire avec Communication (Rumeur) et Joker Patrick: Troisième série.
      • Vote Majoritaire avec Joker Patrick Permanent: Quatrième série.
      • Récompense: Si les participants réussissent les 12 questions sans erreur, le présentateur prononcera un mot de leur choix dans sa prochaine vidéo. Le mot choisi fut "Hexacosiohexacontahexaphobie".
      • Jokers Disponibles:Le 50/50 (élimine deux mauvaises réponses).

      Le changement de question.

      L'avis de l'invité Patrick Baud.

      • Activation Joker: La moitié des participants doivent en faire la demande via leur système de vote.
      • Rôle de Patrick Baud: Invité et "joker" pour donner son avis sur les questions. Il souligne la "pression monumentale" que représente ce rôle.
      • Durée de Réponse: 60 secondes par question, sans influence extérieure pendant ce temps.

      II. Série 1: Le Vote Majoritaire Simple

      • Principe Théorique: Le vote majoritaire repose sur un "mécanisme de dilution des erreurs". Les participants ne connaissant pas la réponse votent au hasard et leurs votes se dispersent, tandis que ceux qui savent concentrent leurs votes sur la bonne option, créant ainsi une majorité pour la bonne réponse. Ce système est "vieux comme le monde", remontant aux démocraties athéniennes et observé chez des animaux sociaux.
      • Observations et Limites:Questions Faciles: Le vote majoritaire fonctionne très bien pour les questions faciles (ex: "Bichon frisé", "Inox tague", "MII" pour 2002, "Mouche" comme mot sans masculin).
      • Excès de Prudence: Pour la 6ème question ("Quel événement s'est produit en premier?"), les participants ont utilisé un 50/50 alors que leur vote "penchait déjà vers la bonne réponse".

      Le présentateur le qualifie de "petit excès de prudence qui pourrait leur coûter cher".

      La bonne réponse (Assassinat de Martin Luther King en 1968) était très proche du premier homme sur la lune (1969).

      • Pièges et Fausses Connaissances: Le vote majoritaire est vulnérable aux pièges où les gens "pensent connaître la bonne réponse mais qui se trompent". C'est le cas pour la question sur le prénom féminin désignant une couleur de robe de cheval. "Claris" a attiré des votes, mais la bonne réponse était "Isabelle".
      • Perception Erratique des Capitales: La 10ème question sur les capitales (Johannesburg, Dodoma, Abidjan, Rangoun) a révélé une faiblesse majeure. De nombreux participants ont voté pour Abidjan, pensant que c'était la capitale de la Côte d'Ivoire, alors que c'est Yamoussoukro. "Oh, c'est Dodoma ! Waouh ! Et oui, dans ce domaine, c'est presque trop facile de faire chuter la majorité."
      • Résultat: La première série s'arrête à la 10ème question. "Désolé, vous étiez à la 10e question mais on va devoir redescendre... tout en bas !"

      III. Série 2: Le Vote Pondéré par Degré de Confiance

      • Nouvelle Méthode: Les participants indiquent leur réponse et leur "degré de confiance" (de 1 à 5). Les votes sont pondérés par cette confiance. "Si vous mettez confiance 5, c'est comme si vous aviez voté cinq fois."
      • Fiabilité de l'Indice de Confiance: Un nouvel indicateur est introduit pour mesurer "à quel point une personne plus confiante qu'une autre aura plus souvent raison".
      • Pour la question facile "Martine à la mer", la fiabilité était de 96%.
      • Pour la question orthographique "Toboggan", la fiabilité a chuté à 72%, car "une part considérable de gens qui se trompent tout en étant sur d'eux".
      • Modèle Mathématique: Des modèles prédisent l'efficacité de cette méthode basée sur le pourcentage de bonnes réponses et la fiabilité de la confiance.
      • Conditions Idéales (haut à droite): Majorité correcte et confiance fiable.
      • Conditions Difficiles (bas à gauche): Majorité incorrecte et confiance non fiable (échec).
      • Bonus (haut à gauche): Majorité incorrecte mais confiance fiable (compense les erreurs).
      • Malus (bas à droite): Majorité correcte mais confiance non fiable (perd de la performance par rapport au vote simple).
      • Observations: "Plus les questions deviennent difficiles, plus on s'approche de la frontière où cette méthode ne marche plus."
      • Résultat: Le vote avec confiance est "un tout petit peu meilleur" que le vote simple (environ 70% de bonnes réponses contre 65%), mais le bénéfice est "assez marginal".

      La série s'arrête à la 10ème question ("Qui devient le premier secrétaire du parti communiste du RSS ?"), où la confiance a "fait basculer" le vote vers la bonne orthographe (Nikita Khrouchtchev). Pour la 11ème question ("Lequel de ces pays n'est pas traversé par l'Équateur?"), l'intuition de Patrick sur la Thaïlande s'avère correcte.

      La 12ème question sur la pièce de Tennessee Williams est changée.

      Finalement, la dernière question sur la Pierre de Rosette ("Quelle système d'écriture n'apparaît pas?") échoue, la fiabilité de l'indice de confiance n'étant plus qu'à 30%.

      IV. Série 3: La Communication entre Participants (Esprit de Ruche) et Joker Patrick Permanent

      • Nouvelle Méthode: Les participants ont le droit de se parler avec leurs voisins ("créer une sorte de rumeur qui circule de proche en proche"). Le joker Patrick est permanent pour chaque question, il peut s'exprimer librement.
      • Impact de la Communication:Amplification des Réponses: Pour la question sur "Mille Bornes" ("Quelle carte n'existe pas?"), la "rumeur a tendance à amplifier la réponse collective". La bonne réponse (station service) est trouvée à une très grande majorité, alors que le groupe contrôle n'avait que 58% de bonnes réponses.
      • Consensus Fort: Pour "Talking to me" de Robert de Niro, "seul 60% des gens trouvent la bonne réponse dans le groupe contrôle, l'effet de la rumeur pousse l'intégralité des participants vers la bonne réponse."
      • Impact de l'Influenceur (Patrick):Confirmation: Pour des questions où il est sûr (ex: "Mandela" n'a pas été assassiné, "Campbell's" pour Andy Warhol), Patrick renforce considérablement le vote collectif. "L'influence de Patrick se fait clairement ressentir puisqu'il a convaincu 19 personnes de suivre son avis."
      • Risque de l'Erreur de l'Influenceur: Pour la question sur l'accord de "petit suisse" ("Si vous mangez un petit suisse et un autre petit suisse, on dit que vous aurez donc mangé..."), Patrick donne une réponse incorrecte, reflétant une erreur commune. "Si la majorité a tendance à se tromper comme ici, et bien l'influenceur se trompera généralement de la même façon et ça ne résout donc pas notre problème."
      • Stratégie de l'Influenceur Non-Sachant: Pour la dernière question ("Lequel de ces animaux n'existe pas?"), Patrick ne connaît pas la réponse et décide de ne pas influencer, recommandant de faire "confiance à la puissance de votre esprit de ruche". Cette stratégie est jugée "parfaitement bien joué" par le présentateur.
      • Résultat: Cette méthode s'avère "extrêmement efficace". Les participants atteignent la 12ème question et la réussissent, même si la difficulté est élevée.
      • La question sur Babar/Bob l'éponge (cravate rouge) est résolue avec un 50/50, avec un équilibre parfait 50/50 initial entre les deux bonnes options.
      • La question sur Trondheim (Norvège) est réussie.
      • La question sur 77° Fahrenheit en Celsius est changée.
      • La question sur la divinité hindoue à plusieurs têtes (Brahma) est réussie.
      • La dernière question sur l'animal qui n'existe pas (le serpent hibou) est réussie, malgré l'hésitation.

      V. Conclusions Générales

      • L'Esprit de Ruche: Patrick Baud est "assez ému" par la performance de la dernière série, constatant que l'esprit de ruche rend les participants "quasiment incollable". "Quand vous mettez en commun toutes vos connaissances c'est quasiment impossible de vous piéger quoi et c'est fascinant à voir."
      • Limites du Vote de Confiance: Bien que légèrement supérieur au vote simple, le vote pondéré par la confiance a un "bénéfice assez marginal" et sa fiabilité diminue avec la difficulté des questions.
      • Bénéfices de la Communication: La discussion entre les participants s'est avérée être la méthode la plus efficace, permettant d'atteindre le succès final.
      • Rôle de l'Influenceur: L'influenceur peut être très utile s'il est sûr de la bonne réponse, mais peut induire en erreur s'il se trompe. Sa meilleure stratégie lorsqu'il ne sait pas est de ne pas influencer.
      • Application dans le Monde Réel: EDF, partenaire de la vidéo, développe des "méthodes d'intelligence collective dans l'entreprise directement inspiré de ce qu'on fait au labo".
      Recherche scientifique démocratie vote 2025 vidéo sociologie psychologie
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    youtube.com/watch
  17. revistas.univalle.edu revistas.univalle.edu
    2.pdf
    2
    1. Ana_Zamora 06 Sep 2025
      En la Tabla 3 se muestra que la correlación entre la COI y la SL el p-valor = 0,000 indica que existe relación significativa y el Rho Spearman = ,693** indica que la relación es positiva; en cuanto a la relación entre la comunicación descendente y la SL el p-valor = 0,000 indica que existe relación significativa y el Rho Spearman = ,594** indica que la relación es positiva; la relación entre la comunicación ascendente y la SL el p-valor = 0,000 indica que existe relación significativa y el Rho Spearman = ,799** indica que la relación es positiva, entre la comunicación horizontal y la SL el p-valor = 0,000 indica que existe relación significativa y el Rho Spearman = ,513** indica que la relación es positiva.Tabla 3. Determinación de las correlaciones,693**,594**,799**,513**00001201209595COIComunicación ascendenteComunicación descendenteComunicación horizontalSatisfacción laboralRho Spearmanp - valorN**. La correlación es significativa

      Estos resultados confirman algo fundamental: una buena comunicación es la base de un equipo satisfecho. Es revelador que todos los tipos de comunicación estén directamente ligados a una mayor satisfacción laboral. La comunicación ascendente (de empleados a jefes) muestra la correlación más fuerte. Esto demuestra que, más allá de dar órdenes, lo que realmente motiva a las personas es sentirse escuchadas y valoradas.

    2. Yairrr 05 Sep 2025
      la Tabla 3 se muestra que la correlación entre la COI y la SL el p-valor = 0,000 indica que existe relación significativa y el Rho Spearman = ,693** indica que la relación es positiva; en cuanto a la relación entre la comunicación descendente y la SL el p-valor = 0,000 indica que existe relación significativa y el Rho Spearman = ,594** indica que la relación es positiva; la relación entre la comunicación ascendente y la SL el p-valor = 0,000 indica que existe relación significativa y el Rho Spearman = ,799** indica que la relación es positiva, entre la comunicación horizontal y la SL el p-valor = 0,000 indica que existe

      Aquí se resume el contenido de la tabla 3, en la que se muestra una clara correlación positiva (según los modelos estadísticos aplicados) entre la SL y la comunicación tanto horizontal como vertical ascendente y vertical descendíente.

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    revistas.univalle.edu/index.php/compas/article/download/223/283/1507
  18. inpahuedu-my.sharepoint.com inpahuedu-my.sharepoint.com
    Untitled
    2
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    inpahuedu-my.sharepoint.com/personal/alejandrocuervo_uninpahu_edu_co/Documents/Periodismo transmedia y crossmedia/Documentos/Seminario/3/Leídos/Periodismo_en_la_Web_edited.pdf
  19. openstax.org openstax.org
    1.1 "Reading" to Understand and Respond - Writing Guide with Handbook | OpenStax
    4
    1. seepaulk1 05 Sep 2025
      open public resource made up of stories from people just like you about their experiences learning to read, write, and generally communicate with the world around them.”

      I never knew what DALN was until I read this. It seems like it could be something I'd be interested in. Seems to be something purposefully comfortable and down to earth

    2. seepaulk1 05 Sep 2025
      The audience should feel a connection to the main character or characters.

      This I feel is super important to the reader/audience. Wether we like or dislike a character especially the main antagonist Because that could end up being the whole point.

    3. seepaulk1 05 Sep 2025
      You likely express your identity, or ideas about who you are, through language.

      Language is the way we speak to one another about any sort of our ideas, feelings and information.

    4. seepaulk1 03 Sep 2025
      looking at a photograph or drawing, you find different information presented simultaneously. This

      I never noticed this. I feel like this is really true. I could read a text and then see an image and it presents a whole new and even better idea in front of me than what I was thinking.

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    openstax.org/books/writing-guide/pages/3-3-glance-at-genre-the-literacy-narrative
  20. www.biorxiv.org www.biorxiv.org
    Determinants of mutation load in birds
    1
    1. george_sandler 05 Sep 2025
      Chromosome-level recombination rates were high across all comparisons (median R2=0.668, Supplementary Figure 3)

      It would be nice to get an idea of how stable this porting over of the estimated recombination map is across phylogenetic distance. There is considerable variation in the correlation among species pairs which would be good to understand. Also would be valuable to show the reader how the phylogenetic distance of these comparisons relates to the distances used for comparisons of the main analysis.

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    biorxiv.org/content/10.1101/2025.08.18.670172v1
  21. www.biorxiv.org www.biorxiv.org
    How bats exit a crowded colony when relying on echolocation only - a modeling approach
    1
    1. Public_Reviews 05 Sep 2025

      Author response:

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

      Reviewer #1 (Public review):

      We thank the reviewer for his valuable input and careful assessment, which have significantly improved the clarity and rigor of our manuscript.

      Summary:

      Mazer & Yovel 2025 dissect the inverse problem of how echolocators in groups manage to navigate their surroundings despite intense jamming using computational simulations.

      The authors show that despite the 'noisy' sensory environments that echolocating groups present, agents can still access some amount of echo-related information and use it to navigate their local environment. It is known that echolocating bats have strong small and large-scale spatial memory that plays an important role for individuals. The results from this paper also point to the potential importance of an even lower-level, short-term role of memory in the form of echo 'integration' across multiple calls, despite the unpredictability of echo detection in groups. The paper generates a useful basis to think about the mechanisms in echolocating groups for experimental investigations too.

      Strengths:

      (1) The paper builds on biologically well-motivated and parametrised 2D acoustics and sensory simulation setup to investigate the various key parameters of interest

      (2) The 'null-model' of echolocators not being able to tell apart objects & conspecifics while echolocating still shows agents successfully emerge from groups - even though the probability of emergence drops severely in comparison to cognitively more 'capable' agents. This is nonetheless an important result showing the directionof-arrival of a sound itself is the 'minimum' set of ingredients needed for echolocators navigating their environment.

      (3) The results generate an important basis in unraveling how agents may navigate in sensorially noisy environments with a lot of irrelevant and very few relevant cues.

      (4) The 2D simulation framework is simple and computationally tractable enough to perform multiple runs to investigate many variables - while also remaining true to the aim of the investigation.

      Weaknesses:

      There are a few places in the paper that can be misunderstood or don't provide complete details. Here is a selection:

      (1) Line 61: '... studies have focused on movement algorithms while overlooking the sensory challenges involved' : This statement does not match the recent state of the literature. While the previous models may have had the assumption that all neighbours can be detected, there are models that specifically study the role of limited interaction arising from a potential inability to track all neighbours due to occlusion, and the effect of responding to only one/few neighbours at a time e.g. Bode et al. 2011 R. Soc. Interface, Rosenthal et al. 2015 PNAS, Jhawar et al. 2020 Nature Physics.

      We appreciate the reviewer's comment and the relevant references. We have revised the manuscript accordingly to clarify the distinction between studies that incorporate limited interactions and those that explicitly analyze sensory constraints and interference. We have refined our statement to acknowledge these contributions while maintaining our focus on sensory challenges beyond limited neighbor detection, such as signal degradation, occlusion effects, and multimodal sensory integration (see lines 58-64):

      (2) The word 'interference' is used loosely places (Line 89: '...took all interference signals...', Line 319: 'spatial interference') - this is confusing as it is not clear whether the authors refer to interference in the physics/acoustics sense, or broadly speaking as a synonym for reflections and/or jamming.

      To improve clarity, we have revised the manuscript to distinguish between different types of interference:

      • Acoustic interference (jamming): Overlapping calls that completely obscure echo detection, preventing bats from perceiving necessary environmental cues.

      • Acoustic interference (masking): Partial reduction in signal clarity due to competing calls.

      • Spatial interference: Physical obstruction by conspecifics affecting movement and navigation.

      We have updated the manuscript to use these terms consistently and explicitly define them in relevant sections (see lines 84-85, 119-120). This distinction ensures that the reader can differentiate between interference as an acoustic phenomenon and its broader implications in navigation.

      (3) The paper discusses original results without reference to how they were obtained or what was done. The lack of detail here must be considered while interpreting the Discussion e.g. Line 302 ('our model suggests...increasing the call-rate..' - no clear mention of how/where call-rate was varied) & Line 323 '..no benefit beyond a certain level..' - also no clear mention of how/where call-level was manipulated in the simulations.

      All tested parameters, including call rate dynamics and call intensity variations, are detailed in the Methods section and Tables 1 and 2. Specifically:

      • Call Rate Variation: The Inter-Pulse Interval (IPI) was modeled based on documented echolocation behavior, decreasing from 100 msec during the search phase to 35 msec (~28 calls per second) at the end of the approach phase, and to 5 msec (200 calls per second) during the final buzz (see Table 2). This natural variation in call rate was not manually manipulated in the model but emerged from the simulated bat behavior.

      • Call Intensity Variation: The tested call intensity levels (100, 110, 120, 130 dB SPL) are presented in Table 1 under the “Call Level” parameter. The effect of increasing call intensity was analyzed in relation to exit probability, jamming probability, and collision rate. This is now explicitly referenced in the Discussion. We have revised the manuscript to explicitly reference these aspects in the Results and Discussion sections – see lines 346-349, 372-375.

      Reviewer #2 (Public review):

      We are grateful for the reviewer’s insightful feedback, which has helped us clarify key aspects of our research and strengthen our conclusions.

      This manuscript describes a detailed model of bats flying together through a fixed geometry. The model considers elements that are faithful to both bat biosonar production and reception and the acoustics governing how sound moves in the air and interacts with obstacles. The model also incorporates behavioral patterns observed in bats, like one-dimensional feature following and temporal integration of cognitive maps. From a simulation study of the model and comparison of the results with the literature, the authors gain insight into how often bats may experience destructive interference of their acoustic signals and those of their peers, and how much such interference may actually negatively affect the groups' ability to navigate effectively. The authors use generalized linear models to test the significance of the effects they observe.

      In terms of its strengths, the work relies on a thoughtful and detailed model that faithfully incorporates salient features, such as acoustic elements like the filter for a biological receiver and temporal aggregation as a kind of memory in the system. At the same time, the authors' abstract features are complicating without being expected to give additional insights, as can be seen in the choice of a twodimensional rather than three-dimensional system. I thought that the level of abstraction in the model was perfect, enough to demonstrate their results without needless details. The results are compelling and interesting, and the authors do a great job discussing them in the context of the biological literature. 

      The most notable weakness I found in this work was that some aspects of the model were not entirely clear to me. 

      For example, the directionality of the bat's sonar call in relation to its velocity. Are these the same?

      For simplicity, in our model, the head is aligned with the body, therefore the direction of the echolocation beam is the same as the direction of the flight. 

      Moreover, call directionality (directivity) is not directly influenced by velocity. Instead, directionality is estimated using the piston model, as described in the Methods section. The directionality is based on the emission frequency and is thus primarily linked to the behavioral phases of the bat, with frequency shifts occurring as the bat transitions from search to approach to buzz phases. During the approach phase, the bat emits calls with higher frequencies, resulting in increased directionality. This is supported by the literature (Jakobsen and Surlykke, 2010; Jakobsen, Brinkløv and Surlykke, 2013). This phase is also associated with a natural reduction in flight speed, which is a well-documented behavioral adaptation in echolocating bats(Jakobsen et al., 2024).

      To clarify this in the manuscript, we have updated the text to explicitly state that directionality follows phase-dependent frequency changes rather than being a direct function of velocity, see lines 543-545. 

      If so, what is the difference between phi_target and phi_tx in the model equations? 

      𝝓<sub>𝒕𝒂𝒓𝒈𝒆𝒕</sub> represents the angle between the bat and the reflected object (target).

      𝝓<sub>𝑻𝒙</sub> the angle [rad], between the masking bat and target (from the transmitter’s perspective)

      𝝓<sub>𝑻𝒙𝑹𝒙</sub> refers to the angle between the transmitting conspecific and the receiving focal bat, from the transmitter’s point of view.

      𝝓<sub>𝑹𝒙𝑻𝒙</sub> represents the angle between the receiving bat and the transmitting bat, from the receiver’s point of view.

      These definitions have been explicitly stated in the revised manuscript to prevent any ambiguity (lines 525-530). Additionally, a Supplementary figure demonstrating the geometrical relations has been added to the manuscript.

      What is a bat's response to colliding with a conspecific (rather than a wall)? 

      In nature, minor collisions between bats are common and typically do not result in significant disruptions to flight (Boerma et al., 2019; Roy et al., 2019; Goldshtein et al., 2025). Given this, our model does not explicitly simulate the physical impact of a collision event. Instead, during the collision event the bat keeps decreasing its velocity and changing its flight direction until the distance between bats is above the threshold (0.4 m). We assume that the primary cost of such interactions arises from the effort required to avoid collisions, rather than from the collision itself. This assumption aligns with observations of bat behavior in dense flight environments, where individuals prioritize collision avoidance rather than modeling post-collision dynamics. See lines 479-484.

      From the statistical side, it was not clear if replicate simulations were performed. If they were, which I believe is the right way due to stochasticity in the model, how many replicates were used, and are the standard errors referred to throughout the paper between individuals in the same simulation or between independent simulations, or both? 

      The number of repetitions for each scenario is detailed in Table 1, but we included it in a more prominent location in the text for clarity. Specifically, we now state (Lines 110-111):

      "The number of repetitions for each scenario was as follows: 1 bat: 240; 2 bats: 120; 5 bats: 48; 10 bats: 24; 20 bats: 12; 40 bats: 12; 100 bats: 6."

      Regarding the reported standard errors, they are calculated across all individuals within each scenario, without distinguishing between different simulation trials. 

      We clarified in the revised text (Lines 627-628 in Statistical Analysis) 

      Overall, I found these weaknesses to be superficial and easily remedied by the authors. The authors presented well-reasoned arguments that were supported by their results, and which were used to demonstrate how call interference impacts the collective's roost exit as measured by several variables. As the authors highlight, I think this work is valuable to individuals interested in bat biology and behavior, as well as to applications in engineered multi-agent systems like robotic swarms.

      Reviewer #3 (Public review):

      We sincerely appreciate the reviewer’s thoughtful comments and the time invested in evaluating our work, which have greatly contributed to refining our study.

      We would like to note that in general, our model often simplifies some of the bats’ abilities, under the assumption that if the simulated bats manage to perform this difficult task with simpler mechanisms, real better adapted bats will probably perform even better. This thought strategy will be repeated in several of the s below.

      Summary:

      The authors describe a model to mimic bat echolocation behavior and flight under high-density conditions and conclude that the problem of acoustic jamming is less severe than previously thought, conflating the success of their simulations (as described in the manuscript) with hard evidence for what real bats are actually doing. The authors base their model on two species of bats that fly at "high densities" (defined by the authors as colony sizes from tens to tens of thousands of individuals and densities of up to 33.3 bats/m2), Pipistrellus kuhli and Rhinopoma microphyllum. This work fits into the broader discussion of bat sensorimotor strategies during collective flight, and simulations are important to try to understand bat behavior, especially given a lack of empirical data. However, I have major concerns about the assumptions of the parameters used for the simulation, which significantly impact both the results of the simulation and the conclusions that can be made from the data. These details are elaborated upon below, along with key recommendations the authors should consider to guide the refinement of the model.

      Strengths:

      This paper carries out a simulation of bat behavior in dense swarms as a way to explain how jamming does not pose a problem in dense groups. Simulations are important when we lack empirical data. The simulation aims to model two different species with different echolocation signals, which is very important when trying to model echolocation behavior. The analyses are fairly systematic in testing all ranges of parameters used and discussing the differential results.

      Weaknesses:

      The justification for how the different foraging phase call types were chosen for different object detection distances in the simulation is unclear. Do these distances match those recorded from empirical studies, and if so, are they identical for both species used in the simulation? 

      The distances at which bats transition between echolocation phases are identical for both species in our model (see Table 2). These distances are based on welldocumented empirical studies of bat hunting and obstacle avoidance behavior (Griffin, Webster and Michael, 1958; Simmons and Kick, 1983; Schnitzler et al., 1987; Kalko, 1995; Hiryu et al., 2008; Vanderelst and Peremans, 2018). These references provide extensive evidence that insectivorous bats systematically adjust their echolocation calls in response to object proximity, following the characteristic phases of search, approach, and buzz.

      To improve clarity, we have updated the text to explicitly state that the phase transition distances are empirically grounded and apply equally to both modeled species (lines 499-508).

      What reasoning do the authors have for a bat using the same call characteristics to detect a cave wall as they would for detecting a small insect? 

      In echolocating bats, call parameters are primarily shaped by the target distance and echo strength. Accordingly, there is little difference in call structure between prey capture and obstacles-related maneuvers, aside from intensity adjustments based on target strength (Hagino et al., 2007; Hiryu et al., 2008; Surlykke, Ghose and Moss, 2009; Kothari et al., 2014). In our study, due to the dense cave environment, the bats are found to operate in the approach phase most of the time, which is consistent with natural cave emergence, where they are navigating through a cluttered environment rather than engaging in open-space search. For one of the species (Rhinopoma), we also have empirical recordings of individuals flying under similar conditions (Goldshtein et al., 2025). Our model was designed to remain as simple as possible while relying on conservative assumptions that may underestimate bat performance. If, in reality, bats fine-tune their echolocation calls even earlier or more precisely during navigation than assumed, our model would still conservatively reflect their actual capabilities. See lines 500-508.

      The two species modeled have different calls. In particular, the bandwidth varies by a factor of 10, meaning the species' sonars will have different spatial resolutions. Range resolution is about 10x better for PK compared to RM, but the authors appear to use the same thresholds for "correct detection" for both, which doesn't seem appropriate.

      The detection process in our model is based on Saillant’s method using a filterbank, as detailed in the paper (Saillant et al., 1993; Neretti et al., 2003; Sanderson et al., 2003). This approach inherently incorporates the advantages of a wider bandwidth, meaning that the differences in range resolution between the species are already accounted for within the signal-processing framework. Thus, there is no need to explicitly adjust the model parameters for bandwidth variations, as these effects emerge from the applied method.

      Also, the authors did not mention incorporating/correcting for/exploiting Doppler, which leads me to assume they did not model it.

      The reviewer is correct. To maintain model simplicity, we did not incorporate the Doppler effect or its impact on echolocation. The exclusion of Doppler effects was based on the assumption that while Doppler shifts can influence frequency perception, their impact on jamming and overall navigation performance is minor within the modelled context.

      The maximal Doppler shifts expected for the bats in this scenario are of ~ 1kHz. These shifts would be applied variably across signals due to the semi-random relative velocities between bats, leading to a mixed effect on frequency changes. This variability would likely result in an overall reduction in jamming rather than exacerbating it, aligning with our previous statement that our model may overestimate the severity of acoustic interference. Such Doppler shifts would result in errors of 2-4 cm in localization (i.e., 200-400 micro-seconds) (Boonman, Parsons and Jones, 2003).

      We have now explicitly highlighted this in the revised version (see 548-581).

      The success of the simulation may very well be due to variation in the calls of the bats, which ironically enough demonstrates the importance of a jamming avoidance response in dense flight. This explains why the performance of the simulation falls when bats are not able to distinguish their own echoes from other signals. For example, in Figure C2, there are calls that are labeled as conspecific calls and have markedly shorter durations and wider bandwidths than others. These three phases for call types used by the authors may be responsible for some (or most) of the performance of the model since the correlation between different call types is unlikely to exceed the detection threshold. But it turns out this variation in and of itself is what a jamming avoidance response may consist of. So, in essence, the authors are incorporating a jamming avoidance response into their simulation. 

      We fully agree that the natural variations in call design between the phases contribute significantly to interference reduction (see our discussion in a previous paper in Mazar & Yovel, 2020). However, we emphasize that this cannot be classified as a Jamming Avoidance Response (JAR). In our model, bats respond only to the physical presence of objects and not to the acoustic environment or interference itself. There is no active or adaptive adjustment of call design to minimize jamming beyond the natural phase-dependent variations in call structure. Therefore, while variation in call types does inherently reduce interference, this effect emerges passively from the modeled behavior rather than as an intentional strategy to avoid jamming. 

      The authors claim that integration over multiple pings (though I was not able to determine the specifics of this integration algorithm) reduces the masking problem. Indeed, it should: if you have two chances at detection, you've effectively increased your SNR by 3dB.  

      The reviewer is correct. Indeed, integration over multiple calls improves signal-tonoise ratio (SNR), effectively increasing it by approximately 3 dB per doubling of observations. The specifics of the integration algorithm are detailed in the Methods section, where we describe how sensory information is aggregated across multiple time steps to enhance detection reliability.

      They also claim - although it is almost an afterthought - that integration dramatically reduces the degradation caused by false echoes. This also makes sense: from one ping to the next, the bat's own echo delays will correlate extremely well with the bat's flight path. Echo delays due to conspecifics will jump around kind of randomly. However, the main concern is regarding the time interval and number of pings of the integration, especially in the context of the bat's flight speed. The authors say that a 1s integration interval (5-10 pings) dramatically reduces jamming probability and echo confusion. This number of pings isn't very high, and it occurs over a time interval during which the bat has moved 5-10m. This distance is large compared to the 0.4m distance-to-obstacle that triggers an evasive maneuver from the bat, so integration should produce a latency in navigation that significantly hinders the ability to avoid obstacles. Can the authors provide statistics that describe this latency, and discussion about why it doesn't seem to be a problem? 

      As described in the Methods section, the bat’s collision avoidance response does not solely rely on the integration process. Instead, the model incorporates real-time echoes from the last calls, which are used independently of the integration process for immediate obstacle avoidance maneuvers. This ensures that bats can react to nearby obstacles without being hindered by the integration latency. The slower integration on the other hand is used for clustering, outlier removal and estimation wall directions to support the pathfinding process, as illustrated in Supplementary Figure 1.

      Additionally, our model assumes that bats store the physical positions of echoes in an allocentric coordinate system (x-y). The integration occurs after transforming these detections from a local relative reference frame to a global spatial representation. This allows for stable environmental mapping while maintaining responsiveness to immediate changes in the bat’s surroundings.

      See lines 600-616 in the revised version.

      The authors are using a 2D simulation, but this very much simplifies the challenge of a 3D navigation task, and there is an explanation as to why this is appropriate. Bat densities and bat behavior are discussed per unit area when realistically it should be per unit volume. In fact, the authors reference studies to justify the densities used in the simulation, but these studies were done in a 3D world. If the authors have justification for why it is realistic to model a 3D world in a 2D simulation, I encourage them to provide references justifying this approach. 

      We acknowledge that this is a simplification; however, from an echolocation perspective, a 2D framework represents a worst-case scenario in terms of bat densities and maneuverability:

      • Higher Effective Density: A 2D model forces all bats into a single plane rather than distributing them through a 3D volume, increasing the likelihood of overlap in calls and echoes and making jamming more severe. As described in the text: the average distance to the nearest bat in our simulation is 0.27m (with 100 bats), whereas reported distances in very dense colonies are 0.5m (Fujioka et al., 2021), as observed in Myotis grisescens (Sabol and Hudson, 1995) and Tadarida brasiliensis (Theriault et al., no date; Betke et al., 2008; Gillam et al., 2010)

      • Reduced Maneuverability: In 3D space, bats can use vertical movement to avoid obstacles and conspecifics. A 2D constraint eliminates this degree of freedom, increasing collision risk and limiting escape options.

      Thus, our 2D model provides a conservative difficult test case, ensuring that our findings are valid under conditions where jamming and collision risks are maximized. Additionally, the 2D framework is computationally efficient, allowing us to perform multiple simulation runs to explore a broad parameter space and systematically test the impact of different variables.

      To address the reviewer’s concern, we have clarified this justification in the revised text and will provide supporting references where applicable (see Methods lines 450455).

      The focus on "masking" (which appears to be just in-band noise), especially relative to the problem of misassigned echoes, is concerning. If the bat calls are all the same waveform (downsweep linear FM of some duration, I assume - it's not clear from the text), false echoes would be a major problem. Masking, as the authors define it, just reduces SNR. This reduction is something like sqrt(N), where N is the number of conspecifics whose echoes are audible to the bat, so this allows the detection threshold to be set lower, increasing the probability that a bat's echo will exceed a detection threshold. False echoes present a very different problem. They do not reduce SNR per se, but rather they cause spurious threshold excursions (N of them!) that the bat cannot help but interpret as obstacle detection. I would argue that in dense groups the mis-assignment problem is much more important than the SNR problem. 

      There is substantial literature supporting the assumption that bats can recognize their own echoes and distinguish them from conspecific signals (Schnitzler, Bioscience and 2001, no date; Kazial, Burnett and Masters, 2001; Burnett and Masters, 2002; Kazial, Kenny and Burnett, 2008; Chili, Xian and Moss, 2009; Yovel et al., 2009; Beetz and Hechavarría, 2022)). However, we acknowledge that false echoes may present a major challenge in dense groups. To address this, we explicitly tested the impact of the self-echo identification assumption in our study see Results Figure 1: The impact of confusion on performance, and lines 399-404 in the Discussion.

      Furthermore, we examined a full confusion scenario, where all reflected echoes from conspecifics were misinterpreted as obstacle reflections (i.e., 100% confusion). Our results show that this significantly degrades navigation performance, supporting the argument that echo misassignment is a critical issue. However, we also explored a simple mitigation strategy based on temporal integration with outlier rejection, which provided some improvement in performance. This suggests that real bats may possess additional mechanisms to enhance self-echo identification and reduce false detections. See lines 411-420 in the manuscript for further discussion. 

      We actually used logarithmically frequency modulated (FM) chirps, generated using the MATLAB built-in function chirp(t, f0, t1, f1, 'logarithmic'). This method aligns with the nonlinear FM characteristics of Pipistrellus kuhlii (PK) and Rhinopoma microphyllum (RM) and provides a realistic approximation of their echolocation signals. We acknowledge that this was not sufficiently emphasized in the original text, and we have now explicitly highlighted this in the revised version to ensure clarity (see Lines 509-512 in Methods).

      The criteria set for flight behavior (lines 393-406) are not justified with any empirical evidence of the flight behavior of wild bats in collective flight. How did the authors determine the avoidance distances? Also, what is the justification for the time limit of 15 seconds to emerge from the opening? Instead of an exit probability, why not instead use a time criterion, similar to "How long does it take X% of bats to exit?"  :

      While we acknowledge that wild bats may employ more complex behaviors for collision avoidance, we chose to implement a simplified decision-making rule in our model to maintain computational tractability.

      The avoidance distances (1.5 m from walls and 0.4 m from other bats) were selected as internal parameters to support stable and realistic flight trajectories while maintaining a reasonable collision rate. These values reflect a trade-off between maneuverability and behavioral coherence under crowding. To address this point, we added a sensitivity analysis to the revised manuscript. Specifically, we tested the effect of varying the conspecific avoidance distance from 0.2 to 1.6 meters at bat densities of 2 to 40 bats/3m². The only statistically significant impact was at the highest density (40 bats/3m²), where exit probability increased slightly from 82% to 88% (p = 0.024, t = 2.25, DF = 958). No significant changes were observed in exit time, collision rate, or jamming probability across other densities or conditions (GLM, see revised Methods). These results suggest that the selected avoidance distances are robust and not a major driver of model performance, see lines 469-47.

      The 15-second exit limit was determined as described in the text (Lines 489-491): “A 15-second window was chosen because it is approximately twice the average exit time for 40 bats and allows for a second corrective maneuver if needed.” In other words, it allowed each bat to circle the ‘cave’ twice to exit even in the most crowded environment. This threshold was set to keep simulation time reasonable while allowing sufficient time for most bats to exit successfully.

      We acknowledge that the alternative approach suggested by the reviewer— measuring the time taken for a certain percentage of bats to exit—is also valid. However, in our model, some outlier bats fail to exit and continue flying for many minutes, such simulations would lead to excessive simulation times making it difficult to generate repetitions and not teaching us much – they usually resulted from the bat slightly missing the opening (see video S1. Our chosen approach ensures practical runtime constraints while still capturing relevant performance metrics.

      What is the empirical justification for the 1-10 calls used for integration?  

      The "average exit time for 40 bats" is also confusing and not well explained. Was this determined empirically? From the simulation? If the latter, what are the conditions?

      Does it include masking, no masking, or which species? 

      Previous studies have demonstrated that bats integrate acoustic information received sequentially over several echolocation calls (2-15), effectively constructing an auditory scene in complex environments (Ulanovsky and Moss, 2008; Chili, Xian and Moss, 2009; Moss and Surlykke, 2010; Yovel and Ulanovsky, 2017; Salles, Diebold and Moss, 2020). Additionally, bats are known to produce echolocation sound groups when spatiotemporal localization demands are high (Kothari et al., 2014). Studies have documented call sequences ranging from 2 to 15 grouped calls (Moss and Surlykke, 2010), and it has been hypothesized that grouping facilitates echo segregation.

      We did not use a single integration window - we tested integration sizes between 1 and 10 calls and presented the results in Figure 3A. This range was chosen based on prior empirical findings and to explore how different levels of temporal aggregation impact navigation performance. Indeed, the results showed that the performance levels between 5-10 calls integration window (Figure 3A)

      Regarding the average exit time for 40 bats, this value was determined from our simulations, where it represents the mean time for successful exits under standard conditions with masking. We have revised the text to clarify these details see, lines 489-491.

      Reviewer #1 (Recommendations for the authors):

      (1) Data Availability:

      As it stands now, this reviewer cannot vouch for the uploaded code as it wasn't accessible according to F.A.I.R principles. The link to the code/data points to a private company's file-hosting account that requires logging in or account creation to see its contents, and thus cannot be accessed.

      This reviewer urges the authors to consider uploading the code onto an academic data repository from the many on offer (e.g. Dryad, Zenodo, OSF). Some repositories offer an option to share a private link (e.g. Zenodo) to the folder that can then be shared only with reviewers so it is not completely public.

      This is a computational paper, and the credibility of the results is based on the code used to generate them.

      The code is available at GitHub as required:

      https://github.com/omermazar/Colony-Exit-Bat-Simulation

      (2) Abstract:

      Line 22: 'To explore whether..' - replace 'whether' with 'how'?

      The sentence was rephrased as suggested by the reviewer.

      (2) Main text:

      Line 43: '...which may share...' - correct to '...which share...', as elegantly framed in the authors' previous work - jamming avoidance is unavoidable because all FM bats of a species still share >90% of spectral bandwidth despite a few kHz shift here and there.

      The sentence was rephrased as suggested by the reviewer.

      Line 49: The authors may wish to additionally cite the work of Fawcett et al. 2015 (J. Comp. Phys A & Biology Open)

      Thank you for the suggestion. We have included a citation to the work of Fawcett et al. (2015) in the revised manuscript.

      Line 61: This statement does not match the recent state of the literature. While the previous models may have assumed that all neighbours can be detected, there are models that specifically study the role of limited interaction arising from the potential inability to track all neighbours, and the effect of responding to only one/few neighbours at a time e.g. Bode et al. 2011 R. Soc. Interface, Jhawar et al. 2020 Nature Physics.

      We have added citations to the important studies suggested by the reviewer, as detailed in the Public Review above.

      Line 89: '..took all interference signals into account...' - what is meant by 'interference signals' - are the authors referring to reflections, unclear.

      We have revised the sentence and detailed the acoustic signals involved in the process: self-generated echoes, calls from conspecifics, and echoes from cave walls and other bats evoked by those calls, see lines 99-106.

      Figure 1A: The colour scheme with overlapping points makes the figure very hard to understand what is happening. The legend has colours from subfigures B-D, adding to the confusion.

      What does the yellow colour represent? This is not clear. Also, in general, the color schemes in the simulation trajectories and the legend are not the same, creating some amount of confusion for the reader. It would be good to make the colour schemes consistent and visually separable (e.g. consp. call direct is very similar to consp. echo from consp. call), and perhaps also if possible add a higher resolution simulation visualisation. Maybe it is best to separate out the colour legends for each sub-figure.

      The updated figure now includes clearer, more visually separable colors, and consistent color coding across all sub-panels. The yellow trajectory representing the focal bat’s flight path is now explicitly labeled, and we adjusted the color mapping of acoustic signals (e.g., conspecific calls vs. echoes) to improve distinction. We also revised the figure caption accordingly and ensured that the legend is aligned with the updated visuals. These modifications aim to enhance interpretability and reduce ambiguity for the reader.

      Figure C3: What is 'FB Channel', this is not explained in the legend.

      FB Channel’ stands for ‘Filter Bank Channel’. This clarification has been added to the caption of Figure 1. 

      Figure 3: Visually noticing that the colour legend is placed only on sub-figure A is tricky and readers may be left searching for the colour legend. Maybe lay out the legend horizontally on top of the entire figure, so it stands out?

      We have adjusted the placement of the color legend in Figure 3 to improve visibility and consistency.

      Line 141: '..the probability of exiting..' - how is this probability calculated - not clear.

      We have clarified in the revised text that the probability of exiting the cave within 15 seconds is defined as the number of bats that exited the cave within that time divided by the total number of bats in each scenario, see lines 159160.

      Line 142: What are the sample sizes here - i.e. how many simulation replicates were performed?

      We have clarified the number of repetitions in each scenario the revised text, as detailed in the Public Review above.

      Line 151: 'The jamming probability,...number of jammed echoes divided by the total number of reflected echoes' - it seems like these are referring to 'own' echoes or first-order reflections, it is important to clarify this.

      The reviewer is right. We have clarified it in the revised text, see lines 173175.

      Line 153: '..with a maximum difference of ...' - how is this difference calculated? What two quantities are being compared - not clear.

      We have revised the text to clarify that the 14.3% value reflects the maximum difference in jamming probability between the RM and PK models, which occurred at a density of 10 bats. The values at each density are shown in Figure 2D, see lines 175-177.

      Line 221: '..temporal aggregation helps..' - I'm assuming the authors meant temporal integration? However, I would caution against using the exact term 'temporal integration' as it is used in the field of audition to mean something different. Perhaps something like 'sensory integration' , or 'multi-call integration'

      To avoid ambiguity and better reflect the process modeled in our work, we have replaced the term "temporal aggregation" with "multi-call integration" throughout the revised manuscript. This term more accurately conveys the idea of combining information from multiple echolocation calls without conflicting with existing terminology.

      (4) Discussion

      Lines 302: 'Our model suggests...increasing the call-rate..' - not clear where this is explicitly tested or referred to in this manuscript. Can't see what was done to measure/quantify the effect of this variable in the Methods or anywhere else.

      We have rephrased this paragraph as detailed in the Public Review above, see lines 346-349.

      Line 319: 'spatial interference' - unclear what this means. This reviewer would strongly caution against creating new terms unless there is an absolute need for it. What is meant by 'interference' in this paper is hard to assess given that the word seems to be used as a synonym for jamming and also for actual physical wave-based interference.

      We have rephrased this paragraph as detailed in the Public Review above, see line 119-120, 366-367.

      Line 323: '..no benefit beyond a certain level...' - also not clear where this is explicitly tested. It seems like there was a set of simulations run for a variety of parameters but this is not written anywhere explicitly. What type of parameter search was done, was it all possible parameter combinations - or only a subset? This is not clear.

      We have rephrased this paragraph as detailed in the Public Review above, see lines 372-375.

      Line 324: '..ca. 110 dB-SPL.' - what reference distance?

      All call levels were simulated and reported in dB-SPL, referenced at 0.1 meters from the emitting bat. We have clarified it in the revised text in the relevant contexts and specifically in line 529.

      (5) Methods

      Line 389 : '...over a 2 x 1.5 m2 area..' It took a while to understand this statement and put it in context. Since there is no previous description of the entire L-arena, the reviewer took it to mean the simulations happened over the space of a 2 x 1.5 m2 area. Include a top-down description of the simulation's spatial setup and rephrase this sentence.

      To address the confusion, we revised the text to clarify that the full simulation environment represents a corridor-shaped cave measuring 14.5 × 2.5 meters, with a right-angle turn located 5.5 meters before the exit, as shown in Figure 1A. The 2 × 1.5 m area refers specifically to the small zone at the far end of the cave where bats begin their flight. The revised description now includes a clearer spatial overview to prevent ambiguity, see lines 456-460.

      Line 398: Replace 'High proximity' with 'Close proximity'

      Replaced.

      Line 427: 'uniform target strength of -23 dB' - at what distance is this target strength defined? Given the reference distance can vary by echolocation convention (0.1 or 1 m), one can't assess if this is a reasonable value or not.

      The reference distance for the reported target strength is 1 meter, in line with standard acoustic conventions. We have revised the text to clarify this explicitly (line 531).

      Also, independent of the reference distance, particularly with reference to bats, the target strength is geometry-dependent, based on whether the wings are open or not. Using the entire wingspan of a bat to parametrise the target strength is an overestimate of the available reflective area. The effective reflective area is likely to be somewhere closer to the surface area of the body and a fraction of the wingspan together. This is important to note and/or mention explicitly since the value is not experimentally parametrised.

      For comparison, experimentally based measurements used in Goetze et al. 2016 are -40 dB (presumably at 1 m since the source level is also defined at 1 m?), and Beleyur & Goerlitz 2019 show a range between -43 to -34 dB at 1 m.

      We agree with the reviewer that target strength in bats is strongly influenced by their geometry, particularly wing posture during flight. In our model, we simplified this aspect by using a constant target strength, as the detailed temporal variation in body and wing geometry is pseudo-random and not explicitly modeled. We acknowledge that this is a simplification, and have now stated this limitation clearly in the revised manuscript. We chose a fixed value of –23 dB at 1 meter to reflect a plausible mid-range estimate, informed by anatomical data and consistent with values reported for similarly sized species (Beleyur and Goerlitz, 2019). To support this, we directly measured the target strength of a 3D-printed RM bat model, obtaining –32dB. 

      Moreover, a sensitivity analysis across a wide range (–49 to –23 dB) confirmed that performance metrics remain largely stable, indicating that our conclusions are not sensitive to this parameter, and suggesting that our results hold for different-sized bats. See lines 384-390, 533-538, and Supplementary Figures 3 and 4 in the revised article. 

      Line 434: 'To model the bat's cochlea...'. Bats have two cochleas. This model only describes one, while the agents are also endowed with the ability to detect sound direction - which requires two ears/cochleas.... There is missing information about the steps in between that needs to be provided.

      We appreciate the reviewer’s observation. Indeed, our model is monaural, and simulates detection using a single cochlear-like filter bank receiver. We have clarified this in the revised text to avoid confusion. This paragraph specifically describes the detection stage of the auditory processing pipeline. The localization process, which builds on detection and includes directional estimation, is described in the following paragraph (see line 583 onward), as discussed in the next comment and response.

      Line 457: 'After detection, the bat estimates the range and Direction of Arrival...' This paragraph describes the overall idea, but not the implementation. What were the inputs and outputs for the range and DOA calculation performed by the agent? Or was this information 'fed' in by the simulation framework? If there was no explicit DOA step that the agent performed, but it was assumed that agents can detect DOA, then this needs to be stated.

      In the current simulation, the Direction of Arrival (DOA) was not modeled via an explicit binaural processing mechanism. Instead, based on experimental studies (Simmons et al., 1983; Popper and Fay, 1995).  we assumed that bats can estimate the direction of an echo with an angular error that depends on the signal-to-noise ratio (SNR). Accordingly, the inputs to the DOA estimation were the peak level of the desired echo, noise level, and the level of acoustic interference. The output was an estimated direction of arrival that included a random angular error, drawn from a normal distribution whose standard deviation varied with the SNR. We have revised the relevant paragraph (Lines 583-592) to clarify this implementation.

      Line 464: 'To evaluate the impact of the assumption...' - the 'self' and 'non-self' echoes can be distinguished perhaps using pragmatic time-delay cues, but also using spectro-temporal differences in individual calls/echoes. Do the agents have individual call structures, or do all the agents have the same call 'shape'? The echolocation parameters for the two modelled species are given, but whether there is call parameter variation implemented in the agents is not mentioned.

      In our relatively simple model, all individuals emit the same type of chirp call, with parameters adapted only based on the distance to the nearest detected object. However, individual variation is introduced by assigning each bat a terminal frequency drawn from a normal distribution with a standard deviation of 1 kHz, as described in the revised version -lines 519-520. This small variation is not used explicitly as a spectro-temporal cue for echo discrimination.

      In our model, all spectro-temporal variations—whether due to call structure or variations resulting from overlapping echoes from nearby reflectors—are processed through the filter bank, which compares the received echoes to the transmitted call during the detection stage. As such, the detection process itself can act as a discriminative filter, to some extent, based on similarity to the emitted call.

      We acknowledge that real bats likely rely on a variety of spectro-temporal features for distinguishing self from non-self-echoes—such as call duration, received level, multi-harmonic structure, or amplitude modulation. In our simulation, we focus on comparing two limiting conditions: full recognition of self-generated echoes versus full confusion. Implementing a more nuanced self-recognition mechanism based on temporal or spectral cues would be a valuable extension for future work.

      (6) References

      Reference 22: Formatting error - and extra '4' in the reference.

      The error has been fixed.

      (7) Thoughts/comments

      Even without 'recogntion' of walls & conspecifics, bats may be able to avoid obstacles - this is a neat result. Also, using their framework the authors show that successful 'blind' object-agnostic obstacle avoidance can occur only when supported by some sort of memory. In some sense, this is a nice intermediate step showing the role of memory in bat navigation. We know that bats have good long-term and long-spatial scale memory, and here the authors show that short-term spatial memory is important in situations where immediate sensory information is unreliable or unavailable.

      We appreciate the reviewer’s thoughtful summary. Indeed, one of the main takeaways of our study is that successful obstacle avoidance can occur even without explicit recognition of walls or conspecifics—provided that a clustered multi-call integration is in place. Our model shows that when immediate sensory information is unreliable, integrating detections over time becomes essential for effective navigation. This supports the broader view that memory, even on short timescales, plays an important role in bat behavior.

      (8) Reporting GLM results

      The p-value, t-statistic, and degrees of freedom are reported consistently across multiple GLM results. However, the most important part which is the effect size is not consistently reported - and this needs to be included in all results, and even in the table. The effect size provides an indicator of the parameter's magnitude, and thus scientific context.

      We agree that the effect size provides essential scientific context. In fact, we already include the effect size explicitly in Table 1, as shown in the “Effect Size” column for each tested parameter. These values describe the magnitude of each parameter’s effect on exit probability, jamming probability, and collision rate. In the main text, effect sizes are presented as concrete changes in performance metrics (e.g., “exit probability increased from 20% to 87%,” or “with a decrease of 3.5%±8% to 5.5%±5% (mean ± s.e.)”), which we believe improves interpretability and scientific relevance.  

      To further clarify this in the main text, we have reviewed the reported results and ensured that effect sizes are mentioned more consistently wherever GLM outcomes are discussed. Additionally, we have added a brief note in the table caption to emphasize that effect sizes are provided for all tested parameters.

      The 'tStat' appears multiple times and seems to be the output of the MATLAB GLM function. This acronym is specific to the MATLAB implementation and needs to be replaced with a conventionally used acronym such as 't', or the full form 't-statistic' too. This step is to keep the results independent of the programming language used.

      We have replaced all instances of tStat with the more conventional term ‘t’ throughout the manuscript to maintain consistency with standard reporting practices.

      Reviewer #2 (Recommendations for the authors):

      In addition to my public review, I had a few minor points that the authors may want to consider when revising their paper.

      (1) Figures 2, 3, and 4 may benefit from using different marker styles, in addition to different colors, to show the different cases.

      Thank you for the suggestion. In Figures 2–4, the markers represent means with standard error bars. To maintain clarity and consistency across all conditions, we have chosen to keep a standardized marker style – and we clarify this in the legend. We found that varying only the colors is sufficient for distinguishing between conditions without introducing visual clutter.

      (2) The text "PK" in the inset for Figure 2A is very difficult to read. I would suggest using grey as with "RM" in the other inset.

      We have updated the insert in Figure 2A to improve legibility.

      (3) Are the error bars in Figure 3 very small? I wasn't able to see them. If that is the case, the authors may want to mention this in the caption.

      You are correct—the error bars are present in all plots but appear very small due to the large number of simulation repetitions and low variability. We have revised the caption to explicitly mention this.

      (4) The species name of PK is spelled inconsistently (kuhli, khulli, and kuhlii).

      We have corrected the species name throughout the manuscript.

      (5) Table 1 is a great condensation of all the results, but the time to exit is missing. It may be helpful if summary statistics on that were here as well.

      We have added time-to-exit to the effect size column in Table 1, alongside the other performance metrics, to provide a more complete summary of the simulation results.

      (6) I may have missed it, but why are there two values for the exit probability when nominal flight speed is varied?

      The exit probability was not monotonic with flight speed, but rather showed a parabolic trend with a clear optimum. Therefore, we reported two values representing the effect before and after the peak. We have clarified this in the revised table and updated the caption accordingly.

      (7) Table 2 has an extra header after the page break on page 18.

      The extra header in Table 2 after the page break has been removed in the revised manuscript.

      (8) The G functions have 2 arguments in their definitions and Equation 1, but only one argument in Equations 2 and 3. I wasn't able to see why.

      Thank you for pointing this out. You are correct—this was a typographical error. We have corrected the argument notation in Equations 2 and 3 and explicitly included the frequency dependence of the gain (G) functions in both equations.

      (9) D_txrx was not defined but it was used in Equation 2.

      The variable D_txrx is defined in the equation notation section as: D<sub>₍ₜₓ</sub>r<sub>ₓ</sub> – the distance [m] between the transmitting conspecific and the receiving focal bat, from the transmitter’s perspective. We have now ensured that this definition is clearly linked to Equation 2 in the revised text. Moreover, we have added a supplementary figure that illustrates the geometric configuration defined by the equations to further support clarity, as described in the Public Review above.

      (10) It was hard for me to understand what was meant by phi_rx and phi_tx. These were described as angles between the rx or tx bats and the target, but I couldn't tell what the point defining the angle was. Perhaps a diagram would help, or more precise definitions.

      We have revised the caption to provide clearer and more precise definitions Additionally, we have included a geometric diagram as a supplementary figure, as noted in the Public Review above, to visually clarify the spatial relationships and angle definitions used in the equations, see lines 498-499.

      (11) Was the hearing threshold the same for both species?

      Yes. We have clarified it in the revised version.

      (12) Collision avoidance is described as turning to the "opposite direction" in the supplemental figure explaining the model. Is this 90 degrees or 180 degrees? If 90 degrees, how do these turns decide between right and left?

      In our model, the bat does not perform a fixed 90° or 180° turn. Instead, the avoidance behavior is implemented by setting the maximum angular velocity in the direction opposite to the detected echo. For example, if the obstacle or conspecific is detected on the bat’s right side, the bat begins turning left, and vice versa.

      This turning direction is re-evaluated at each decision step, which occurs after every echolocation pulse. The bat continues turning in the same direction if the obstacle remains in front, otherwise it resumes regular pathfinding. We have clarified this behavior in the updated figure caption and model description, see lines 478-493.

      Reviewer #3 (Recommendations for the authors):

      (1) Lines 27-31: These sentences mischaracterize the results. This claim appears to equate "the model works" with "this is what bats actually do." Also, the model does not indicate that bats' echolocation strategies are robust enough to mitigate the effects of jamming - this is self-evident from the fact that bats navigate successfully via echolocation in dense groups.

      Thank you for the comment. Our aim was not to claim that the model confirms actual bat behavior, but rather to demonstrate that simple and biologically plausible strategies—such as signal redundancy and basic pathfinding—are sufficient to explain how bats might cope with acoustic interference in dense settings. We have revised the wording to better reflect this goal and to avoid overinterpreting the model's implications.

      See abstract in the revised version.  

      (2) Line 37: This number underestimates the number of bats that form some of the largest aggregations of individuals worldwide - the free-tailed bats can form aggregations exceeding several million bats.

      We have revised the text to reflect that some bat species, such as free-tailed bats, are known to form colonies of several million individuals, which exceed the typical range. The updated sentence accounts for these extreme cases, see lines 36-37.

      (3) The flight densities explained in the introduction and chosen references are not representative of the literature - without providing additional justification for the chosen species, it can be interpreted that the selection of the species for the simulation is somewhat arbitrary. If the goal is to model dense emergence flight, why not use a species that has been studied in terms of acoustic and flight behavior during dense emergence flights---such as Tadarida brasiliensis?

      Our goal was to develop a general model applicable to a broad class of FMecholocating bat species. The two species we selected—Pipistrellus kuhlii (PK) and Rhinopoma microphyllum (RM)—span a wide range of signal characteristics: from wideband (PK) to narrowband (RM), providing a representative contrast in call structure. 

      Although we did not include Tadarida brasiliensis (TB) specifically, its echolocation calls are acoustically similar to RM in terminal frequency and fall between PK and RM in bandwidth. Therefore, we believe our findings are likely to generalize to TB and other FM-bats.

      Moreover, as noted in a previous response, the average inter-bat distance in our highest-density simulations (0.27 m) is still smaller than those reported for Tadarida brasiliensis during dense emergences—further supporting the relevance of our model to such scenarios.

      To support broader applicability, we also provide a supplementary graphical user interface (GUI) that allows users to modify key echolocation parameters and explore their impact on behavior—making the framework adaptable to additional species, including TB.

      (4) Line 78: It is not clear how (or even if) the simulated bats estimate the direction of obstacles. The explanation given in lines 457-463 is quite confusing. What is the acoustic/neurological mechanism that enables this direction estimation? If there is some mechanism (such as binaural processing), how does this extrapolate to 3D?

      This comment echoes a similar concern raised by a previous reviewer. As explained earlier, in the current simulation, the Direction of Arrival (DOA) was not modeled via an explicit binaural processing mechanism. The complete  is detailed in  to Reviewer #1, Line 457. This implementation is now clarified in the revised text, and a detailed description of the localization process is also provided in the Methods section (lines 583-592).

      (5) The authors propose they are modeling the dynamic echolocation of bats in the simulation (line 79), but it appears (whether this is due to a lack of information in the manuscript or true lack in the simulation) that the authors only modeled a flight response. How did the authors account for bats dynamically changing their echolocation? This is unclear and from what I can tell may just mean that the bats can switch between foraging phase call types depending on the distance to a detected obstacle. Can the authors elaborate more on this?

      The echolocation behavior of the bats—including dynamic call adjustments— was implemented in the simulation and is described in detail in the Methods section (lines 498-520 and Table 2). To avoid redundancy, the Results chapter originally referred to this section, but we have now added a brief explanation in the Results to clarify that the bats’ call parameters (IPI, duration, and frequency range) adapt based on the distance to detected objects, following empirically documented echolocation phases ("search," "approach," "buzz"). These dynamics are consistent with established bat behavior during navigation in cluttered environments such as caves.

      (6) Figure 1 C3: "Detection threshold": what is this and how was it derived?

      The caption also mentions yellow arrows, but they are absent from the figure. C4: Each threshold excursion is marked with an asterisk, but there are many more excursions than asterisks. Why are only some marked? Unclear.

      C3: The detection threshold is determined dynamically. It is set to the greater of either 7 dB above the noise level (0 dB-SPL)(Kick, 1982; Saillant et al., 1993; Sanderson et al., 2003; Boonman et al., 2013) or the maximal received level minus 70 dB, effectively applying a dynamic range of 70 dB. This clarification has been added to the Methods section. The yellow arrow has been added.

      C4: Thank you for this important observation. Only peaks marked with asterisks represent successful detections—those that were identified in both the interference-free and full detection conditions, as explained in the Methods. Other visible peaks result from masking signals or overlapping echoes from nearby reflectors, but they do not meet the detection criteria. To keep the figure caption concise, we have elaborated on this process more clearly in the revised Methods section. We added this information to the legend

      (7) Figure 2: A line indicating RM, No Masking is absent

      Thank you for pointing this out. The missing line for RM, No Masking has now been added in the revised version of Figure 2.

      (8) Line 121: "reflected off conspecifics". Does this mean echoes due to conspecifics?

      The phrase "reflected off conspecifics" refers to echoes originating from the bat’s own call and reflected off the bodies of nearby conspecifics. We have clarified the wording in the revised text to avoid confusion

      (9) Line 125: Why are low-frequency channels stimulated by higher frequencies? This needs further clarification.

      The cochlear filter bank in our model is implemented using gammatone filters, each modeled as an 8th-order Butterworth filter. Due to the non-ideal filter response and relatively broad bandwidths—especially in the lower-frequency channels—strong energy from the beginning of the downward FM chirp (at higher frequencies) can still produce residual activation in lower-frequency channels. While these stimulations are usually below the detection threshold, they may still be visible as early sub-threshold responses. Given the technical nature of this explanation (a property of the filter implementation) and it does not influence the detection outcomes, we have chosen not to elaborate on it in the figure caption or Methods.

      (10) Lines 146-150: This is an interesting finding. Is there a theoretical justification for it?

      This outcome arises directly from the simulation results. As noted in the Discussion (lines 359-365), although Pipistrellus kuhlii (PK) shows a modest advantage in jamming resistance due to its broader bandwidth, the redundancy in sensory information across calls—enabled by frequent echolocation—appears to compensate for these signal differences. As a result, the small variations in echo quality between species do not translate into significant differences in performance. We speculate that if the difference in jamming probability had been larger, performance disparities would likely have emerged.

      (11) Line 151: The authors define a jammed echo as an echo entirely missed due to masking. Is this appropriate? Doesn't echo mis-assignment also constitute jamming?

      We agree that echo mis-assignment can also degrade performance; however, in our model, we distinguish between two outcomes: (1) complete masking (echo not detected), and (2) detection with a localization error. As explained in the Methods (lines 500–507), we run the detection analysis twice—once with only desired echoes (“interference-free detection”) and once including masking signals (“full detection”). If a previously detected echo is no longer detected, it is classified as a jammed echo. If the echo is still detected but the delay shifts by more than 100 µs compared to the interference-free condition, it is also considered jammed. If the delay shift is smaller, it is treated as a detection with localization error rather than full jamming. We have clarified this distinction in the revised Methods section.

      (12) Figure 2-E: Detection probability statistics are of limited usefulness without accompanying false alarm rate (FAR) statistics. Do the authors have FAR numbers?

      We understand FAR to refer to instances where masking signals or other acoustic phenomena are mistakenly interpreted as real echoes from physical objects. As explained in the manuscript, we implemented two model versions: one without confusion, and one with full confusion.

      Figure 2E reports detection performance under the non-confusion model, in which only echoes from actual physical reflectors are used, and no false detections occur—hence, the false alarm rate is effectively zero in this condition. In the full-confusion model, all detected echoes—including those originating from masking signals or conspecific calls—are treated as valid detections, which may include false alarms. However, we did not explicitly quantify the false alarm rate as a separate metric in this simulation.

      We agree that tracking FAR could be informative and will consider incorporating it into future versions of the model.

      (13) Line 161: RM bats suffered from a significantly higher probability of the "desired conspecific's echoes" being jammed. What does "desired conspecific's echoes" mean? This is unclear.

      The term “desired conspecific's echoes” refers to echoes originating from the bat’s own call, reflected off nearby conspecifics, which are treated as relevant reflectors for collision avoidance. We have revised the wording in the text for clarity.

      (14) Line 188: Why didn't the size of the integration window affect jamming probability? I couldn't find this explained in the discussion.

      The jamming probability in our analysis is computed at the individual-echo level, prior to any temporal integration. Since the integration window is applied after the detection step, it does not influence whether a specific echo is masked (i.e., jammed) or not. Therefore, as expected, we did not observe a significant effect of integration window size on jamming probability.

      (15) Line 217-218: Why do the authors think this would be?

      Thank you for the thoughtful question. We agree that, in theory, increasing call intensity should raise the levels of both desired echoes and masking signals proportionally. However, in our model, the environmental noise floor and detection threshold remain constant, meaning that higher call intensities increase the signal-to-noise ratio (SNR) more effectively for weaker echoes, especially those at longer distances or with low reflectivity. This could lead to a higher likelihood of those echoes crossing the detection threshold, resulting in a small but measurable reduction in jamming probability.

      Additionally, the non-linear behavior of the filter-bank receiver—including such as thresholding at multiple stages—can introduce asymmetries in how increased signal levels affect the detection of target versus masking signals.

      That said, the effect size was small, and the improvement in jamming probability did not translate into any significant gain in behavioral performance (e.g., exit probability or collision rate), as shown in Figure 3C.

      (16) Line 233: I'm not sure I understand how a slightly improved aggregation model that clustered detected reflectors over one-second periods is different. Doesn't this just lead to on average more calls integrated into memory?

      While increasing the memory duration does lead to more detections being available, the enhanced aggregation model (we now refer to as multi-call clustering) differs fundamentally from the simpler one. As detailed in the Methods, it includes additional processing steps: clustering spatially close detections, removing outliers, and estimating wall directions based on the spatial structure of clustered echoes. In contrast, the simpler model treats each detection as an isolated point without estimating obstacle orientation. These additional steps allow for more robust environmental interpretation and significantly improve performance under high-confusion conditions. We have clarified it in revised text (lines 606-616) and added a Supplementary Figure 2B.

      (17) Table 1: What about conspecific target strength?

      We have now added the conspecific target strength as a tested parameter in Table 1, along with its tested range, default value, and measured effect sizes. A detailed sensitivity analysis is also presented in Supplementary Figure 4, demonstrating that variations in conspecific target strength had relatively minor effects on performance metrics.  

      (18) Figure 3-A: The x-axis is the number of calls in the integration window. But the leftmost sample on each curve is at 0 calls. Shouldn't this be 1?

      “0 calls” refers to the case where only the most recent call is used for pathfinding—without integrating any information from prior calls. The x-axis reflects the number of previous calls stored in memory, so a value of 0 still includes the current call. We’ve clarified this terminology in the figure caption.

      (19) Lines 282-283: This statement needs to be clarified that it is with the constraints of using a 2D simulation with at most 33 bats/m^2. It also should be clarified that it is assumed the bat can reliably distinguish between its own echoes and conspecific echoes, which is a very important caveat.

      We have revised the text to clarify that the results are based on a 2D simulation with a maximum tested density of 33 bats/m². We also now explicitly state that the model assumes bats can distinguish between their own echoes and those generated by conspecifics—an assumption we recognize as a simplification. These clarifications help place the results within the scope and constraints of the simulation. Moreover, as described in the text (and noted in previous response): the average distance to the nearest bat in our simulation is 0.27m (with 100 bats), whereas reported distances in very dense colonies are 0.5m

      (20) Line 294: What is this sentence referring to?

      The sentence refers to the finding that, even under high bat densities, a substantial portion of the echoes—particularly those reflected from nearby obstacles (e.g., 1 m away)—were jammed due to masking. Nevertheless, the bats in the simulation were still able to navigate successfully using partial sensory input. We have clarified the sentence in the revised text to make this point more explicit, see line 333-336.

      (21) Line 302: Was jamming less likely when IPI was higher or lower? I could not find this demonstrated anywhere in the manuscript.

      We agree that the original text was not sufficiently clear on this point. While we did not explicitly test fixed IPI values as a parameter, the model does simulate the natural behavior of decreasing IPI as bats approach obstacles. This behavior is supported by empirical observations and is incorporated into the echolocation dynamics of the simulation. We have clarified this point in the revised text (see Lines 346-351) and explained that while lower IPI introduces more acoustic overlap, it also increases redundancy and improves detection through temporal integration.

      (22) Lines 313-314: This is an interesting assumption, but it is not evident that is substantiated by the references.

      The claim is based on well-established principles in signal processing and bioacoustics. Wideband signals—such as those emitted by PK bats— distribute their energy over a broader frequency range, which makes them inherently more resistant to narrowband interference and masking. This concept is commonly applied in both biological and artificial sonar systems and is supported by empirical studies in bats and theory in acoustic sensing.

      For example, Beleyur & Goerlitz (2019) demonstrate that broader bandwidth calls improve detection in cluttered and jamming-prone environments. Similarly, Ulanovsky et al. (2004) and Schnitzler & Kalko (200) discuss how FM bats' wideband calls enhance temporal and spatial resolution, helping to reduce the impact of overlapping signals from conspecifics. These findings align with communication theory where spread-spectrum techniques improve robustness in noisy environments.

      We agree with the reviewer that this is an important point and we have updated the manuscript to clarify this rationale and cite the relevant literature accordingly – lines 631-363,

      (23) Lines 318-319: What is the justification for "probably"? Isn't this just a supposition?

      We agree with the reviewer’s point and have rephrased the sentence

      (24) Line 320: How does this 63% performance match the sentence in line 295?

      The sentence in Line 295 refers to the overall ability of the bats to navigate successfully despite high jamming levels, highlighting the robustness of the strategy under challenging conditions. The figure in Line 320 (63%) quantifies this performance under the most extreme simulated scenario (100 bats / 3 m²), where both spatial and acoustic interferences are maximal. We have rephrased the text in the revised version (lines 324-327).

      (25) Lines 341-345: It seems like this is more likely to be the main takeaway of the paper.

      As noted in the Public Review above, there is substantial literature supporting the assumption that bats can recognize their own echoes and distinguish them from those of conspecifics (e.g., Schnitzler, Bioscience, 2001; Kazial et al., 2001, 2008; Burnett & Masters, 2002; Chiu et al., 2009; Yovel et al., 2009; Beetz & Hechavarría, 2022). Therefore, we consider our assumption of selfrecognition to be well-supported, at least under typical conditions. That said, we agree that the impact of echo confusion on performance is significant and highlights a critical challenge in dense environments.

      To our knowledge, this is the first computational model to explicitly simulate both self-recognition and full echo confusion under high-density conditions. We believe that the combination of modeled constraints and the demonstrated robustness of simple sensorimotor strategies, even under worst-case assumptions, is what makes this contribution both novel and meaningful.

      (26) Lines 349-350: What is the aggregation model? What is meant by "integration"?

      We have revised the text to clarify that the “aggregation model” refers to a multi-call clustering process that includes clustering of detections, removal of outliers, and estimation of wall orientation, as described in detail in the revised Methods and Results sections.

      (27) Line 354: Again, why isn't this the assumption we're working under?

      As addressed in our response to Comment 25, our primary model assumes that bats can recognize their own echoes—an assumption supported by substantial empirical evidence. The alternative "full confusion" model was included to explore a worst-case scenario and highlight the behavioral consequences of failing to distinguish self from conspecific echoes. We assume that real bats may experience some degree of echo misidentification; however, our assumption of full confusion represents a worst-case scenario.

      (28) Line 382: "Under the assumption that..." I agree that bats probably can, but if we assume they can differentiate them all, where's the jamming problem?

      The assumption that bats can theoretically distinguish between different signal sources applies after successful detection. However, the jamming problem arises during the detection and localization stages, where acoustic interference can prevent echoes from crossing the detection threshold or distort their timing.

      (29) Lines 386-387: The paper referenced focused on JAR in the context of foraging. What changes were made to the simulation to switch to obstacle avoidance?

      While the simulation framework in Mazar & Yovel (2020) was developed to study jamming avoidance during foraging, the core components—such as the acoustic calculations, receiver model, and echolocation behavior—remain applicable. For the current study, we adapted the simulation extensively to address colony-exit behavior. These modifications include modeling cave walls as acoustic reflectors, implementing a pathfinding algorithm, integrating obstacle-avoidance maneuvers, and adapting the integration window and integration processes. These updates are detailed throughout the Methods section.

      (30) Line 400-402: Something doesn't add up with the statement: each decision relies on an integration window that records estimated locations of detected reflectors from the last five echolocation calls, with the parameter being tested between 1 and 10 calls. Can the authors reword this to make it less confusing?

      We have reworded the sentence to clarify that the default integration window includes five calls, while we systematically tested the effect of using 1 to 10 calls, see lines 486-487.

      (31) Line 393: "30 deg/sec" why was this value chosen?

      The turning rate of 30 deg/sec was manually selected to approximate the curvature of natural foraging flight paths observed in Rhinopoma microphyllum using on-board tags. Moreover, in Mazar & Yovel (2020), we showed that the flight dynamics of simulated bats in a closed room closely matched those of Pipistrellus kuhlii flying in a room of similar dimensions. However, in the current simulation, bats rarely follow a random-walk trajectory due to the structured environment and frequent obstacle detection. As a result, this parameter has no meaningful impact on the simulation outcomes.

      (32) Line 412: "Harmony" --- do you mean harmonic? And what is the empirical evidence that RM bats use the 2nd harmonic compared to the 1st?

      Perhaps showing a spectrogram of a real RM signal would be helpful.

      The typo-error was corrected. For reference See (Goldshtein et al., 2025)

      (33) Table 2: Something is incorrect with the table. The first row on the next page is the wrong species name. Also, where are the citations for these parameter values?

      The table header has been corrected in the revised version. The parameter values for flight and echolocation behavior were derived from existing literature and empirical data: Pipistrellus kuhlii parameters were based on Kalko (1995), and Rhinopoma microphyllum parameters were extracted from our own recordings using on-board tags, as described in Goldstein et al. (2025). We have added the appropriate citations to Table 2.

      (34) Line 442: How was the threshold level chosen?

      The detection threshold in each level is set to the greater of either 7 dB above the noise level (0 dB-SPL) or the maximal received level minus 70 dB, effectively applying a dynamic range of 70 dB.

      (35) Line 445: 100 micros: This is about 3cm. The resolution of PK is about 1cm. For RM it's about 10cm. So, this window is generous for PK, but too strict for RM.

      To keep the model simple and avoid introducing species-specific detection thresholds, we selected a biologically plausible compromise that could reasonably apply to both species. This simplification ensures consistency across simulations while remaining within the known behavioral range.

      (36) Line 448: What is the spectrum of the Gaussian noise, and did it change between PK and RM?

      We used the same white Gaussian noise with a flat spectrum across the relevant frequency range (10–80 kHz) for both species. We have clarified this in the revised text in lines 570-572.

      (37) Line 451: 4 milliseconds is 1.3m. Is this appropriate?

      The 4 milliseconds window was selected based on established auditory masking thresholds described in Mazar & Yovel (2020), and supported by (Popper and Fay, 1995) ch. 2.4.5, ((Blauert, 1997),  ch. 3.1 and (Mohl and Surlykke, 1989). These values provide conservative lower bounds on bats’ ability to cope with masking (Beleyur and Goerlitz, 2019). For simplicity, we used constant thresholds within each window, see lines 574-576.  

      (38) Line 452: Citation for the forward and backward masking durations?

      See the  to the previous comment.

      (39) Lines 460-461: This is unclear. How does the bat get directional information? The authors claim to be able to measure direction-of-arrival for each detection, but it is not clear how this is done

      As noted in our response to Reviewer 1 (Comment on Line 457), directional information is not computed via an explicit binaural model. Instead, we assume the bat estimates the direction of arrival with an angular error that depends on the SNR, based on established studies (e.g., Simmons et al., 1983; Popper & Fay, 1995). We have clarified this in the revised text in lines 583-592.

      (40) Line 467: It seems like the authors are modeling pulse-echo ambiguity, at least in this one alternative model, which is good! However the alternative model doesn't get much attention in the paper. Is there a reason for this?

      We would like to clarify that we did not model pulse-echo. In our confusion model, all echoes received within the IPI are attributed to the bat’s most recent call. This includes echoes that may in fact originate from conspecific calls, but the model does not assign self-echoes to earlier pulses or span multiple IPIs. Therefore, while the model captures echo confusion, it does not include true pulse-echo ambiguity. We have clarified this point in the revised text in lines 551-553.

      (41) Line 41: "continuous" is more appropriate than "constant".

      Thank you, we have rephrased the text accordingly.

      (42) Line 69: "band width" should be one word.

      Thank you, we have corrected it to “bandwidth”.

      (43) Line 79: "bats" should be in the possessive.

      Thank you, the text has been rephrased.

      (44) Line 128: "convoluted" don't you mean "convolved"?

      We have replaced “convoluted” with the correct term “convolved” in the revised text.

      (45) Please check your references, as there are some incomplete citations and typos.

      Thank you, we have reviewed and corrected all references for completeness and consistency.

      References

      Beetz, M.J. and Hechavarría, J.C. (2022) ‘Neural Processing of Naturalistic Echolocation Signals in Bats’, Frontiers in Neural Circuits, 16, p. 899370. Available at: https://doi.org/10.3389/FNCIR.2022.899370/BIBTEX.

      Beleyur, T. and Goerlitz, H.R. (2019) ‘Modeling active sensing reveals echo detection even in large groups of bats’, Proceedings of the National Academy of Sciences of the United States of America, 116(52), pp. 26662–26668. Available at: https://doi.org/10.1073/pnas.1821722116.

      Betke, M. et al. (2008) ‘Thermal Imaging Reveals Significantly Smaller Brazilian Free-Tailed Bat Colonies Than Previously Estimated’, Journal of Mammalogy, 89(1), pp. 18–24. Available at: https://doi.org/10.1644/07-MAMM-A-011.1.

      Blauert, J. (1997) ‘Spatial Hearing: The Psychophysics of Human Sound Localization (rev. ed.)’.

      Boerma, D.B. et al. (2019) ‘Wings as inertial appendages: How bats recover from aerial stumbles’, Journal of Experimental Biology, 222(20). Available at: https://doi.org/10.1242/JEB.204255/VIDEO-3.

      Boonman, A. et al. (2013) ‘It’s not black or white-on the range of vision and echolocation in echolocating bats’, Frontiers in Physiology, 4 SEP(September), pp. 1–12. Available at: https://doi.org/10.3389/fphys.2013.00248.

      Boonman, A.M., Parsons, S. and Jones, G. (2003) ‘The influence of flight speed on the ranging performance of bats using frequency modulated echolocation pulses’, The Journal of the Acoustical Society of America, 113(1), p. 617. Available at: https://doi.org/10.1121/1.1528175.

      Burnett, S.C. and Masters, W.M. (2002) ‘Identifying Bats Using Computerized Analysis and Artificial Neural Networks’, North American Symposium on Bat Research, 9.

      Chili, C., Xian, W. and Moss, C.F. (2009) ‘Adaptive echolocation behavior in bats for the analysis of auditory scenes’, Journal of Experimental Biology, 212(9), pp. 1392–1404. Available at: https://doi.org/10.1242/jeb.027045.

      Fujioka, E. et al. (2021) ‘Three-Dimensional Trajectory Construction and Observation of Group Behavior of Wild Bats During Cave Emergence’, Journal of Robotics and Mechatronics, 33(3), pp. 556–563. Available at: https://doi.org/10.20965/jrm.2021.p0556.

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    Unbiased whole genome comparison of Pan paniscus (bonobo) and Homo sapiens (human) through a novel sequence match-based approach
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      The observation that certain chromosomes, such as chromosome 3, exhibit significantly higher similarity than others, such as chromosome 5, highlights the importance of analyzing chromosome-specific homology rather than relying on averaged genome-wide comparisons. This heterogeneity suggests that different genomic regions have experienced varying rates of evolution and may be subject to different selective pressures. Further investigation is warranted to understand the underlying mechanisms driving these differences. Potential factors could include varying rates of mutation, recombination, gene duplication, transposition, and horizontal gene transfer.

      Measuring gene similarity within each Chromosome (the traditional method of detecting relatedness) would be a very strong supplemental figure to establish a baseline of comparison to GeneCompare. How does the previously biased approach compare to this new unbiased approach?

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      focus on setting aside enough time for reading and breaking your assignments into manageable chunks. If you are assigned a seventy-page chapter to read for next week’s class, try not to wait until the night before to get started. Give yourself at least a few days and tackle one section at a time.

      break down the 70 page chapter down over so many days (2 or 3)

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      From these images, we obtain a pixel-wise dynamic frequency response given by the absolute value of the Fourier transform of the temporal phase signal, <img class="highwire-embed" alt="Embedded Image" src="https://www.biorxiv.org/sites/default/files/highwire/biorxiv/early/2025/04/26/2025.04.22.649403/embed/inline-graphic-3.gif.backup.1745987201.1834"/> for each spatial pixel in the image.

      Just to clarify: are these the absolute pixels in the entire imaging field? or the pixels of individual segmented cells? if segmented cells, was there any registration of the images? Are the cells moving or do you have evidence that some of these changes in the phasor analysis don't result from jitter in the positions of the cells?

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    The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGF&#x03B2;- dependent but monocyte-independent process.
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    1. EMBOpress 05 Sep 2025

      Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons

      Reply to the reviewers

      Based on the below reviews, we propose the following revision plan. Briefly:

      • We will re-focus the manuscript on the developmental data providing a molecular and cellular blueprint __of lining macrophage development. The __novelty and relevance of our developmental data have been highlighted by all three reviewers, and they have also praised the rigor of these experiments and their interpretation. We thus believe that this re-focus will improve the manuscript's message.
      • We will include our data on CSF1 as a key signal. Whilst previously appreciated as a factor required for tissue-resident macrophages, including those in the joint, our study is the first to show the requirement of lining macrophages over a complete developmental time course, using modern readouts, and in a model that circumvents the limitations of previously used approaches (see point-by-point response for details).
      • However, we will remove the functional data on TGFβ signaling and mechanical loading/mechanosensing. We agree with the reviewers that we would need to generate additional histological and molecular data from conditional knockout mice, antibody and (ant)agonist treatments and the optogenetic model to determine their exact involvement in lining macrophage maturation. These experiments require significant time and other resources. We would therefore like to uncouple this question for a follow-on manuscript, and to re-focus the current study as a developmental atlas. Removal of (some) of these data has been suggested in the reviewers' comments as well.
      • To further elevate our developmental atlas, we are proposing to include additional data and new analyses delineating the developmental dynamics of synovial fibroblasts on single cell (transcriptomic) level. This change to the original manuscript had not been requested by the reviewers, but we are proposing this pro-actively because we believe this would be an impactful addition to a revised version of our study, providing data also on the maturation of the synovial (lining) macrophage niche. Again, this will re-focus the manuscript on the developmental data and provide a novel, valuable resource for those interested in joint biology.
      • We will otherwise respond to all individual reviewer comments and implement the requested changes, unless technically not possible. We are convinced that this revision plan will result in a manuscript that fits very well with the remit of Genes & Development.

      Please find below detailed point-by-point answers.

      Reviewer #1

      Evidence, reproducibility and clarity

      In their manuscript entitled "The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ-dependent but monocyte-independent process," the authors explore the developmental trajectory of synovial lining macrophages. They demonstrate that the formation of this specialized macrophage layer is age-dependent and governed by a distinct developmental program that proceeds independently of circulating monocytes. Through scRNA-Seq, the authors show that synovial lining macrophages originate locally from Aqp1⁺ macrophages and are marked by the expression of Csf1r, Tgfbr, and Piezo1. Notably, genetic ablation of each of these factors impaired the development of lining macrophages to varying degrees, suggesting differential contributions of CSF1, TGFβ, and PIEZO1 signaling pathways to their maturation and maintenance.

      The manuscript is well written, and the data quality and representation is of a high standard. The authors have employed a sophisticated array of state-of-the-art mouse models and cutting-edge technologies to elucidate the developmental origin of synovial lining macrophages. Notably, the supporting scRNA-Seq datasets are of excellence and provide valuable insights that will likely be of significant interest to researchers in the field of immunology and joint biology. Accordingly, the experimental approach and interpretations regarding macrophage origin are well-founded and compelling. However, in the eye of the reviewer, the section addressing the underlying molecular mechanisms is a bit less convincing. This part of the study appears slightly underdeveloped, and some of the mechanistic claims lack sufficient experimental clarity. A more rigorous experimental investigation would be essential to reinforce the manuscript's conclusions, particularly concerning the data related to Tgfbr and Piezo1, where the current evidence appears insufficiently substantiated.

      We thank the reviewer for their positive and constructive evaluation of our manuscript. We agree with them (and the other reviewers) that our functional data on the involvement of TGFβ signaling and mechanical loading/mechanosensing are comparably less convincing and substantiated than our developmental data. We are very grateful for their (and the other reviewers') suggestions to provide more support for the involvement of these factors in lining macrophage development. However, we think that carrying this out to the same high standard will require substantial time and other resources. We have therefore decided to uncouple this from the developmental data and pursue this in follow-up work. We will re-focus the current manuscript on the developmental data. We have proposed to the editors to instead include additional data on synovial fibroblast development, to complement our macrophage data and also delineate the maturation of their niche, thereby providing a conclusive developmental atlas.

      Major point:

      1. The numbers of VSIG4⁺ macrophages appear either unaffected or only minimally altered in both Csf1rMerCreMer Tgfbr2floxed and Fcgr1Cre Piezo1floxed mouse models, respectively. This raises an important question: was the gene deletion efficiency sufficient in each model? Accordingly, the authors are encouraged to include quantitative data on gene deletion efficiency for both mouse models, as this information is critical for interpreting the observed phenotypic outcomes and validating the conclusions regarding gene function. Furthermore, to better assess the impact of Tgfbr2 and Piezo1 disruption, the authors should provide more comprehensive flow cytometry analyses and histological data for these mouse models. Given the apparent homogeneity of VSIG4⁺ macrophages (as shown by the authors themselves), bulk RNA-Seq of sorted Tgfbr2- and Piezo1-deficient VSIG4⁺ macrophages (or from TGFβ-treated animals) would offer valuable insights into both the effectiveness of gene deletion and the molecular pathways governed by TGFβ and PIEZO1 in lining macrophages.

      As outlined above, we have decided to uncouple our functional data on TGFβ, Piezo1 and mechanical loading. The points raised here are all very valid, and we will implement your suggestions in our follow-up functional work focusing on signaling events regulating lining macrophage development. On the suggestion to perform bulk RNA sequencing for VSIG4+ macrophages: This is a good one in principle - although we will not be able to use this strategy where we want to assess the consequences of experimental treatments or genetic models on lining macrophage maturation, because acquisition of VSIG4 is a key maturation event that might be impaired in these conditions.

      Minor points:

      Consistent usage of Cx3cr1-GFP+ nomenclature (for instance: Fig. S1 legend "adult mouse synovial tissue, showing PDGFRα⁺ fibroblasts (yellow) and CX3CR1-GFP⁺ cells (cyan)." versus Fig. 1 legend "Automated spot detection highlights Cx3cr1-GFP⁺ macrophages)".

      We will implement these changes.

      Unclear Fig. 3 legend: "Representative immunofluorescence images of synovial tissue from Clec9aCre:Rosa26lsl-tdT mice at 3 weeks and in adulthood, showing and tdTomato (yellow) and stained for DAPI (blue), VSIG4 (cyan)" Check 'showing and tdTomato.'

      We will implement these changes.

      For greater clarity, it would have been helpful if the transcript names had been directly included within Figures 3C, S3A, and S3C.

      We will implement these changes.

      Page 24: "(Mki67CreERT2:Rosa26lsl-tdT)" Last bracket not superscript.

      We will implement these changes.

      Page 25: "we again leveraged our scRNAsequencing dataset" Missing punctuation.

      We will implement these changes.

      Page 27: Fig. 5C legend: " of synovial tissue of 1 week-old, 3 weeks-old and adult mice." Please specify and change to 'adult Csf1rΔFIRE/ΔFIRE mice'.

      We will implement these changes.

      Page 30: The outcome observed in the Acta1-rtTA:tetO-Cre:ChR2-V5fl mouse model appears to be inconclusive: "This approach resulted in an increased density of VSIG4+ and total (F4/80+) macrophages in the exposed leg of some 5 days-old pups, but others showed the opposite trend (Figure S5D)." This variability may reflect low efficiency of the model or other technical limitations (e.g. muscle contractions frequency or time point of analysis). Given this ambiguity, it is worth reconsidering whether the data are sufficiently robust to warrant inclusion. Should the authors choose to include these findings, further experimentation of appropriate depth and precision is required to allow a conclusive interpretation (either it increases the density of VSIG4+ macrophages or not). The same applies to the Yoda1-treated mice, for which additional data are needed to determine whether VSIG4⁺ macrophage density is truly affected.

      We have decided to remove the data on the optogenetic mouse model and Yoda1 treatment and follow-on separately, implementing these suggestions, including proof of concept data for optogenetically induced muscle contractions.

      Significance

      General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This is a well-designed study that uses cutting-edge methodologies to investigate the developmental trajectory of synovial lining macrophages under homeostatic conditions. The authors present robust experimental evidence and compelling interpretations concerning synovial macrophage origin, which are both well-substantiated and impactful. Nonetheless, from the reviewer's perspective, the section exploring the molecular mechanisms underlying macrophage differentiation is comparatively less convincing. This section appears somewhat underdeveloped, as some of the mechanistic claims lack sufficient depth and experimental rigor to fully substantiate the conclusions.

      Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field: In contrast to earlier studies (PMID: 31391580, 32601335), the inclusion of fate-mapping experiments adds an important dimension, offering novel insight into the ontogeny of synovial macrophages. This expanded perspective may prove particularly valuable in advancing our understanding of joint immunology, especially regarding the local origins and lineage relationships of macrophage populations.

      Furthermore, the authors present novel insights into the molecular pathways underlying the differentiation and development of synovial lining macrophages. By demonstrating previously unrecognized regulatory mechanisms, this work significantly deepens our understanding of the cellular and transcriptional programs that drive macrophage specialization within the joint microenvironment.

      Place the work in the context of the existing literature (provide references, where appropriate): This study builds upon previous work characterizing the macrophage compartment in the joint (PMID: 31391580, 32601335), yet provides a substantially more comprehensive dataset that spans multiple developmental time points and data on the origin of this specialized macrophage subset.

      State what audience might be interested in and influenced by the reported findings: Immunologist, clinicians

      Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. This study falls well within the scope of the reviewer's expertise in innate immunity.

      Reviewer #2

      Evidence, reproducibility and clarity

      In the manuscript „The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ- dependent but monocyte-independent process", Magalhaes Pinto and colleagues carefully employ a wide range of technologies including single cell profiling, imaging and an exceptional combination of fate mapping models to characterize the ontogeny and development of lining macrophages in the joint, thus dissecting their maturation during postnatal development. Over the last decade, several landmark studies highlighted the imprinting of tissue-resident macrophages by a combination of ontogenetic and tissue-specific niche factors during development. So far, the ontogeny and the tissue niche factors governing the development and maturation of lining macrophages have not been described. Therefore, the results of this study offers insights on a small highly adapted macrophage population with relevance in many disease settings in the joint. Furthermore, the findings are nicely showcasing how macrophages are specializing to even very small tissue niches across development within one bigger anatomical compartment to serve dedicated functions within this niche.

      This manuscript is beautifully written and highlights many novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages.

      In particular, the combination of scRNA-seq and fate mapping is providing a unique the link of transcriptional programs to ontogeny within the tissue niche. Furthermore, the integrative use of distinct fate mapping strategies, transgenic mouse lines, and treatment paradigms to elucidate key niche factors guiding the development and maturation of lining macrophages provides many interesting findings and data that are highly relevant to the field. I really enjoyed reading this manuscript.

      Thank you for your complimentary and constructive assessment of our manuscript, and the detailed comments below, which are very helpful. Please find point-by-point responses below.

      Major points:

      The authors show dynamic regulation of VSIG4 in lining macrophages during development, therefore VSIG4 is maybe not an ideal choice for gating strategies to define lining macrophages or to show as a single markers in immunofluorescence (IF) stainings to demonstrate their abundance across development (even though it is clear that this is the reason why the F4/80 staining is shown next to it). To demonstrate the increase of lining macrophages during development in IF, it would be more helpful if the authors would show quantifications of all F4/80+ cells and additionally VSIG4+ as a proportion of F4/80+ cells (or VSIG4+ F4/80+ and all F4/80+ in a stacked bar plot). We agree with the assessment of VSIG4 not being ideal since this is a key marker of mature lining macrophages only.

      We agree with the assessment of VSIG4 not being ideal since this is a key marker of mature lining macrophages only. We will provide additional data and analyses.

      In Figure 1C, the authors nicely demonstrate that the lining macrophages get closer in their distance across development to build the epithelial-like macrophage structure along the adult lining. Is the close proximity between lining macrophages already fully "matured" at 3 weeks of age and comparable to adults? Please quantify the distance in adult linings.

      We will provide additional data for adult joints.

      Can the authors explain how the grouping was performed between the analyzed human fetal joints? It is not clear why the cut was chosen between the groups at 16/17 weeks of age. Maybe it would be also beneficial if the authors would consider not grouping these samples but rather show the specific quantifications for each samples individually and estimate via linear regression the expansion over time across human development. Furthermore, can the authors give additional information about the distancing of lining macrophages in the human fetal samples, it would be great to see if they follow the same dynamics as in mouse. Maybe comparison to human juvenile/adult joints would also add on to substantiate the findings in human samples (if possible).

      We will show samples ungrouped and perform new linear regression analysis as suggested.

      The scRNA-seq analysis leaves several questions open and some conclusions and workflows cannot be easily followed.

      We appreciate this comment and the complexity of the data, and will implement the below recommendations, and clarify the issues raised. Detailed:

      a. It is not clear how and especially why the signature genes to define macrophages vs. monocytes were chosen. Especially as the signature genes for monocytes would not include patrolling monocytes and the macrophage signature genes seem to be highly regulated during development, see also Apoe expression in NB vs. adult in Figure S2e. Why did the authors not take classical markers such as Itgam, Fcgr1a, Csf1r?

      We will include new analyses using these markers.

      b. Can dendritic cell signatures be excluded? Cluster 11 and 12 show indeed some DC markers, are these really macrophages?

      We will include new analyses to account for DC markers.

      c. The authors provide several figure panels showing TOP marker genes or key marker genes for the identified clusters, however it is not clear if these are TOP DE genes or if the genes were hand chosen. Somehow, the authors give the impression that the clusters were chosen and labeled not based on DE genes, but more on existing literature that previously reported these macrophage populations. DE gene lists for all annotated cell types and macrophage clusters need to be provided within the manuscript.

      We will provide the full DEG analysis results.

      d. The authors claim that Clusters 1 and 4 are "developing" macrophages. How is this defined? Why are these developing cells compared to other clusters? And why are these clusters later on not considered as progenitors of Aqp1 macrophages and Vsig4 macrophages? Why are Aqp1+ macrophages not labeled as developing when they are later on in the manuscript shown as potential intermediate progenitors of lining macrophages?

      As per below comment, we will expand on this and clarify nomenclature and (potential) relationships between these and other macrophages.

      e. Furthermore, it is again confusing that markers are used throughout Figure 2 which are labeled as "key marker genes" for a population and then later on they are claimed to be regulated during development within this population, see for example Figure 2D and 2H.

      We will clarify this as per above answer.

      f. It is appreciated that the authors distinguished cycling clusters such as 8, 9, and 10 based on their cycling gene signature. Here it would be very exciting to see a cell cycle analysis across all clusters and time points to see when exactly the cells are expanding during development; this would also substantiate the data later shown for the Mki67-CreERT2 mouse model.

      We will perform the proposed cell cycle analysis, and implement this and the other reviewer's suggestions for marker selection and cluster annotation (this is also covered in below comments from other reviewers).

      g. Can the authors identify certain gene modules during development of lining macrophages (and/or their progenitors) which are associated with certain functions (e.g. GO terms, GSEA enrichment)?

      This will be included in the revised manuscript.

      To determine the actual presence of the identified macrophage clusters from the scRNA-seq as macrophage populations in the joint, the authors should perform IF or FACS for key markers. Especially, Aqp1+ macrophages should be shown in the developing joint.

      We will provide additional data on Aqp1+ macrophages in the developing joint, and related these to a study by collaborators currently in revision at Immunity, which characterizes the Aqp1+ population in detail (we are hoping to have a doi available during our revision process).

      The authors used a wide range of fate mapping models, which is quite unique and highly appreciated. The obtained results and the conclusions made from the models raise a couple of questions: Whereas contribution of HSC-derived/monocyte-derived macrophages to the lining compartment seems to be minor, there is still labeling across different models. Various aspects would need to be clarified.

      We will clarify these data throughout as per below suggestions.

      a. For example, the authors employ Ms4a3-Cre as a tracing model for GMP-derived monocytes, however all quantifications of the labeling efficiency are not normalized to the labeling in monocytes or another highly recombined cell population. This should be shown, similar to the other fate mapping models (Figure 3 F-I).

      Labelling efficacy for Ms4a3-Cre is near complete for GMP-derived monocytes (and neutrophils) with the Rosa-lsl-tdT (aka Ai14) reporter we have used (see also PMID: 31491389 and doi: 10.1101/2024.12.03.626330); but we will include normalized data as requested.

      b. Please show Ms4a3 expression across clusters across time points, to exclude expression in fetal-derived clusters.

      We will include this in the revised supplementary information, but there is indeed very little at birth (in line with the original report for other tissues PMID: 31491389).

      c. In line with the question raised above, if the authors can exclude a development of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages and subsequently into Vsig4 lining macrophages, the obtained data from the Ms4a3-Cre model highly suggests a correlative labeling across these clusters what could implicate a relation. However, the authors do not discuss throughout the manuscript the role of these developing macrophages. It is highly encouraged to include this into the manuscript and it would be of high relevance to understand lining macrophage development.

      This is an interesting point and we agree it deserves consideration in the revised manuscript. Indeed, our trajectory analyses do not predict differentiation of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages, and hence, ultimately lining macrophages. Conversely, Aqp1+ cells might also convert into Egfr1+ and Clec4n+ developing macrophages. We will elaborate on this more in the revised manuscript.

      d. The authors conclude from the pseudo bulk transcriptomic profiling of the different macrophage clusters that TdT+ and TdT- macrophages do not differ in their gene expression profile and that this is due to niche imprinting rather than origin imprinting. Even though the data supports that conclusion, the authors should verify if inkling cells early during development also show this similar gene expression profile and gene expression should be compared at the different developmental time points. Tissue niche imprinting is happening within the niche during development, most likely in a stepwise progress, and therefore there should be differences in the beginning.

      This is another important point that we will address in the revised manuscript by performing additional differential gene expression analyses at the different developmental time points, including the earliest stages, as suggested.

      The trajectorial analysis using different pseudotime pipelines is very interesting and nicely points out the potential role of Aqp1 macrophages as intermediates of Vsig4 lining macrophages. From my point of view, all trajectories seem to suggest that Egfr1 developing macrophages and Clec4n developing macrophages might differentiate into Aqp1 macrophages, however the authors are not exploring this further and the role of both developing macrophage clusters is not further discussed (see also comments above).

      We will address and discuss this in the revised manuscript.

      How was the starting point of the trajectorial analyses defined and is it the same for each pipeline used?

      We will clarify this in the revised manuscript.

      Are there potentially two trajectories? It looks like there is one in the beginning of postnatal life and a second one appearing from the monocyte-compartment later in life. If this is true, that would rather speak for a dual ontogeny of Vsig4+ macrophages, wouldn't it?

      We will discuss this in the revised manuscript.

      A heatmap (transcriptional shift) of trajectories between more clusters should be shown at least for Cluster 0,1,2, and 3. It is not sufficient to demonstrate this only between two clusters.

      We will add these analyses during revision.

      To show the similarity between Aqp1 macrophages and proliferating macrophage clusters, the authors should remove the cycling signature and compare these clusters to show that the cycling cells might be Aqp1 macrophages or earlier developing macrophage progenitors aka Clec4n or Egfr1 macrophages.

      We will address this in the revised manuscript.

      The conclusions made from the Mki67-CreERT2 data are a bit difficult to understand, whereas all progenitors (monocyte progenitors and macrophage progenitors will proliferate at the neonatal time point and no conclusions can be made if the cells expand in the niche. The authors should employ Confetti mice or other models (Ubow mice) to analyze clonal expansion in the niche.

      We acknowledge that interpretation of the Mki67-CreERT2 data is complicated by labeling of other cells, and notably, labeling observed in BM-derived cells. To complement the Mki67-CreERT2 data, and specifically account for proliferation of BM-derived cells, we have tried using Ms4a3-Cre:Ubow mice to quantify expansion of the few monocyte-derived macrophages in the joint (lining). However, this yielded

      All predicted cell-cell interactions between macrophages and fibroblasts should be provided in a supplementary table. Are the interactions shown in Figure 5 chosen interactions or the TOP predicted ones? Whereas the authors show different numbers of interactions, it is most likely hand-picked and therefore biased.

      We will provide a full list of all predicted interactions in the revised supplementary material in addition to a list of the full differential gene expression analysis.

      The authors further aim to dissect the factors involved in the developmental niche imprinting of lining macrophages. Even though it is highly appreciated that the authors used so many experimental setups to show the reliance of lining macrophages on Csf1 and TGF-beta as well as mechanosensation, the wide range of models the different methods used and selected developmental time points make it very difficult to really interpret the data. The authors should carefully choose time points and methods (either FACS analysis across all models or IF across all, or both). Often deletion efficiencies for transgenic models and proof of concept that the inhibitors and agonists are working in the treatment paradigm are not provided. For example, Csf1rMer-iCre-Mer Tgfbr2fl/fl mice are used but no deletion efficiency is shown or different time points of analysis, maybe the macrophages are not properly targeted in the set up.

      We have decided to uncouple our experimental data on Tgfb, Piezo1 and mechanosensing/mechanical loading, but are taking this into consideration for revision. In many cases, we have in fact performed flow cytometry and imaging analyses, and agree, we should be showing this consistently.

      The authors have shown the role of Csf1 and Tgfbr2 only for lining macrophages, is this specific in the joint to this population of are subliming macrophages affected in a similar manner.

      We will include data on sublining macrophages in the revised figure (for CSF1; Tgfb data will be uncoupled from this current manuscript).

      Can the authors confirm their results in CSF1R-FIRE mice with anti-Csf1 injections or in Csf1op/op mice?

      We will expand our discussion of the Csf1 findings, and aim to include data for anti-CSF1 antibody treatment during revision. Csf1 has previously been reported as a key factor required for maintenance of tissue-resident macrophages, including those in the joint (lining). Indeed, Csf1op/op mice are deficient in synovial lining macrophages, from 2 days of age onwards (PMID: 8050349), and lining macrophages are also absent from 2-weeks-old and adult Csf1r-/- mice (PMID: 11756160). However, a full developmental analysis has not been performed. We are thus the first to show a full developmental time course, using state-of-the-art experimental readouts, and specifically focusing on the early postnatal window of lining maturation that we have identified here in this study. Moreover, we have used a more specific model, Csf1rFIRE ko, in which Csf1 deficiency is restricted to myeloid cells. This model circumvents issues with other models, which show many developmental defects, some of which unrelated to macrophages. These include growth retardation and skeletal defects, which may influence joint macrophage development. Therefore, although Csf1 dependence of synovial lining macrophage had indeed been previously reported in principle, our data substantially expand on and solidify these findings, thereby adding novelty.

      The setup in Figure S5G is very interesting to test the role of movement and mechanical load on the joint, however, there is basically no data on the model provided showing the efficiency of the induced optogenetic muscle contractions, and only one time point is shown.

      Data on mechanical loading will be uncoupled from the current manuscript and substantiated in a separate follow-up.

      The results regarding the role of Piezo1 and mechanosensation vary a lot. Could it be that analyses were done too early or that actually proper weight load on the joint must be applied for the maturation of the macrophages? The authors should test this to.

      We will uncouple these data from the current manuscript during revision in order to investigate the contribution of these (and other) factors in sufficient detail. However, this is a possibility that we have discussed. In fact, the most appropriate experimental approach to address the involvement of mechanical loading, onset of walking and specifically, weight bearing would be a loss-of-function approach (i.e. paralysis at the newborn stage), for which we unfortunately could not obtain ethics approval from the UK Home Office.

      The Rolipram experiment is shown in Figure S5G, but is not described in the result section. It only appears at some point in the discussion part. The authors should move it to results or remove it from the manuscript.

      We will incorporate these data with the revised section on developing synovial macrophage populations.

      Minor points:

      Please reference the Figure panels in numeric order throughout the text.

      We will change this where not the case already.

      Figure 2a and 2b are a bit out of the storyline, it is not obvious why this is shown here and maybe it would be good to move it to the supplements. Gating strategy is also not used for scRNA-seq. Therefore, it would better fit to the later analysis of joint macrophages across different transgenic mouse models and treatment paradigms. The gating strategies are changing across different experiments throughout the figures, it would be nice to have a similar gating strategy for all experiments, see also Figure 3 where the defining markers for joint macrophages are changing between models.

      We will revise Figures 2, 3 and the related supplementary figures.

      A lot of figure panels have very small labeling that is basically unreadable. Axes at FACS plots for example. Sometimes, it is even impossible to distinguish cluster labels especially when they have similar colors.

      We will revise this, thanks for pointing it out.

      In the text on page 14, many markers are named which are specifically regulated during development in lining macrophages, but these factors are not labeled anywhere in the volcano plot. It would be good to showcase at least some of these named genes in the figure panel, e.g. Trem2.

      We will do this for revision.

      Figure 2F and Figure S2F are really nicely showing the percentage of cells per cluster in each analyzed biological sample. Maybe the authors could additionally consider to show a stacked bar plot with the mean percentage of cells per cluster and how the clusters are distributed across time points?

      We will include this in the revised manuscript.

      Figure 3A: IF for adult lining macrophages and the quantification are missing.

      This will be included in the revised version.

      Reviewer #3 - Major

      Generally, the story could be more streamlined by introducing earlier reporter lines and lineage-origin logic. Clearly state which reporter/CreERT2 lines and acrosses are used. It was unclear in Figure 2 that cells of the cross of the Cx3cr1-GFP and Ms4a3Cre:Rosa26lsl-tdT reporter lines were used for the scRNA-seq. The principle that there are fetal-derived and bone marrow (GMP)-derived monocytes and macrophages doesn't need to be "hidden" until Figure 3. For example, also the imaging of Ms4a3Cre could be introduced before the scRNA-seq.

      We will revise the structure and order of the manuscript during revision. However, we will streamline this between reviewer comments, and would also like to point out that the 2 other reviewers were very complimentary about the writing and clarity, i.e. we may not follow every specific suggestion of reviewer 3, but are very much taking on board their overall comment on structure and clarity.

      Figure 1 could benefit from a cartoon visualizing the anatomy of the knee joint. The terms "sublining" and "synovium" are now a bit unclear, as it appears that sometimes the synovium is indicated as sublining and vice versa. Additionally, a schematic developmental timeline could be added to indicate the parallels between mouse and human development (fetal and postnatal development in mouse versus gestational age in human). Also, the various waves of hematopoiesis could be indicated in this timeline, which would be particularly helpful for Figure 3 for the lineage-tracing readouts. Lastly, the authors could end the manuscript (a new Figure 6) with a general cartoon summarizing all the results presented.

      We will include these illustrations as suggested.

      Figure 1 could be rearranged: first introduce the markers CX3CR1 and VSIG4 (Figure 1D) and then present the quantifications (Figure 1B/E). Where possible, co-visualization CX3CR1-GFP and VSIG4 on tissue sections to strengthen the claims on the relationship between these 2 markers. Tying the scRNA-seq insights (Figure 2) to the imaging would be elegant. Moreover, it would be informative to represent the CX3CR1+ and VSIG4+ macrophages as a percentage of F4/80+ macrophages (Figure 1B/E). Similarly, for the flow cytometry data in Figure 2, the relationship between the markers CX3CR1 and VSIG4 on macrophages could be more clearly displayed and discussed.

      Thanks for this remark. We will endeavour to show co-localization and analysis of both markers wherever possible. However, where we did not use Cx3cr1gfp mice, co-staining was limited by antibody choice and availability.

      The 3D imaging of the joint is a nice addition to the manuscript, as it provides more context to the anatomical structure; however, while the text suggests several newborn joints were imaged, Figure 1F visualizes (again) the knee joint. Could other joints also be represented by 3D imaging? If the knee joint is the only joint available for imaging, and previous confocal imaging focused specifically on the meniscus in the knee joint, could the meniscus also be highlighted in the lightsheet imaging?

      Apologies if this was not clear from the original manuscript text, but we have only imaged the knee joint in 3D. We will clarify this during revision. Whilst we want to maintain the focus on knee joints throughout this manuscript, but we will include additional 3D lightsheet imaging data from micro-dissected knee joints to further substantiate the original data.

      Clarification is requested regarding the imaging quantification representation. The M&M section under "Statistical analysis and reproducibility" states that individual data points are displayed, and bars represent the mean. However, some of the Figure legends (e.g., Figures 1B and S1C) specify that each dot corresponds to an individual mouse, with quantification based on 2-3 sections per mouse. While this appears to be a very reasonable representation of the data, does this mean that for each dot, the mean value from the 2-3 sections per mouse was calculated and plotted?

      We will clarify this.

      It is not clear how the differential expression analysis was performed on the Vsig4+ cells. Please specify if Cluster 0 was used for analysis, or all Vsig4-expressing cells? Not all cells in Cluster 0 have Vsig4+ expression. The authors described the expression dynamics of Aqp1 as intriguing, but lack a reasoning on why this is interesting.

      We will revise this section.

      Figure S3E: In line with the previous comment, can the authors justify that the tdTomato+/- comparisons are not biased by scRNA-seq dropout (scRNA-seq is zero-inflated, so some tdTomato- cells could be false negatives), and provide methodological details (thresholds, ambient RNA correction, etc.) to support this?

      We will clarify this and include additional representations of the tdTomato transcript data.

      Although the sex-related differences in macrophage composition and the absence of differential expression are interesting, they distract from the manuscript's main messages. Moreover, the Discussion does not elaborate on how these observations relate to joint (disease) biology. Consider removing this section or integrating it clearly into the relevant biological context.

      We will remove this section as suggested.

      CreERT2 transgenic lines are often not 100% efficient in recombination, also depending on whether tamoxifen or 4-OHT is used. Could the authors report the percentage of tdTomato+ cells in the joints and compare them to the recombination efficiencies in the monocytes/microglia under the same tamoxifen or 4-OHT conditions? This would help clarify how the interpret the macrophage labeling %'s.

      We will report labelling efficacies and/or show normalized data in the revised manuscript.

      Could the authors draw parallels between the observations in the mouse knee joint macrophage populations and literature on other joints in mouse and the knee joint in human (for example, as described in Alivernini et al., 2020 and in the very recent Raut et al., 2025)?

      We will include a section on this in the revised manuscript.

      Reviewer #3 - Minor comments:

      In general, the authors should clarify in the Results what each marker used for imaging, flow cytometry, or in the mouse reporter lines delineates. For example, mention that F4/80 is a marker for tissue-resident macrophages (correct?) in immunofluorescence, that IBA1 is a marker for macrophages on human tissue sections (Figure S1), and PDPN is GP38 (Figure S2 - align usage of marker reference across main text and figures).

      We will implement this request.

      Figure S1B: Is CX3CR1 also restricted to the lining macrophages in human? Could a co-staining with IBA1 be performed to strengthen the species similarities?

      To our knowledge, there is no antibody available that works for imaging of human CX3CR1. Moreover, CX3CR1 is only limited to the lining population in adult joints, in fetal and newborn (mouse) joints, all macrophages express this receptor, as do fetal progenitors to macrophages. However, Alivernini and colleagues have reported that TREM2high macrophages are the human counterpart of the mouse CX3CR1+ lining population (PMID: 32601335). We do not have access to postnatal human joint tissue samples, unfortunately, but we will attempt to stain for and quantify TREM2+ macrophages in human fetal joints for the revised manuscript.

      Adipocyte diameter quantification: Avoid plotting individual adipocytes from 2 mice without per-mouse visualization. Instead, report the mean adipocyte diameter per mouse and plot those means.

      We will implement this change.

      A little typo was spotted in the "Statistical analysis and reproducibility" section: it is Dunn's, not Bunn's multiple-comparison correction.

      Thanks for spotting this.

      Figure 2A: The gating strategy for the CX3CR1-GFP cells is missing.

      We will provide this in the revised manuscript or supplementary material.

      Improve the visualization of some plots. For example, Figure 2F is hard to read because of the big dot size. The dots seem to add no information to the graph and could be removed. Additionally, for comparing the clusters across the different time points, one could project the cells from the other time points in grey in the background.

      We will revise the presentation of these data.

      Figure S2: The dotplot is more informative than the heatmap, consider removing the heatmap.

      We will do that.

      Figure 3A: If technically feasible, image and visualize both the GFP and tdTomato expression. It would be informative to see the Cx3cr1+ and Ms4a3-derived cells in the same specimen.

      We will strive to show this in the revised manuscript.

      Figure 3C: Highlight that tdTomato expression is visualized here.

      We will do that.

      Figure 3G,F: The authors should place the schematics and graphs next to each other, so the data points can be more easily compared.

      We aim to do this in the revised manuscript.

      Figure 4B: Which co-staining was performed for the immunofluorescence to quantify the % of tdTomato+ cells?

      We co-stained for F4/80 and assessed localization in the lining or sublining. This will be clarified in the revised Figure legend.

      Figure 4C: The trajectory analysis appears to have an arrow pointing from the Ccr2+ macrophages to the Ly6c+ monocytes. Please verify this directionality, as its seems against the known biology.

      This will be addressed during revision.

      Figure 5 mentions that the Csfr1 levels were reduced in a tissue-specific manner, but it is unclear how this tissue specificity was achieved.

      We apologize for this misunderstanding. Csfr1FIRE mice are not tissue-specific knockouts, but they are more specific than global knockout mice, since only a (myeloid-specific) enhancer is affected. We will clarify this in the relevant section.

      For the TGFb perturbations (Tgfbr2 KO and systemic TGFb depletion): did the authors validate reduced TGFb pathway activity in the macrophages, for example, reduced pSMAD2/3 levels? This would validate the effectiveness of the perturbations.

      This is an important point, and assessing signaling events downstream of TGFb is a very good suggestion. As per above comment, we have decided to uncouple the functional data with exception of CSF1 from the revised version of the current manuscript, but we will be taking this into account for substantiating our functional data in follow-up work.

      Figure 5F could benefit from a timeline of the treatment.

      As for 15., we will be taking this into account for follow-up work on the uncoupled functional data.

      The Methods mention that Gene Ontology analysis was performed on the single-cell data, but the results are not plotted in a figure. It would be informative to include this GO/pathway analysis in the appropriate figure(s).

      We will include this in the revised (supplementary) information.

      PeerReviewed
    2. EMBOpress 05 Sep 2025

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

      Learn more at Review Commons

      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Magalhaes Pinto, Malengier-Devlies, and co-authors investigated the developmental origins and maturation of synovial (lining and sublining) macrophages across embryonic, newborn, and postnatal stages in mouse. The authors used multiple transgenic reporter lines, lineage tracing, scRNA-seq, 2D confocal and 3D lightsheet imaging, and perturbations to delineate the macrophage states and ontogeny. They propose a model in which the majority of the joint lining macrophages has a fetal (EMP-derived) origin and a small proportion has a definitive HSC-derived monocyte origin, which both seed and mature within the synovial space in the postnatal period in the first 3 weeks of life. Using cell-cell communication analysis on their scRNA-seq data, they identified Fgf2, Csf1, and Tgfb as candidate signaling pathways that support (lining) macrophage development and maturation. Functional experiments indicate that the process is CSF1 and TGFb-dependent and also partly dependent on mechanosensing through Piezo1. The key conclusions on the composition of the synovial macrophages are convincing based on the presented results, and are carefully phrased. The study is very comprehensive, yet the description and organization of the results of the different mouse models could be altered to improve the storyline. Several refinements in data presentation, formulation, and minor validation experiments would further improve the clarity of the story, as well as summary recaps of the major findings throughout the text.

      Major comments:

      1. Generally, the story could be more streamlined by introducing earlier reporter lines and lineage-origin logic. Clearly state which reporter/CreERT2 lines and acrosses are used. It was unclear in Figure 2 that cells of the cross of the Cx3cr1-GFP and Ms4a3Cre:Rosa26lsl-tdT reporter lines were used for the scRNA-seq. The principle that there are fetal-derived and bone marrow (GMP)-derived monocytes and macrophages doesn't need to be "hidden" until Figure 3. For example, also the imaging of Ms4a3Cre could be introduced before the scRNA-seq.
      2. Figure 1 could benefit from a cartoon visualizing the anatomy of the knee joint. The terms "sublining" and "synovium" are now a bit unclear, as it appears that sometimes the synovium is indicated as sublining and vice versa. Additionally, a schematic developmental timeline could be added to indicate the parallels between mouse and human development (fetal and postnatal development in mouse versus gestational age in human). Also, the various waves of hematopoiesis could be indicated in this timeline, which would be particularly helpful for Figure 3 for the lineage-tracing readouts. Lastly, the authors could end the manuscript (a new Figure 6) with a general cartoon summarizing all the results presented.
      3. Figure 1 could be rearranged: first introduce the markers CX3CR1 and VSIG4 (Figure 1D) and then present the quantifications (Figure 1B/E). Where possible, co-visualization CX3CR1-GFP and VSIG4 on tissue sections to strengthen the claims on the relationship between these 2 markers. Tying the scRNA-seq insights (Figure 2) to the imaging would be elegant. Moreover, it would be informative to represent the CX3CR1+ and VSIG4+ macrophages as a percentage of F4/80+ macrophages (Figure 1B/E). Similarly, for the flow cytometry data in Figure 2, the relationship between the markers CX3CR1 and VSIG4 on macrophages could be more clearly displayed and discussed.
      4. The 3D imaging of the joint is a nice addition to the manuscript, as it provides more context to the anatomical structure; however, while the text suggests several newborn joints were imaged, Figure 1F visualizes (again) the knee joint. Could other joints also be represented by 3D imaging? If the knee joint is the only joint available for imaging, and previous confocal imaging focused specifically on the meniscus in the knee joint, could the meniscus also be highlighted in the lightsheet imaging?
      5. Clarification is requested regarding the imaging quantification representation. The M&M section under "Statistical analysis and reproducibility" states that individual data points are displayed, and bars represent the mean. However, some of the Figure legends (e.g., Figures 1B and S1C) specify that each dot corresponds to an individual mouse, with quantification based on 2-3 sections per mouse. While this appears to be a very reasonable representation of the data, does this mean that for each dot, the mean value from the 2-3 sections per mouse was calculated and plotted?
      6. It is not clear how the differential expression analysis was performed on the Vsig4+ cells. Please specify if Cluster 0 was used for analysis, or all Vsig4-expressing cells? Not all cells in Cluster 0 have Vsig4+ expression. The authors described the expression dynamics of Aqp1 as intriguing, but lack a reasoning on why this is interesting.
      7. Figure S3E: In line with the previous comment, can the authors justify that the tdTomato+/- comparisons are not biased by scRNA-seq dropout (scRNA-seq is zero-inflated, so some tdTomato- cells could be false negatives), and provide methodological details (thresholds, ambient RNA correction, etc.) to support this?
      8. Although the sex-related differences in macrophage composition and the absence of differential expression are interesting, they distract from the manuscript's main messages. Moreover, the Discussion does not elaborate on how these observations relate to joint (disease) biology. Consider removing this section or integrating it clearly into the relevant biological context.
      9. CreERT2 transgenic lines are often not 100% efficient in recombination, also depending on whether tamoxifen or 4-OHT is used. Could the authors report the percentage of tdTomato+ cells in the joints and compare them to the recombination efficiencies in the monocytes/microglia under the same tamoxifen or 4-OHT conditions? This would help clarify how the interpret the macrophage labeling %'s.
      10. Could the authors draw parallels between the observations in the mouse knee joint macrophage populations and literature on other joints in mouse and the knee joint in human (for example, as described in Alivernini et al., 2020 and in the very recent Raut et al., 2025)?

      Minor comments:

      1. In general, the authors should clarify in the Results what each marker used for imaging, flow cytometry, or in the mouse reporter lines delineates. For example, mention that F4/80 is a marker for tissue-resident macrophages (correct?) in immunofluorescence, that IBA1 is a marker for macrophages on human tissue sections (Figure S1), and PDPN is GP38 (Figure S2 - align usage of marker reference across main text and figures).
      2. For clarity in the microscopy representation, the single channels should be represented in a grey scale.
      3. Figure S1B: Is CX3CR1 also restricted to the lining macrophages in human? Could a co-staining with IBA1 be performed to strengthen the species similarities?
      4. Adipocyte diameter quantification: Avoid plotting individual adipocytes from 2 mice without per-mouse visualization. Instead, report the mean adipocyte diameter per mouse and plot those means.
      5. A little typo was spotted in the "Statistical analysis and reproducibility" section: it is Dunn's, not Bunn's multiple-comparison correction.
      6. Figure 2A: The gating strategy for the CX3CR1-GFP cells is missing.
      7. Improve the visualization of some plots. For example, Figure 2F is hard to read because of the big dot size. The dots seem to add no information to the graph and could be removed. Additionally, for comparing the clusters across the different time points, one could project the cells from the other time points in grey in the background.
      8. Figure S2: The dotplot is more informative than the heatmap, consider removing the heatmap.
      9. Figure 3A: If technically feasible, image and visualize both the GFP and tdTomato expression. It would be informative to see the Cx3cr1+ and Ms4a3-derived cells in the same specimen.
      10. Figure 3C: Highlight that tdTomato expression is visualized here.
      11. Figure 3G,F: The authors should place the schematics and graphs next to each other, so the data points can be more easily compared.
      12. Figure 4B: Which co-staining was performed for the immunofluorescence to quantify the % of tdTomato+ cells?
      13. Figure 4C: The trajectory analysis appears to have an arrow pointing from the Ccr2+ macrophages to the Ly6c+ monocytes. Please verify this directionality, as its seems against the known biology.
      14. Figure 5 mentions that the Csfr1 levels were reduced in a tissue-specific manner, but it is unclear how this tissue specificity was achieved.
      15. For the TGFb perturbations (Tgfbr2 KO and systemic TGFb depletion): did the authors validate reduced TGFb pathway activity in the macrophages, for example, reduced pSMAD2/3 levels? This would validate the effectiveness of the perturbations.
      16. Figure 5F could benefit from a timeline of the treatment.
      17. The Methods mention that Gene Ontology analysis was performed on the single-cell data, but the results are not plotted in a figure. It would be informative to include this GO/pathway analysis in the appropriate figure(s).

      Significance

      This work provides a high temporal-resolution and "spatial" resolution reference map of the ontogeny and maturation of the synovial lining macrophages in the knee joint. It complements existing literature that demonstrated the presence of tissue-resident macrophages in the synovial space and lining (Culemann, et al., 2019 and others) by charting the embryonic-to-postnatal emergence of lining and sublining subsets. In particular, this mouse work identified some key signaling pathways in shaping this tissue compartment. This dataset serves as a robust, steady-state reference for joint pathology and can be implemented with human studies on disease biology of the knee joint (e.g., Alivernini et al., 2020; Raut et al., 2025). Insights into the exact developmental origins, mechanisms contributing to diverse or seemingly similar cell types, and distinct maturation processes are crucial to understanding disease biology, in which developmental processes can be hijacked/reactivated.

      These findings will interest researchers in joint disease biology (osteoarthritis and immune-mediated arthritides such as RA and psoriasis), macrophage development (tissue-resident vs monocyte-derived lineages), the bone/joint microenvironment, and joint mechanobiology.

      The reviewer's expertise is in developmental biology, mesoderm, bone biology, hematopoiesis, and monocyte/macrophage biology in disease

      PeerReviewed
    3. EMBOpress 05 Sep 2025

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

      Learn more at Review Commons

      Referee #2

      Evidence, reproducibility and clarity

      In the manuscript „The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ- dependent but monocyte-independent process", Magalhaes Pinto and colleagues carefully employ a wide range of technologies including single cell profiling, imaging and an exceptional combination of fate mapping models to characterize the ontogeny and development of lining macrophages in the joint, thus dissecting their maturation during postnatal development. Over the last decade, several landmark studies highlighted the imprinting of tissue-resident macrophages by a combination of ontogenetic and tissue-specific niche factors during development. So far, the ontogeny and the tissue niche factors governing the development and maturation of lining macrophages have not been described. Therefore, the results of this study offers insights on a small highly adapted macrophage population with relevance in many disease settings in the joint. Furthermore, the findings are nicely showcasing how macrophages are specializing to even very small tissue niches across development within one bigger anatomical compartment to serve dedicated functions within this niche.

      This manuscript is beautifully written and highlights many novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages.

      In particular, the combination of scRNA-seq and fate mapping is providing a unique the link of transcriptional programs to ontogeny within the tissue niche. Furthermore, the integrative use of distinct fate mapping strategies, transgenic mouse lines, and treatment paradigms to elucidate key niche factors guiding the development and maturation of lining macrophages provides many interesting findings and data that are highly relevant to the field. I really enjoyed reading this manuscript.

      Major points:

      1) The authors show dynamic regulation of VSIG4 in lining macrophages during development, therefore VSIG4 is maybe not an ideal choice for gating strategies to define lining macrophages or to show as a single markers in immunofluorescence (IF) stainings to demonstrate their abundance across development (even though it is clear that this is the reason why the F4/80 staining is shown next to it). To demonstrate the increase of lining macrophages during development in IF, it would be more helpful if the authors would show quantifications of all F4/80+ cells and additionally VSIG4+ as a proportion of F4/80+ cells (or VSIG4+ F4/80+ and all F4/80+ in a stacked bar plot).

      2) In Figure 1C, the authors nicely demonstrate that the lining macrophages get closer in their distance across development to build the epithelial-like macrophage structure along the adult lining. Is the close proximity between lining macrophages already fully "matured" at 3 weeks of age and comparable to adults? Please quantify the distance in adult linings.

      3) Can the authors explain how the grouping was performed between the analyzed human fetal joints? It is not clear why the cut was chosen between the groups at 16/17 weeks of age. Maybe it would be also beneficial if the authors would consider not grouping these samples but rather show the specific quantifications for each samples individually and estimate via linear regression the expansion over time across human development. Furthermore, can the authors give additional information about the distancing of lining macrophages in the human fetal samples, it would be great to see if they follow the same dynamics as in mouse. Maybe comparison to human juvenile/adult joints would also add on to substantiate the findings in human samples (if possible).

      4) The scRNA-seq analysis leaves several questions open and some conclusions and workflows cannot be easily followed.

      a. It is not clear how and especially why the signature genes to define macrophages vs. monocytes were chosen. Especially as the signature genes for monocytes would not include patrolling monocytes and the macrophage signature genes seem to be highly regulated during development, see also Apoe expression in NB vs. adult in Figure S2e. Why did the authors not take classical markers such as Itgam, Fcgr1a, Csf1r?

      b. Can dendritic cell signatures be excluded? Cluster 11 and 12 show indeed some DC markers, are these really macrophages?

      c. The authors provide several figure panels showing TOP marker genes or key marker genes for the identified clusters, however it is not clear if these are TOP DE genes or if the genes were hand chosen. Somehow, the authors give the impression that the clusters were chosen and labeled not based on DE genes, but more on existing literature that previously reported these macrophage populations. DE gene lists for all annotated cell types and macrophage clusters need to be provided within the manuscript.

      d. The authors claim that Clusters 1 and 4 are "developing" macrophages. How is this defined? Why are these developing cells compared to other clusters? And why are these clusters later on not considered as progenitors of Aqp1 macrophages and Vsig4 macrophages? Why are Aqp1+ macrophages not labeled as developing when they are later on in the manuscript shown as potential intermediate progenitors of lining macrophages?

      e. Furthermore, it is again confusing that markers are used throughout Figure 2 which are labeled as "key marker genes" for a population and then later on they are claimed to be regulated during development within this population, see for example Figure 2D and 2H.

      f. It is appreciated that the authors distinguished cycling clusters such as 8, 9, and 10 based on their cycling gene signature. Here it would be very exciting to see a cell cycle analysis across all clusters and time points to see when exactly the cells are expanding during development; this would also substantiate the data later shown for the Mki67-CreERT2 mouse model.

      g. Can the authors identify certain gene modules during development of lining macrophages (and/or their progenitors) which are associated with certain functions (e.g. GO terms, GSEA enrichment)?

      5) To determine the actual presence of the identified macrophage clusters from the scRNA-seq as macrophage populations in the joint, the authors should perform IF or FACS for key markers. Especially, Aqp1+ macrophages should be shown in the developing joint.

      6) The authors used a wide range of fate mapping models, which is quite unique and highly appreciated. The obtained results and the conclusions made from the models raise a couple of questions: Whereas contribution of HSC-derived/monocyte-derived macrophages to the lining compartment seems to be minor, there is still labeling across different models. Various aspects would need to be clarified.

      a. For example, the authors employ Ms4a3-Cre as a tracing model for GMP-derived monocytes, however all quantifications of the labeling efficiency are not normalized to the labeling in monocytes or another highly recombined cell population. This should be shown, similar to the other fate mapping models (Figure 3 F-I).

      b. Please show Ms4a3 expression across clusters across time points, to exclude expression in fetal-derived clusters.

      c. In line with the question raised above, if the authors can exclude a development of the Egfr1+ and Clec4n+ developing macrophages into Aqp1+ macrophages and subsequently into Vsig4 lining macrophages, the obtained data from the Ms4a3-Cre model highly suggests a correlative labeling across these clusters what could implicate a relation. However, the authors do not discuss throughout the manuscript the role of these developing macrophages. It is highly encouraged to include this into the manuscript and it would be of high relevance to understand lining macrophage development.

      d. The authors conclude from the pseudo bulk transcriptomic profiling of the different macrophage clusters that TdT+ and TdT- macrophages do not differ in their gene expression profile and that this is due to niche imprinting rather than origin imprinting. Even though the data supports that conclusion, the authors should verify if inkling cells early during development also show this similar gene expression profile and gene expression should be compared at the different developmental time points. Tissue niche imprinting is happening within the niche during development, most likely in a stepwise progress, and therefore there should be differences in the beginning.

      7) The trajectorial analysis using different pseudotime pipelines is very interesting and nicely points out the potential role of Aqp1 macrophages as intermediates of Vsig4 lining macrophages. From my point of view, all trajectories seem to suggest that Egfr1 developing macrophages and Clec4n developing macrophages might differentiate into Aqp1 macrophages, however the authors are not exploring this further and the role of both developing macrophage clusters is not further discussed (see also comments above).

      8) How was the starting point of the trajectorial analyses defined and is it the same for each pipeline used?

      9) Are there potentially two trajectories? It looks like there is one in the beginning of postnatal life and a second one appearing from the monocyte-compartment later in life. If this is true, that would rather speak for a dual ontogeny of Vsig4+ macrophages, wouldn't it?

      10) A heatmap (transcriptional shift) of trajectories between more clusters should be shown at least for Cluster 0,1,2, and 3. It is not sufficient to demonstrate this only between two clusters.

      11) To show the similarity between Aqp1 macrophages and proliferating macrophage clusters, the authors should remove the cycling signature and compare these clusters to show that the cycling cells might be Aqp1 macrophages or earlier developing macrophage progenitors aka Clec4n or Egfr1 macrophages.

      12) The conclusions made from the Mki67-CreERT2 data are a bit difficult to understand, whereas all progenitors (monocyte progenitors and macrophage progenitors will proliferate at the neonatal time point and no conclusions can be made if the cells expand in the niche. The authors should employ Confetti mice or other models (Ubow mice) to analyze clonal expansion in the niche.

      13) All predicted cell-cell interactions between macrophages and fibroblasts should be provided in a supplementary table. Are the interactions shown in Figure 5 chosen interactions or the TOP predicted ones? Whereas the authors show different numbers of interactions, it is most likely hand-picked and therefore biased.

      14) The authors further aim to dissect the factors involved in the developmental niche imprinting of lining macrophages. Even though it is highly appreciated that the authors used so many experimental setups to show the reliance of lining macrophages on Csf1 and TGF-beta as well as mechanosensation, the wide range of models the different methods used and selected developmental time points make it very difficult to really interpret the data. The authors should carefully choose time points and methods (either FACS analysis across all models or IF across all, or both). Often deletion efficiencies for transgenic models and proof of concept that the inhibitors and agonists are working in the treatment paradigm are not provided. For example, Csf1rMer-iCre-Mer Tgfbr2fl/fl mice are used but no deletion efficiency is shown or different time points of analysis, maybe the macrophages are not properly targeted in the set up.

      15) The authors have shown the role of Csf1 and Tgfbr2 only for lining macrophages, is this specific in the joint to this population of are subliming macrophages affected in a similar manner.

      16) Can the authors confirm their results in CSF1R-FIRE mice with anti-Csf1 injections or in Csf1op/op mice?

      17) The setup in Figure S5G is very interesting to test the role of movement and mechanical load on the joint, however, there is basically no data on the model provided showing the efficiency of the induced optogenetic muscle contractions, and only one time point is shown.

      18) The results regarding the role of Piezo1 and mechanosensation vary a lot. Could it be that analyses were done too early or that actually proper weight load on the joint must be applied for the maturation of the macrophages? The authors should test this to

      19) The Rolipram experiment is shown in Figure S5G, but is not described in the result section. It only appears at some point in the discussion part. The authors should move it to results or remove it from the manuscript.

      Minor points:

      1) Please reference the Figure panels in numeric order throughout the text.

      2) Figure 2a and 2b are a bit out of the storyline, it is not obvious why this is shown here and maybe it would be good to move it to the supplements. Gating strategy is also not used for scRNA-seq. Therefore, it would better fit to the later analysis of joint macrophages across different transgenic mouse models and treatment paradigms. The gating strategies are changing across different experiments throughout the figures, it would be nice to have a similar gating strategy for all experiments, see also Figure 3 where the defining markers for joint macrophages are changing between models.

      3) A lot of figure panels have very small labeling that is basically unreadable. Axes at FACS plots for example. Sometimes, it is even impossible to distinguish cluster labels especially when they have similar colors.

      4) In the text on page 14, many markers are named which are specifically regulated during development in lining macrophages, but these factors are not labeled anywhere in the volcano plot. It would be good to showcase at least some of these named genes in the figure panel, e.g. Trem2.

      5) Figure 2F and Figure S2F are really nicely showing the percentage of cells per cluster in each analyzed biological sample. Maybe the authors could additionally consider to show a stacked bar plot with the mean percentage of cells per cluster and how the clusters are distributed across time points?

      6) Figure 3A: IF for adult lining macrophages and the quantification are missing

      Significance

      This manuscript highlights novel, highly relevant findings on lining macrophage biology and the authors employ a wide range of different technologies to carefully dissect the postnatal development of lining macrophages. Furthermore, this study showcases in a very elegant and detailed way the adaptation of macrophage progenitors to a highly specific anatomical tissue niche.

      The manuscript is of high interest to basic scientists focussing on macrophage biology and immune cell development and clinicians and clinician scientists focussing on joint diseases such as RA

      Therefore the manuscript is of interest to a wide community working in immunology.

      PeerReviewed
    4. EMBOpress 05 Sep 2025

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

      Learn more at Review Commons

      Referee #1

      Evidence, reproducibility and clarity

      In their manuscript entitled "The synovial lining macrophage layer develops in the first weeks of life in a CSF1- and TGFβ-dependent but monocyte-independent process," the authors explore the developmental trajectory of synovial lining macrophages. They demonstrate that the formation of this specialized macrophage layer is age-dependent and governed by a distinct developmental program that proceeds independently of circulating monocytes. Through scRNA-Seq, the authors show that synovial lining macrophages originate locally from Aqp1⁺ macrophages and are marked by the expression of Csf1r, Tgfbr, and Piezo1. Notably, genetic ablation of each of these factors impaired the development of lining macrophages to varying degrees, suggesting differential contributions of CSF1, TGFβ, and PIEZO1 signaling pathways to their maturation and maintenance.

      The manuscript is well written, and the data quality and representation is of a high standard. The authors have employed a sophisticated array of state-of-the-art mouse models and cutting-edge technologies to elucidate the developmental origin of synovial lining macrophages. Notably, the supporting scRNA-Seq datasets are of excellence and provide valuable insights that will likely be of significant interest to researchers in the field of immunology and joint biology. Accordingly, the experimental approach and interpretations regarding macrophage origin are well-founded and compelling. However, in the eye of the reviewer, the section addressing the underlying molecular mechanisms is a bit less convincing. This part of the study appears slightly underdeveloped, and some of the mechanistic claims lack sufficient experimental clarity. A more rigorous experimental investigation would be essential to reinforce the manuscript's conclusions, particularly concerning the data related to Tgfbr and Piezo1, where the current evidence appears insufficiently substantiated.

      Major point:

      • The numbers of VSIG4⁺ macrophages appear either unaffected or only minimally altered in both Csf1rMerCreMer Tgfbr2floxed and Fcgr1Cre Piezo1floxed mouse models, respectively. This raises an important question: was the gene deletion efficiency sufficient in each model? Accordingly, the authors are encouraged to include quantitative data on gene deletion efficiency for both mouse models, as this information is critical for interpreting the observed phenotypic outcomes and validating the conclusions regarding gene function. Furthermore, to better assess the impact of Tgfbr2 and Piezo1 disruption, the authors should provide more comprehensive flow cytometry analyses and histological data for these mouse models. Given the apparent homogeneity of VSIG4⁺ macrophages (as shown by the authors themselves), bulk RNA-Seq of sorted Tgfbr2- and Piezo1-deficient VSIG4⁺ macrophages (or from TGFβ-treated animals) would offer valuable insights into both the effectiveness of gene deletion and the molecular pathways governed by TGFβ and PIEZO1 in lining macrophages.

      Minor points:

      • Consistent usage of Cx3cr1-GFP+ nomenclature (for instance: Fig. S1 legend "adult mouse synovial tissue, showing PDGFRα⁺ fibroblasts (yellow) and CX3CR1-GFP⁺ cells (cyan)." versus Fig. 1 legend "Automated spot detection highlights Cx3cr1-GFP⁺ macrophages)"
      • Unclear Fig. 3 legend: "Representative immunofluorescence images of synovial tissue from Clec9aCre:Rosa26lsl-tdT mice at 3 weeks and in adulthood, showing and tdTomato (yellow) and stained for DAPI (blue), VSIG4 (cyan)" Check 'showing and tdTomato.'
      • For greater clarity, it would have been helpful if the transcript names had been directly included within Figures 3C, S3A, and S3C.
      • Page 24: "(Mki67CreERT2:Rosa26lsl-tdT)" Last bracket not superscript.
      • Page 25: "we again leveraged our scRNAsequencing dataset" Missing punctuation.
      • Page 27: Fig. 5C legend: " of synovial tissue of 1 week-old, 3 weeks-old and adult mice." Please specify and change to 'adult Csf1rΔFIRE/ΔFIRE mice'.
      • Page 30: The outcome observed in the Acta1-rtTA:tetO-Cre:ChR2-V5fl mouse model appears to be inconclusive: "This approach resulted in an increased density of VSIG4+ and total (F4/80+) macrophages in the exposed leg of some 5 days-old pups, but others showed the opposite trend (Figure S5D)." This variability may reflect low efficiency of the model or other technical limitations (e.g. muscle contractions frequency or time point of analysis). Given this ambiguity, it is worth reconsidering whether the data are sufficiently robust to warrant inclusion. Should the authors choose to include these findings, further experimentation of appropriate depth and precision is required to allow a conclusive interpretation (either it increases the density of VSIG4+ macrophages or not). The same applies to the Yoda1-treated mice, for which additional data are needed to determine whether VSIG4⁺ macrophage density is truly affected.

      Significance

      • General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

      This is a well-designed study that uses cutting-edge methodologies to investigate the developmental trajectory of synovial lining macrophages under homeostatic conditions. The authors present robust experimental evidence and compelling interpretations concerning synovial macrophage origin, which are both well-substantiated and impactful. Nonetheless, from the reviewer's perspective, the section exploring the molecular mechanisms underlying macrophage differentiation is comparatively less convincing. This section appears somewhat underdeveloped, as some of the mechanistic claims lack sufficient depth and experimental rigor to fully substantiate the conclusions. - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field:

      In contrast to earlier studies (PMID: 31391580, 32601335), the inclusion of fate-mapping experiments adds an important dimension, offering novel insight into the ontogeny of synovial macrophages. This expanded perspective may prove particularly valuable in advancing our understanding of joint immunology, especially regarding the local origins and lineage relationships of macrophage populations. Furthermore, the authors present novel insights into the molecular pathways underlying the differentiation and development of synovial lining macrophages. By demonstrating previously unrecognized regulatory mechanisms, this work significantly deepens our understanding of the cellular and transcriptional programs that drive macrophage specialization within the joint microenvironment. -Place the work in the context of the existing literature (provide references, where appropriate):

      This study builds upon previous work characterizing the macrophage compartment in the joint (PMID: 31391580, 32601335), yet provides a substantially more comprehensive dataset that spans multiple developmental time points and data on the origin of this specialized macrophage subset. - State what audience might be interested in and influenced by the reported findings:

      Immunologist, clinicians - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      This study falls well within the scope of the reviewer's expertise in innate immunity.

      PeerReviewed
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    4  Cohesión Horizontal: Seguridad – Medición de la cohesión social con ELSOC
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    1. tomasurzuam 05 Sep 2025
      Cohesión Horizontal: Seguridad

      corregir la sección de los títulos acorde a la estructuración de Andreas en el issue: 1.- medición de seguridad; 2.- seguridad y migración; 3.- seguridad y migración en el tiempo

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    Unify OpenScape Maintenance FA469025Q0062
    1
    1. parths301 05 Sep 2025
      Assistant linked to federal registration and award history for Arayna Technology Solutions (UEI LQKTFZMD19B3), update here. Upload a capability statement or catalogue for your company here or upload an opportunity-specific document here Some suggestions:Please summarize the work to be completed under this opportunityDo the documents mention an incumbent contractor?Does this contract have any security clearance requirements?I'd like to anonymously submit a question to the procurement officer(s) Do they need certified people or something similar Loading Yes, the solicitation explicitly requires that all primary technical services be performed by individuals who are Mitel/Unify certified, qualified, and experienced in performing maintenance and repair of the specified equipment. Specifically, the contractor must:- Be a Mitel/Unify Systems certified partner authorized to perform on-network installations and maintenance.- Ensure that technicians have a complete understanding of system design, installation diagrams, and maintenance records for the equipment listed in the PWS.- Meet the following Air Force requirements for access: - Air Force Cyber Readiness Training (Annual) - CompTIA Security+ CertificationThese requirements are detailed in Section 4.0 of the [Performance Work Statement (PWS)](https://www.highergov.com/document/attachment-1-ellsworth-28th-bw-unify-sustainment-pws-pdf-4c2c3b/). Submit × Opportunity Assistant Document Library Capability Statements Opportunity Documents Drop or click to upload a Capability Statement (.pdf, .docx, or .xlsx) Close Select Document for Processing Select a document for analysis. Submit Match Score Factors Contributing to Match Registered with primary NAICS 541519 matches this opportunity. Registered for NAICS 541519 - Other Computer Related Services This opportunity is open to competition Potential Issues or Gaps Minimal or no matching keywords found for your capabilities May have limited or no recent experience with NAICS 541519 - Other Computer Related Services Registration not found for PSC DG10 - IT And Telecom - Network As A Service May have limited or no recent experience with PSC DG10 - IT And Telecom - Network As A Service Little or no prior performance with Air Combat Command This opportunity was marked No Bid by: Arayna Description Original Summary Original Summary The 28th Contracting Squadron, Ellsworth AFB, South Dakota, has issued this Solicitation, FA469025Q0062, to compete and award a Firm-Fixed Price Contract for sustainment and repair for the Mitel/Unify OpenScape Systems located at Ellsworth Air Force Base, South Dakota. This requriement is being solicited Full & Open (No Small Business Set-Aside), limited to Unify OpenScape Brand Name items and certified Technicians. Attached to this Solicitation Notice are: Standard Form 1449 Solicitation Attachment 1 - Performance Work Statement (PWS) Attachment 2 Wage Determination All questions and comments on this solicitation must be submitted in writing to joshua.johnson.233@us.af.mil no later than Wednesday, 10 September 2025 by 12:00pm MDT. Please title all emails with questions with the following subject line: FA469025Q0062 - Unify Mainteance. All interested and responsible entities are invited to submit a quote that will be considered by the 28th Contracting Office at Ellsworth AFB, South Dakota. The award will be based on the criteria established in the solicitation. Vendor quotes and all items required as listed within the Addendum to 52.212-1, Instruction to Offerors, are due to be submitted no later than Wednesday, 17 September 2025 by 3:00pm MDT to joshua.johnson.233@us.af.mil. Please title all submissions with the following subject line: FA469025Q0062 - Unify Maintenance Contractors submitting a quote must have and list within the quote their assigned Cage Code and be registered and ACTIVE in the System of Award Management (SAM) at www.sam.gov to be eligible for award. Auto-generated summaries available on select opportunities Background The 28th Contracting Squadron at Ellsworth Air Force Base, South Dakota, is issuing Solicitation FA469025Q0062 for a Firm-Fixed Price Contract aimed at the sustainment and repair of Mitel/Unify OpenScape Systems. This requirement is open to all vendors (Full & Open) but is limited to Unify OpenScape Brand Name items and certified technicians. The goal of this contract is to ensure the operational integrity of the telecommunications systems at the base.Work Details The contractor shall provide all personnel, equipment, tools, materials, supervision, and any other items and services necessary to ensure that the Unify system is operational. Key tasks include: - Performing maintenance and repair on Mitel/Unify telecommunications hardware and software installed throughout Ellsworth AFB. - Diagnosing and resolving system issues for applications such as OpenScape Voice, OpenScape Xpert, iNemsoft radio interface, and ASC voice. - Ensuring that all technical services are performed by individuals who are certified, qualified, and experienced in Mitel/Unify systems. - The scope of work includes sustainment and repair of specific equipment listed in Section 6.0 Equipment List of the Performance Work Statement (PWS).Period of Performance The contract will have a base period of 12 months with four additional option years, each lasting 12 months.Place of Performance Ellsworth Air Force Base, South Dakota. Show Less List Text Overview Agency Air Combat Command (ACC) [DoD - USAF] Response Deadline Sept. 17, 2025, 5:00 p.m. EDT Due in 12 Days Posted Sept. 4, 2025, 12:14 p.m. EDT Set Aside None NAICS 541519 - Other Computer Related Services PSC DG10 - IT And Telecom - Network As A Service Place of Performance Ellsworth AFB, SD 57706 United States Source Open Current SBA Size Standard $34 Million Pricing Fixed Price Est. Level of Competition Average Est. Value Range Experimental $50,000 - $150,000 (AI estimate) On 9/4/25 Air Combat Command issued Solicitation FA469025Q0062 for Unify OpenScape Maintenance due 9/17/25. The opportunity was issued full & open with NAICS 541519 and PSC DG10. Primary Contact Name Marc L Bellucci   Profile Email marc.bellucci.1@us.af.mil Phone (605) 385-1782 Secondary Contact Name Joshua Johnson   Profile Email joshua.johnson.233@us.af.mil Phone (605) 385-1734 Download All Explore Documents Posted documents for Solicitation FA469025Q0062 5102050100 ShownDocumentAgencyPosted DateSourceDownloadDocumentAgencyPosted DateSourceDownload Attachment 1 - Ellsworth 28th BW Unify Sustainment PWS.pdf Air Combat Command 09/04/25Contract Opportunity Text Snapshot This performance work statement (PWS) outlines the requirements for the sustainment and repair of Mitel/Unify systems at Ellsworth Air Force Base, specifically for the 28th Bomb Wing (BW). The contractor is tasked with providing all necessary personnel, equipment, tools, materials, and supervision to ensure the operational status of the Unify system. Key services include maintenance and repair... Attachment 3 - single source justification Redacted.pdf Air Combat Command 09/04/25Contract Opportunity Text Snapshot This single source justification is for a simplified acquisition related to the unify maintenance contract at ellsworth air force base (afb). the contracting activity is managed by the 28th contracting squadron, and the justification outlines the necessity for continuous maintenance, warranty support, software licenses, and timely updates for the existing unify openscape voice communication... Solicitation - FA469025Q0062.pdf Air Combat Command 09/04/25Contract Opportunity Text Snapshot This solicitation (FA469025Q0062) is for commercial products and services, specifically focused on Unify maintenance services. The solicitation outlines the requirements for a 12-month maintenance contract with options for four additional 12-month periods. The pricing arrangement is firm fixed price, and the total quantity required is specified as one unit for each period. The solicitation is... Attachment 2 - WD 15-5367 (Rev 29) dated 08jul25 (1).pdf Air Combat Command 09/04/25Contract Opportunity Text Snapshot This wage determination is issued by the U.S. Department of Labor, specifically under the Service Contract Act, with wage determination no. 2015-5367 and revision no. 29, dated July 8, 2025. It outlines the minimum wage rates and fringe benefits that contractors must pay to workers performing on federal service contracts in South Dakota, particularly in the counties of Custer, Meade, and... Question & Answer The AI Q&A Assistant has moved to the bottom right of the page Export Visible Records Clipboard CSV Excel All Records CSV Excel Opportunity Lifecycle Procurement notices related to Solicitation FA469025Q0062 5102050100 ShownTitleTypeAgencySet AsidePostedDeadlineDescriptionTitleTypeAgencySet AsidePostedDeadlineDescription Unify OpenScape Maintenance  25%Solicitation Air Combat Command None09/04/2509/17/25Description The 28th Contracting Squadron, Ellsworth AFB, South Dakota, has issued this Solicitation, FA469025Q0062, to compete and award a Firm-Fixed Price Contract for sustainment and repair for the Mitel/Unify OpenScape Systems located at Ellsworth Air Force Base, South Dakota. This requriement is being solicited Full & Open (No Small Business Set-Aside), limited to Unify OpenScape Brand Name items and certified Technicians. Attached to this Solicitation Notice are: Standard Form 1449 Solicitation Attachment 1 - Performance Work Statement (PWS) Attachment 2 Wage Determination All questions ...show moreThe 28th Contracting Squadron, Ellsworth AFB, South Dakota, has issued this Solicitation, FA469025Q0062, to compete and award a Firm-Fixed Price Contract for sustainment and repair for the Mitel/Unify OpenScape Systems located at Ellsworth Air Force Base, South Dakota. This requriement is being solicited Full & Open (No Small Business Set-Aside), limited to Unify OpenScape Brand Name items and certified Technicians. Attached to this Solicitation Notice are: Standard Form 1449 Solicitation Attachment 1 - Performance Work Statement (PWS) Attachment 2 Wage Determination All questions and comments on this solicitation must be submitted in writing to joshua.johnson.233@us.af.mil no later than Wednesday, 10 September 2025 by 12:00pm MDT. Please title all emails with questions with the following subject line: FA469025Q0062 - Unify Mainteance. All interested and responsible entities are invited to submit a quote that will be considered by the 28th Contracting Office at Ellsworth AFB, South Dakota. The award will be based on the criteria established in the solicitation. Vendor quotes and all items required as listed within the Addendum to 52.212-1, Instruction to Offerors, are due to be submitted no later than Wednesday, 17 September 2025 by 3:00pm MDT to joshua.johnson.233@us.af.mil. Please title all submissions with the following subject line: FA469025Q0062 - Unify Maintenance Contractors submitting a quote must have and list within the quote their assigned Cage Code and be registered and ACTIVE in the System of Award Management (SAM) at www.sam.gov to be eligible for award. Unify OpenScape Maintenance  0%Sources Sought Air Combat Command None04/30/2505/09/25Description This Sources Sought / RFI is issued solely for market research purposes in accordance with Federal Acquisition Regulation (FAR) Part 10 and is not a solicitation for proposals. This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government will use information received in response to this notice to determine the appropriate acquisition strategy for the requirement. The 28th Maintenance Group (28 MXG) at Ellsworth AFB, South Dakota requires the contractor to provide all personnel, equipment, tools, materials, ...show moreThis Sources Sought / RFI is issued solely for market research purposes in accordance with Federal Acquisition Regulation (FAR) Part 10 and is not a solicitation for proposals. This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government will use information received in response to this notice to determine the appropriate acquisition strategy for the requirement. The 28th Maintenance Group (28 MXG) at Ellsworth AFB, South Dakota requires the contractor to provide all personnel, equipment, tools, materials, supervision and any other items and services necessary to accomplish maintenance required. The primary technical services shall be performed by individuals who are Mitel/Unify certified, qualified, and experienced in performing maintenance and repair of equipment, crisis management, dispatch consoles, and all associated Unify OpenScape telecommunications hardware and software installed throughout Ellsworth 28 BW. PWS is attached to this RFI. NOTE: IF YOU DO NOT INTEND TO SUBMIT A PROPOSAL FOR THIS PROJECT WHEN IT IS FORMALLY ADVERTISED, PLEASE DO NOT SUBMIT A RESPONSE TO THIS SOURCES SOUGHT / RFI. Information requested: All interested parties are invited to provide information about your company/institution, or any teaming or joint venture partners. Interested vendors are requested to submit the following information, clearly indicating whether you are providing information: Company name, address, point of contact with phone number and email address, CAGE code, business size status (e.g., small business, large business), and website (if applicable). Manufacturer and model number. Detailed product specifications and brochures. Maintenance requirements and service support Availability and lead time. The 28 MXG will review all vendors who respond to this sources sought to determine if other companies can perform the required repairs; and if aftermarket parts can meet the government’s needs. Submission Instructions: All responses must be submitted electronically to marc.bellucci.1@us.af.mil and joshua.johnson.233@us.af.mil no later than 4:00 PM MDT, Thursday, 09 May 2025. 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    From cognition to compensation: Neurocomputational mechanisms of guilt-driven and shame-driven altruistic behavior
    2
    1. Public_Reviews 05 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      This work provides important new evidence of the cognitive and neural mechanisms that give rise to feelings of shame and guilt, as well as their transformation into compensatory behavior. The authors use a well-designed interpersonal task to manipulate responsibility and harm, eliciting varying levels of shame and guilt in participants. The study combines behavioral, computational, and neuroimaging approaches to offer a comprehensive account of how these emotions are experienced and acted upon. Notably, the findings reveal distinct patterns in how harm and responsibility contribute to guilt and shame and how these factors are integrated into compensatory decision-making.

      Strengths:

      (1) Investigating both guilt and shame in a single experimental framework allows for a direct comparison of their behavioral and neural effects while minimizing confounds.

      (2) The study provides a novel contribution to the literature by exploring the neural bases underlying the conversion of shame into behavior.

      (3) The task is creative and ecologically valid, simulating a realistic social situation while retaining experimental control.

      (4) Computational modeling and fMRI analysis yield converging evidence for a quotient-based integration of harm and responsibility in guiding compensatory behavior.

      Weaknesses:

      (1) Post-experimental self-reports rely both on memory and on the understanding of the conceptual difference between the two emotions. Additionally, it is unclear whether the 16 scenarios were presented in random order; sequential presentation could have introduced contrast effects or demand characteristics.

      (2) In the neural analysis of emotion sensitivity, the authors identify brain regions correlated with responsibility-driven shame sensitivity and then use those brain regions as masks to test whether they were more involved in the responsibility-driven shame sensitivity than the other types of emotion sensitivity. I wonder if this is biasing the results. Would it be better to use a cross-validation approach? A similar issue might arise in "Activation analysis (neural basis of compensatory sensitivity)."

      Additional comments and questions:

      (1) Regarding the traits of guilt and shame, I appreciate using the scores from the subscales (evaluations and action tendencies) separately for the analyses (instead of a composite score). An issue with using the actions subscales when measuring guilt and shame proneness is that the behavioral tendencies for each emotion get conflated with their definitions, risking circularity. It is reassuring that the behavior evaluation subscale was significantly correlated with compensatory behavior (not only the action tendencies subscale). However, the absence of significant neural correlates for the behavior evaluation subscale raises questions: Do the authors have thoughts on why this might be the case, and any implications?

      (2) Regarding the computational model finding that participants seem to disregard self-interest, do the authors believe it may reflect the relatively small endowment at stake? Do the authors believe this behavior would persist if the stakes were higher? Additionally, might the type of harm inflicted (e.g., electric shock vs. less stigmatized/less ethically charged harm like placing a hand in ice-cold water) influence the weight of self-interest in decision-making?

      Taken together, the conclusions of the paper are well supported by the data. It would be valuable for future studies to validate these findings using alternative tasks or paradigms to ensure the robustness and generalizability of the observed behavioral and neural mechanisms.

      Review 1
    2. Public_Reviews 05 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      Zhu et al. set out to elucidate how the moral emotions of guilt and shame emerge from specific cognitive antecedents - harm and responsibility - and how these emotions subsequently drive compensatory behavior. Consistent with their prediction derived from functionalist theories of emotion, their behavioral findings indicate that guilt is more influenced by harm, whereas shame is more influenced by responsibility. In line with previous research, their results also demonstrate that guilt has a stronger facilitating effect on compensatory behavior than shame. Furthermore, computational modeling and neuroimaging results suggest that individuals integrate harm and responsibility information into a composite representation of the individual's share of the harm caused. Brain areas such as the striatum, insula, temporoparietal junction, lateral prefrontal cortex, and cingulate cortex were implicated in distinct stages of the processing of guilt and/or shame. In general, this work makes an important contribution to the field of moral emotions. Its impact could be further enhanced by clarifying methodological details, offering a more nuanced interpretation of the findings, and discussing their potential practical implications in greater depth.

      Strengths:

      First, this work conceptualizes guilt and shame as processes unfolding across distinct stages (cognitive appraisal, emotional experience, and behavioral response) and investigates the psychological and neural characteristics associated with their transitions from one stage to the next.

      Second, the well-designed experiment effectively manipulates harm and responsibility - two critical antecedents of guilt and shame.

      Third, the findings deepen our understanding of the mechanisms underlying guilt and shame beyond what has been established in previous research.

      Weaknesses:

      (1) Over the course of the task, participants may gradually become aware of their high error rate in the dot estimation task. This could lead them to discount their own judgments and become inclined to rely on the choices of other deciders. It is unclear whether participants in the experiment had the opportunity to observe or inquire about others' choices. This point is important, as the compensatory decision-making process may differ depending on whether choices are made independently or influenced by external input.

      (2) Given the inherent complexity of human decision-making, it is crucial to acknowledge that, although the authors compared eight candidate models, other plausible alternatives may exist. As such, caution is warranted when interpreting the computational modeling results.

      (3) I do not agree with the authors' claim that "computational modeling results indicated that individuals integrate harm and responsibility in the form of a quotient" (i.e., harm/responsibility). Rather, the findings appear to suggest that individuals may form a composite representation of the harm attributable to each individual (i.e., harm/the number of people involved). The explanation of the modeling results ought to be precise.

      (4) Many studies have reported positive associations between trait gratitude, social value orientation, and altruistic behavior. It would be helpful if the authors could provide an explanation about why this study failed to replicate these associations.

      (5) As the authors noted, guilt and shame are closely linked to various psychiatric disorders. It would be valuable to discuss whether this study has any implications for understanding or even informing the treatment of these disorders.

      Review 3
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    Eye-specific active zone clustering underlies synaptic competition in the developing visual system
    2
    1. Public_Reviews 05 Sep 2025

      Reviewer #1 (Public review):

      Summary

      The authors previously published a study of RGC boutons in the dLGN in developing wild-type mice and developing mutant mice with disrupted spontaneous activity. In the current manuscript, they have broken down their analysis of RGC boutons according to the number of Homer/Bassoon puncta associated with each vGlut3 cluster.

      The authors find that, in the first post-natal week, RGC boutons with multiple active zones (mAZs) are about a third as common as boutons with a single active zone (sAZ). The size of the vGluT2 cluster associated with each bouton was proportional to the number of active zones present in each bouton. Within the author's ability to estimate these values (n=3 per group, 95% of results expected to be within ~2.5 standard deviations), these results are consistent across groups: 1) dominant eye vs. non-dominant eye, 2) wild-type mice vs. mice with activity blocked, and at 3) ages P2, P4, and P8. The authors also found that mAZs and sAZs also have roughly the same number (about 1.5) of sAZs clustered around them (within 1.5 um).

      However, the authors do not interpret this consistency between groups as evidence that active zone clustering is not a specific marker or driver of activity dependent synaptic segregation. Rather, the authors perform a large number of tests for statistical significance and cite the presence or absence of statistical significance as evidence that "Eye-specific active zone clustering underlies synaptic competition in the developing visual system (title)". I don't believe this conclusion is supported by the evidence.

      Strengths

      The source dataset is high resolution data showing the colocalization of multiple synaptic proteins across development. Added to this data is labeling that distinguishes axons from the right eye from axons from the left eye. The first order analysis of this data showing changes in synapse density and in the occurrence of multi-active zone synapses is useful information about the development of an important model for activity dependent synaptic remodeling.

      Weaknesses

      In my previous review I argued that it was not possible to determine, from their analysis, whether the differences they were reporting between groups was important to the biology of the system. The authors have made some changes to their statistics (paired t-tests) and use some less derived measures of clustering. However, they still fail to present a meaningfully quantitative argument that the observed group differences are important. The authors base most of their claims on small differences between groups. There are two big problems with this practice. First, the differences between groups appear too small to be biologically important. Second, the differences between groups that are used as evidence for how the biology works are generally smaller than the precision of the author's sampling. That is, the differences are as likely to be false positives as true positives.

      (1) Effect size. The title claims: "Eye-specific active zone clustering underlies synaptic competition in the developing visual system". Such a claim might be supported if the authors found that mAZs are only found in dominant-eye RGCs and that eye-specific segregation doesn't begin until some threshold of mAZ frequency is reached. Instead, the behavior of mAZs is roughly the same across all conditions. For example, the clear trend in Figure 4C and D is that measures of clustering between mAZ and sAZ are as similar as could reasonably be expected by the experimental design. However, some of the comparisons of very similar values produced p-values < 0.05. The authors use this fact to argue that the negligible differences between mAZ and sAZs explain the development of the dramatic differences in the distribution of ipsilateral and contralateral RGCs.

      (2) Sample size. Performing a large number of significance tests and comparing p-values is not hypothesis testing and is not descriptive science. At best, with large sample sizes and controls for multiple tests, this approach could be considered exploratory. With n=3 for each group, many comparisons of many derived measures, among many groups, and no control for multiple testing, this approach constitutes a random result generator.

      The authors argue that n=3 is a large sample size for the type of high resolution / large volume data being used. It is true that many electron microscopy studies with n=1 are used to reveal the patterns of organization that are possible within an individual. However, such studies cannot control individual variation and are, therefore, not appropriate for identifying subtle differences between groups.<br /> In response to previous critiques along these lines, the authors argue they have dealt with this issue by limiting their analysis to within-individual paired comparisons. There are several problems with their thinking in this approach. The main problem is that they did not change the logic of their arguments, only which direction they pointed the t-tests. Instead of claiming that two groups are different because p < 0.05, they say that two groups are different because one produced p < 0.05 and the other produced p > 0.05. These arguments are not statistically valid or biologically meaningful.

      To the best of my understanding, the results are consistent with the following model:

      • RGCs form mAZs at large boutons (known)

      • About a quarter of week-one RGC boutons are mAZs (new observation)

      • Vesicle clustering is proportional to active zone number (~new observation)

      • RGC synapse density increases during the first post-week (known)

      • Blocking activity reduces synapse density (known)

      • Contralateral eye RGCs for more and larger synapses in the lateral dLGN (known)

      • With n=3 and effect sizes smaller than 1 standard deviation, a statistically significant result is about as likely to be a false positive as a true positive.

      • A true-positive statistically significant result does is not evidence of a meaningful deviation from a biological model.

      Providing plots that show the number of active zones present in boutons across these various conditions is useful. However, I could find no compelling deviation from the above default predictions that would influence how I see the role of mAZs in activity dependent eye-specific segregation.

      Below are critiques of most of the claims of the manuscript.

      Claim (abstract): individual retinogeniculate boutons begin forming multiple nearby presynaptic active zones during the first postnatal week.

      Confirmed by data.

      Claim (abstract): the dominant-eye forms more numerous mAZ contacts,

      Misleading: The dominant-eye (by definition) forms more contacts than the non-dominant eye. That includes mAZ.

      Claim (abstract): At the height of competition, the non-dominant-eye projection adds many single active zone (sAZ) synapses

      Weak: While the individual observation is strong, it is a surprising deviation based on a single n=3 experiment in a study that performed twelve such experiments (six ages, mutant/wildtype, sAZ/mAZ)

      Claim (abstract): Together, these findings reveal eye-specific differences in release site addition during synaptic competition in circuits essential for visual perception and behavior.

      False: This claim is unambiguously false. The above findings, even if true, do not argue for any functional significance to active zone clustering.

      Claim (line 84): "At the peak of synaptic competition midway through the first postnatal week, the non-dominant-eye formed numerous sAZ inputs, equalizing the global synapse density between the two eyes"

      Weak: At one of twelve measures (age, bouton type, genotype) performed with 3 mice each, one density measure was about twice as high as expected.

      Claim (line 172): "In WT mice, both mAZ (Fig. 3A, left) and sAZ (Fig. 3B, left) inputs showed significant eye-specific volume differences at each age."

      Questionable: There appears to be a trend, but the size and consistency is unclear.

      Claim (line 175): "the median VGluT2 cluster volume in dominant-eye mAZ inputs was 3.72 fold larger than that of non-dominant-eye inputs (Fig. 3A, left)."

      Cherry picking. Twelve differences were measured with an n of 3, 3 each time. The biggest difference of the group was cited. No analysis is provided for the range of uncertainty about this measure (2.5 standard deviations) as an individual sample or as one of twelve comparisons.

      Claim (line 174): "In the middle of eye-specific competition at P4 in WT mice, the median VGluT2 cluster volume in dominant-eye mAZ inputs was 3.72 fold larger than that of non-dominant-eye inputs (Fig. 3A, left). In contrast, β2KO mice showed a smaller 1.1 fold difference at the same age (Fig. 3A, right panel). For sAZ synapses at P4, the magnitudes of eye-specific differences in VGluT2 volume were smaller: 1.35-fold in WT (Fig. 3B, left) and 0.41-fold in β2KO mice (Fig. 3B, right). Thus, both mAZ and sAZ input size favors the dominant eye, with larger eye-specific differences seen in WT mice (see Table S3)."

      No way to judge the reliability of the analysis and trivial conclusion: To analyze effect size the authors choose the median value of three measures (whatever the middle value is). They then make four comparisons at the time point where they observed the biggest difference in favor of their hypothesis. There is no way to determine how much we should trust these numbers besides spending time with the mislabeled scatter plots. The authors then claim that this analysis provides evidence that there is a difference in vGluT2 cluster volume between dominant and non-dominant RGCs and that that difference is activity dependent. The conclusion that dominant axons have bigger boutons and that mutants that lack the property that would drive segregation would show less of a difference is very consistent with the literature. Moreover, there is no context provided about what 1.35 or 1.1 fold difference means for the biology of the system.

      Claim (189): "This shows that vesicle docking at release sites favors the dominant-eye as we previously reported but is similar for like eye type inputs regardless of AZ number."

      Contradicts core claim of manuscript: Consistent with previous literature, there is an activity dependent relative increase in vGlut2 clustering of dominant eye RGCs. The new information is that that activity dependence is more or less the same in sAZ and mAZ. The only plausible alternative is that vGlut2 scaling only increases in mAZ which would be consistent with the claims of their paper. That is not what they found. To the extent that the analysis presented in this manuscript tests a hypothesis, this is it. The claim of the title has been refuted by figure 3.

      Claim (line 235): "For the non-dominant eye projection, however, clustered mAZ inputs outnumbered clustered sAZ inputs at P4 (Fig. 4C, bottom left panel), the age when this eye adds sAZ synapses (Fig. 2C)."

      Misleading: The overwhelming trend across 24 comparisons is that the sAZ clustering looks like mAZ clustering. That is the objective and unambiguous result. Among these 24 underpowered tests (n=3), there were a few p-values < 0.05. The authors base their interpretation of cell behavior on these crossings.

      Claim (line 328): "The failure to add synapses reduced synaptic clustering and more inputs formed in isolation in the mutants compared to controls."

      Trivially true: Density was lower in mutant.

      Claim (line 332): "While our findings support a role for spontaneous retinal activity in presynaptic release site addition and clustering..."

      Not meaningfully supported by evidence: I could not find meaningful differences between WT and mutant beside the already known dramatic difference in synapse density.

      Review 1
    2. Public_Reviews 05 Sep 2025

      Reviewer #3 (Public review):

      This study is a follow-up to a recent study of synaptic development based on a powerful data set that combines anterograde labeling, immunofluorescence labeling of synaptic proteins, and STORM imaging (Cell Reports, 2023). Specifically, they use anti-Vglut2 label to determine the size of the presynaptic structure (which they describe as the vesicle pool size), anti-Bassoon to label active zones with the resolution to count them, and anti-Homer to identify postsynaptic densities. Their previous study compared the detailed synaptic structure across the development of synapses made with contra-projecting vs. ipsi-projecting RGCs and compared this developmental profile with a mouse model with reduced retinal waves. In this study, they produce a new detailed analysis on the same data set in which they classify synapses into "multi-active zone" vs. "single-active zone" synapses and assess the number and spacing of these synapses. The authors use measurements to make conclusions about the role of retinal waves in the generation of same-eye synaptic clusters. The authors interpret these results as providing insight into how neural activity drives synapse maturation, the strength of their conclusions is not directly tested by their analysis.

      Strengths:

      This is a fantastic data set for describing the structural details of synapse development in a part of the brain undergoing activity-dependent synaptic rearrangements. The fact that they can differentiate the eye of origin is what makes this data set unique over previous structural work. The addition of example images from the EM dataset provides confidence in their categorization scheme.

      Weaknesses:

      Though the descriptions of single vs multi-active zone synapses are important and represent a significant advance, the authors continue to make unsupported conclusions regarding the biological processes driving these changes. Although this revision includes additional information about the populations tested and the tests conducted, the authors do not address the issue raised by previous reviews. Specifically, they provide no assessment of what effect size represents a biologically meaningful result. For example, a more appropriate title is "The distribution of eye-specific single vs multi-active zone is altered in mice with reduced spontaneous activity" rather than concluding that this difference in clustering is somehow related to synaptic competition. Of course, the authors are free to speculate, but many of the conclusions of the paper are not supported by their results.

      Review 3
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    Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling
    2
    1. EMBOpress 05 Sep 2025

      Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons

      Reply to the reviewers

      Manuscript number: RC-2025-02879 Corresponding author(s): Matteo Allegretti; Alia dos Santos

      1. General Statements

      In this study, we investigated the effects of paclitaxel on both healthy and cancerous cells, focusing on alterations in nuclear architecture. Our novel findings show that:

      • Paclitaxel-induced microtubule reorganisation during interphase alters the perinuclear distribution of actin and vimentin. The formation of extensive microtubule bundles, in paclitaxel or following GFP-Tau overexpression, coincides with nuclear shape deformation, loss of regulation of nuclear envelope spacing, and alteration of the nuclear lamina.

      • Paclitaxel treatment reduces Lamin A/C protein levels via a SUN2-dependent mechanism. SUN2, which links the lamina to the cytoskeleton, undergoes ubiquitination and consequent degradation following paclitaxel exposure.

      • Lamin A/C expression, frequently dysregulated in cancer cells, is a key determinant of cellular sensitivity to, and recovery from, paclitaxel treatment.

      Collectively, our data support a model in which paclitaxel disrupts nuclear architecture through two mechanisms: (i) aberrant nuclear-cytoskeletal coupling during interphase, and (ii) multimicronucleation following defective mitotic exit. This represents an additional mode of action for paclitaxel beyond its well-established mechanism of mitotic arrest.

      We thank the reviewers for their time and constructive feedback. We have carefully considered all comments and have carried out a full revision. The updated manuscript now includes additional data showing:

      • Overexpression of microtubule-associated protein Tau causes similar nuclear aberration phenotypes to paclitaxel. This supports our hypothesis that increased microtubule bundling directly leads to nuclear disruption in paclitaxel during interphase.

      • Paclitaxel's effects on nuclear shape and Lamin A/C and SUN2 expression levels occur independently of cell division.

      • Reduced levels of Lamin A/C and SUN2 upon paclitaxel treatment occur at the protein level via ubiquitination of SUN2.

      • The effects of paclitaxel on the nucleus are conserved in breast cancer cells.

      Full Revision

      We have also edited our text and added further detail to clarify points raised by the reviewers. We believe that our revised manuscript is overall more complete, solid and compelling thanks to the reviewers' comments.

      1. Point-by-point description of the revisions

      Reviewer #1 Evidence, reproducibility and clarity

      This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown.

      We thank the reviewer for this valuable comment. To elucidate the mechanism behind the decrease in Lamin A/C and SUN2 levels, we have now performed several additional experiments. First, we performed RT-qPCR to quantify mRNA levels of these genes, relative to the housekeeping gene GAPDH (Supplementary Figure 3B and O). The levels of SUN2 and LMNA mRNA remained the same between control and paclitaxel-treated cells, indicating that this effect instead occurs at the protein level. We have also tested post-translational modifications as a potential regulatory mechanism for Lamin A/C and SUN2. In addition to the phosphorylation of Ser404 which we had already tested (Supplementary Figure 3C), we have now included additional Phos-tag gel and Western blotting data showing that the overall phosphorylation status of Lamin A/C is not affected by paclitaxel (Supplementary Figure 3E and F). We also pulled-down Lamin A/C from cell lysates and then Western blotted for polyubiquitin and acetyl-lysine, which showed that the ubiquitination and acetylation states of Lamin A/C are also not affected by paclitaxel (Supplementary Figure 3G-I). However, Western blots for polyubiquitin of SUN2 pulled down from cell lysates showed that paclitaxel treatment results in significant SUN2 ubiquitination (Figure 3M and N). Therefore, we propose that the downregulation of SUN2 following paclitaxel treatment occurs by ubiquitin-mediated proteolysis.

      The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

      We addressed this important point by using an alternative method to stabilise/bundle microtubules in interphase, namely by overexpressing GFP-Tau, as suggested by reviewer 2. Following GFP- Tau overexpression, large microtubule bundles were observed throughout the cytoplasm (Figure 4A), and this resulted in a significant decrease in nuclear solidity (Figure 4B). Furthermore, in cells where microtubule bundles extensively contacted the nucleus, the nuclear lamina became unevenly distributed and appeared patchy (Figure 4C). This supports our hypothesis that the aberrations to nuclear shape and Lamin A/C localisation in paclitaxel-treated cells are due to the presence of microtubules bundles surrounding the nucleus.

      The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

      We thank the reviewer for raising this important point. We have addressed this by conducting additional repeats for the cell confluency measurements to increase the statistical power of our experiments (Figure 5A). Our data now show that GFP-lamin A/C had a statistically significant effect on rescuing cell growth at both 1 nM and 5 nM paclitaxel, while Lamin A/C knockdown exacerbated the inhibition of cell growth at 5 nM paclitaxel but not 1 nM paclitaxel (Figure 5A). In addition, we note that the duration of paclitaxel treatment before assessing viability was specified in the figure legend: "Bar graph comparing cell viability between wild-type (red), GFP-Lamin A/C overexpression (green), and Lamin A/C knockdown (blue) cells following 20 h incubation in 0, 1, 5, or 10 nM paclitaxel." We also repeated cell viability analysis after 48 h incubation in paclitaxel instead of 20 h to allow for a longer time for differences to take effect (Figure 5B).

      We also added figures showing the cytoskeletal reorganisation at both 1 and 10 nM in addition to 0 and 5 nM (Supplementary Figure 1A) showing that microtubule bundling and condensation of actin into puncta correlated with increased paclitaxel concentration. Vimentin colocalised well with microtubules at all concentrations.

      We have also included in our results section further clarification for the use of 5nM paclitaxel in this study. The new section reads as follows: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

      Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

      We thank the reviewer for pointing out the need for clarification in the wording of our manuscript. We have reworded the title and relevant sections of our abstract, introduction, and discussion to make it clearer that the effects of paclitaxel on the nucleus are due to a combination of aberrant nuclear cytoskeletal coupling during interphase and multimicronucleation following mitotic slippage. We have also added additional data in support of the effect of paclitaxel on nuclear architecture during interphase. For this, we used serum-starved cells (which divide only very slowly such that the majority of cells do not pass through mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data confirmed that paclitaxel's effects on nuclear solidity, and Lamin A/C and SUN2 proteins levels can occur independently of cell division (Figure 2C; Figure 3H-J). Finally, when we overexpressed GFP-Tau (as discussed above) we observed similar aberrations to nuclear solidity and Lamin A/C localisation. This indicates that these effects occur due to microtubule bundling in interphase, especially as in our study GFP-Tau did not lead to multimicronucleation or appear to affect mitosis (Figure 4).

      Below are the main changes to the text regarding the interphase effect of paclitaxel:

      • Title: "Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling"

      • Abstract: "Overall, our data supports nuclear architecture disruption, caused by both aberrant nuclear-cytoskeletal coupling during interphase and exit from defective mitosis, as an additional mechanism for paclitaxel beyond mitotic arrest."

      • Introduction: "Here we propose that cancer cells have increased vulnerability to paclitaxel both during interphase and following aberrant mitosis due to pre-existing defects in their NE and nuclear lamina."

      • Discussion: "Overall, our work builds on previous studies investigating loss of nuclear integrity as an anti-cancer mechanism of paclitaxel separate from mitotic arrest14,20,21. We propose that cancer cells show increased sensitivity to nuclear deformation induced by aberrant nuclear-cytoskeletal coupling and multimicronucleation following mitotic slippage. Therefore, we conclude that paclitaxel functions in interphase as well as mitosis, elucidating how slowly growing tumours are targeted."

      minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

      As mentioned above, we have now included additional information in our Results section to clarify our paclitaxel dose range: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

      Significance

      In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

      We appreciate the reviewer's summary and thank them for their time. We believe our comprehensive revisions have addressed all comments, strengthening the manuscript and making it more robust and compelling.

      Reviewer #2 Evidence, reproducibility and clarity This study investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state- of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

      We thank the reviewer for the summary and for highlighting our efforts in using the latest imaging technical advances.

      Major comments:

      The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

      However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

      Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

      We thank the reviewer for highlighting the lack of clarity in our wording. We have revised the title, abstract and relevant sections of the introduction and discussion to clarify our message that the effects of paclitaxel on the nucleus arise from a combination of aberrant nuclear-cytoskeletal coupling during interphase and multimicronucleation following exit from defective mitosis. We have also included additional data where we used slow-dividing, serum-starved cells (under these conditions, the majority of cells do not undergo mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data show that even in these cells there is a clear effect of paclitaxel on nuclear solidity, and Lamin A/C and SUN2 protein levels, further supporting our hypothesis that these phenotypes can occur independently of cell division (Figure 2C; Figure 3H-J). Furthermore, we performed additional experiments where we used overexpression of GFP-Tau as an alternative method of stabilising microtubules in interphase and observed similar aberrations to nuclear solidity and Lamin A/C localisation. As GFP-Tau overexpression did not lead to micronucleation or appear to affect mitosis, these data support the hypothesis that nuclear aberrations occur due to microtubule bundling in interphase (Figure 4). We discuss these experiments in more detail below. Finally, we have reworded the introduction to better introduce alternative hypotheses and mechanisms for paclitaxel's activity.

      The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

      We would like to note that the paper from Xiang-Xi Xu's lab (Smith et al, 2021) was cited in our original manuscript (reference 14 in both the original and revised manuscripts). We have now also included additional review articles from the Xiang-Xi Xu lab (PMID:36368286 20 and PMID: 35048083 21). Furthermore, we have clarified the wording in both the introduction and discussion to better reflect the current understanding of paclitaxel's mechanism and alternative hypotheses.

      The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

      We thank the reviewer for suggesting this control experiment using the microtubule stabiliser Tau. We have now included these experiments in the revised version of the manuscript (Figure 4). The overexpression of GFP-Tau supports our hypothesis that cytoskeletal reorganisation in paclitaxel exerts mechanical stress on the nucleus during interphase, resulting in nuclear deformation and aberrations to the nuclear lamina. In particular, GFP-Tau overexpression resulted in large microtubule bundles throughout the cytoplasm (Figure 4A). Notably, in cells where these bundles extensively contacted the nucleus, we observed a significant decrease in nuclear solidity (Figure 4B) accompanied by changes in nuclear lamina organisation, including a patchy lamina phenotype, similar to that induced by paclitaxel (Figure 4C).

      The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

      We thank the reviewer for raising important points regarding the specificity of paclitaxel's effects. We agree that microtubule stabilisation can induce myriad cellular changes, including alterations to focal adhesions and other cytoskeletal components. Our focus on Lamin A/C and nuclear morphology is grounded both in the established clinical relevance of nuclear mechanics in cancer and builds on mechanistic work from other groups.

      Lamin A/C expression is commonly altered in cancer, and nuclear morphology is frequently used in cancer diagnosis35. Lamin A/C also plays a crucial role in regulating nuclear mechanics32 and, importantly, determines cell sensitivity to paclitaxel14. However, the mechanism by which Lamin A/C determines sensitivity of cancer cells to paclitaxel is unclear.

      Our data are consistent with Lamin A/C being a determinant of paclitaxel survival sensitivity. We also provide evidence that paclitaxel itself reduces Lamin A/C protein levels and disrupts its organisation at the nuclear envelope. We directly link these effects to microtubule bundling around the nucleus and degradation of force-sensing LINC component SUN2, highlighting the importance of nuclear architecture and mechanics to overall cellular function. Furthermore, we show that recovery from paclitaxel treatment depends on Lamin A/C expression levels. This has clinical relevance, as unlike cancer cells, healthy tissue with non-aberrant lamina would be able to selectively recover from paclitaxel treatment.

      Minor comments:

      While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

      We agree with the reviewer that Supplementary Figure 6 does not add significant value to the manuscript and thank the reviewer for pointing this out. We have removed it from the manuscript accordingly.

      I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

      We thank the reviewer for pointing out the missing information. For our cryo-ET experiments, we collected data from 9 lamellae from paclitaxel-treated cells and 6 lamellae from control cells, with each lamella derived from a single cell. This information has now been added to the figure legend (Figure 2F).

      Significance

      The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

      As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

      We appreciate the reviewer's concerns and have performed major revisions to strengthen the significance of our study. Specifically, we conducted two key sets of experiments to validate our original conclusions: serum starvation to control for the effects of cell division, and overexpression of the microtubule stabiliser Tau to demonstrate that paclitaxel can affect the nucleus via its microtubule bundling activity in interphase.

      By elucidating the mechanistic link between microtubule stabilisation and nuclear-cytoskeletal coupling, our findings contribute to our understanding of paclitaxel's multifaceted actions in cancer cells.

      My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

      Reviewer #3 Evidence, reproducibility and clarity The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

      We thank the reviewer for the positive feedback.

      Although similar ideas are published, which may be suitable to be cited? • Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review. • Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

      We thank the reviewer for bringing to our attention these important review articles. In our initial manuscript, we only cited the original paper (14, also reference 14 in the original manuscript). We have now included citations to the suggested publications (20,21).

      We would also like to emphasise how our manuscript distinguishes itself from the work of Smith et al.14,20,21:

      • Cell-type focus: In their study 14, Smith et al. examined the effect of paclitaxel on malignant ovarian cancer cells and proposed that paclitaxel's effects on the nucleus are limited to cancer cells. However, our data extends these findings by demonstrating paclitaxel's effects in both cancerous and non-cancerous backgrounds.

      • Cytoskeletal reorganisation: Smith et al. show reorganisation of microtubules in paclitaxel-treated cells14. Our data show re-organisation of other cytoskeletal components, including F-actin and vimentin.

      • Multimicronucleation: Smith et al. propose that paclitaxel-induced multimicronucleation occurs independently of cell division14. Although we observe progressive nuclear abnormalities during interphase over the course of paclitaxel treatment, our data do not support this conclusion; we find that multimicronucleation occurs only following mitosis.

      • Direct link between microtubule bundling and nuclear aberrations: We show that nuclear aberrations caused by paclitaxel during interphase (distinct from multimicronucleation) are directly linked to microtubule bundling around the nucleus, suggesting they result from mechanical disruption and altered force propagation.

      • Lamin A/C regulation: Consistent with Smith et al.14, we show that Lamin A/C depletion leads to increased sensitivity to paclitaxel treatment. However, we further demonstrate that paclitaxel itself leads to reduced levels of Lamin A/C and that this effect occurs independently of mitosis and is mediated via force-sensing LINC component SUN2. Upon SUN2 knockdown, Lamin A/C levels are no longer affected by paclitaxel treatment.

      • Recovery: Finally, our work reveals that cells expressing low levels of Lamin A/C recover less efficiently after paclitaxel removal. This might help explain how cancer cells could be more susceptible to paclitaxel.

      Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

      We thank the reviewer for this comment. Our initial study aimed to understand the effects of paclitaxel on nuclear architecture in non-aberrant backgrounds. To show that the observed effects of paclitaxel are also applicable to cancer cells, we have now repeated our main experiments using MDA-MB-231 human breast cancer cells (Supplementary Figure 1B; Supplementary Figure 3P-T). Similar to our findings in human fibroblasts, paclitaxel treatment of MDA-MB-231 led to cytoskeletal reorganisation (Supplementary Figure 1B), a decrease in nuclear solidity (Supplementary Figure 3P), aberrant (patchy) localisation of Lamin A/C (Supplementary Figure 3Q), and a reduction in Lamin A/C and SUN2 levels (Supplementary Figure 3R-T).

      "Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

      We have added further detail to the figure legend for clarification: "Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Different colours distinguish individual α-tubulin clusters, representing individual microtubule filaments or filament bundles."

      Generally, the figures need additional description to be clear.

      We have added further clarification and detail to our figure legends.

      "Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

      We changed this sentence to: "Figure 3 - Paclitaxel treatment results in aberrant organisation of the nuclear lamina and decreased Lamin A/C levels via SUN2."

      Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?.

      We thank the reviewer for pointing this out and we agree that the ratio of Lamin A to Lamin C can vary with culture conditions. To confirm that paclitaxel treatment reduces total Lamin A/C levels regardless of this ratio, we repeated the Western blot analysis in three additional biological replicates using cells in which Lamin C levels exceeded Lamin A levels. These experiments confirmed a comparable decrease in total Lamin A/C levels. Figure 3B and 3C have been updated accordingly.

      Also, the effect on Lamin A/C and SUN2 levels are not significant of robust.

      Decreased Lamin A/C and SUN2 levels following paclitaxel treatment were consistently seen across three or more biological repeats (Figure 3B-C), and this could be replicated in a different cell type (MDA-MB-231) (Supplementary Figure 3R-T). Furthermore, Western blotting results are consistent with the patchy Lamin A/C distribution observed using confocal and STORM following paclitaxel treatment (Figure 3A; Supplementary Figure 3A), where Lamin A/C appears to be absent from discrete areas of the lamina.

      Any mechanisms are speculated for the reason for the reduction?

      We have now included additional data which aims to shed light on the mechanism behind the decrease in Lamin A/C and SUN2 levels following paclitaxel treatment. We found that SUN2 is selectively degraded during paclitaxel treatment. Immunoprecipitation of SUN2 followed by Western blotting against Polyubiquitin C showed increased SUN2 ubiquitination in paclitaxel (Figure 3M and N). Furthermore, in our original manuscript, we showed that Lamina A/C levels remained unaltered during paclitaxel treatment in cells where SUN2 had been knocked down. We propose that changes in microtubule organisation affect force propagation to Lamin A/C specifically via SUN2 and that this leads to Lamina A/C removal and depletion. Future work will be needed to fully understand this mechanism.

      In addition to the findings described above, we report no significant changes in mRNA levels for LMNA or SUN2 in paclitaxel (Supplementary Figure 3B and O). Phos-tag gels followed by Western blotting analysis for Lamin A/C also did not detect changes to the overall phosphorylation status of Lamin A/C due to paclitaxel treatment. This is in agreement with our initial data showing no changes to Lamin A/C Ser 404 phosphorylation levels (Supplementary Figure 3E and F). Finally, Lamin A/C immunoprecipitation experiments followed by Western blotting for Polyubiquitin C and acetyl-lysine showed no significant changes in the ubiquitination and acetylation state of Lamin A/C in paclitaxel-treated cells (Supplementary Figure 3G-I).

      Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

      The nuclear lamina and LINC complex proteins play a critical role in regulating nuclear integrity, stiffness and mechanical responsiveness to external forces28,31-33,54,75, as well as in maintaining the nuclear intermembrane distance69,74. In particular, SUN-domain proteins physically bridge the nuclear lamina to the cytoskeleton through interactions with Nesprins, thereby preserving the perinuclear space distance30,69,74. Mutations in Lamins have been shown to disrupt chromatin organization, alter gene expression, and compromise nuclear structural integrity, and experiments with LMNA knockout cells reveal that nuclear mechanical fragility is closely coupled to nuclear deformation47. Furthermore, nuclear-cytoskeletal coupling is essential during processes such as cell migration, where cells undergo stretching and compression of the nucleus; weakening or loss of the lamina in such cases compromises cell movement47,73. In our work, we show that alterations to nuclear Lamin A/C and SUN2 by paclitaxel treatment coincide with nuclear deformations (Figure 2A-D, F, G; Figure 3A-D, F, G; Supplementary Figure 3A, P-T) and that these deformations are reversible following paclitaxel removal (Supplementary Figure 4B-D). Our experiments also demonstrate that Lamin A/C expression levels significantly influence cell growth, cell viability, and cell recovery in paclitaxel (Figure 5). Therefore, drawing on current literature and our results, we propose that, during interphase, paclitaxel induces severe nuclear aberrations through the combined effects of: i) increased cytoskeletal forces on the NE caused by microtubule bundling; ii) loss of ~50% Lamin A/C and SUN2; iii) reorganisation of nucleo-cytoskeletal components.

      Significance

      The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

      The data may be improved to provide stronger support.

      Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

      We thank the reviewer for the feedback and valuable suggestions. In response, we have included experiments using human breast cancer cell line MDA-MB-231 to further corroborate our findings and interpretations. We believe these additions have improved the clarity, robustness and impact of our manuscript, and we are grateful for the reviewer's contributions to its improvement.

      PeerReviewed
    2. EMBOpress 05 Sep 2025

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

      Learn more at Review Commons

      Referee #3

      Evidence, reproducibility and clarity

      The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years. Although similar ideas are published, which may be suitable to be cited?

      • Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review.
      • Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

      Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

      "Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

      Generally, the figures need additional description to be clear.

      "Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

      Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?. Also, the effect on Lamin A/C and SUN2 levels are not significant of robust. Any mechanisms are speculated for the reason for the reduction? Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

      Significance

      The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

      The data may be improved to provide stronger support.

      Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

      PeerReviewed
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    Criticality-driven enhancer-promoter dynamics in Drosophila chromosomes
    2
    1. Public_Reviews 05 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      This computational study investigates the physical mechanisms underlying enhancer-promoter (E-P) interactions across genomic distances in Drosophila chromosomes, motivated by a previously published study that revealed unexpectedly frequent long-range contacts challenging classical polymer models. The authors performed coarse-grained polymer simulations testing three chromatin organization models: ideal polymers, loop extrusion, and compartmental segregation, comparing their predictions to experimental Hi-C contact maps, mean E-P distances, and two-locus mean-squared displacement dynamics. They found that compartmental segregation best captured both the structural and dynamic features observed experimentally, while neither ideal chains nor loop extrusion alone could reproduce all experimental observables. The combination of compartmental segregation with loop extrusion further improved agreement with experimental data, suggesting these mechanisms might be involved in Drosophila chromatin organization.

      Strengths:

      The paper has two primary strengths:

      (1) The simulations are based on biologically interpretable mechanisms (compartmentalization and loop extrusion), which may facilitate making specific experimentally testable predictions.

      (2) The work uses a systematic approach to increase model complexity by directly fitting to data, first establishing that simple models fail to capture the data until arriving at a more complex model that does capture the data.

      Weaknesses:

      I have two major concerns (detailed below) and multiple minor concerns.

      Major concerns:

      (1) While the upside of the mechanistic simulations is that they are interpretable, the downside is that specific choices for the considered mechanism were made, and conclusions drawn from it are necessarily biased by the initial choices. In this paper, only two mechanisms were considered: loop extrusion and compartmentalization. Yet, it is not clear why these are the most likely underlying mechanisms that might determine the chromosome dynamics. Indeed, previous work (not cited in this paper) showed that Drosophila chromosome structure is not determined by loop extrusion: https://elifesciences.org/articles/94070.

      This should be acknowledged, and the main reasons for choosing these particular mechanisms should be laid out. The conclusions of the paper must then necessarily always be seen under the caveat that only these two mechanisms were considered.

      (2) Even within the framework of the approach, insufficient evidence is given to support the title of the paper "Criticality-driven enhancer-promoter dynamics in Drosophila chromosomes" for two reasons:

      (a) The fact that the best-fit parameters are near a coil-globule transition does not mean that the resulting dynamics are criticality-driven. To claim criticality, one would usually expect much more direct evidence, such as diverging correlation lengths. Furthermore, it would need to be shown that the key features of the dynamics (which should be defined, presumably the static and dynamic exponents) indeed depend on the parameters being at this transition. i.e., when tuning the simulations away from this parameter point, does the behaviour disappear? Only in this case can it be claimed that the behaviour is driven by this phenomenon.

      (b) The results section actually contains no mention of the coil-globule transition, and it is not clear in what way the parameters are close to this transition.

      Thus, three things are necessary:

      (i) How the parameters are close to the transition needs to be explained in detail.

      (ii) The divergence of observed dynamics whenever the parameters are tuned away from the transition needs to be demonstrated.

      (iii) Even if 1 and 2 are fulfilled, a more careful title should be chosen, such as "Polymer simulations near the coil-globule transition are consistent with enhancer-promoter dynamics in Drosophila chromosomes."

      Many of the results in the figures and results section are rather repetitive and could be compressed. The main result of Figure 1 - that the data are not described by an ideal chain - was already fully shown and established in the original paper from which the data are taken. Figure 2 is a negative result with near-identical panels to Figure 3. Figure 4B is hard to interpret.

      The paper makes no concrete suggestions for new experiments to test the hypotheses formulated. Since the paper can only claim that the simulations are consistent with the data, it would significantly strengthen the paper if testable predictions could be made.

      Review 1
    2. Public_Reviews 05 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      In this work, Ganesh and colleagues use experimental data from Hi-C and from live-cell imaging to evaluate different polymer models of 3D genome organization in Drosophila based on both structural and dynamic properties. The authors consider several leading hypotheses, which are examined sequentially in increasing level of complexity - from the minimal Rouse polymer, to a model combining sequence-specific compartmentalization and loop-extrusion without extrusion blockers. They conclude that the combination of both compartmentalization and loop-extrusion gives the best agreement with the data. Their analysis also leads to concrete predictions about the processivity of cohesin loop extrusion in Drosophila, and a conclusion that the compartmental interaction strength is poised near criticality in the coil-globule phase space.

      Strengths:

      There is considerable interest in the field in understanding the mechanisms responsible for the 3D spatial organization genome and the dynamic movement of the genome, which has major implications for our understanding of long-range transcriptional regulation and other genome behaviors. The live-cell experimental work on which this study draws highlights the limitations of existing models to explain even the dynamic behaviors observed in the data, further exciting interest in further exploration. Therefore, this paper seeks to address an important gap in the field. The work is written in a well-organized, well-illustrated fashion. The text and figures are nicely integrated, easy to read, and explain challenging concepts with elegance and brevity in a manner that will be accessible to a broad audience.

      Weaknesses:

      The validity and utility of these conclusions are, in my view, substantially undermined by what appears to be unappreciated peculiarities of the live-cell data set that was used to constrain the model. The live-cell data comes from embryos were edited in a way that intentionally substantively changed both the 3D genome structure and dynamics specifically at the loci which are imaged, a case which is not at all explained by any of the models suggested nor acknowledged in the current work, nor compatible with the Hi-C data that simultaneously used to explain these models. As these ignored synthetic alterations have been previously shown to be determinative of transcriptional activity, the relevance of the author's work to transcriptional control (a prime motivation in the introduction) is unclear.

      The agreement in 3D organization, as represented in chromosome-scale contact frequency heatmaps, is substantially less impressive than the agreement seen in prior work with similar models. This discrepancy appears to be due in part to the unappreciated effects of the mentioned in the previous limitation, as well as inappropriate choices in metrics used to evaluate agreement. It is also not particularly surprising that combining more models, with more free parameters, results in an improvement in the quality of fit.

      Some major results, including both theoretical works and experimental ones, are ignored, despite their relevance to the stated objective of the work. The current manuscript and analysis could be improved substantially by a consideration of these works.

      I describe these issues in more detail below.

      Major issues:

      (1) The genetic element "homie" is present in a subset of the data: The experimental data used in this analysis come from different fly lines, half of which have been edited explicitly to alter genome structure and consequent transcriptional behavior, yet the authors are trying to fit with a common model - a problem which substantially undermines the utility of the analysis.

      Specifically, the authors evaluate the various models/simulations by comparing them to Hi-C from wildtype Drosophila embryos on the chromosome scale and 3D distances and dynamics from live cell imaging in genetically edited embryos, to a series of models in turn. The exercise fatally overlooks a critical fact, (admittedly not easily noticed in the work from Bruckner et al), that the fly embryos used for nearly all their analyses contain not only fluorescent labels, but also contain two copies of a powerful genetic sequence, "homie", known for its ability to dramatically change the 3D organization and dynamics of the genome. Whether or not the fluorescent labels themselves used in the study further alter structure and dynamics is not entirely clear (and will require further work beyond the scope of either study), but at least these fluorescent labels aren't known to dramatically affect 3D structure and dynamics the way homie is. The critical problem is that adding or removing the "homie", as shown in a collection of prior works I describe below in more detail, dramatically affects structure, dynamics, and gene expression. Whether or not the genome contains two distal cis-linked copies of homie fundamentally changes genome structure and dynamics, so to use one dataset which has this edit (the live-cell data) and one dataset which lacks it (the Hi-C data) is, in some sense, to guarantee failure of any model to match all the data.

      If the authors had chosen instead to focus exclusively on the 'no homie' genetic lines in the Brukner data, they would have a much smaller dataset (just 2 distances), which would not cover all the length scales of interest, but it would at least be a dataset not known to be contradictory to the Hi-C. The two 'no homie' lines make much more plausible candidates for the sort of generalizable polymer dynamics these authors seek to explain, as will hopefully be made more clear by a brief review of what is known about homie. I next describe the published data that support these conclusions about how homie affects 3D genome spatial organization and dynamics:

      What is "homie" and how does it affect 3D genome distances, dynamics, and gene expression?

      The genetic element "homie" was named by James Jaynes' lab ( Fujioka...Jaynes 2009) in reference to its remarkable "homing" ability - a fascinating and still poorly understood biological observation that some genetic sequences from Drosophila, when cloned on plasmids and reintegrated into the genome with p-elements, had a remarkable propensity to re-integrate near their endogenous sequence, (Hama et al., 1990; Kassis, 2002; Taillebourg and Dura, 1999; Bender and Hudson, 2000; Fujioka...Jaynes 2009). By contrast, most genetic elements tend to incorporate at random across the genome in such assays (with some bias for active chromatin).

      The Jaynes lab subsequently showed that flies carrying two copies of homie, one integrated in cis, ~140 kb distal from the endogenous element, formed preferential cis contacts with one another. Indeed, if a promoter and reporter gene were included at this distal integration site, the reporter gene would activate gene expression in the pattern normally seen by the gene, even-skipped. The endogenous copy of homie marks one border of ~16 kb mini-TAD which contains the even-skipped gene, (eve), and its developmental enhancers, so this functional interaction provides further evidence of physical proximity (as was also shown by 3C by Jaynes (Fujioka..., Schedl, Jaynes 2016), and later with elegant live imaging, by Jaynes and Gregor (Chen 2018)).

      Critically, if either copy of homie is deleted or substantially mutated, the 3D proximity is lost (Fujioka 2016, Chen 2018, Bruckner 2023), and the expression of the transgene is dramatically reduced (at 58 kb) or lost. Given the author's motivation of understanding "E-P" interactions, the fact that the increased 3D proximity provided by homie is as essential for transcription as the promoter itself at the ~150 kb distance, underscores that these are not negligible changes.

      These effects can be seen by plotting the data from Bruckner 2023, which includes data from labels with separations of 58 kb and ~150 kb "no homie" as well as homie. Unfortunately, the authors don't plot this data in the manuscript in the comparison of 3D distances, though the two-point MSD can be seen in Figure S13C, and laudably, the data is made public in a well-annotated repository on Zenodo, noted in the study. Note that the distance data in Figure S13 were filtered to exclude the transcriptionally off state, and are thus not the quantity the current authors are interested in. If they plot the published data for no homie, they will see the clear effect on the average 3D distance, R(s), and a somewhat stronger effect on the contact frequency P(s), which causes significant deviation from the trend-line followed by the homie-containing data.

      (2) The agreement between the "best performing" simulations for all models and the Hi-C data is not on par with prior studies using similar approaches, apparently due to some erroneous choices in how the optimization is carried out:

      Hi-C-comparison

      The 'best fit' simulation Hi-C looks strikingly different from the biological data in all comparisons, with clearly lower agreement than other authors have shown using highly similar methods (e.g., Shi and Thirumalai 2023; Di Pierro et al. 2017; Nuebler et al. 2018; Esposito et al. 2022; Conte et al. 2022), among many others. I believe this results from a few issues with how the current authors select and evaluate the data in their work:

      (a) Most works have used Pearson's correlation rather than Spearman's correlation when comparing simulation and Hi-C contact frequencies. Pearson's correlation is more appropriate when we expect the values to be linearly related, which they should be in this case, as they are constructed indeed to be measuring the same thing (contact frequency), just derived from two different methods. Spearman's correlation would have been justifiable for comparing how transcription output correlates with contact frequency. This may fix the bafflingly low correlations reported at lower adhesion values in Figure S2C.

      (b) Choice of adhesion strengths - The Hi-C map comparison in Figure 3 strongly suggests that a much more striking visual agreement would have been achieved if much weaker (but still non-zero) homotypic monomer affinity had been selected. In the authors' simulation, the monomer state (A/B identity) strongly dominates polymer position, resulting in the visual appearance of an almost black-and-white checkerboard. The data, meanwhile, look like a weak checkerboard superimposed on the polymer.

      (c) A further confounding problem is the aforementioned issue that the Hi-C data don't come from the edited cell lines, and that the interaction of the two Homie sites is vastly stronger than the compartment interactions of this region of the genome.

      (3) Some important concepts from the field are ignored:

      The crumpled/fractal globule model is widely discussed in the literature (including the work containing the data used in this study) - its exclusion from this analysis thus appears as a substantial gap/oversight:

      A natural alternative to the much-discussed Rouse polymer model is the "crumpled polymer" (Grosberg et al. 1988; Grosberg 2016; Halverson et al. 2011; Halverson et al. 2011), also known as the "fractal globule" (Lieberman-Aiden et al. 2009; Mirny 2011; Dekker and Mirny 2016; Boettiger et al. 2016), much discussed for the way it captures the ⅓ scaling of R(s), found for much of the genome (or, equivalently, the -1 exponent of the probability of contact as a function of genome separation, P(s)). Given the 1/3rd scaling in the data, and the fact that the original authors highlighted the crumpled model in addition to the Rouse model, it seems that this comparison would be instructive and the lack of discussion an oversight. Moreover, while prior works (e.g., Buckner, Gregor, 2023) used some traditional simplifying assumptions to estimate the MSD and relaxation time scaling of this model, I believe a more rigorous analysis with explicit simulations (as in Figure 1 for the Rouse model) would be instructive for the crumpled polymer simulations. Note the crumpled globule is not necessarily the same as the globule in the coil-globule transition discussed here - it requires some assumptions about non-entanglement to stay trapped in the meta-stable state which has the 1/3rd R(s) scaling that is indicative of this model, and not the 1/2 exhibited by equilibrium globules (for s<< length of the polymer) and dilute polymers alike.

      While the fit in Figure 2 appears to get closer to the 1/3rd exponent (B= 0.32), this appears to be a largely coincidental allusion of agreement - the simulation data in truth shows a systematic deviation, returning to the 1/2 scaling for distances from 500 kb to whole chromosomes. This feature is not very evident as the authors restrict the analysis to only the few points available in the experimental data, though had they tested intervening distances I expect they would show log-log P(s) is nonlinear (non-powerlaw) for distances less than the typical loop length up to a few fold larger than the loop length, and thereafter returns to the scaling provided by the 'base' polymer behavior. This appears to be Rouse-like in these authors' model, with R(s) going like 1/2, even though the data are closer to 1/3rd, as indeed most published simulated P(s) curves based on loop extrusion - e.g., (Fudenberg et al. 2016; Nuebler et al. 2018). In this vein, it would be instructive to the readers if the authors would include additional predictions from the simulation on the plot that lie at genomic separation distances not tested in the data, to better appreciate the predictions.

      Minor issues

      (1) I think it is too misleading to only describe the experimental data from Brukner as "E-P" interactions from Drosophila. It is important to note somewhere that this is not an endogenous interaction with a functional role in Drosophila - it is a synthetic interaction between enhancers in the vicinity of the eve gene and a synthetic promoter placed at a variable distance away. The uniformity is elegant - (it is the same pair of elements being studied at all distances), but also provides limited scope for generalization as suggested by the current text. Moreover, the enhancers were not directly labeled; rather, the 3D position of nascent RNA transcribed from eve was tracked with an RNA-binding protein and used as a proxy for the 3D position of the enhancers. There is not an individual enhancer at the eve locus that interacts with the transgene, but rather a collection of enhancers is distributed at different positions throughout the entire TAD, which contains eve, and must form separate loops to reach eve. Indeed, it was previously reported that differences in the local position of these enhancers, relative to eve, affect their ability to interact with the distal reporter gene and the endogenous eve gene (Chen 2018). There is also reported competition between these enhancers and the distal gene, which further complicates the analysis (especially since the state of eve and of its enhancers varies among the different cells as a function of stripe position) - see Chen 2018. All of this is ignored in the current work, despite the assertion of the application to understanding E-P interaction. A detailed discussion of these issues is not necessary, but I fear that ignoring them entirely is to invite further confusion and error.

      (2) I believe this sentence is overstated, given available data: " TAD borders are characterized by transitions between epigenetic states rather than by preferentially-bound CTCF [4, 23, 24]." Indeed, this claim has been repeatedly made in the literature as cited here. However, other data clearly demonstrate a strong enrichment of CTCF at TAD borders (and at epigenetic borders, which in Drosophila have a high correspondence with TAD borders, as the authors have already appropriately noted). See, for example, Figure 4 of Sexton Cell 2012, and compare to Figure 2 of Dixon 2012. Of minor note, CTCF peaks co-occupied by the Zinc Finger TF CP190 are more likely to be TAD borders than CTCF alone. How big a species-specific difference this is remains unclear, as it appears some mammalian CTCF-marked TAD boundaries may be co-occupied by additional ZNFs. While plenty of Drosophila TAD boundaries indeed lack CTCF, many are marked by CTCF, this is enriched relative to what would be expected by chance (or relative to the alignment of other TFs, like Twist or Eve with TAD boundaries), and it has been shown that CTCF loss is sufficient to remove a subset of these, see for example Figure 5 of (Kaushal et al. 2021) (though it is possible, most will require mutation of the all the border-associated factors that collectively bind many of the borders, dCTCF, CP190, mod(mdg4) and others).

      (3) This assertion is overstated given available data: "Although TAD boundaries in Drosophila are often associated with insulator proteins [20], there is no direct evidence that these elements block LEFs in vivo. Therefore, we did not impose boundary constraints in our simulations; LEFs were allowed to move freely unless stalled by collisions with other LEFs, with the possibility of crossover.". Deletion of insulator in Drosophila that lie within a common epigenetic state leads to fusion of TADs (e.g., Mateo et al., 2019 - deletion of the CTCF-marked Fub insulator, in posterior tissues where both flanks of Fub are active; Kaushal, 2021, has examples as well). Loss of CTCF causes a small number of TADs to fuse as measured by Hi-C. This is far from 'direct evidence that insulators block LEFs' - as the authors have already noted, even the idea that cohesin extrudes loops in Drosophila in the first place is indeed controversial. However, LEF activity and stalling at insulators would provide a very natural explanation of why chromatin in a shared epigenetic state should form distinct TADs, and why these TADs should fuse upon insulator deletion. Justifying the lack of stalling sites based on empirical data is thus not very convincing to this reviewer. I believe it would be more apt to simply describe this as a simplifying assumption, rather than the above phrase, which may be misleading.

      Review 2
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    Matrix-associated extracellular vesicles modulate smooth muscle cell adhesion and directionality by presenting collagen VI
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    1. Public_Reviews 05 Sep 2025

      Author response:

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

      Reviewer #1 (Public Review):

      Summary

      In this investigation Kapustin et al. demonstrate that vascular smooth muscle cells (VSMCs) exposed to the extracellular matrix fibronectin stimulates the release of small extracellular vesicles (sEVs). The authors provide experimental evidence that stimulation of the actin cytoskeleton boosts sEV secretion and posit that sEVs harbor both fibronectin and collagen IV protein themselves which also, in turn, alter cell migration parameters. It is well established that fibronectin is associated with increased cell migration and adherence; therefore, this association with VSMCs is not novel.

      The reviewer is correct that FN has been associated with migration and adherence in previous studies.  However we have extended these observations to show that the extracellular fibronectin matrix stimulates small extracellular vesicle (sEVs) secretion by modulating the actin cytoskeleton. We also showed that sEVs are trapped in the extracellular matrix and that by presenting collagen VI induce early focal adhesion formation, reduce excessive cellular spreading and guide cell invasion directionality though a 3D matrix. Hence, sEVs mediate cell-matrix cross talk and change cell behaviour in the context of fibronectin matrix. This is critically important for vasculature where regulated VSMC invasion is essential for repair with its deregulation leading to pathology.

      The authors purport that sEV are largely born of filopodia origin; however, this data is not well executed and seems generally at odds with the presented data.

      Our experimental data showed that CD63 MVs are associated with filopodia in fixed and live cells (Fig 2E, 2F and Video S1) and that inhibition of filopodia formation using the formin inhibitor, SMIFH2 reduced sEV secretion on FN (Fig 2B). However, we agree with the reviewer that further studies are required to connect sEV secretion to filopodia.  To address this we have provided further data analysis but also toned down our conclusions regarding this point: . Changes include:

      (1) Title: Matrix-associated extracellular vesicles modulate smooth muscle cell adhesion and directionality by presenting collagen VI.

      (2) Results, section title: 2. FN-induced sEV secretion is modulated by Arp2/3 and formin-dependent actin cytoskeleton remodelling

      (3) Results, page 6 Line 27-44 and conclusion page 7, Ln 3 “Interestingly, CD63+ MVBs can be observed in filopodia-like structures suggesting that sEV secretion can also occur spatially via cellular protrusion-like filopodia but more studies are needed to confirm this hypothesis.”

      (4) Discussion, page 12, line 19. “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”

      Similarly, the effect of sEVs on parameters of cell migration has almost no magnitude of effect, making mechanism exploration somewhat nebulous.

      VSMC are mesenchymal-type cells with a low migration rate and we agree that the changes in the motility are not of great magnitude even for the positive controls suggesting that this is a complex, multifactorial process for VSMCs. In our experiments we collected data from >5000 individual cells to measure the average speed and found that fibronectin matrix on its own increased VSMC speed from ~0.61 um/min to ~0.68 μm/min (~12% raise) which was statistically significant (Fig 5A). Addition of a sEV inhibitor caused a modest but significant decrease in cellular speed. Interestingly, addition of ECM-associated sEVs did not influence cell speed in 2D or 3D assays. However in a 3D model we observed a 22% change in cell directionality (Fig 5G) and  a 235% change in cell alignment index (FMI, Fig 5H) which we believe is very strong evidence that VSMC-derived sEVs are involved in a regulation of VSMC invasion directionality.  These data are also in agreement with sEV effects in tumour cells (Sung et al., 2015) though this previous study did not identify the factor driving the directionality and we think our Collagen VI data extends significantly these previous observations. 

      Results, page 9: “Hence, ECM-associated sEVs have modest influence on VSMC speed but influence VSMC invasion directionality.”.  

      Lastly, the proposed mechanism of VSMCs responding to, and depositing, ECM proteins via sEVs was not rigorously executed; again, making the conclusions challenging for the reader to interpret.

      We appreciate the reviewer’s comment regarding the mechanistic aspects of VSMCs responding to and depositing ECM proteins via sEVs. In our revised manuscript, we have expanded the data demonstrating that sEVs can be retained within the extracellular matrix (see Figs 3A, 3B, S3A, S3B). Additionally, we show that collagen VI is present on the surface of sEVs, where it may modulate cell adhesion and influence the directionality of cell invasion (Fig 7E). Our results further indicate that both fibronectin (FN) and collagen VI can be recycled through multivesicular bodies (see Figs S3C, S3D, S3E–S3G). However, we acknowledge that the precise mechanisms governing the selective loading of ECM proteins onto sEVs, as well as the specific contributions of sEVs to overall ECM organization, remain to be fully elucidated and warrant further investigation. Based on our current evidence, we propose that collagen VI–loaded sEVs act primarily in a signaling capacity by modulating focal adhesion formation but are not directly involved in ECM structural remodeling.

      Results, page 7: To quantify ECM-trapped sEVs we applied a modified protocol for the sequential extraction of extracellular proteins using salt buffer (0.5M NaCl) to release sEVs which are loosely-attached to ECM via ionic interactions, followed by 4M guanidine HCl buffer (GuHCl) treatment to solubilize strongly-bound sEVs (Fig S3A) [42]. We quantified total sEV and characterised the sEV tetraspanin profile in conditioned media, and the 0.5M NaCl and GuHCl fractions using ExoView. The total particle count showed that EVs are both loosely bound and strongly trapped within the ECM. sEV tetraspanin profiling showed differences between these 3 EV populations.  While there was close similarity between the conditioned media and the 0.5M NaCl fraction with high abundance of CD63+/CD81+ sEVs as well as CD63+/CD81+/CD9+ in both fractions (Fig S3A). In contrast, the GuHCl fraction was particularly enriched with CD63+ and CD63+/CD81+ sEVs with very low abundance of CD9+ EVs (Fig S3A). The abundance of CD63+/CD81+ sEVs was confirmed independently by a CD63+ bead capture assay in the media and loosely bound fractions (Fig S3B).

      Results, page 7: We previously found that the serum protein prothrombin binds to the sEV surface both in the media and MVB lumen showing it is recycled in sEVs and catalyses thrombogenesis being on the sEV surface43. So we investigated whether FN can also be associated with sEV surface where it can be directly involved in sEV-cell cross-talk43.   We treated serum-deprived primary human aortic VSMCs with FN-Alexa568 and found that it was endocytosed and subsequently delivered to early and late endosomes together with fetuin A, another abundant serum protein that is a recycled sEV cargo and elevated in plaques (Figs S3C and S3D). CD63 visualisation with a different fluorophore (Alexa488) confirmed FN colocalization with CD63+ MVBs (Fig S3E). Next, we stained non-serum deprived VSMC cultured in normal growth media (RPMI supplemented with 20% FBS) with an anti-FN antibody and observed colocalization of CD63 and serum-derived FN.  Co-localisation was reducd likely due to competitive bulk protein uptake by non-deprived cells (Fig S3F). Notably, when we compared FN distribution in sparsely growing VSMCs versus confluent cells we found that FN intracellular spots, as well as colocalization with CD63, completely disappeared in the confluent state (Fig S3F and S3G). This correlated with nearly complete loss of CD63+/CD81+ sEV secretion by the confluent cells indicating that confluence abrogates intracellular FN trafficking as well as sEV secretion by VSMCs (Fig S3H). Finally, FN could be co-purified with sEVs from VSMC conditioned media (Fig S3I) and detected on the surface of sEVs by flow cytometry confirming its loading and secretion via sEVs (Fig 3C).

      Results: page 10  Collagen VI was the most abundant protein in VSMC-derived sEVs (Fig 7B, Table S7) and  was previously implicated in the interaction with the proteoglycan NG2[53] and suppression of cell spreading on FN[54]. To confirm the presence of collagen VI in ECM-associated sEVs we analysed sEVs extracted from the 3D matrix using 0.5M NaCl treatment and showed that both collagen VI and FN are present (Fig 7D). Next, we analysed the distribution of collagen VI using dot-blot. Alix staining was bright only upon permeabilization of sEV indicating that it is preferentially a luminal protein (Fig 7E). On the contrary, CD63 staining was similar in both conditions showing that it is surface protein (Fig 7E). Interestingly, collagen VI staining revealed that 40% of the protein is located on the outside surface with 60% in the sEV lumen (Fig 7E). 

      Discussion page 12. “In fact, we observed that an extensive secretion of sEVs effectively ceased protrusion activity; also VSMCs acquired a rounded morphology when “hovering” over the FN matrix decorated with sEVs (data not shown). Hence, it will be interesting in future studies to investigate whether sEVs can stimulate Rho activity by presenting adhesion modulators—particularly collagen VI—on their surface, thereby guiding cell directionality during invasion..”

      Discussion, page 14 “In summary, cooperative activation of integrin signalling and F-actin cytoskeleton pathways results in the secretion of sEVs which associate with the ECM and play a signalling role by controling FA formation and cell-ECM crosstalk. Further studies are needed to test these mechanisms across various cell types and ECM matrices.     

      Strengths

      The authors provide a comprehensive battery of cytoskeletal experiments to test how fibronectin and sEVs impact both sEV release and vascular smooth muscle cell migratory activation.

      We appreciate this comment reflecting our efforts to apply a range of orthogonal methods to show the role of the integrin/actin cytoskeleton in ECM-stimulated sEV secretion.

      Weaknesses

      Unfortunately, this article suffers from many weaknesses. First, the rigor of the experimental approach is low, which calls into question the merit of the conclusions. In this vein, there is a lack of proper controls or inclusion of experiments addressing alternative explanations for the phenotype or lack thereof.

      We acknowledge this comment and agree that there was not sufficient evidence to conclude that sEV secretion occurs via filopodia despite the microscopy/inhibitory data so this claim has now been excluded from the study. However we believe that our experimental data does clearly show that FN stimulates the secretion of collagenVI-loaded sEVs which are trapped by the ECM and have the capacity to modulate VSMC adhesion and invasion directionality. To support this, we have now extended the dataset in the revised version:

      (1) In addition to the use of inhibitors and live cell analysis we have added quantitative data confirming that a large proportion of CD63+ endosomes are associated with F-actin/cortactin tails and this colocalization is increased upon the inhibition of sEV secretion with 3-OMS (Fig  2D, Fig S2B).

      (2) We developed a method to extract ECM-associated sEVs and quantified/characterized these using ExoView Assays further confirming significant sEV entrapment by the ECM (Figs 3B, S3A, S3B).    

      (3) We extended the controls to confirm FN delivery to CD63+ endosomes and showed that FN recycling is stopped upon reaching cell confluence (Figs S3F, S3G and Fig S3H).

      (4) We included more intensive characterisation of human atherosclerotic plaque morphology (H&E, Masson’s trichrome staining, Orcein, elastin fibers staining) to confirm predominant accumulation of sEV in the neointima (Figs S4A, S4B and S4C). We also excluded an endothelial origin for the  CD81+ sEVs (Fig 4G).

      (5) We included individual cellular tracks to the 2D migration analysis to confirm the statistical significance and concluded that ECM-associated sEVs regulate cell invasion directionality but not the cell speed (Figs 5A and 5B).

      (6) We showed surface localisation of collagen VI on sEVs confirming that it can activate signalling pathways leading to early FA formation on the FN matrix  (Figs 7D and 7E).

      (7) We included alternative explanations for some of our data in the discussion.      

      Reviewer #2 (Public Review):

      Extracellular vesicles have recently gained significant attention across a wide variety of fields, and they have therefore been implicated in numerous physiological and pathophysiological processes. When such a discovery and an explosion of interest occur in science, there is often much excitement and hope for answers to mechanisms that have remained elusive and poorly understood. Unfortunately, there is an equal amount of hype and overstatement that may also be put forth in the name of "impact", but this temptation must be avoided so that scientists and the broader public are not misled by overreaching interpretations and statements that lack rigorous and fully convincing evidence.

      Thank you for your comment and we agree that investigating sEVs is particularly challenging due to the their heterogeneity and nano-size, as well as complex biogenesis mechanisms. ECM-associated sEVs is a very new direction for the EV field but one that is particularly relevant to the vasculature where cells must invade through a thick ECM and where the accumulation of ECM-bound EVs is a unique and documented phenomenon.  To further strengthen out conclusions we have included new data to support our statements but also excluded statements re: filopodia as the origin of sEVs, that are out of scope of our study and need to be investigated further.

      The study presented by Kapustin et al. is certainly intriguing and timely, and it offers an interesting working hypothesis for the fields of extracellular vesicles and vascular biology to consider. The authors do a reasonable job at detecting these small extracellular vesicles, though some aspects of data presentation are missing such as full Western blots with accompanying size markers for the viewer to more fully appreciate that data and comparisons being made (see Figures 1 and 7).

      We agree with the reviewer and have now included molecular weight markers (Fig 1F, 7C, 7D, S3I, S4E) and provided all original western blot scans (uncropped and unedited) to the eLife editor. 

      Much of the imaging data from cell-based experiments is strong and conducted with many cutting-edge tools and approaches. That said, the static images and the dynamic imaging fall short of being fully convincing that the small extracellular vesicles found in the neighboring extracellular matrix are indeed being deposited there via the smooth muscle cell filopodia. Many of the lines of evidence presented suggest that this could occur, but alternative hypotheses also exist that were not fully ruled out, such as the ECM-deposited vesicles were secreted more from the soma and/or the lamellipodia that are also emitted and retracted from the cells. In particular, the authors show very nice dynamic imaging (Supplementary Figure S2A and Supplemental Video S1) that is interpreted as "extracellular vesicles being released from the cell" and these are seen as "bursts" of fluorescent signal; however, none of these appear to occur in filopodia as they appear within the cell proper (a "burst" of signal vs. a more intense "streak" of signal), which would be a stronger and more consistent observation predicted by the working model proposed by the authors.

      Our live and fixed cell microscope data as well as inhibitor analysis showed that sEV secretion can be associated with the filopodia. However we agree with the reviewer that the data generated using pHluoron GFP marker clearly indicate that the majority of sEVs are secreted from the cell soma toward the ECM:

      To reflect this, we have added further changes:

      (1) Title: Matrix-associated extracellular vesicles modulate smooth muscle cell adhesion and directionality by presenting collagen VI.

      (2) Results, section title: 2. FN-induced sEV secretion is modulated by Arp2/3 and formin-dependent actin cytoskeleton remodelling

      (3)  Results, page 6 Line 27-36 “Formins and the Arp2/3 complex play a crucial role in the formation of filopodia, a cellular protrusion required for sensing the extracellular environment and cell-ECM interactions36. To test whether MVBs can be delivered to filopodia, we stained VSMCs for Myosin-10 (Myo10)37. We observed no difference between total filopodia number per cell on plastic or FN matrices (n=18±8 and n=14±3, respectively) however the presence of endogenous CD63+ MVBs along the Myo10-positive filopodia were observed in both conditions (Fig 2E, arrows). Filopodia have been implicated in sEV capture and delivery to endocytosis “hot-spots”38, so next we examined the directionality of CD63+ MVB movement in filopodia by overexpressing Myo10-GFP and CD63-RFP in live VSMCs. Importantly, we observed anterograde MVB transport toward the filopodia tip (Fig 2F and Supplementary Video S2) indicative of MVB secretion”.

      (4) Results, page 6, Ln 37-44 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)”.

      (5) Results, page 7 Ln 3 “Interestingly, CD63+ MVBs can be observed in filopodia-like structures suggesting that sEV secretion can also occur spatially via cellular protrusion-like filopodia but more studies are needed to confirm this hypothesis.”

      (6) Discussion, page 12, line 19. “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”

      Imaging of related human samples is certainly a strength of the paper, and the authors are commended for attempting to connect the findings from their cell culture experiments to an important clinical scenario. However, the marker selected for marking extracellular vesicles is CD81, which has been described as present on the endothelium of atherosclerotic plaques with a proposed role in the recruitment of monocytes into diseased arteries (Rohlena et al. Cardiovasc Res 2009). More data should address this potentially confounding interpretation of the signals presented in images within Figure 4.

      We thank the reviewer for this insightful comment that the  sEV marker CD81 can originate from endothelial cells in agreement with Rohlena et al., 2009.   To address this we investigated the spatial overlap between CD81 and the endothelial marker, CD31. We observed very strong CD81 staining in the intact endothelial cell (intima) layer and occasional CD31 positive cells in the neointima. Importantly, quantification of colocalization confirmed that 80% of CD81 in the neointima does not overlap with CD31 excluding an endothelial origin of these sEVs. (Fig 4G).  Moreover, we included complete morphological characterisation of the atherosclerotic plaques confirming that CD81 sEVs were primarily observed in the neointima where VSMCs constitute the cellular majority (Fig S4A, S4B, S4C and S4D).

      On a conceptual level, the idea that the small extracellular vesicles contain Type VI Collagen, and this element of their cargo is modulating smooth muscle cell migration, is an intriguing aspect of the authors' working model. Nevertheless, the evidence supporting this potential mechanism does not quite fit together as presented. It is not entirely clear how the collagen VI within the vesicles is somehow accessed by the smooth muscle cell filopodia during migration. Are the vesicles lysed open once on the extracellular matrix? If so, what is the proposed mechanism for that to occur? If not, how are the adhesion molecules on the smooth muscle cell surface engaging the collagen VI fibers that are contained within the vesicles? This aspect of the model does not quite fit together with the proposed mechanism and may be an interesting speculative interpretation, warranting further investigation, but it should not be considered a strong conclusion with sufficient convincing data supporting this idea.

      We thank the reviewer for their insightful comments regarding the mechanism by which collagen VI associated with sEVs could modulate smooth muscle cell adhesion and migration. To clarify, our new data suggest that collagen VI is predominantly present on the surface of the sEVs, as evidenced by Fig 7E. This surface localization strongly implies that collagen VI can be directly accessed by cell surface adhesion receptors, without the need for vesicle lysis or opening. While we cannot entirely rule out all alternative mechanisms, we consider vesicle rupture or lysis within the extracellular matrix to be a highly unlikely route for collagen VI exposure, given the known stability of sEVs under physiological conditions. We have added these points to clarify:

      (1) Results, page 10, Ln 45 “To confirm the presence of collagen VI in ECM-associated sEVs we analysed sEVs extracted from the 3D matrix using 0.5M NaCl treatment and showed that both collagen VI and FN are present (Fig 7D). Next, we analysed the distribution of collagen VI using dot-blot. Alix staining was bright only upon permeabilization of sEV indicating that it is preferentially a luminal protein (Fig 7E). On the contrary, CD63 staining was similar in both conditions showing that it is surface protein (Fig 7E). Interestingly, collagen VI staining revealed that 40% of the protein is located on the outside surface with 60% in the sEV lumen (Fig 7E).”

      (2) Discussion, page 13, Ln 2 “Hence, it will be interesting in future studies to investigate whether sEVs can stimulate Rho activity by presenting adhesion modulators—particularly collagen VI—on their surface, thereby guiding cell directionality during invasion..”

      (3) Discussion, page 14, Ln 30: In addition to collagen VI the unique adhesion cluster in VSMC-derived sEVS also includes EGF-like repeat and discoidin I-like domain-containing protein (EDIL3), transforming growth factor-beta-induced protein ig-h3 (TGFBI) and the lectin galactoside-binding soluble 3 binding protein (LGALS3BP) and these proteins are also directly implicated in activation of integrin signalling and cellular invasiveness85-87. Although we found that collagen VI plays the key role in sEV-induced early formation of FAs in VSMCs, it is tempting to speculate that the high sEV efficacy in stimulating FA formation is driven by cooperative action of this unique adhesion complex on the sEVs surface and targeting this novel sEV-dependent mechanism of VSMC invasion may open-up new therapeutic opportunities to modulate atherosclerotic plaque development or even to prevent undesired VSMC motility in restenosis.    .   

      (4) Abstract Figure

      On a technical level, some of the statistical analysis is not readily understood from the data presented. It is very much appreciated that the authors show many of the graphs with technical and biological replicate values in addition to the means and standard deviations (though this is not clearly stated in all figure legends). However, in figures such as Figure 5, there are bars shown and indicated to be different by statistical comparison (see panel B in Figure 5). It is not clear how the values for Group 1 (no FN, no 3-OMS, no sEV) are statistically different (denoted by three asterisks but no p value provided in the legend) than Group 3 (no FN, 3-OMS added, no sEV), when their means and standard deviations appear almost identical. If this is an oversight, this needs to be corrected. If this is truly the outcome, further explanation is warranted. A higher level of transparency in such instances would certainly go a long way in helping address the current crisis of mistrust within the scientific community and at the interface with society at-large.

      We thank the reviewer for their careful reading and important comments on the statistical analysis. We acknowledge that the technical and biological replicate data were not clearly reported in all figure legends and that the statistical approach for Figures 5A and 5B required clarification. In response, we have made several changes for greater transparency and rigor:

      First, we have now explicitly included the numbers of biological replicates (N) and technical replicates (n) in all relevant figure legends for Figures 1–7. In addition, the number of individual cell tracks is now annotated for the migration/invasion analyses, along with the mean values for each dataset.

      Upon review, we found that the original statistical analyses for Figures 5A and 5B were conducted using pooled averaged data. To address this, we have repeated the statistical tests using pooled individual cell track data, applying the Kruskal–Wallis test with Dunn’s multiple comparison correction. This more stringent approach revealed revised p-values, which are now indicated in Figures 5A and 5B.

      With these corrections, we reconfirm our major findings: In the 2D model, fibronectin (FN) coating promotes VSMC velocity, while inhibition of sEV secretion with 3-OMS leads to reduced cell speed (Fig. 5A). Addition of sEVs to the ECM had no effect on VSMC speed at baseline but did rescue cell speed and distance in the presence of 3-OMS, consistent with EVs acting primarily on invasion directionality rather than speed in both 2D and 3D models (Fig. 5A, 5D). Furthermore, sEVs continue to significantly impact VSMC invasion directionality (Figs. 5G, 5H), in agreement with previous reports in tumor cells (Sung et al., 2015).

      In summary, we have implemented the following revisions:

      (1) Figures 5A and 5B: Individual cell track data are now shown, and statistical analyses have been repeated using the Kruskal–Wallis test with Dunn’s multiple comparisons.

      (2) Figure legends and results sections: Numbers of biological and technical replicates, as well as individual data points, are now clearly stated.

      Results, page 9, line 14: The text has been updated to clarify the statistical approach and major findings as described above.

      We hope that these changes address the reviewer’s concerns and improve the transparency and reproducibility of our data presentation

      Reviewer #1 (Recommendations For The Authors):

      We are very thankful for the comprehensive review and comments which helped to improve our data.

      Figure 1.<br /> The authors clearly show that FN stimulation (immobilized or cell-derived) promotes sEV secretion via canonical integrin pathways. FN is a promigratory substrate, hence its extensive use as a cell adhesion aid; thus one could assume that simply plating on FN induces a pro-migratory phenotype (later data supports this notion). Does the addition of growth factors also increase sEV release? An endogenous function of FN is siloing of various GFs during clot formation. Also, FAK and SRC networks intersect with canonical RTK signaling in terms of promoting Rac1, CDC42 and other migration mediators. The reason I believe this is important is because the data could be interpreted in two ways: 1) FN induces pro-migration signaling and then sEVs are released, or visa versa, FN induces sEV release and migration is initiated. GF supplementation in the absence of FN would clarify this relationship.

      We thank the reviewer for this insightful comment regarding the possible role of growth factors (GFs) and the mechanistic relationship between FN stimulation, sEV secretion, and cell migration. We agree that FN is a well-established promoter of cell migration, and it is important to distinguish whether FN directly induces a pro-migratory phenotype or does so via sEV-mediated signaling.

      Our data show that FN stimulation markedly increases VSMC motility, as reflected by enhanced cell speed (Fig. 5A), an increased number of focal adhesions (Fig. 6E), and facilitated centripetal movement of FAs (Fig. 6F). Interestingly, ECM-associated sEVs appear to play a complementary but distinct role: they do not significantly affect cell migration speed (Fig. 5A) but instead guide cell invasion directionality (Figs. 5G, 5H), reduce the number of FAs per cell (Fig. 6E), and promote early peripheral FA formation (Fig. 6F). In light of these findings, we have updated our graphical abstract to reflect the unique cross-talk mediated by sEVs between VSMCs and the ECM.

      Regarding the influence of growth factors, we acknowledge that FN can bind and present different GFs, which could also contribute to changes in sEV secretion. Although our inhibition studies and integrin-blocking antibody results support a primary role for β1 integrin activation and actin assembly in triggering sEV secretion, we cannot entirely exclude the possibility that FN-bound growth factors play a role in this process. We have now incorporated this point into the discussion to address the reviewer’s suggestion.

      Discussion, page 14 , Ln 7 “Although our small inhibitors and integrin modulating antibody data clearly indicate that β1 activation triggers sEV secretion via activation of actin assembly we cannot fully rule out that FN may also be modulating growth factor activity which in turn contributes to sEV secretion by VSMCs<sup>23</sup>.  Excessive collagen and elastin matrix breakdown in atheroma has been tightly linked to acute coronary events hence it will be interesting to study the possible link between sEV secretion and plaque stability as sEV-dependent invasion is also likely to influence the necessary ECM degradation induced by invading cells<sup>96</sup>

      Figure 2.<br /> • The authors provide no evidence (or references) that SMIFH2 or CK666 halts filopodia extensions.

      Thank you for this important note. We have included the corresponding references:

      Results, page 5: “So next we tested the contribution of Arp2/3 and formins by using the small molecule inhibitors, CK666 and SMIFH2, respectively31, 32”.  

      • Is there an increase in filopodia density when plated on FN vs plastic? Similarly, if there are more filopodia present is that associated with more sEV? Please provide evidence in this regard.

      We agree that connecting the number of filopodia with the secretion level of sEVs may be an important clue if sEV secretion can be driven by FN-induced filopodia formation. However, Myosin10 staining to quantify filopodia (Fig 2E) showed no difference between VSMCs plated on plastic versus FN matrix. Therefore, we agree with the reviewer that the filopodia contribution to sEV secretion needs to be investigated further.  This idea is reflected in the following comments:

      (1) Results, page 6, Ln 29 “We observed no difference between total filopodia number per cell on plastic or FN matrices (n=18±8 and n=14±3, respectively) however the presence of endogenous CD63+ MVBs along the Myo10-positive filopodia were observed in both conditions (Fig 2E, arrows).

      (2) Results, page 6, Ln 37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

      (3) Discussion, page 12, Ln 15 : “Focal complexes either disassemble or mature into the elongated centripetally located FAs48. In turn, these mature FAs anchor the ECM to actin stress fibres and the traction force generated by actomyosin-mediated contractility pulls the FAs rearward and the cell body forward12, 13. Here we report that β1 integrin activation triggers sEV release followed by sEV entrapment by the ECM. Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells..”

      As hinted above, this data could be interpreted in the light of generally inhibiting cell migration to blunt sEV shedding. Does cell confluence affect sEV release? If cells are cultured to 100% confluency this would limit filopodia formation regardless of ECM type. If sEV secretion remains elevated on FN in this culture condition it would suggest a lack of dependency on filopodia.

      We thank the reviewer for this thoughtful suggestion regarding the influence of cell confluence on sEV release and filopodia formation. To directly address this hypothesis, we performed additional experiments comparing VSMCs cultured at low and high confluency. As described in the revised Results (page 7, line 39), we found that high cellular confluency reduced FN recycling, as indicated by the marked decrease in intracellular FN-positive spots and loss of colocalization with CD63 (Figs S3F, S3G). Importantly, this was accompanied by a significant reduction in CD63+/CD81+ sEV secretion by confluent cells (Fig S3H). These results suggest that VSMC confluence, which suppresses filopodia formation, nearly abolishes both intracellular FN trafficking and sEV secretion, even in the presence of FN. Thus, under our experimental conditions, sEV secretion by VSMCs appears to be closely linked to dynamic cell–matrix interactions and is dramatically reduced when these processes are limited by confluence:

      (1) Results, page 7, Ln 39 : “Notably, when we compared FN distribution in sparsely growing VSMCs versus confluent cells we found that FN intracellular spots, as well as colocalization with CD63, completely disappeared in the confluent state (Fig S3F and S3G). This correlated with nearly complete loss of CD63+/CD81+ sEV secretion by the confluent cells indicating that confluence abrogates intracellular FN trafficking as well as sEV secretion by VSMCs (Fig S3H)..  

      • Inhibition of branched actin polymerization has been shown to reduce both exocytic and endocytic activity. Thus, it is hard to interpret the results of Fig. 2B than anything more than a generalized effect of losing actin.

      We thank the reviewer for this important point regarding the broad cellular functions of branched actin polymerization, and agree that generalized actin loss can influence both exocytic and endocytic pathways. To address this, we performed additional experiments and analyses to better define the relationship between branched actin structures and sEV-related processes in VSMCs.

      As described in the revised Results (page 6), we overexpressed ARPC2-GFP (an Arp2/3 subunit) together with F-tractin-RFP in VSMCs and carried out live-cell imaging. This approach revealed that Arp2/3 and F-actin organize into lamellipodial scaffolds at the cell cortex, as expected (Fig. S2A; Supplementary Video S2). Additionally, and more unexpectedly, we observed numerous Arp2/3– and F-actin–positive dynamic spots within the VSMC cytoplasm. These structures resemble actin comet tails seen in other systems, previously implicated in endosomal propulsion (Fig. S2A, arrow; Supplementary Video S2).

      Quantitative analysis confirmed that a substantial fraction of these dynamic F-actin/cortactin spots colocalized with CD63+ endosomes (Fig. 2D), and that these structures are indeed branched actin tails based on cortactin immunostaining. Furthermore, inhibition of SMPD3 (with 3-OMS) induced enlarged cortactin/F-actin/CD63+ complexes, morphologically similar to invadopodia (Fig. 2D, arrowheads), supporting a functional link between actin branching and MVB dynamics.

      To quantify the association, we calculated Manders’ colocalization coefficients for F-actin tails and CD63+ endosomal structures in fixed VSMCs, observing that ~50% of F-actin tails were associated with ~13% of endosomes. Upon 3-OMS treatment, this overlap increased further (Fig. S2B).

      Finally, using live-cell imaging (Fig 2C; Supplementary Video S4), we directly observed CD63+ MVBs being propelled through the cytoplasm by Arp2/3-driven actin tails, suggesting a mechanistic role for branched actin assembly in MVB intracellular transport, rather than a generalized effect of actin disruption alone.

      We believe these combined data reinforce a more specific mechanistic role for Arp2/3-mediated branched actin in MVB/endosome transport and, consequently, in sEV secretion in VSMCs—over and above an indirect effect of global actin loss. We hope these additional experiments and quantitative analyses address the reviewer’s concern and clarify the functional relevance of branched actin structures to sEV trafficking:

      (1) Results, page 6, Ln 3 “As regulators of branched actin assembly, the Arp2/3 complex and cortactin are thought to contribute to sEV secretion in tumour cells by mediating MVB intracellular transport and plasma membrane docking[28, 33]. Therefore, we overexpressed the Arp2/3 subunit, ARPC2-GFP and the F-actin marker, F-tractin-RFP in VSMCs and performed live-cell imaging. As expected, Arp2/3 and F-actin bundles formed a distinct lamellipodia scaffold in the cellular cortex (Fig S2A and Supplementary Video S2). Unexpectedly, we also observed numerous  Arp2/3/F-actin positive spots moving  through the VSMC cytoplasm that resembled previously described endosome actin tails observed in Xenopus eggs[33] and parasite infected cells where actin comet tails propel parasites via filopodia to neighbouring cells[34, 35] (Fig S2A, arrow, and Supplementary Video S2). Analysis of the intracellular distribution of Arp2/3 and CD63-positive endosomes in VSMCs showed CD63-MVB propulsion by the F-actin tail in live cells (Fig 2C and Supplementary Video S4).”

      (2) Results, New data Fig 2D, page 6, Ln 14. “we observed numerous F-actin spots in fixed VSMCs that were positive both for F-actin and cortactin indicating that these are branched-actin tails (Fig 2D). Moreover, cortactin/F-actin spots colocalised with CD63+ endosomes and addition of the SMPD3 inhibitor, 3-OMS, induced the appearance of enlarged doughnut-like cortactin/F-actin/CD63 complexes resembling invadopodia-like structures similar to those observed in tumour cells (Fig 2D, arrowheads)[18].”

      (3) Results, New data Fig S2B, page 6, Ln 19 “To quantify CD63 overlap with the actin tail-like structures, we extracted round-shaped actin structures and calculated the thresholded Manders colocalization coefficient (Fig S2B).  We observed overlap between F-actin tails and CD63 as well as close proximity of these markers in fixed VSMCs (Fig S2B). Approximately 50% of the F-actin tails were associated with 13% of all endosomes (tM1=0.44±0.23 and tM2= 0.13±0.06, respectively, N=3). Addition of 3-OMS enhanced this overlap further (tM1=0.75±0.18 and tM2=0.25±0.09) suggesting that Arp2/3-driven branched F-actin tails are involved in CD63+ MVB intracellular transport in VSMCs”

      • In video 1 the author states (lines 8-9; pg6) "intense CD63 staining along filopodia" Although, there is some fluorescence (not strong) in these structures, there was no visible exocytic activity. This data is more suggestive that sEVs (marked by CD63) are not associated with filopodia. The following conclusion statement the authors make is overreaching given this result.

      We thank the reviewer for this careful observation and agree that the previous conclusion regarding sEV release from filopodia was overstated. In response, we have revised both the Results and Discussion sections to more accurately reflect the data..

      (1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

      (2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

      • Fig 2D and video 2 are wholly unconvincing with regard to sEV secretion sites. The authors could use their CD63-pHluroin construct to count exocytic events in the filopodia vs the whole cell. Given the movie, I have a suspicion this would not be significant. The authors could also perform staining CD63 in non-permeabilized cells to capture and count exocytic events at the plasma membrane as well as their location between groups.

      We thank the reviewer for these constructive suggestions and their critical assessment of our current data regarding the sites of sEV secretion. We agree that our CD63-pHluorin approach clearly indicates sEV secretion events in the soma at the cell–ECM interface, while we did not observe comparable events in filopodia. Accordingly, we have clarified these points in the revised manuscript.

      (1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

      (2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

      • Fig. 2E and video 4. Again, the conclusions drawn from this data are very strained. First, no co-localization quantification is presented on the proportion of CD63 vesicles with actin. Once again, the movie, if anything convinces the reader that 95-99% of all CD63 vesicles are not associated with actin; therefore, this is an unlikely mechanism of transport.

      We thank the reviewer for this valuable comment and for highlighting the need for quantitative co-localization analysis. In response, we developed a method to systematically quantify F-actin and CD63 co-localization in fixed VSMCs, as now presented in new Figures 2D and S2B. We acknowledge that the majority of CD63+ endosomes are not associated with F-actin, consistent with the reviewer’s interpretation. However, our quantitative data now show that a specific subpopulation of MVBs appears to utilize this actin-based mechanism for transport. We believe this addresses the concern and more accurately reflects the prevalence and significance of the mechanism described.

      (1) Results, page 6 , Ln 19. “To quantify CD63 overlap with the actin tail-like structures, we extracted round-shaped actin structures and calculated the thresholded Manders colocalization coefficient (Fig S2B).  We observed overlap between F-actin tails and CD63 as well as close proximity of these markers in fixed VSMCs (Fig S2B). Approximately 50% of the F-actin tails were associated with 13% of all endosomes (tM1=0.44±0.23 and tM2= 0.13±0.06, respectively, N=3). Addition of 3-OMS enhanced this overlap further (tM1=0.75+/-0.18 and tM2=0.25+/-0.09) suggesting that Arp2/3-driven branched F-actin tails are involved in CD63+ MVB intracellular transport in VSMCs.”

      • Are there perturbations that increase filopodia numbers? A gain of function experiment would be valuable here.

      We thank the reviewer for this important suggestion regarding the potential value of gain-of-function experiments to clarify filopodia’s contribution to sEV release. In agreement with the reviewer’s scepticism, we have removed statements linking filopodia to sEV release from both the title and abstract to avoid overinterpretation. At present, our understanding of filopodia biology and the lack of robust tools to selectively and substantially increase filopodia numbers in VSMCs prevent us from directly addressing this question through gain-of-function assays. We acknowledge that future studies using established methods—such as overexpression of filopodia-inducing proteins (e.g., mDia2 or fascin)—could provide insight into whether an increased number of filopodia affects sEV release. However, such experiments are beyond the scope of the current manuscript. We have made the following changes to clarify these points:

      (1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

      (2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

      Figure 3<br /> • Fig 3A. The CD63 staining is strongly associated with the entire plasma membrane. How are the authors distinguishing between normal membrane shedding and bona fida sEVs based on this staining alone (?)- this is insufficient as all membrane structures are seemingly positive. Additionally, there are very few sEVs in scrutinizing the provided images. For the "sEV secretion, fold change" graphs in previous figures, could the authors provide absolute values, or an indication of what these values are in absolute terms?

      We thank the reviewer for raising this important point regarding the specificity of CD63 staining and the need to distinguish bona fide sEVs from membrane fragments or general membrane shedding. We agree that CD63 staining alone at the plasma membrane or in the extracellular matrix is not sufficient to unequivocally identify sEVs. To address this, we employed several complementary approaches to rigorously characterize ECM-associated sEVs:

      First, using high-resolution iSIM imaging, we confirmed the association of CD63-positive particles specifically with the FN-rich matrix, and demonstrated that SMPD3 knockdown significantly reduced the number of CD63+ particles in the matrix (Fig. 3B; revised from Fig. S3A).

      Second, by incubating FN matrices with purified and fluorescently labeled sEVs, we directly observed efficient entrapment of these labeled sEVs within the matrices (Fig. 3E), confirming that sEVs can interact with and be retained by the ECM.

      Third, we developed and applied a sequential extraction protocol using mild salt buffer (0.5M NaCl) and strong denaturant (4M guanidine HCl) to selectively extract ECM-associated sEVs based on the strength of their association (see new Figs. S3A and S3B). Extracted vesicles were then characterized by ExoView analysis, which demonstrated a tetraspanin profile (CD63+/CD81+/CD9+) closely matching that of sEVs from conditioned media, providing evidence that these particles are true sEVs and not merely membrane debris. We also found that the more weakly bound (NaCl-extracted) fraction closely resembles media-derived sEVs, while the strongly bound (GuHCl-extracted) fraction is more enriched in CD63+ and CD63+/CD81+ sEVs but contains very few CD9+ vesicles, further supporting distinct extracellular vesicle subpopulations within the ECM.

      In addition, the abundance of CD63+/CD81+ sEVs in both media and ECM-derived fractions was independently validated by CD63 bead-capture assay (Fig. S3B).

      We hope these clarifications and the expanded data set address the reviewer’s concerns about sEV identification and quantification in the extracellular matrix:

      (1) Results, page 7, Ln 16. To quantify ECM-trapped sEVs we applied a modified protocol for the sequential extraction of extracellular proteins using salt buffer (0.5M NaCl) to release sEVs which are loosely-attached to ECM via ionic interactions, followed by 4M guanidine HCl buffer (GuHCl) treatment to solubilize strongly-bound sEVs (Fig S3A) 42. We quantified total sEV and characterised the sEV tetraspanin profile in conditioned media, and the 0.5M NaCl and GuHCl fractions using ExoView. The total particle count showed that EVs are both loosely bound and strongly trapped within the ECM. sEV tetraspanin profiling showed differences between these 3 EV populations.  While there was close similarity between the conditioned media and the 0.5M NaCl fraction with high abundance of CD63+/CD81+ sEVs as well as CD63+/CD81+/CD9+ in both fractions (Fig S3A). In contrast, the GuHCl fraction was particularly enriched with CD63+ and CD63+/CD81+ sEVs with very low abundance of CD9+ EVs (Fig S3A). The abundance of CD63+/CD81+ sEVs was confirmed independently by a CD63+ bead capture assay in the media and loosely bound fractions (Fig S3B).

      • A control of fig 3b would be helpful to parse out random uptake of extracellular debris verses targeted sEV internalization. It would be helpful if the authors added particles of similar size to that of the sEVs to test whether these structures are endocytosed/micropinocytosed at similar levels.

      We thank the reviewer for this useful suggestion regarding the need for better controls to distinguish specific sEV uptake from nonspecific internalization of extracellular debris or similarly sized particles. As a comparison, in our study we analyzed the uptake of both sEVs and serum proteins such as fibronectin and fetuin-A (Figs S3C and S3D), and observed similar patterns of intracellular trafficking. However, we acknowledge that inert nanoparticles or beads of a similar size to sEVs could serve as potential controls to assess nonspecific micropinocytosis or endocytosis.

      It is important to note, however, that the uptake of sEVs is strongly influenced by their surface protein composition and the so-called “protein corona.” Recent work from Prof. Khuloud T. Al-Jamal’s group underscores that exosome uptake mechanisms may be highly specific (Liam-Or et al., 2024), and studies from Mattias Belting’s lab have also shown the importance of heparan sulfate proteoglycans in exosome endocytosis (Cerezo-Magana et al., 2021). As a result, uptake comparisons with inert particles or beads may not fully recapitulate the specificity of sEV internalization, and distinct nanoparticle classes may rely on different uptake pathways.

      Figure 4<br /> • Fig. 4E,F,G. How are the authors determining the neointima and media compartments without ancillary staining for basement membrane or endothelial markers? Anatomic specific markers need to be incorporated here for the reader to evaluate the specificity of the FN and CD81 staining. It is also hard to understand the severity of the atherosclerotic lesion without a companion H&E cross section.

      We thank the reviewer for highlighting the need for more rigorous characterization of atherosclerotic lesion architecture and anatomical compartments in our study. In response, we have incorporated additional histological analyses and now provide ancillary staining and companion images to enable clear identification of the neointima and medial compartments, as well as to assess lesion severity (see new Figs S4A–S4D):

      (1)Results, page  8, Ln 28. . “To test if FN associates with sEV markers in atherosclerosis, we investigated the spatial association of FN with sEV markers using the sEV-specific marker CD81. Staining of atherosclerotic plaques with haematoxylin and eosin revealed well-defined regions with the neointima as well as tunica media layers formed by phenotypically transitioned or contractile VSMCs, respectively (Fig S4A). Masson's trichrome staining of atherosclerotic plaques showed abundant haemorrhages in the neointima, and sporadic haemorrhages in the tunica media (Fig S4B). Staining of atherosclerotic plaques with orcein indicated weak connective tissue staining in the atheroma with a confluent extracellular lipid core, and strong specific staining at the tunica media containing elastic fibres which correlated well with the intact elastin fibrils in the tunica media (Figs S4C and S4D). Using this clear morphological demarcation, we found that FN accumulated both in the neointima and the tunica media where it was significantly colocalised with the sEV marker, CD81 (Fig. 4D, 4E and 4F). Notably CD81 and FN colocalization was particularly prominent in cell-free, matrix-rich plaque regions (Figs. 4E and 4F).”

      • Figs s4c, S4d- proper controls are not provided. Again, a non-FN internalization control as well as a 4oC cold block negative control is required to interpret this data.

      We thank the reviewer for this valuable suggestion. To enhance the rigor of our internalization assays, we have now included several additional controls using alternative treatments, fluorophore combinations, and internalization conditions:

      a) We performed FN-Alexa568 uptake assays, followed by immunostaining for CD63 with a distinct fluorophore (Alexa488), to confirm the colocalization of internalized FN with CD63+ endosomal compartments in VSMCs (new Fig. S3E).

      b) We also stained VSMCs, cultured under normal growth conditions, with an anti-FN antibody to visualize intracellular serum-derived FN and again observed colocalization with CD63 (new Figs. S3F and S3G). Notably, in cells grown to confluence, we observed a complete loss of intracellular FN staining and FN/CD63 colocalization, suggesting that FN recycling is prominent in sparse, motile cells, but not in confluent populations.

      These additional controls strengthen our conclusions regarding FN internalization pathways and the conditions under which FN trafficking to the endosomal system occurs:

      (1) Results, page 7, Ln 31  We treated serum-deprived primary human aortic VSMCs with FN-Alexa568 and found that it was endocytosed and subsequently delivered to early and late endosomes together with fetuin A, another abundant serum protein that is a recycled sEV cargo and elevated in plaques (Figs S3C and S3D). CD63 visualisation with a different fluorophore (Alexa488) confirmed FN colocalization with CD63+ MVBs (Fig S3E). Next, we stained non-serum deprived VSMC cultured in normal growth media (RPMI supplemented with 20% FBS) with an anti-FN antibody and observed colocalization of CD63 and serum-derived FN.  Co-localisation was reduced likely due to competitive bulk protein uptake by non-deprived cells (Fig S3F). Notably, when we compared FN distribution in sparsely growing VSMCs versus confluent cells we found that FN intracellular spots, as well as colocalization with CD63, completely disappeared in the confluent state (Fig S3F and S3G)..

      • Can the authors please provide live and fixed imaging of FN and CD63-mediate filopodial secretion to amply support their conclusions.

      We have observed CD63 MVBs in both fixed (Fig 2E) and live VSMCs (Fig 2F) yet we agree that further studies are required to establish the contribution of filopodia to sEV secretion. Therefore, we have added the following changes:

      (1) Results, page 6, Ln37 “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)..”

      (2) Discussion, page 12, Ln19 “Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells.”. 

      Figure 5

      • Fig. 5A,B. The authors claim that sEV supplementation enhances VSMC migration speed and distance. The provided graphs show only a marginal increase in speed with sEV addition (A) but, concerningly, there is a four-star significant difference between the FN condition compared with FN+sEV (B) while the means appear the same. How are these conditions statistically different? The statistics seem off for these comparisons.

      We thank the reviewer for highlighting concerns regarding the statistical analysis in Figures 5A and 5B. In response, we have carefully re-examined our data and statistical approach to ensure accuracy and transparency.

      First, we have now included all individual cell migration tracks in the data representation for these figures. The statistical tests were repeated using the Kruskal–Wallis test with Dunn’s multiple comparison correction across all groups. This more stringent analysis confirmed our key findings: fibronectin (FN) stimulates VSMC migration speed, while inhibition of sEV secretion (with 3-OMS) reduces cellular speed (Fig. 5A). Addition of exogenous ECM-associated sEVs modestly restored cell speed in the presence of 3-OMS, but had no effect on baseline migration speed in 2D or 3D models (Figs. 5A, 5D).

      Regarding the four-star significance observed in the original Fig. 5B, the previous result reflected an analysis based on pooled group averages, which may have overstated marginal differences. The revised analysis, based on individual cell tracks, does not support a substantial difference between FN and FN+sEV groups. The revised p-values and comparisons are now provided directly on the figures and described in the figure legends. We also clearly report the numbers of biological replicates, technical replicates, and individual data points for every condition.

      Further, the modest effect of ECM-associated sEVs on speed is consistent with our observation that sEVs influence invasion directionality rather than baseline migration velocity, in agreement with previous findings in tumor models (Sung et al., 2015).

      The manuscript has been revised accordingly, with updates in:

      (1) Figures 5A and 5B: Individual cell track data are now shown, and statistical analyses have been repeated using the Kruskal–Wallis test with Dunn’s multiple comparisons.

      (2) Figure legends and results sections: Numbers of biological and technical replicates, as well as individual data points, are now clearly stated.

      (3) Results, page 9, line 14:  “FN as a cargo in sEVs promotes FA formation in tumour cells and increases cell speed14, 15. As we found that FN is loaded into VSMC-derived sEVs we hypothesized that ECM-entrapped sEVs can enhance cell migration by increasing cell adhesion and FA formation in the context of a FN-rich ECM. Therefore, we tested the effect of sEV deposition onto the FN matrix on VSMC migration in 2D and 3D models. We found that FN coating promoted VSMC velocity and inhibition of bulk sEV secretion with 3-OMS reduced VSMC speed in a 2D single-cell migration model (Figs. 5A, 5B) in agreement with previous studies using tumour cells14, 15. However, addition of sEVs to the ECM had no effect on VSMC speed at baseline but rescued cell speed and distance in the presence of the sEV secretion inhibitor, 3-OMS suggesting the EVs are not primarily regulating cell speed (Figs 5A and 5B).”

      (4) Results, page 9, Ln 29 “Hence, ECM-associated sEVs have modest influence on VSMC speed but influence VSMC invasion directionality.”.

      We hope that these changes address the reviewer’s concerns and improve the transparency and reproducibility of our data presentation

      • Fig d-h. Generally, the magnitude of the difference between the presented conditions are biologically insignificant. Several of the graphs show a four-star difference with means that appear equivalent with overlapping error bars. Do the authors conclude that a 0.1%, or less, effect between groups is biologically meaningful?

      We thank the reviewer for drawing attention to the apparent mismatch between statistical significance and biological relevance in Figures 5d–h. In response, we have reanalyzed the data using individual cell tracks and more stringent non-parametric statistical tests, as described above. This reanalysis confirmed that the magnitude of differences in migration speed and related parameters between the groups is minimal and not biologically meaningful. Thus, we no longer claim that sEVs significantly affect VSMC migration speed under these conditions in either 2D or 3D assays. Our revised manuscript now accurately reflects this finding in both the Results and Discussion sections, and the updated figures and legends clarify the true extent of any differences observed.

      Figure 6

      • Generally, the author's logic for looking into adhesion, focal adhesion and traction forces is hard to follow. If there are sEV-mediated migration differences, then there would inexorably be focal adhesion alterations. However, the data indicates few differences brought on by sEVs, which speaks to the lack of migration differences presented in Fig. 5. Overall, the sEV migration phenotype has so little of an effect, to then search for a mechanism seems destine to not turn up anything significant.

      We thank the reviewer for highlighting the importance of connecting the observed phenotypic effects of sEVs to the investigation of adhesion and focal adhesion mechanisms. While our revised analysis confirms that sEVs have little to no effect on VSMC migration speed or distance in 2D and 3D models, we did observe a robust effect of sEVs on the directionality of cell invasion (Figs. 5G and 5H). This prompted us to look more closely at pathways involved in cell guidance rather than bulk cell motility.

      Our proteomic comparison between larger EVs (10K fraction) and sEVs (100K fraction) revealed a unique adhesion complex present specifically on the sEVs—comprising collagen VI, TGFBI, LGALS3BP, and EDIL3 (Figs. 7A–C)—each of which has previously been implicated in integrin signaling, cell adhesion, or invasion. Functional blocking and knockdown studies further identified collagen VI as a key mediator in the regulation of cell adhesion and invasion directionality influenced by sEVs (Figs. 7F and 7I).

      In response to this mechanistic insight, we have modified the graphical abstract and discussion to clarify our approach:

      We now explicitly state that our focus has shifted from analyzing baseline migration speed to mechanisms guiding invasion directionality, in line with our key phenotypic findings.We highlight that the unique adhesion cluster identified on sEVs—including collagen VI and its cooperative partners—provides a strong mechanistic rationale for examining focal adhesion dynamics and ECM interactions, even in the absence of changes in migration velocity.Discussion excerpts (pages 13–14) have been updated to reflect this rationale and to summarize the potential significance of these findings for vascular biology and disease.

      We hope this clarifies the logic underlying our approach and justifies the mechanistic studies performed in this context:

      (1) Discussion, page 13, Ln 2  “Hence, it will be interesting in future studies to investigate whether sEVs can stimulate Rho activity by presenting adhesion modulators—particularly collagen VI—on their surface, thereby guiding cell directionality during invasion.”

      (2) Discussion, page 13, Ln 30  “In addition to collagen VI the unique adhesion cluster in VSMC-derived sEVS also includes EGF-like repeat and discoidin I-like domain-containing protein (EDIL3), transforming growth factor-beta-induced protein ig-h3 (TGFBI) and the lectin galactoside-binding soluble 3 binding protein (LGALS3BP) and these proteins are also directly implicated in activation of integrin signalling and cellular invasiveness85-87. Although we found that collagen VI plays the key role in sEV-induced early formation of FAs in VSMCs, it is tempting to speculate that the high sEV efficacy in stimulating FA formation is driven by cooperative action of this unique adhesion complex on the sEVs surface and targeting this novel sEV-dependent mechanism of VSMC invasion may open-up new therapeutic opportunities to modulate atherosclerotic plaque development or even to prevent undesired VSMC motility in restenosis”.    . 

      (3) Discussion, page 14, Ln 14 “In summary, cooperative activation of integrin signalling and F-actin cytoskeleton pathways results in the secretion of sEVs which associate with the ECM and play a signalling role by controlling FA formation and cell-ECM crosstalk. Further studies are needed to test these mechanisms across various cell types and ECM matrices.     ”.    

      Figure 7<br /> • The authors need to provide additional evidence Col IV is harbored in sEVs and not a contaminant of sEV isolation as VSMCs secrete a copious amount of this in culture. For instance, IHC of isolated sEVs stained for CD63 and Col IV as well as single cell staining of the same sort.

      We thank the reviewer for this important comment regarding the specificity of collagen VI detection in sEVs. To ensure that collagen VI is associated with bona fide sEVs—rather than being a contaminant resulting from high extracellular abundance—we performed a comparative analysis of vesicles isolated from the same conditioned media. Both proteomic mass spectrometry and western blotting revealed that collagen VI was exclusively present in the small EV (100K pellet) fraction and not in the larger EVs (10K pellet), as shown in Figs. 7B and 7C. Collagen VI was further identified in sEVs extracted from the ECM using our salt/guanidine protocol (new Fig. 7D).

      Reviewer #2 (Recommendations For The Authors):

      The authors have presented a nice collection of data with strong approaches to address their hypotheses. Nevertheless, an additional section within the Discussion would be welcome in addressing the potential limitations and important caveats to be considered alongside their study. These caveats and limitations could be reshaped by additional data supporting the ideas that: (1) small extracellular vesicles can be directly observed during their secretion from filopodia, (2) CD81 labeling in tissue can be interpreted clearly as extracellular vesicles and not the cell surface of other cell types (co-staining with an endothelial cell marker such as PECAM-1 perhaps), and (3) collagen VI within the vesicles is somehow accessed by adhesion molecules on the cell surface of migrating cells.

      We thank the reviewer for these important suggestions and we have now added further studies and modified our conclusions to reflect the data more accurately:

      (1) Results. Page 6, Ln37  “We also attempted to visualise sEV release in filopodia using CD63-pHluorin where fluorescence is only observed upon the fusion of MVBs with the plasma membrane39. Using total internal reflection fluorescence microscopy (TIRF) we observed the typical “burst”-like appearance of sEV secretion at the cell-ECM interface in full agreement with an earlier report showing MVB recruitment to invadopodia-like structures in tumor cells18 (Fig S2B and Supplementary Video S1). Although we also observed an intense CD63-pHluorin staining along filopodia-like structures we were not able to detect typical “burst”-like events to confirm sEV secretion in filopodia. (Fig S2C and Supplemental Video S1)”..  

      (2) Discussion, page 12, Ln18: “Here we report that β1 integrin activation triggers sEV release followed by sEV entrapment by the ECM. Curiously we observed CD63+ MVB transport toward the filopodia tips as well as inhibition of sEV-secretion with filopodia formation inhibitors suggesting that sEV secretion can be directly linked to filopodia but further studies are needed to define the contribution of this pathway to the overall sEV secretion by cells”..

      We quantified the colocalization of CD81 and CD31 to exclude the endothelial cell origin of sEVs and extended the characterisation of the atherosclerotic matrix as well as highlighting any limitations to interpretation ie re  CD81 ECM localisation: 

      (1) Results, page 8, Ln 43 “An enhanced expression of CD81 by endothelial cells in early atheroma has been previously reported so to study the contribution of CD81+ sEVs derived from endothelial cells  we investigated the localisation of CD31 and CD8145. In agreement with a previous study, we found that the majority of CD31 colocalises with CD81 (Thresholded Mander's split colocalization coefficient 0.54±0.11, N=6) indicating that endothelial cells express CD81 (Fig 4G)45. However, only a minor fraction of total CD81 colocalised with CD31 (Thresholded Mander's split colocalization coefficient 0.24±0.06, N=6) confirming that the majority of CD81 in the neointima is originating from the most abundant VSMCs.. 

      (2) Results, page 8, Ln 28: “To test if FN associates with sEV markers in atherosclerosis, we investigated the spatial association of FN with sEV markers using the sEV-specific marker CD81. Staining of atherosclerotic plaques with haematoxylin and eosin revealed well-defined regions with the neointima as well as tunica media layers formed by phenotypically transitioned or contractile VSMCs, respectively (Fig S4A). Masson's trichrome staining of atherosclerotic plaques showed abundant haemorrhages in the neointima, and sporadic haemorrhages in the tunica media (Fig S4B). Staining of atherosclerotic plaques with orcein indicated weak connective tissue staining in the atheroma with a confluent extracellular lipid core, and strong specific staining at the tunica media containing elastic fibres which correlated well with the intact elastin fibrils in the tunica media (Figs S4C and S4D). Using this clear morphological demarcation, we found that FN accumulated both in the neointima and the tunica media where it was significantly colocalised with the sEV marker, CD81 (Fig. 4D, 4E and 4F). Notably CD81 and FN colocalization was particularly prominent in cell-free, matrix-rich plaque regions (Figs. 4E and 4F). .”

      We showed that collagen VI is presented on the surface of sEVs:

      (1) Results, page 10, Ln43: “Collagen VI was the most abundant protein in VSMC-derived sEVs (Fig 7B, Table S7) and  was previously implicated in the interaction with the proteoglycan NG253 and suppression of cell spreading on FN54. To confirm the presence of collagen VI in ECM-associated sEVs we analysed sEVs extracted from the 3D matrix using 0.5M NaCl treatment and showed that both collagen VI and FN are present (Fig 7D). Next, we analysed the distribution of collagen VI using dot-blot. Alix staining was bright only upon permeabilization of sEV indicating that it is preferentially a luminal protein (Fig 7E). On the contrary, CD63 staining was similar in both conditions showing that it is surface protein (Fig 7E). Interestingly, collagen VI staining revealed that 40% of the protein is located on the outside surface with 60% in the sEV lumen (Fig 7E)

      AuthorResponse
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  37. osf.io osf.io
    Common psychiatric treatments alter affective dynamics
    2
    1. Public_Reviews 05 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      This study examines how two common psychiatric treatments, antidepressant medication and cognitive distancing, influence baseline levels and moment-to-moment changes in happiness, confidence, and engagement during a reinforcement learning task. Combining a probabilistic selection task, trial-by-trial affect ratings, psychiatric questionnaires, and computational modeling, the authors demonstrate that each treatment has distinct effects on affective dynamics. Notably, the results highlight the key role of affective biases in how people with mental health conditions experience and update their feelings over time, and suggest that interventions like cognitive distancing and antidepressant medication may work, at least in part, by shifting these biases.

      Strengths:

      (1) Addresses an important question: how common psychiatric treatments impact affective biases, with potential translational relevance for understanding and improving mental health interventions.

      (2) The introduction is strong, clear, and accessible, making the study approachable for readers less familiar with the underlying literature.

      (3) Utilizes a large sample that is broadly representative of the UK population in terms of age and psychiatric symptom history, enhancing generalizability.

      (4) Employs a theory-driven computational modeling framework that links learning processes with subjective emotional experiences.

      (5) Uses cross-validation to support the robustness and generalizability of model comparisons and findings.

      Weaknesses:

      The authors acknowledge the limitations in the discussion section.

      Additional questions:

      (1) Group Balance & Screening for Medication Use: How many participants in the cognitive distancing and control groups were taking antidepressant medication? Why wasn't medication use included as part of the screening to ensure both groups had a similar number of participants taking medication?

      (2) Assessment of the Practice of Cognitive Distancing: Is there a direct or more objective method to evaluate whether participants actively engaged in cognitive distancing during the task, and to what extent? Currently, the study infers engagement indirectly through the outcomes, but does not include explicit measures of participants' use of the technique. Would including self-report check-ins throughout the task, asking participants whether they were actively engaging in cognitive distancing, have been useful? However, including frequent self-report check-ins would increase procedural differences between groups, making perhaps the tasks less comparable beyond the intended treatment manipulation. Maybe incorporating a question at the end of the task, asking how much they engaged in cognitive distancing, could offer a useful measure of subjective engagement without overly disrupting the task flow.

      Conclusion:

      This study advances our understanding of the mechanisms underlying mental health interventions. The combination of computational modeling with behavioral and affective data offers a powerful framework for understanding how treatments influence affective biases and dynamics. These findings are of broad interest across clinical and mental health sciences, cognitive and affective research, and applied translational fields focused on improving psychological well-being.

      Review 1
    2. Public_Reviews 05 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      The present manuscript investigates and proposes different mechanisms for the effects of two therapeutic approaches - cognitive distancing technique and use of antidepressants - on subjective ratings of happiness, confidence, and task engagement, and on the influence of such subjective experiences on choice behavior. Both approaches were found to link to changes in affective state dynamics in a choice task, specifically reduced drift (cognitive distancing) and increased baseline (antidepressant use). Results also suggest that cognitive distancing may reduce the weighing of recent expected values in the happiness model, while antidepressant use may reduce forgetting of choices and outcomes.

      Strengths:

      This is a timely topic and a significant contribution to ongoing efforts to improve our mechanistic understanding of psychopathology and devise effective novel interventions. The relevance of the manuscript's central question is clear, and the links to previous literature and the broader field of computational psychiatry are well established. The modelling approaches are thoughtful and rigorously tested, with appropriate model checks and persuasive evidence that modelling complements the theoretical argument and empirical findings.

      Weaknesses:

      Some vagueness and lack of clarity in theoretical mechanisms and interpretation of results leave outstanding questions regarding (a) the specific links drawn between affective biases, therapies aimed at mitigating them, and mental health function, and (b) the structure and assumptions of the modelling, and how they support the manuscript's central claims. Broadly, I do not fully understand the distinction between how choice behavior vs. affect are impacted separately or together by cognitive distancing. Clarification on this point is needed, possibly through a more explicit proposal of a mechanism (or several alternative mechanisms?) in the introduction and more explicit interpretation of the modelling results in the context of the cyclical choice-affect mechanism.

      (1) Theoretical framework and proposed mechanisms

      The link between affective biases and negative thinking patterns is a bit unclear. The authors seem to make a causal claim that "affective biases are precipitated and maintained by negative thinking patterns", but it is unclear what precisely these negative patterns are; earlier in the same paragraph, they state that affective biases "cause low mood" and possibly shift choices toward those that maintain low mood. So the directionality of the mechanism here is unclear - possibly explaining a bit more of the cyclic nature of this mechanism, and maybe clarifying what "negative thinking patterns" refer to will be helpful.

      More generally, this link between affect and choices, especially given the modelling results later on, should be clarified further. What is the mechanism by which these two impact each other? How do the models of choice and affect ratings in the RL task test this mechanism? I'm not quite sure the paper answers these questions clearly right now.

      The authors also seem to implicitly make the claim that symptoms of mental ill-health are at least in part related to choice behavior. I find this a persuasive claim generally; however, it is understated and undersupported in the introduction, to the point where a reader may need to rely on significant prior knowledge to understand why mitigating the impact of affective biases on choice behavior would make sense as the target of therapeutic interventions. This is a core tenet of the paper, and it would be beneficial to clarify this earlier on.

      It would be helpful to interpret a bit more clearly the findings from 3.4. on decreased drift in all three subjective assessments in the cognitive distancing group. What is the proposed mechanism for this? The discussion mentions that "attenuated declines [...] over time, [add] to our previously reported findings that this psychotherapeutic technique alters aspects of reward learning" - but this is vague and I do not understand, if an explanation for how this happens is offered, what that explanation is. Given the strong correlation of the drift with fatigue, is the explanation that cognitive distancing mitigates affect drift under fatigue? Or is this merely reporting the result without an interpretation around potential mechanisms?

      (Relatedly, aside from possibly explaining the drift parameter, do the fatigue ratings link with choice behavior in any way? Is it possible that the cognitive distancing was helping participants improve choices under fatigue?)

      (2) Task Structure and Modelling

      It is unclear what counted as a "rewarding" vs. "unrewarding" trial in the model. From my understanding of the task description, participants obtained positive or no reward (no losses), and verbal feedback, Correct/Incorrect. But given the probabilistic nature of the task, it follows that even some correct choices likely had unrewarding results. Was the verbal feedback still "Correct" in those cases, but with no points shown? I did not see any discussion on whether it is the #points earned or the verbal feedback that is considered a reward in the model. I am assuming the former, but based on previous literature, likely both play a role; so it would be interesting - and possibly necessary to strengthen the paper's argument - to see a model that assigns value to positive/negative feedback and earned points separately.

      From a theory perspective, it's interesting that the authors chose to assume separate learning rates for rewarding and non-rewarding trials. Why not, for example, separate reward sensitivity parameters? E.g., rather than a scaling parameter on the PE, a parameter modifying the r term inside the PE equation to, perhaps, assign different values to positive and zero points? (While I think overall the math works out similarly at the fitting time, this type of model should be less flexible on scaling the expected value and more flexible on scaling the actual #points / the subjective experience of the obtained verbal feedback, which seems more in line with the theoretical argument made in the introduction). The introduction explicitly states that negative biases "may cause low mood by making outcomes appear less rewarding" - which in modelling equations seems more likely to translate to different reward-perception biases, and not different learning rates. Alternatively, one might incorporate a perseveration parameter (e.g., similar to Collins et al. 2014) that would also accomplish a negative bias. Either of these two mechanisms seems perhaps worth testing out in a model - especially in a model that defines more clearly what rewarding vs. unrewarding may mean to the participant.

      If I understand correctly, the affect ratings models assume that the Q-value and the PE independently impact rating (so they have different weights, w2 and w3), but there is no parameter allowing for different impact for perceived rewarding and unrewarding outcomes? (I may be misreading equations 4-5, but if not, Q-value and PE impact the model via static rather than dynamic parameters.) Given the joint RL-affect fit, this seems to carry the assumption that any perceptual processing differences leading to different subjective perceptions of reward associated with each outcome only impact choice behavior, but not affect? (whereas affect is more broadly impacted, if I'm understanding this correctly, just by the magnitude of the values and PEs?) This is an interesting assumption, and the authors seem to have tested it a bit more in the Supplementary material, as shown in Figure S4. I'm wondering why this was excluded from the main text - it seems like the more flexible model found some potentially interesting differences which may be worth including, especially as they might shed additional insight into the influence of cognitive distancing on the cyclical choice-affect mechanisms proposed.

      Minor comments:

      If fatigue ratings were strongly associated with drift in the best-fitting model (as per page 13), I wonder if it would make sense to use those fatigue ratings as a proxy rather than allow the parameter to vary freely? (This does not in any way detract from the winning model's explanatory power, but if a parameter seems to be strongly explained by a variable we have empirical data for, it's not clear what extra benefit is earned by having that parameter in the model).

      Review 3
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  38. ia601403.us.archive.org ia601403.us.archive.org
    Public_Syllabus_Aug 7 of Community_syllabus 2025 AI without Bosses - Google Docs
    1
    1. gyuri 05 Sep 2025
      Week 14: PEER-TO-PEER WRITING (Thursday, December 4, 2025)

      peer-to-peer writing

      If only we could have that in an atuonomous setting that would be the right counterballance

      Gives a good target date

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  39. socialsci.libretexts.org socialsci.libretexts.org
    1.1: Who Are You, Really?
    13
    2. imartinez006103 05 Sep 2025
      ________________________________________________________
      1. I anticipate being in college for 2-4 years to become a labor and delivery nurse.
      2. I would probably have to take about 3-5 per term to finish in my time period. 3.I think the most difficult part will be making time for everything
      3. I do believe I can overcome any difficulties that will come my way.
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  40. www.jasonwei.net www.jasonwei.net
    Some intuitions about large language models — Jason Wei
    1
    1. tiendat 05 Sep 2025
      Intuition 3. Tokens can have very different information density, so give language models time to think.

      important I guess he realized this by examing the behaviors and output of LLMs. Then what happens next is that it inspires him about the intermediate thinking chains of thoughts

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  41. novaonline.nvcc.edu novaonline.nvcc.edu
    M2 Hypothesis Annotation Two Articles March 2024.pdf
    1
    1. AmyStieg 05 Sep 2025
      Sen. Bill Cassidy (R-La.), the ranking member of the HELP Committee, tore into the plan

      The word, "tore" is an example of bias and slant because it tries to show that Cassidy is more powerful because he is in a position to rip someone else's work apart. It makes one think of the teacher/student or parent/child role where the more powerful person would be the one who is able to tear a plan apart. This also comes on the heals of 3 quotes that are sensational in themselves because they use words like "insane", "(Sanders) he would know nothing about this," and likens the idea to elementary school student government. All of these are meant to minimize Sanders as a serious person and minimize the issue a flippant and silly.

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  42. socialsci.libretexts.org socialsci.libretexts.org
    3.4: Language, Society, and Culture
    1
    1. ajaunijohnson123 04 Sep 2025
      Discuss some of the social norms that guide conversational interaction. Identify some of the ways in which language varies based on cultural context. Explain the role that accommodation and code-switching play in communication. Discuss cultural bias in relation to specific cultural identities.

      When we talk with others, there are social rules like taking turns and making eye contact that help things flow smoothly. How people use language changes a lot depending on their culture—some are more formal, others more laid-back, and what’s polite somewhere might not be in another place. Sometimes we switch up how we talk or change our style to fit in better, which is called accommodation or code-switching. Cultural bias is when we judge people unfairly based on where they come from, and that can cause misunderstandings. Knowing about these biases helps us be more respectful and avoid stepping on toes.

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  43. socialsci.libretexts.org socialsci.libretexts.org
    3.3: Using Words Well
    1
    1. ajaunijohnson123 04 Sep 2025
      Discuss how the process of abstraction and the creation of whole messages relate to language clarity. Employ figurative and evocative language. Identify strategies for using language ethically.

      Sure thing! Here’s a more casual, natural version of that summary:

      Abstraction is when we use general or vague words, which can make things confusing sometimes. To get your point across clearly, it helps to add specific details along with the bigger ideas. Using things like metaphors or colorful descriptions makes what you’re saying more interesting and easier to remember. It’s also important to be honest and respectful with your words, so you don’t mislead or hurt anyone. When you communicate clearly and ethically, it builds trust and helps people understand each other better.

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  44. socialsci.libretexts.org socialsci.libretexts.org
    3.2: Functions of Language
    1
    1. ajaunijohnson123 04 Sep 2025
      Identify and discuss the four main types of linguistic expressions. Discuss the power of language to express our identities, affect our credibility, control others, and perform actions. Discuss some of the sources of fun within language. Explain how neologisms and slang contribute to the dynamic nature of language. Identify the ways in which language can separate people and bring them together.

      Linguistic expressions come in different forms words, phrases, sentences, and whole conversations. Language is a way we show who we are, earn trust, influence people, and even do things like make promises just by talking. It’s also a lot of fun, with jokes, puns, and playful ways of using words that keep things interesting. New slang and made-up words keep language fresh and always changing. Sometimes language can make people feel left out, but it also has the power to bring us together and create a sense of belonging.

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    socialsci.libretexts.org/Bookshelves/Communication/Introduction_to_Communication/Communication_in_the_Real_World_-_An_Introduction_to_Communication_Studies/03:_Verbal_Communication/3.02:_Functions_of_Language
  45. socialsci.libretexts.org socialsci.libretexts.org
    3.1: Language and Meaning
    1
    1. ajaunijohnson123 04 Sep 2025
      Explain how the triangle of meaning describes the symbolic nature of language. Distinguish between denotation and connotation. Discuss the function of the rules of language. Describe the process of language acquisition.

      The triangle of meaning is all about how words are just symbols that connect to the ideas we have, not the actual things themselves. Denotation is the plain, dictionary meaning of a word, while connotation is the extra feelings or thoughts that come with it. Language has rules like grammar and word order that help us make sense of what people are saying. We pick up language naturally as kids by listening and copying the people around us. Over time, this helps us learn new words and figure out how to put them together the right way.

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  46. www.biorxiv.org www.biorxiv.org
    Remote automated delivery of mechanical stimuli coupled to brain recordings in behaving mice
    2
    1. Public_Reviews 04 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      This report describes the development and initial applications of the ARM (Automated Reproducible Mechano-stimulator), a programmable tool that delivers various mechanical stimuli to a select target (most frequently, a rodent hindpaw). Comparisons to traditional testing methods (e.g., experimenter application of stimuli) reveal that the ARM reduces variability in the anatomical targeting, height, velocity, and total time of stimulus application. Given that the ARM can be controlled remotely, this device was also used to assess effects of experimenter presence on reflexive responses to mechanical stimulation. Although not every experimenter had notable sex-dependent effects on animal behavior, use of the ARM never had this effect (for obvious reasons!). Lastly, the ARM was used to stimulate rodent hindpaws while measuring neuronal activity in the basolateral nucleus of the amygdala (BLA), a brain region that is associated with the negative affect of pain. This device, and similar automated devices, will undoubtedly reduce experimenter-related variability in reflexive mechanical behavior tests; this may increase experimental reproducibility between laboratories who are able to invest in this type of technology.

      Strengths:

      Clear examples of variability in experimenter stimulus application are provided and then contrasted with uniform stimulus application that is inherent to the ARM.

      The ARM is able to quickly oscillate between delivery of various mechanical stimuli; this is advantageous for experimental efficiency.

      New additions to the ARM and PAWS platforms have been methodically tested to ensure reproducibility and reliability.

      Review 3
    2. Public_Reviews 04 Sep 2025

      Author response:

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

      Reviewer #1 (Public review):

      (1) Given the mechanical nature of the device and the propensity for mice to urinate on things, I also wonder how frequently the device breaks/needs to be repaired. Perhaps some details regarding the cost and reliability of the device would be helpful to include, as these are the two things that could make researchers hesitant to adopt immediately.

      We thank the reviewer for their astute observations. We also noted the problem of mouse waste and incorporated this concern into the redesign we mention in the text.

      “Mouse waste getting on mechanical parts was found to be a major concern for the initial version of the device. As part of the redesign, the linear stages were moved out from under the mice to avoid this problem. Despite this problem, the original version of the device has not had any of its stages break down yet. A common problem though was that stimulus tips would blunt or break if they hit the mesh of the mesh table, requiring replacement. This has been solved in the latest version through a new feature where the mesh is detected via the force sensor, prompting immediate stimulus withdrawal, avoiding damage.”

      In regards to cost and adoption, we have added this sentence to the final line of the discussion:

      “To promote wide adaptation of this device across as many labs as possible, a company, Tactorum Inc., has been formed.”

      (2) The only major technical concern, which is easy to address, is whether the device generates ultrasonic sounds that rodents can hear when idle or operational, across the ultrasonic frequencies that are of biological relevance (20-110 kHz). These sounds are generally alarm vocalizations and can create stress in animals, and/or serve as cues of an impending stimulus (if indeed they are produced by the device).

      The reviewer brings up an interesting question. The ARM does not make a lot of noise, but some of the noise it emits does range into the 20-110 kHz range, though besides this does not qualitatively have other similarities to a mouse vocalization. Based on this we tested whether the noise produced by the ARM causes stress in naïve mice.

      “A concern was raised that the noise of the ARM may cause stress in the mice tested. To test this, the open field test was performed with naïve mice (n=10) 2 feet from the ARM while the ARM either sat silent or ran through its habituation program, producing noise. The mouse's center point movement was then tracked in relation to the chamber, its edges, and center. No significant differences were found in distance traveled, center entrances, center, time in center, and latency to center entrance based on a student’s two-tailed t-test (Figure S1D-G). Based on this, neither stress nor locomotion differences were detected by this test, indicating the ARM does not induce an increased stress state due to its noise, even in non-habituated mice.”

      (3) This sentence in the intro may be inaccurate: "or the recent emergence of a therapeutic targeting voltage-gated sodium channels, that block pain in both rodents and humans such as VX-548 for NaV1.8 (Jones 2023)" Despite extensive searching, I have been unable to find a reference showing that VX-548 is antinociceptive in rodents (rats or mice). As for why this is the case, I do not know. One speculation: this drug may be selective for the human Nav1.8 channel (but again, I have found no references comparing specificity on human vs rodent Nav1.8 channels). To not mislead the field into thinking VX-548 works for rodents and humans, please remove "both rodents and" from the sentence above (unless you find a reference supporting VX-548 as being effective in pain assays with rodents. There is a PK/PD paper with rodents, but that only looks at drug metabolism, not efficacy with pain assays).

      We agree with the reviewer and have removed mention of the new Nav1.8 therapeutic also working in rodents.

      (4) In the intro paragraph where variability in measuring mechanical stimuli is described, there is a new reference from the Stucky lab that further supports the need for an automated way to measure allodynia, as they also found variability between experimenters. This would be a relevant reference to include: Rodriguez Garcia (2024) PMID: 38314814.

      Thanks to the reviewer for this relevant citation and we have updated the text to incorporate this:

      “Recent studies utilizing the manual highspeed analysis of withdrawal behavior analysis developed by Abdus-Saboor et al. 2019 has reproduced this sizable experimenter effect using the new technique. (Rodríguez García 2024)”

      (5) "a simple sin wave motion": should be "sine", correct throughout (multiple instances of "sin")

      Corrections made where relevant.

      Reviewer #2 (Public review):

      (1) ARM seems like a fantastic system that could be widely adopted, but no details are given on how a lab could build ARM, thus its usefulness is limited.

      The reviewer raises a good point, unfortunately the authors are constrained by university policies around patent law. That said efforts are being made to make the ARM widely available to interested researchers. As mentioned above to Reviewer 1’s comments, we end the discussion section with this sentence:

      “To promote wide adaptation of this device across as many labs as possible, a company, Tactorum Inc., has been formed.”

      (2) The ARM system appears to stop short of hitting the desired forces that von Frey filaments are calibrated toward (Figure 2). This may affect the interpretation of results.

      The reviewer gives an important observation. We amended the text to include more clarity on the max forces induced, and comments on causes beyond the delivery mechanism. It should be noted that a newly bought fresh set of von Frey’s was used.

      “With the same 1.4 and 2 g von Frey filaments Researcher 1 delivered max average forces of 1.5 g and 2.7 g, and Researcher 2 1.35 g and 2.4 g. The ARM delivered average max forces closest to the targeted forces, with 1.36 g and 1.9 g. (Figure 2C) Some of the error observed could be due to the error rate (+/- 0.05 g) in the force gauge and the von Frey set used.”

      (3) The authors mention that ARM generates minimal noise; however, if those sounds are paired with stimulus presentation, they could still prompt a withdrawal response. Including some 'catch' trials in an experiment could test for this.

      The reviewer makes a very useful suggestion that we incorporated into our carrageenan experiments. This new data can be found in Supplemental Figure 3F.

      “For the carrageenan model, three replicates of the force ramp stimulus were delivered to each paw, and catch trials were performed every 3<sup>rd</sup> trial to test whether the mice would respond to the noise of the ARM alone. During catch trials, the stimulus was delivered to the open air behind the mouse, and any movement within 5 seconds of stimulus delivery was counted as a response. These trials found a 96% response rate in true trials, with only a 7% rate in catch trials, indicating responses were not being driven by device noise.”

      (4) The experimental design in Figure 2 is unclear- did each experimenter have their own cohort of 10 mice, or was a single cohort of mice shared? If shared, there's some concern about repeat testing.

      Further clarification was added to avoid confusion on the methods used here.

      “Separate cohorts of 10 mice were used for ARM and manual delivery, with a week given between each researcher to avoid sensitization.”

      (5) In Figure 5 and S4, the order of the legends does not match the order of the graphs. This can be particularly confusing as the color scheme is not colorblind-friendly. Please consider revising the presentation of these figures.

      Corrections made where relevant.

      Reviewer #3 (Public review):

      (1) Limited details are provided for statistical tests and inappropriate claims are cited for individual tests. For example, in Figure 2, differences between researchers at specific forces are reported to be supported by a 2-way ANOVA; these differences should be derived from a post-hoc test that was completed only if the independent variable effects (or interaction effect) were found to be significant in the 2-way ANOVA. In other instances, statistical test details are not provided at all (e.g., Figures 3B, 3C, Figure 4, Figure 6G).

      We would like to thank the reviewer for pointing out the lack of clarity in the text on these statistical methods. We have added further details across the manuscript and shown below here in order to address this concern.

      “Both manual delivery and the ARM produced significant paw withdrawal percentage curves, a standard traditional measurement of mechanical sensitivity in the field (von Frey 1896, Dixon 1980, Chaplan 1994)(Figure 2E), with a 2-way ANOVA and a posthoc Tukey test detecting significant increases in comparing the 3 lower force VFH’s (0.02g, 0.07g, 0.16g) to the 2 highest force VFH’s (1g, 1.4g). This demonstrates that the ARM delivers results comparable to highly experienced researchers. However, a 2-way ANOVA and a posthoc Tukey test found that Researcher 2 elicited a significantly higher (p=0.0008) paw withdrawal frequency than Researcher 1 (Figure S2A) which corresponded with Researcher 2’s higher VFH application time as measured by the force sensor (Figure 2B).”

      “Adjustments were then made to the PAWS software to automate the measurement of withdrawal latency based on pose tracking data of the withdrawal response and the trajectory of the stimulus delivery encoded into the ARM. Testing of C57/BL6J (n=15) at baseline found significant decreases in withdrawal latency for pinprick compared to cotton swab stimuli delivered in identical ways by the ARM (Figure 3B) based on a 2-tailed student t-test.”

      “Mice injected with carrageenan (n=15) showed elevated shaking behavior (p=0.0385, 2-way ANOVA and a posthoc Tukey test) in response to pinprick stimuli in comparison to measurements at baseline (Figure 3C).”

      “Remote habituated mice showed a significant decrease (p=0.0217, 2-way ANOVA) in time to rest over the 3 days (Figure 4B), but no significant differences for any single day. The number of turns was measured for each group during the first 10 minutes of day 1 to act as a baseline, and then from 20 to 30 minutes for each day. Turn counts were then compared as a percentage of the baseline count for each group. This period was chosen as it the period when experiments start after the day of habituation on experimental days. It was found that remote-habituated mice showed significantly less turning on day 2 compared to mice habituated with a researcher present (p=0.024, 2-way ANOVA posthoc Tukey test), and that only the remote-habituated mice showed significantly decreased turning behavior on day 3 compared to day 1 (p=0.0234, 2-way ANOVA posthoc Tukey test) (Figure 4C).”

      “Sex-dependent differences were found in reflexive and affective behavioral components of the mouse withdrawal response when a researcher was present versus not for both reactions to innocuous and noxious stimuli. A 2-way ANOVA and a posthoc Tukey test found that cotton swab stimuli elicited increased male mouse reflexive paw withdrawal features, including max paw height (p=0.0413) and max paw velocity (Y-axis) (p=0.0424) when Researcher 1 was present compared to when no researcher was present (Figure 4E-F). Pinprick stimuli (Figure 4H-I) on the other hand led to increased max paw height (p=0.0436) and max paw velocity (Y-axis) (p=0.0406) in male mice compared to female mice when Researcher 1 was present.

      Analysis of the shaking behavior elicited by cotton swab and pinprick stimuli found no significant differences in shaking behavior duration (Figure 4SA-B) but found sex-dependent differences in paw distance traveled after the initial withdrawal, including during shaking and guarding behaviors. For cotton swab (Figure 4G) male mice showed significantly increased paw distance traveled compared to female mice when Researcher 2 was present (p=0.0468, 2-way ANOVA posthoc Tukey test) but not when Researcher 2 was present or no researcher was present. Pinprick stimuli also elicited sex-based increases in paw distance traveled (Figure 4J) in male mice when Researcher 2 was present compared to both male mice when no researcher was present (p=0.0149, 2-way ANOVA posthoc Tukey test) and female mice when Researcher 1 was present (p=0.0038, 2-way ANOVA posthoc Tukey test).”

      (2) In the current manuscript, the effects of the experimenter's presence on both habituation time and aspects of the withdrawal reflex are minimal for Researcher 2 and non-existent for Research 1. This is surprising given that Researcher 2 is female; the effect of experimenter presence was previously documented for male experiments as the authors appropriately point out (Sorge et al. PMID: 24776635). In general, this argument could be strengthened (or perhaps negated) if more than N=2 experiments were included in this assessment.

      The reviewer makes an important point regarding this data and the need for further experiments. We designed a new set of experiments to examine the effect of male and female researchers overall. It should be noted that this is rather noisy data given it was collected by three sets of male and female researchers over 3 weeks. That said a significant difference was found between mouse sexes when a male researcher was present. This is consistent with previous data, but as we discuss this does not invalidate previous data as researcher gender appears to be only one of the factors at work in researcher presence effects on mouse behavior, leading to individuals having the potential for greater or lesser effects than their overall gender. Our new results can be found in Figure 4K.

      “These results indicate that researcher presence at baseline can lead to significant differences in reflexive and affective pain behavior. In this case, male mice showed increased behavioral responses to both touch and pain behavior depending on whether the researcher was present. This led to sex differences in the affective and reflexive component of the withdrawal response when a researcher is present, which disappears when no researcher is present, or a different researcher is present. For this set of researchers, the female researcher elicited the greater behavioral effect. This appeared at first to contradict previous findings (Sorge 2024, Sorge 2014), but it was hypothesized that the effect of an individual researcher could easily vary compared to their larger gender group. To test this, 6 new researchers, half male and half female, were recruited and a new cohort of mice (n=15 male, n=15 female) was tested in each of their presence over the course of 3 weeks, controlling for circadian rhythms (Figure 4K). The newly added force ramp stimulus type was used for these experiments, with three replicates per trial, to efficiently measure mechanical threshold in a manner comparable to previous work. It was found that female mice showed significantly decreased mechanical threshold compared to male mice (p=0.034, Šídák's multiple comparisons test and student’s t-test) when a male researcher was present. This did not occur when a female researcher or no researcher was present. In the latter case of slight trend towards this effect was observed, but it was not significant (p=0.21), and may be the result of a single male researcher being responsible for handling and setting up the mice for all experiments.”

      “These findings indicate that sex-dependent differences in evoked pain behavior can appear and disappear based on which researcher/s are in the room. There is a trend towards male researchers overall having a greater effect, but individuals may have a greater or lesser effect on mouse behavior, independent of the gender or sex. This presents a confound that must be considered in the analysis of sex differences in pain and touch behavior which may explain some of the variation in findings from different researchers. Together, these results suggest that remote stimulus delivery may be the best way to eliminate variation caused by experimenter presence while making it easier to compare with data from researchers in your lab and others.”

      (3) The in vivo BLA calcium imaging data feel out of place in this manuscript. Is the point of Figure 6 to illustrate how the ARM can be coupled to Inscopix (or other external inputs) software? If yes, the following should be addressed: why do the up-regulated and down-regulated cell activities start increasing/decreasing before the "event" (i.e., stimulus application) in Figure 6F? Why are the paw withdrawal latencies and paw distanced travelled values in Figures 6I and 6J respectively so much faster/shorter than those illustrated in Figure 5 where the same approach was used?

      Thanks to the reviewer for bringing up this concern. We have included further text discussing this behavioral data and how it compares to previous work in this study.

      “Paw height and paw velocity were found to be consistent with data from figures 4E-I (male researcher and male mice) and 5C (stimulus intensity 2.5 and 4.5) for similar data, with slightly elevated measures of paw distance traveled and decreased paw withdrawal latency for the pinprick stimulus. This was likely caused by sensitization due to multiple stimulus deliveries over the course of the experiment, as due to logistics, 30 stimulus trials were delivered per session due to logistical constraints vs the max of 3 that were performed during previous experiments.”

      “This data indicates that the ARM is an effective tool for efficiently correlating in vivo imaging data with evoked behavioral data, including sub-second behavior. One limitation is that the neural response appears to begin slightly before stimulus impact (Figure 6F, 6SB). This was likely caused by a combination of the imprecise nature of ARM v1 paw contact detection and slight delays in the paw contact signal reaching the Inscopix device due to flaws in the software and hardware used, slowing down the signal. Improvements have been made to eliminate this delay as part of the ARM v2, which have been shown to eliminate this delay in vivo fiber photometry data recorded as part of new projects using the device.”

      (4) Another advance of this manuscript is the integration of a 500 fps camera (as opposed to a 2000 fps camera) in the PAWS platform. To convince readers that the use of this more accessible camera yields similar data, a comparison of the results for cotton swabs and pinprick should be completed between the 500 fps and 2000 fps cameras. In other words, repeat Supplementary Figure 3 with the 2000 fps camera and compare those results to the data currently illustrated in this figure.

      The reviewer makes a good point about the need for direct comparison between 500 fps and 2000 fps data. To address this we added data from same mice, from 2 weeks prior with a comparable set up. These new results can be found in Supplemental Figure 3.

      “Changes were made to PAWS to make it compatible with framerates lower than 2000 fps. This was tested using a 0.4 MP, 522 FPS, Sony IMX287 camera recording at 500 fps, and data recorded at 2000 fps by the previously used photron fastcam (Figure 3SC-F). The camera paired with PAWS was found to be sufficient to separate between cotton swab and pinprick withdrawal responses, suggesting it may be a useful tool for labs that cannot invest in a more expensive device. PAWS features measured from 500 fps video data were not significantly different from the 2000 fps data based on a 2 way ANOVA.”

      (5) In Figure 2F, the authors demonstrate that a von Frey experiment can be completed much faster with the ARM vs. manually. I don't disagree with that fact - the data clearly show this. I do, however, wonder if the framing of this feature is perhaps too positive; many labs wait > 30 s between von Frey filament applications to prevent receptive field sensitization. The fact that an entire set of ten filaments can be applied in < 50 s (< 3 s between filaments given that each filament is applied for 2 s), while impressive, may never be a feature that is used in a real experiment.

      The reviewer makes an important point about how different researchers perform these tests and the relevant timings. We have moderated the framing of these results to address this concern.

      “Further, we found that the ARM decreased the time needed to apply a stimulus 10 times to a mouse paw by 50.9% compared to manual delivery (Figure 2F). This effect size may decrease for researchers who leave longer delays between stimulus delivery, but the device should still speed up experiments by reducing aiming time and allowing researchers to quickly switch to a new mouse while waiting for the first.”

      (6) Why are different affective aspects of the hindpaw withdrawal shown in different figures? For example, the number of paw shakes is shown in Figure 3C, whereas paw shaking duration is shown in Figure 5D. It would be helpful - and strengthen the argument for either of these measures as being a reproducible, reliable measure of pain - if the same measure was used throughout.

      Thanks to the reviewer for pointing out this discrepancy. We have adjusted the figures and text to only use the Number of Paw Shakes for better consistency (Figure 5D and Figure 5-figure supplement 1C).

      (7) Is the distance the paw traveled an effective feature of the paw withdrawal (Figure 5E)? Please provide a reference that supports this statement.

      A relevant citation and discussion of this metric based on previous studies has been added.

      “Mice injected with carrageenan (n=15) showed elevated shaking behavior (p=0.0385) in response to pinprick stimuli in comparison to measurements at baseline (Figure 3C). This aligned with previous findings where PAWS has detected elevations in shaking and/or guarding behavior, examples of affective pain behavior, and post-peak paw distance traveled, which correlates with these behaviors in carrageenan pain models and has been to found to be a good measure of them in past studies (Bohic et al. 2023).”

      (8) Dedek et al. (PMID: 37992707) recently developed a similar robot that can also be used to deliver mechanical stimuli. The authors acknowledge this device's ability to deliver optogenetic and thermal stimuli but fail to mention that this device can deliver mechanical stimuli in a similar manner to the device described in this paper, even without experimenter targeting. Additional discussion of the Dedek et al. device is warranted.

      We would like to thank the reviewer for identifying  this omission. Discussion of this as well as further discussion of Dedek et al.’s automation prototyping work has been added.

      “Previous attempts at automating mechanical stimulus delivery, including the electronic von Frey (Martinov 2013) and dynamic plantar asthesiometer (Nirogi 2012), have focused on eliminating variability in stimulus delivery. In contrast to the ARM, both of these devices rely upon a researcher being present to aim or deliver the stimulus, can only deliver vFH-like touch stimuli, and only measure withdrawal latency/force threshold. Additionally, progress has been made in automating stimulus assays by creating devices with the goal of delivering precise optogenetic and thermal stimuli to the mouse’s hind paw (Dedek 2023, Schorscher-Petchu 2021). The Prescott team went farther and incorporated a component into their design to allow for mechanical stimulation but this piece appears to be limited to a single filament type that can only deliver a force ramp. As a result these devices and those previously discussed lack of customization for delivering distinct modalities of mechanosensation that the ARM allows for. Moreover, in its current form the automated aiming of some of these devices may not provide the same resolution or reliability of the ARM in targeting defined targets (Figure 1C), such as regions of the mouse paw that might be sensitized during chronic pain states. Due to the nature of machine learning pose estimation, substantial work, beyond the capacity of a single academic lab, in standardizing the mouse environment and building a robust model based on an extensive and diverse training data set will be necessary for automated aiming to match the reliability or flexibility of manual aiming. That said, we believe this work along with that of that of the other groups mentioned has set the groundwork from which a new standard for evoked somatosensory behavior experiments in rodents will be built.”

      (9) Page 2: von Frey's reference year should be 1896, not 1986.

      This typo has been fixed, thanks to the reviewer for noting it.

      “For more than 50 years, these stimuli have primarily been the von Frey hair (vFH) filaments that are delivered to the mouse paw from an experimenter below the rodent aiming, poking, and subsequently recording a paw lift (von Frey 1896, Dixon 1980, Chaplan 1994).”

      (10) Page 2: Zumbusch et al. 2024 also demonstrated that experimenter identification can impact mechanical thresholds, not just thermal thresholds.

      Text has been updated in order to note this important point.

      “A meta-analysis of thermal and mechanical sensitivity testing (Chesler 2002, Zumbusch 2024) found that the experimenter has a greater effect on results than the mouse genotype, making data from different individual experimenters difficult to merge.”

      (11) Page 2: One does not "deliver pain in the periphery". Noxious stimuli or injury can be delivered to the periphery, but by definition, pain is a sensation that requires a central nervous system.

      Text has been updated for improved accuracy.

      “Combining approaches to deliver painful stimuli with techniques mapping behavior and brain activity could provide important insights into brain-body connectivity that drives the sensory encoding of pain.”

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    biorxiv.org/content/10.1101/2024.05.06.592101v2
  47. www.planalto.gov.br www.planalto.gov.br
    L5172COMPILADO
    2
    1. hvs 04 Sep 2025
      III
      • Informativo nº 860
      • 2 de setembro de 2025.
      • PRIMEIRA TURMA
      • Processo: AgInt no REsp 2.109.509-RS, Rel. Ministro Sérgio Kukina, Primeira Turma, por unanimidade, julgado em 5/8/2025, DJEN 21/8/2025.

      Ramo do Direito DIREITO TRIBUTÁRIO

      TemaPaz, Justiça e Instituições Eficazes <br /> Processo administrativo fiscal. Crédito de natureza tributária. Prescrição intercorrente. Não Incidência. Ausência de previsão normativa específica.

      Destaque - Não ocorre a incidência da prescrição intercorrente em sede de processo <u>administrativo fiscal</u>, dada a ausência de previsão normativa específica.

      Informações do Inteiro Teor - Cinge-se a controvérsia acerca da perda ou não do direito de exigir crédito tributário lançado em processo administrativo da Receita Federal do Brasil, ao fundamento de que o processo administrativo teria ficado 5 anos e 2 meses parado, sem tramitação, período superior ao prazo prescricional do próprio tributo.

      • Isso posto, tem-se que referida tese traduz-se em pleito de reconhecimento da prescrição intercorrente do processo administrativo fiscal.

      • Com efeito, é firme a jurisprudência do Superior Tribunal de Justiça no sentido de que "o recurso administrativo suspende a exigibilidade do crédito tributário, enquanto perdurar o contencioso administrativo, nos termos do art. 151, III do CTN, desde o lançamento (efetuado concomitantemente com auto de infração), momento em que não se cogita do prazo decadencial, até seu julgamento ou a revisão ex officio, sendo certo que somente a partir da notificação do resultado do recurso ou da sua revisão, tem início a contagem do prazo prescricional, <u>afastando-se a incidência da prescrição intercorrente em sede de processo administrativo fiscal</u>, pela ausência de previsão normativa específica" (REsp 1.113.959/RJ, Rel. Ministro Luiz Fux, Primeira Turma, DJe de 11/3/2010).

      • Dessa forma, <u>não ocorre a incidência da prescrição intercorrente em sede de processo administrativo fiscal</u>, em razão da ausência de previsão normativa específica.

      Obs.: Considerando que inexiste norma que disponha a respeito da incidência de prescrição intercorrente em processos administrativos fiscais, não há como reconhecer a extinção do tributo com este fundamento.

    2. hvs 03 Sep 2025
      leis
      • Informativo 1187
      • ADI 4854 / RS
      • Órgão julgador: Tribunal Pleno
      • Relator(a): Min. NUNES MARQUES
      • Julgamento: 22/08/2025 (Virtual)
      • Ramo do Direito: Tributário
      • Matéria: ICMS; Regime Especial De Fiscalização; Sanção Política; Obrigações Tributárias Acessórias; Devedor Contumaz

      ICMS: regime especial de fiscalização aplicado aos devedores contumazes

      Resumo - É constitucional — e não configura sanção política nem viola os princípios constitucionais da legalidade tributária (CF/1988, art. 150, I), da liberdade de trabalho e comércio (CF/1988, art. 5º, XIII; e 170, parágrafo único), bem como o da igualdade tributária (CF/1988, arts. 5º, caput; e 150, II) — norma estadual que institui Regime Especial de Fiscalização (REF), aplicável aos contribuintes considerados <u>devedores contumazes</u> de ICMS.

      • Conforme jurisprudência desta Corte (1), a submissão de contribuinte inadimplente a regime fiscal diferenciado não configura sanção política, desde que a medida não inviabilize o exercício da atividade empresarial e observe critérios de proporcionalidade e razoabilidade.
      • Na espécie, a norma estadual impugnada estabelece critérios objetivos para a qualificação de contribuintes como devedores contumazes e institui o REF. O seu conteúdo não se sujeita à reserva de lei complementar federal, na medida em que (i) não trata de elementos essenciais do tributo, como fato gerador, lançamento ou crédito tributário (CF/1988, art. 146, III, b); e (ii) não institui mecanismos coercitivos de cobrança, como os vedados pelas Súmulas 70, 323 e 547 do STF (2).
      • Portanto, inexiste violação ao princípio da legalidade tributária. Diante disso, o REF representa instrumento legítimo de controle tributário e sua validade decorre da própria legislação tributária (CTN/1966, art. 96), de modo que é compatível com a competência do ente federado para disciplinar obrigações acessórias (3). As medidas previstas, como a alteração de prazos de recolhimento e a intensificação da fiscalização, não impedem o exercício da atividade econômica, pois se aplicam somente aos casos graves e reiterados de inadimplência.
      • Trata-se, portanto, de mecanismo excepcional e proporcional, voltado à indução de condutas regulares e à preservação da arrecadação. Além disso, a previsão de exclusão do REF pelos titulares originários de créditos decorrentes de precatórios inadimplidos não infringe o princípio da igualdade tributária. Como não há identidade de situações entre credores originários e cessionários de precatórios, é legitimo conferir tratamento diferenciado aos que possuem relação direta e reconhecida com o poder público, especialmente em cenários voltados para a simplificação da fiscalização e proteção do patrimônio público.
      • Com base nesses e em outros entendimentos, o Plenário, por unanimidade, conheceu parcialmente da ação e, nessa extensão, a julgou improcedente para confirmar a presunção de constitucionalidade dos arts. 2º, §§ 1º, 2º e 3º, e art. 3º, ambos da Lei nº 13.711/2011 do Estado do Rio Grande do Sul (4), na redação dada pela Lei gaúcha nº 14.180/2012.

      (1) Precedentes citados: RE 486.175 AgR-EDv, ARE 1.349.448 AgR e ADI 3.952. (2) Enunciados sumulares citados: Súmula 70/STF, Súmula 323/STF e Súmula 547/STF. (3) CTN/1966: “Art. 113. A obrigação tributária é principal ou acessória. (...) § 2º A obrigação acessória decorre da legislação tributária e tem por objeto as prestações, positivas ou negativas, nela previstas no interesse da arrecadação ou da fiscalização dos tributos.” (4) Lei nº 13.711/2011 do Estado do Rio Grande do Sul: “Art. 2.º O contribuinte será considerado como devedor contumaz e ficará submetido a Regime Especial de Fiscalização, conforme disposto em regulamento, quando qualquer de seus estabelecimentos situados no Estado, sistematicamente, deixar de recolher o ICMS devido nos prazos previstos no Regulamento do Imposto sobre Operações Relativas à Circulação de Mercadorias e sobre Prestações de Serviços de Transporte Interestadual e Intermunicipal e de Comunicação - RICMS. § 1.º Para efeitos deste artigo, considera-se como devedor contumaz o contribuinte que: I - deixar de recolher o ICMS declarado em Guia de Informação e Apuração do ICMS - GIA -, em oito meses de apuração do imposto nos últimos doze meses anteriores ao corrente (Redação dada pela Lei n.º 14.180/12); II - tiver créditos tributários inscritos como Dívida Ativa em valor superior a 38.500 UPFs-RS, decorrente de imposto não declarado em GIA, em oito meses de apuração do imposto nos últimos doze meses anteriores ao corrente; ou (Redação dada pela Lei n.º 14.180/12) III - tiver créditos tributários inscritos como Dívida Ativa em valor que ultrapasse: (Incluído pela Lei n.º 14.180/12) a) 30% (trinta por cento) do seu patrimônio conhecido; ou (Incluído pela Lei n.º 14.180/12); b) 25% (vinte e cinco por cento) do faturamento anual declarado em GIA ou em Guia Informativa - GI -. (Incluído pela Lei n.º 14.180/12) § 2.º Não serão considerados devedores contumazes, para os termos a que se refere o ‘caput’ do art. 2.º, as pessoas físicas ou jurídicas, titulares originários de créditos oriundos de precatórios inadimplidos pelo Estado e suas autarquias, até o limite do respectivo débito tributário constante de Dívida Ativa. § 3.º Não serão computados para os efeitos deste artigo os débitos cuja exigibilidade esteja suspensa nos termos do Código Tributário Nacional. (...) Art. 3.º O contribuinte deixará de ser considerado como devedor contumaz se os débitos que motivaram essa condição forem extintos ou tiverem sua exigibilidade suspensa.”

      Legislação: CF/1988: arts. 5º, caput, XIII; 146, III, b; 150, I e II e 170, parágrafo único. CTN/1966: arts. 96 e 113, § 2º. Lei nº 13.711/2011 do Estado do Rio Grande do Sul: arts. 2º, §§ 1º, 2º e 3º, e art. 3º. Lei nº 14.180/2012 do Estado do Rio Grande do Sul.

      Precedentes: RE 486.175 AgR-EDv, ARE 1.349.448 AgR, ADI 3.952, Súmula 70/STF, Súmula 323/STF e Súmula 547/STF.

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  48. uw.pressbooks.pub uw.pressbooks.pub
    The Thyroid Gland
    1
    1. langerUWYO 04 Sep 2025
      Causes of increased TBG and high total T4:Estrogens (e.g. pregnancy, birth control pills) Congenital X-linked disorder causing elevated TBG levelsCauses of decreased TBG and low total T4:GlucocorticoidsProtein loss: malnutrition, wasting, liver failure, nephrotic syndrome

      paragraph duplicates Table 3. Remove.

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  49. www.planalto.gov.br www.planalto.gov.br
    Constituicao-Compilado
    1
    1. hvs 04 Sep 2025
      bens

      **Jurisprudência em Teses - Edição nº 124 - BENS PÚBLICOS**

      • 1) Os bens integrantes do acervo patrimonial de sociedades de economia mista sujeitos a uma destinação pública <u>equiparam-se</u> a bens públicos, sendo, portanto, insuscetíveis de serem adquiridos por meio de usucapião.

      • 2) Os imóveis administrados pela Companhia Imobiliária de Brasília - Terracap são públicos e, portanto, insuscetíveis de aquisição por meio de usucapião.

      • 3) O imóvel vinculado ao Sistema Financeiro de Habitação - SFH, porque afetado à prestação de serviço público, <u>deve ser</u> tratado como bem público, não podendo, pois, ser objeto de usucapião.

      • 4) É possível reconhecer a usucapião do domínio útil de bem público sobre o qual tinha sido, anteriormente, instituída enfiteuse, pois, nessa circunstância, existe apenas a substituição do enfiteuta pelo usucapiente, não havendo qualquer prejuízo ao Estado.

      • 5) É incabível a modificação unilateral pela União do valor do domínio pleno de imóvel aforado, incidindo somente a correção monetária na atualização anual do pagamento do foro na enfiteuse de seus bens (art. 101 do Decreto-Lei n. 9760/1946).

      • 6) As concessões de terras devolutas situadas na faixa de fronteira, feitas pelos Estados, autorizam, <u>apenas</u>, o uso, permanecendo o domínio com a União, ainda que se mantenha inerte ou tolerante, em relação aos possuidores. (Súmula n. 477/STF)

      • 7) Terras em faixas de fronteira e aquelas sem registro imobiliário não são, por si só, terras devolutas, cabendo ao ente federativo comprovar a titularidade desses terrenos.

      • 8) O descumprimento de encargo estabelecido em lei que determinara a doação de bem público enseja, <u>por si só</u>, a sua desconstituição.

      • 9) A ocupação indevida de bem público configura mera detenção, de natureza precária, insuscetível de retenção ou indenização por acessões e benfeitorias. (Súmula n. 619/STJ)

      • 10) Construção ou atividade irregular em bem de uso comum do povo revela <u>dano presumido</u> à coletividade, dispensada prova de prejuízo em concreto.

      • 11) Os registros de propriedade particular de imóveis situados em terrenos de marinha não são oponíveis à União. (Súmula n. 496/ STJ) (Tese julgada sob o rito do art. 543-C do CPC/73 - TEMA 419)

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    Forecasting protein evolution by integrating birth-death population models with structurally constrained substitution models
    2
    1. Public_Reviews 04 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      Ferreiro et al. present a method to simulate protein sequence evolution under a birth-death model where sequence evolution is guided by structural constraints on protein stability. The authors then use this model to explore the predictability of sequence evolution in several viral proteins. In principle, this work is of great interest to molecular evolution and phylodynamics, which has struggled to couple non-neutral models of sequence evolution to phylodynamic models like birth-death processes. Unfortunately, though, the model shows little improvement over neutral models in predicting protein sequence evolution, although it can predict protein stability better than models assuming neutral evolution. It appears that more work is needed to determine exactly what aspects of protein sequence evolution are predictable under such non-neutral phylogenetic models.

      Major concerns:

      (1) The authors have clarified the mapping between birth-death model parameters and fitness, but how fitness is modeled still appears somewhat problematic. The authors assume the death rate = 1 - birth rate. So a variant with a birth rate b = 1 would have a death rate d = 0 and so would be immortal and never die, which does not seem plausible. Also I'm not sure that this would "allow a constant global (birth-death) rate" as stated in line 172, as selection would still act to increase the population mean growth rate r = b - d. It seems more reasonable to assume that protein stability affects only either the birth or death rate and assume the other rate is constant, as in the Neher 2014 model.

      (2) It is difficult to evaluate the predictive performance of protein sequence evolution. This is in part due to the fact that performance is compared in terms of percent divergence, which is difficult to compare across viral proteins and datasets. Some protein sequences would be expected to diverge more because they are evolving over longer time scales, under higher substitution rates or under weaker purifying selection. It might therefore help to normalize the divergence between predicted and observed sequences by the expected or empirically observed amount of divergence seen over the timescale of prediction.

      (3) Predictability may also vary significantly across different sites in a protein. For example, mutations at many sites may have little impact on structural stability (in which case we would expect poor predictive performance) while even conservative changes at other sites may disrupt folding. I therefore feel that there remains much work to be done here in terms of figuring out where and when sequence evolution might be predictable under these types of models, and when sequence evolution might just be fundamentally unpredictable due to the high entropy of sequence space.

      Review 1
    2. Public_Reviews 04 Sep 2025

      Author response:

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

      Reviewer #1 (Public review): 

      Summary: 

      Ferreiro et al. present a method to simulate protein sequence evolution under a birth-death model where sequence evolution is guided by structural constraints on protein stability. The authors then use this model to explore the predictability of sequence evolution in several viral proteins. In principle, this work is of great interest to molecular evolution and phylodynamics, which has struggled to couple non-neutral models of sequence evolution to phylodynamic models like birth-death processes. Unfortunately, though, the model shows little improvement over neutral models in predicting protein sequence evolution, although it can predict protein stability better than models assuming neutral evolution. It appears that more work is needed to determine exactly what aspects of protein sequence evolution are predictable under such non-neutral phylogenetic models. 

      We thank the reviewer for the positive comments about our work. We agree that further work is needed in the field of substitution models of molecular evolution to enable more accurate predictions of specific amino acid sequences in evolutionary processes.

      Major concerns: 

      (1) The authors have clarified the mapping between birth-death model parameters and fitness, but how fitness is modeled still appears somewhat problematic. The authors assume the death rate = 1 - birth rate. So a variant with a birth rate b = 1 would have a death rate d = 0 and so would be immortal and never die, which does not seem plausible. Also I'm not sure that this would "allow a constant global (birth-death) rate" as stated in line 172, as selection would still act to increase the population mean growth rate r = b - d. It seems more reasonable to assume that protein stability affects only either the birth or death rate and assume the other rate is constant, as in the Neher 2014 model. 

      The model proposed by Neher, et al. (2014), which incorporates a death rate (d) higher than 0 for any variant, was implemented and applied in the present method. In general, this model did not yield results different from those obtained using the model that assumes d = 1 – b, suggesting that this aspect may not be crucial for the study system. Next, the imposition of arbitrary death events based on an arbitrary death rate could be a point of concern. Regarding the original model, a variant with d = 0 can experience a decrease in fitness through the mutation process. In an evolutionary process, each variant is subject to mutation, and Markov models allow for the incorporation of mutations that decrease fitness (albeit with lower probability than beneficial ones, but they can still occur). All this information is included in the manuscript.

      (2) It is difficult to evaluate the predictive performance of protein sequence evolution. This is in part due to the fact that performance is compared in terms of percent divergence, which is difficult to compare across viral proteins and datasets. Some protein sequences would be expected to diverge more because they are evolving over longer time scales, under higher substitution rates or under weaker purifying selection. It might therefore help to normalize the divergence between predicted and observed sequences by the expected or empirically observed amount of divergence seen over the timescale of prediction. 

      AU: The study protein datasets showed different levels of sequence divergence over their evolutionary times, as indicated for each dataset in the manuscript. For some metrics, we evaluated the accuracy (or error) of the predictions through direct comparisons between real and predicted protein variants using percentages to facilitate interpretation: 0% indicates a perfect prediction (no error), while 100% indicates a completely incorrect prediction (total error). Regarding normalization of these evaluations, we respectfully disagree with the suggestion because diverse factors can affect (not only the substitution rate, but also the sample size, structural features of the protein that may affect stability when accommodating different sequences, among others) and this complicates defining a consistent and meaningful normalization criterion. Given that the manuscript provides detailed information for each dataset, we believe that the presentation of the prediction accuracy through direct comparisons between real and predicted protein variants, expressed as percentages of similarity, is the clearest way.

      (3) Predictability may also vary significantly across different sites in a protein. For example, mutations at many sites may have little impact on structural stability (in which case we would expect poor predictive performance) while even conservative changes at other sites may disrupt folding. I therefore feel that there remains much work to be done here in terms of figuring out where and when sequence evolution might be predictable under these types of models, and when sequence evolution might just be fundamentally unpredictable due to the high entropy of sequence space. 

      We agree with this reflection. Mutations can have different effects on folding stability, which are accounted for by the model presented in this study. However, accurately predicting the exact sequences of protein variants with similar stability remains difficult with current structurally constrained substitution models, and therefore, further work is needed in this regard. This aspect is indicated in the manuscript.

      We want to thank the reviewer again for taking the time to revise our work and for the insightful and helpful comments.

      Reviewer #2 (Public review): 

      In this study, the authors aim to forecast the evolution of viral proteins by simulating sequence changes under a constraint of folding stability. The central idea is that proteins must retain a certain level of structural stability (quantified by folding free energy, ΔG) to remain functional, and that this constraint can shape and restrict the space of viable evolutionary trajectories. The authors integrate a birth-death population model with a structurally constrained substitution (SCS) model and apply this simulation framework to several viral proteins from HIV-1, SARS-CoV-2, and influenza.

      The motivation to incorporate biophysical constraints into evolutionary models is scientifically sound, and the general approach aligns with a growing interest in bridging molecular evolution and structural biology. The authors focus on proteins where immune pressure is limited and stability is likely to be a dominant constraint, which is conceptually appropriate. The method generates sequence variants that preserve folding stability, suggesting that stability-based filtering may capture certain evolutionary patterns. 

      Correct. We thank the reviewer for the positive comments about our study.

      However, the study does not substantiate its central claim of forecasting. The model does not predict future sequences with measurable accuracy, nor does it reproduce observed evolutionary paths. Validation is limited to endpoint comparisons in a few datasets. While KL divergence is used to compare amino acid distributions, this analysis is only applied to a single protein (HIV-1 MA), and there is no assessment of mutation-level predictive accuracy or quantification of how well simulated sequences recapitulate real evolutionary paths. No comparison is made to real intermediate variants available from extensive viral sequencing datasets which gather thousands of sequences with detailed collection date annotation (SARS-CoV-2, Influenza, RSV). 

      There are several points in this comment.

      The presented method accurately predicts folding stability of forecasted variants, as shown through comparisons between real and predicted protein variants. However, as the reviewer correctly indicates, predicting the exact amino acid sequences remains challenging. This limitation is discussed in detail in the manuscript, where we also suggest that further improvements in substitution models of protein evolution are needed to better capture the evolutionary signatures of amino acid change at the sequence level, even between amino acids with similar physicochemical properties. Regarding the time points used for validation, the studied influenza NS1 dataset included two validation points. A key limitation in increasing the number of time points is the scarcity of datasets derived from monitoring protein evolution with sufficient molecular diversity between samples collected at consecutive time points (i.e., at least more than five polymorphic amino acid sites). 

      As described in the manuscript, calculating Kullback-Leibler (KL) divergence requires more than one sequence per studied time point. However, most datasets in the literature include only a single sequence per time point, typically a consensus sequence derived from bulk population sequencing. Generating multiple sequences per time point is experimentally more demanding, often requiring advanced methods such as single-virus sequencing or amplification of sublineages in viral subpopulations, as was done for the first dataset used in the study (Arenas, et al. 2016), which enabled the calculation of KL divergence. The extent to which the simulated sequences resemble real evolution is evaluated in the method validation. As noted, intermediate time point validation was performed using the influenza NS1 protein dataset. Although, as the reviewer indicates, thousands of viral sequences are available, these are usually consensus sequences from bulk sequencing. Indeed, many viral variants mainly differ through synonymous mutations, where the number of accumulated nonsynonymous mutations is small. For example, from the original Wuhan strain to the Omicron variant, the SARS-CoV-2 proteins Mpro and PLpro accumulated only 10 and 22 amino acid changes, respectively.

      Analyzing intermediate variants of concern (i.e., Gamma or Delta) would reduce this number affecting statistics. In addition, many available viral sequences are not consecutive in evolutionary terms (one dataset does not represent the direct origin of another dataset at a subsequent time point), which further limits their applicability in this study. There is little data from monitored protein evolution with consecutive samples. The most suitable studies usually involve in vitro virus evolution, but the data from these studies often show low genetic variability between samples collected at different time points. Finally, it is important to note that the presented method can only be applied to proteins with known 3D structures, as it relies on selection based on folding stability. Non-structural proteins cannot be analyzed using this approach. Future work could incorporate additional selection constraints, which may improve the accuracy of predictions. These considerations and limitations are indicated in the manuscript.

      The selection of proteins is narrow and the rationale for including or excluding specific proteins is not clearly justified. 

      The viral proteins included in the study were selected based on two main criteria, general interest and data availability. In particular, we included proteins from viruses that affect humans and for which data from monitored protein evolution, with sufficient molecular diversity between consecutive time points, is available. These aspects are indicated in the manuscript.

      The analyzed datasets are also under-characterized: we are not given insight into how variable the sequences are or how surprising the simulated sequences might be relative to natural diversity. Furthermore, the use of consensus sequences to represent timepoints is problematic, particularly in the context of viral evolution, where divergent subclades often coexist - a consensus sequence may not accurately reflect the underlying population structure. 

      The manuscript indicates the sequence identity among protein datasets of different time points, along with other technical details. Next, the evaluation based on comparisons between simulated and real sequences reflects how surprising the simulated sequences might be relative to natural diversity, considering that the real dataset is representative. We believe that the diverse study real datasets are useful to evaluate the accuracy of the method in predicting different molecular patterns. Regarding the use of consensus sequences, we agree that they provide an approximation. However, as previously indicated, most of the available data from monitored protein evolution consist of consensus sequences obtained through bulk sequencing. Additionally, analyzing every individual viral sequence within a viral population, which is typically large, would be ideal but computationally intractable.

      The fitness function used in the main simulations is based on absolute ΔG and rewards increased stability without testing whether real evolutionary trajectories tend to maintain, increase, or reduce folding stability over time for the particular systems (proteins) that are studied. While a variant of the model does attempt to center selection around empirical ΔG values, this more biologically plausible version is underutilized and not well validated.

      The applied fitness function, based on absolute ΔG, is well stablished in the field (Sella and Hirsh 2005; Goldstein 2013). The present study independently predicts ΔG for the real and simulated protein variants at each sampling point. This ΔG prediction accounts not only for negative design, informed by empirical data, but also for positive design based on the study data (Arenas, et al. 2013; Minning, et al. 2013), thereby enabling the detection of variation in folding stability among protein variants. These aspects are indicated in the manuscript. Therefore, in our view, the study provides a proper comparison of real and predicted evolutionary trajectories in terms of folding stability.

      Ultimately, the model constrains sequence evolution to stability-compatible trajectories but does not forecast which of these trajectories are likely to occur. It is better understood as a filter of biophysically plausible outcomes than as a predictive tool. The distinction between constraint-based plausibility and sequence-level forecasting should be made clearer. Despite these limitations, the work may be of interest to researchers developing simulation frameworks or exploring the role of protein stability in viral evolution, and it raises interesting questions about how biophysical constraints shape sequence space over time. 

      The presented method estimates the fitness of each protein variant, which can reflect the relative survival capacity of the variant. Therefore, despite the error due to evolutionary constraints not considered by the method, it indicates which variants are more likely to become fixed over time. In our view, the method does not merely filter plausible variants, rather, it generates predictions of variant survival through predicted fitness based on folding stability and simulations of protein evolution under structurally constrained substitution models integrated with birth-death population genetics approaches. The use of simulation-based approaches for prediction is well established in population genetics. For example, approaches such as approximate Bayesian computation (Beaumont, et al. 2002) rely on this strategy, and it has also been applied in other studies of forecasting evolution (e.g., Neher, et al. 2014). We believe that the distinction between forecasting folding stability and amino acid sequence is clearly shown in the manuscript, including the main text and the figures.

      Reviewer #2 (Recommendations for the authors): 

      I thank the authors for addressing the question about template switching, their clarification was helpful. However, the core concerns I raised remain unresolved: the claim that the method is useful for forecasting is not substantiated.  In order to support the paper's central claims or to prove its usefulness, several key improvements could be incorporated: 

      (1) Systematic analysis of more proteins: 

      The manuscript would be significantly strengthened by a systematic evaluation of model performance across a broader set of viral proteins, beyond the examples currently shown. Many human influenza and SARS-CoV-2 proteins have wellcharacterized structures or high-quality homology templates, making them suitable candidates. In the light of limited success of the method, presenting the model's behavior across a more comprehensive protein set, including those with varying structural constraints and immune pressures, would help assess generalizability and clarify the specific conditions under which the model is applicable. 

      Following a comment from the reviewer in a previous revision of the study, we included the analysis of an influenza NS1 protein dataset that contains two evaluation time points. Next, to validate the prediction method, it is necessary to have monitored protein sequences collected at least at two consecutive time points, with sufficient divergence between them to capture evolutionary signatures that allow for proper evaluation. Additionally, many data involve sequences that are not consecutive in evolutionary terms (one dataset is not a direct ancestor of another dataset existing at a posterior time point), which disallows their applicability in this study. Little data from monitored protein evolution with trustable consecutive (ancestor-descendant) samples exist. The most suitable studies often involve in vitro virus evolution, but they usually show low genetic variability between samples collected at different time points. Although thousands of sequences are available for some viruses, they are usually consensus sequences from bulk sequencing and often show a low number of nonsynonymous mutations at the study protein-coding gene between time points. For example, from the original Wuhan strain and the Omicron variant, the SARS-CoV-2 proteins Mpro and PLpro accumulated only 10 and 22 amino acid changes, respectively. Analyzing intermediate variants of concern (i.e., Gamma or Delta) would reduce this number affecting statistics. Thus, in practice, we found scarcity of data derived from monitoring protein evolution, with trustable ancestor and corresponding descendant data at consecutive time points and with sufficient molecular diversity between them (i.e., at least more than five polymorphic amino acid sites). In all, we believe that the diverse viral protein datasets used in the present study, along with the multiple analyzed datasets collected from monitored HIV-1 populations present in different patients, provide a representative application of the method, since notice that similar patterns were generally generated from the analysis of the different datasets.

      (2) Present clear data statistics: For each analyzed dataset, the authors should provide basic information about the number of unique sequences, levels of variability, and evolutionary divergence between start and end sequences. This would contextualize the forecasting task and clarify whether the simulations are non-trivial. In particular, it should be shown that the consensus sequence is indeed representative of the viral population at a given time point. In viral evolution we frequently observe co-circulation of subclades and the consensus sequence is then not representative. 

      For each dataset analyzed, the manuscript provides the sequence identity between samples at the study time points (which also informs about sequence variability), sample sizes, representative protein structure, and other technical details. The study assumes that consensus sequences, typically generated by bulk sequencing, are representative of the viral population. Next, samples at different time points should involve ancestor-descendant relationships, which is a requirement and one of the limitations to find appropriate data for this study, as noted in our previous response.

      (3) Explore other metrics for population level sequence comparison: 

      In the light of possible existence of subclades, mentioned above, the currently used metrics for sequence comparison may underestimate performance of the simulations. It would be sufficient to see some overlap of simulated clades and and the observed clades. 

      We found this to be a good idea. However, in practice, we believe that the criteria used to define subclades could introduce biases into the results. For some metrics, we evaluated the accuracy of the predictions through direct comparisons between all real and predicted protein variants, using percentages to facilitate interpretation. We believe that using subclades could potentially reduce the current prediction errors, but this would complicate the interpretation of the results, as they would be influenced by the subjective criteria used to define the subclades.

      Currently, the manuscript presents a plausible filtering framework rather than a predictive model. Without these additional analyses, the main claims remain only partially supported. 

      Please see our reply to the comment of the reviewer just before the section titled “Recommendations for the authors”.

      Response to some rebuttal statements: 

      (1) "Sequence comparisons based on the KL divergence require, at the studied time point, an observed distribution of amino acid frequencies among sites and an estimated distribution of amino acid frequencies among sites. In the study datasets, this is only the case for the HIV-1 MA dataset, which belongs to a previous study from one of us and collaborators where we obtained at least 20 independent sequences at each sampling point (Arenas, et al. 2016)" 

      The available Influenza and SARS-CoV-2 data gathers isolates annotated with exact collection dates, providing reach datasets for such analysis. 

      The available influenza and SARS-CoV-2 sequences are typically derived from bulk sequencing and, therefore, they are consensus sequences. As a result, they cannot be used to calculate KL divergence. Additionally, many of the indicated sequences from databases are not demonstrated to be consecutive in evolutionary terms (one dataset is not a direct ancestor of another dataset existing at a posterior time point), which disallows their applicability in this study. The most suitable studies often involve in vitro virus evolution, but they usually show low genetic variability between samples collected at different time points.

      (2) "Regarding extending the analysis to other time points (other variants of concern), we kindly disagree because Omicron is the variant of concern with the highest genetic distance to the Wuhan variant, and a high genetic distance is  required to properly evaluate the prediction method." 

      There have been many more variants of concern subsequent to Omicron which circulated in 2021. 

      A key aspect is the accumulation of diversity in the study proteins across different time points. The SARS-CoV-2 proteins Mpro and PLpro accumulated only 10 and 22 amino acid changes from the original Wuhan variant to Omicron, respectively.

      Analyzing intermediate variants of concern (e.g., Gamma or Delta) or those closely related to Omicron would reduce the number of accumulated mutations even further.   

      We want to thank the reviewer again for taking the time to revise our work and for the insightful and helpful comments.

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

      Reviewer #1 (Public review): 

      Summary: 

      Ferreiro et al. present a method to simulate protein sequence evolution under a birth-death model where sequence evolution is constrained by structural constraints on protein stability. The authors then use this model to explore the predictability of sequence evolution in several viral structural proteins. In principle, this work is of great interest to molecular evolution and phylodynamics, which have struggled to couple non-neutral models of sequence evolution to phylodynamic models like birth-death. Unfortunately, though, the model shows little improvement over neutral models in predicting protein evolution, and this ultimately appears to be due to fundamental conceptual problems with how fitness is modeled and linked to the phylodynamic birth-death model. 

      AU: We thank the reviewer for the positive comments about our work.

      Regarding predictive power, the study showed a good accuracy in predicting the real folding stability of forecasted protein variants under a selection model, but not under a neutral model. Next, predicting the exact sequences was more challenging. In this revised version, where we added additional real data, we found that the accuracy of this prediction can vary among proteins (i.e., the SCS model was more accurate than the neutral model in predicting sequences of the influenza NS1 protein at different time points). Still, we consider that efforts are required in the field of substitution models of molecular evolution. For example, amino acids with similar physicochemical properties can result in predictions with appropriate folding stability while different specific sequence. The development of accurate substitution models of molecular evolution is an active area of research with ongoing progress, but further efforts are still needed. Next, forecasting the folding stability of future real proteins is fundamental for proper forecasting protein evolution, given the essential role of folding stability in protein function and its variety of applications. Regarding the conceptual concerns related to fitness modeling, we clarify them in detail in our responses to the specific comments below.

      Major concerns:

      (1) Fitness model: All lineages have the same growth rate r = b-d because the authors assume b+d=1. But under a birth-death model, the growth r is equivalent to fitness, so this is essentially assuming all lineages have the same absolute fitness since increases in reproductive fitness (b) will simply trade off with decreases in survival (d). Thus, even if the SCS model constrains sequence evolution, the birthdeath model does not really allow for non-neutral evolution such that mutations can feed back and alter the structure of the phylogeny. 

      We thank the reviewer for this comment that aims to improve the realism of our model. In the model presented (but see later another model, derived from the proposal of the reviewer, that we have now implemented into the framework and applied it to the study data), the fitness predicted from a protein variant is used to obtain the corresponding birth rate of that variant. In this way, protein variants with high fitness have high birth rates leading to overall more birth events, while protein variants with low fitness have low birth rates resulting in overall more extinction events, which has biological meaning for the study system. The statement “All lineages have the same growth rate r = b-d” in our model is incorrect because, in our model, b and d can vary among lineages according to the fitness. For example, a lineage might have b=0.9, d=0.1, r=0.8, while another lineage could have b=0.6, d=0.4, r=0.2. Indeed, the statement “this is essentially assuming all lineages have the same absolute fitness” is incorrect. Clearly, assuming that all lineages have the same fitness would not make sense, in that situation the folding stability of the forecasted protein variants would be similar under any model, which is not the case as shown in the results. In our model, the fitness affects the reproductive success, where protein variants with a high fitness have higher birth rates leading to more birth events, while those with lower fitness have higher death rates leading to more extinction events. This parameterization is meaningful for protein evolution because the fitness of a protein variant can affect its survival (birth or extinction) without necessarily affecting its rate of evolution. While faster growth rate can sometimes be associated with higher fitness, a variant with high fitness does not necessarily accumulate substitutions at a faster rate. Regarding the phylogenetic structure, the model presented considers variable birth and death events across different lineages according to the fitness of the corresponding protein variants, and this affects the derived phylogeny (i.e., protein variants selected against can go extinct while others with high fitness can produce descendants). We are not sure about the meaning of the term “mutations can feed back” in the context of our system. Note that we use Markov models of evolution, which are well-stablished in the field (despite their limitations), and substitutions are fixed mutations, which still could be reverted later if selected by the substitution model (Yang 2006). Altogether, we find that the presented birth-death model is technically correct and appropriate for modeling our biological system. Its integration with structurally constrained substitution (SCS) models of protein evolution as Markov models follows general approaches of molecular evolution in population genetics (Yang 2006; Carvajal-Rodriguez 2010; Arenas 2012; Hoban, et al. 2012). We have now provided a more detailed description of the models in the manuscript.

      Apart from these clarifications about the birth-death model used, we could understand the point of the reviewer and following the suggestion we have now incorporated an additional birth-death model that accounts for variable global birth-death rate among lineages. Specifically, we followed the model proposed by Neher et al (2014), where the death rate is considered as 1 and the birth rate is modeled as 1 + fitness. In this model, the global birth-death rate can vary among lineages. We implemented this model into the computer framework and applied it to the data used for the evaluation of the models. The results indicated that, in general, this model yields similar predictive accuracy compared to the previous birth-death model. Thus, accounting for variability in the global birth-death rate does not appear to play a major role in the studied systems of protein evolution. We have now presented this additional birth-death model and its results in the manuscript.

      (2) Predictive performance: Similar performance in predicting amino acid frequencies is observed under both the SCS model and the neutral model. I suspect that this rather disappointing result owes to the fact that the absolute fitness of different viral variants could not actually change during the simulations (see comment #1). 

      As indicated in our previous answer, our study shows a good accuracy in predicting the real folding stability of forecasted protein variants under a selection model, but not under a neutral model. Next, predicting the exact sequences was more challenging, which was not surprising considering previous studies. In particular, inferring specific sequences is considerably challenging even for ancestral molecular reconstruction (Arenas, et al. 2017; Arenas and Bastolla 2020). Indeed, observed sequence diversity is much greater than observed structural diversity (Illergard, et al. 2009; Pascual-Garcia, et al. 2010), and substitutions between amino acids with similar physicochemical properties can yield modeled protein variants with more accurate folding stability, even when the exact amino acid sequences differ. As indicated, further work is demanded in the field of substitution models of molecular evolution. Next, in this revised version, where we included analyses of additional real datasets, we found that the accuracy of sequence prediction can vary among datasets. Notably, the analysis of an influenza NS1 protein dataset, with higher diversity than the other datasets studied, showed that the SCS model was more accurate than the neutral model in predicting sequences across different time points. Datasets with relatively high sequence diversity can contain more evolutionary information, which can improve prediction quality. In any case, as previously indicated, we believe that efforts are required in the field of substitution models of molecular evolution. Apart from that, forecasting the folding stability of future real proteins is an important advance in forecasting protein evolution, given the essential role of folding stability in protein function (Scheiblhofer, et al. 2017; Bloom and Neher 2023) and its variety of applications.

      Next, also as indicated in our previous response, the birth-death model used in this study accounts for variation in fitness among lineages producing variable reproductive success. The additional birth-death model that we have now incorporated, which considers variation of the global birth-death rate among lineages, produced similar prediction accuracy, suggesting a limited role in protein evolution modeling. Molecular evolution parameters, particularly the substitution model, appear to be more critical in this regard. We have now included these aspects in the manuscript.

      (3) Model assessment: It would be interesting to know how much the predictions were informed by the structurally constrained sequence evolution model versus the birth-death model. To explore this, the authors could consider three different models: 1) neutral, 2) SCS, and 3) SCS + BD. Simulations under the SCS model could be performed by simulating molecular evolution along just one hypothetical lineage. Seeing if the SCS + BD model improves over the SCS model alone would be another way of testing whether mutations could actually impact the evolutionary dynamics of lineages in the phylogeny. 

      In the present study, we compared the neutral model + birth-death (BD) with the SCS model + BD. Markov substitution models Q are applied upon an evolutionary time (i.e., branch length, t) and this allows to determine the probability of substitution events during that time period [P(t) = exp (Qt)]. This approach is traditionally used in phylogenetics to model the incorporation of substitution events over time. Therefore, to compare the neutral and SCS models in terms of evolutionary inference, an evolutionary time is required, in this case it is provided by the birth-death process. Thus, the cases 1) and 2) cannot be compared without an underlined evolutionary history. Next, comparisons in terms of likelihood, and other aspects, between models that ignore the protein structure and the implemented SCS models are already available in previous studies based on coalescent simulations or given phylogenetic trees (Arenas, et al. 2013; Arenas, et al. 2015). There, SCS models outperformed models that ignore evolutionary constraints from the protein structure, and those findings are consistent with the results obtained in the present study where we explored the application of these models to forecasting protein evolution. We would like to emphasize that forecasting the folding stability of future real proteins is a significant finding, folding stability is fundamental to protein function and has a variety of applications. We have now indicated these aspects in the manuscript.

      (4) Background fitness effects: The model ignores background genetic variation in fitness. I think this is particularly important as the fitness effects of mutations in any one protein may be overshadowed by the fitness effects of mutations elsewhere in the genome. The model also ignores background changes in fitness due to the environment, but I acknowledge that might be beyond the scope of the current work. 

      AU: This comment made us realize that more information about the features of the implemented SCS models should be included in the manuscript. In particular, the implemented SCS models consider a negative design based on the observed residue contacts in nearly all proteins available in the Protein Data Bank (Arenas, et al. 2013; Arenas, et al. 2015). This data is distributed with the framework, and it can be updated to incorporate new structures (further details are provided in the distributed framework documentation and practical examples). Therefore, the prediction of folding stability is a combination of positive design (direct analysis of the target protein) and negative design (consideration of background proteins from a database to improve the predictions), thus incorporating background molecular diversity. We have now indicated this important aspect in the manuscript. Regarding the fitness caused by the environment, we agree with the reviewer. This is a challenge for any method aiming to forecast evolution, as future environmental shifts are inherently unpredictable and may affect the accuracy of the predictions. Although one might attempt to incorporate such effects into the model, doing so risks overparameterization, especially when the additional factors are uncertain or speculative. We have now mentioned this aspect in the manuscript.

      (5) In contrast to the model explored here, recent work on multi-type birth-death processes has considered models where lineages have type-specific birth and/or death rates and therefore also type-specific growth rates and fitness (Stadler and Bonhoeffer, 2013; Kunhert et al., 2017; Barido-Sottani, 2023). Rasmussen & Stadler (eLife, 2019) even consider a multi-type birth-death model where the fitness effects of multiple mutations in a protein or viral genome collectively determine the overall fitness of a lineage. The key difference with this work presented here is that these models allow lineages to have different growth rates and fitness, so these models truly allow for non-neutral evolutionary dynamics. It would appear the authors might need to adopt a similar approach to successfully predict protein evolution. 

      We agree with the reviewer that robust birth-death models have been developed applying statistics and, in many cases, the primary aim of those studies is the development and refinement of the model itself. Regarding the study by Rasmussen and Stadler 2019, it incorporates an external evaluation of mutation events where the used fitness is specific for the proteins investigated in that study, which may pose challenges for users interested in analyzing other proteins. In contrast, our study takes a different approach. We implement a fitness function that can be predicted and evaluated for any type of structural protein (Goldstein 2013), making it broadly applicable. Actually, in this revised version we added the analysis of additional data of another protein (influenza NS1 protein) with predictions at different time points. In addition, we provide a freely available and well-documented computational framework to facilitate its use. The primary aim of our study is not the development of novel or complex birthdeath models. Rather, we aim to explore the integration of a standard birth-death model with SCS models for the purpose of predicting protein evolution. In the context of protein evolution, substitution models are a critical factor (Liberles, et al. 2012; Wilke 2012; Bordner and Mittelmann 2013; Echave, et al. 2016; Arenas, et al. 2017; Echave and Wilke 2017), and the presented combination with a birth-death model constitutes a first approximation upon which next studies can build to better understand this evolutionary system. We have now indicated these considerations in the manuscript.

      Reviewer #2 (Public review): 

      Summary: 

      In this study, "Forecasting protein evolution by integrating birth-death population models with structurally constrained substitution models", David Ferreiro and coauthors present a forward-in-time evolutionary simulation framework that integrates a birth-death population model with a fitness function based on protein folding stability. By incorporating structurally constrained substitution models and estimating fitness from ΔG values using homology-modeled structures, the authors aim to capture biophysically realistic evolutionary dynamics. The approach is implemented in a new version of their open-source software, ProteinEvolver2, and is applied to four viral proteins from HIV-1 and SARS-CoV-2. 

      Overall, the study presents a compelling rationale for using folding stability as a constraint in evolutionary simulations and offers a novel framework and software to explore such dynamics. While the results are promising, particularly for predicting biophysical properties, the current analysis provides only partial evidence for true evolutionary forecasting, especially at the sequence level. The work offers a meaningful conceptual advance and a useful simulation tool, and sets the stage for more extensive validation in future studies.

      We thank the reviewer for the positive comments on our study. Regarding the predictive power, the results showed good accuracy in predicting the folding stability of the forecasted protein variants. In this revised version, where we included analyses of additional real datasets, we found that the accuracy of sequence prediction can vary among datasets. Notably, the analysis of an influenza NS1 protein dataset, with higher diversity than the other datasets studied, showed that the SCS model was more accurate than the neutral model in predicting sequences across different time points. Datasets with relatively high sequence diversity can contain more evolutionary information, which can improve prediction quality. Still, we believe that further efforts are required in the field in improving the accuracy of substitution models of molecular evolution. Altogether, accurately forecasting the folding stability of future real proteins is fundamental for predicting their protein function and enabling a variety of applications. Also, we implemented the models into a freely available computer framework, with detailed documentation and a variety of practical examples.

      Strengths: 

      The results demonstrate that fitness constraints based on protein stability can prevent the emergence of unrealistic, destabilized variants - a limitation of traditional, neutral substitution models. In particular, the predicted folding stabilities of simulated protein variants closely match those observed in real variants, suggesting that the model captures relevant biophysical constraints. 

      We agree with the reviewer and appreciate the consideration that forecasting the folding stability of future real proteins is a relevant finding. For instance, folding stability is fundamental for protein function and affects several other molecular properties.

      Weaknesses: 

      The predictive scope of the method remains limited. While the model effectively preserves folding stability, its ability to forecast specific sequence content is not well supported. 

      Our study showed a good accuracy in predicting the real folding stability of forecasted protein variants under a selection model, but not under a neutral model. Predicting the exact sequences was more challenging, which was not surprising considering previous studies. In particular, inferring specific sequences is considerably challenging even for ancestral molecular reconstruction (Arenas, et al. 2017; Arenas and Bastolla 2020). Indeed, observed sequence diversity is much greater than observed structural diversity (Illergard, et al. 2009; Pascual-Garcia, et al. 2010), and substitutions between amino acids with similar physicochemical properties can yield modeled protein variants with more accurate folding stability, even when the exact amino acid sequences differ. As indicated, further work is demanded in the field of substitution models of molecular evolution. Next, in this revised version, where we included analyses of additional real datasets, we found that the accuracy of sequence prediction can vary among datasets. Notably, the analysis of an influenza NS1 protein dataset, with higher diversity than the other datasets studied, showed that the SCS model was more accurate than the neutral model in predicting sequences across different time points. Datasets with relatively high sequence diversity can contain more evolutionary information, which can improve prediction quality. In any case, as previously indicated, we believe that efforts are required in the field of substitution models of molecular evolution. Apart from that, forecasting the folding stability of future real proteins is an important advance in forecasting protein evolution, given the essential role of folding stability in protein function (Scheiblhofer, et al. 2017; Bloom and Neher 2023) and its variety of applications. We have now expanded these aspects in the manuscript.

      Only one dataset (HIV-1 MA) is evaluated for sequence-level divergence using KL divergence; this analysis is absent for the other proteins. The authors use a consensus Omicron sequence as a representative endpoint for SARS-CoV-2, which overlooks the rich longitudinal sequence data available from GISAID. The use of just one consensus from a single time point is not fully justified, given the extensive temporal and geographical sampling available. Extending the analysis to include multiple timepoints, particularly for SARS-CoV-2, would strengthen the predictive claims. Similarly, applying the model to other well-sampled viral proteins, such as those from influenza or RSV, would broaden its relevance and test its generalizability. 

      The evaluation of forecasting evolution using real datasets is complex due to several conceptual and practical aspects. In contrast to traditional phylogenetic reconstruction of past evolutionary events and ancestral sequences, forecasting evolution often begins with a variant that is evolved forward in time and requires a rough fitness landscape to select among possible future variants (Lässig, et al. 2017). Another concern for validating the method is the need to know the initial variant that gives rise to the corresponding future (forecasted) variants, and it is not always known. Thus, we investigated systems where the initial variant, or a close approximation, is known, such as scenarios of in vitro monitored evolution. In the case of SARS-CoV-2, the Wuhan variant is commonly used as the starting variant of the pandemic. Next, since forecasting evolution is highly dependent on the used model of evolution, unexpected external factors can be dramatic for the predictions. For this reason, systems with minimal external influences provide a more controlled context for evaluating forecasting evolution. For instance, scenarios of in vitro monitored virus evolution avoid some external factors such as host immune responses. Another important aspect is the availability of data at two (i.e., present and future) or more time points along the evolutionary trajectory, with sufficient genetic diversity between them to identify clear evolutionary signatures. Additionally, using consensus sequences can help mitigate effects from unfixed mutations, which should not be modeled by a substitution model of evolution. Altogether, not all datasets are appropriate to properly evaluate or apply forecasting evolution. These aspects are indicated in the manuscript. Sequence comparisons based on the KL divergence require, at the studied time point, an observed distribution of amino acid frequencies among sites and an estimated distribution of amino acid frequencies among sites. In the study datasets, this is only the case for the HIV-1 MA dataset, which belongs to a previous study from one of us and collaborators where we obtained at least 20 independent sequences at each sampling point (Arenas, et al. 2016). This aspect is now more clearly indicated in the manuscript. Regarding the Omicron datasets, we used 384 curated sequences of the Omicron variant of concern to construct the study data and we believe that it is a representative sample. The sequence used for the initial time point was the Wuhan variant (Wu, et al. 2020), which is commonly assumed to be the origin of the pandemic in SARS-CoV-2 studies. As previously indicated, the use of consensus sequences is convenient to avoid variants with unfixed mutations. Regarding extending the analysis to other time points (other variants of concern), we kindly disagree because Omicron is the variant of concern with the highest genetic distance to the Wuhan variant, and a high genetic distance is required to properly evaluate the prediction method. Actually, we noted that earlier variants of concern show a small number of fixed mutations in the study proteins, despite the availability of large numbers of sequences in databases such as GISAID. Additionally, we investigated the evolutionary trajectories of HIV-1 protease (PR) in 12 intra-host viral populations with predictions for up to four different time points. Apart from those aspects, following the proposal of the reviewer, we have now incorporated the analysis of an additional dataset of influenza NS1 protein (Bao, et al. 2008), with predictions for two different time points, to further assess the generalizability of the method. We have now included details of this influenza NS1 protein dataset and the predictions derived from it in the manuscript.

      It would also be informative to include a retrospective analysis of the evolution of protein stability along known historical trajectories. This would allow the authors to assess whether folding stability is indeed preserved in real-world evolution, as assumed in their model.

      Our present study does not aim to investigate the evolution of the folding stability over time, although it provides this information indirectly at the studied time points. Instead, the present study shows that the folding stability of the forecasted protein variants is similar to the folding stability of the corresponding real protein variants for diverse viral proteins, which provides an important evaluation of the prediction method. Next, the folding stability can indeed vary over time in both real and modeled evolutionary scenarios, and our present study is not in conflict with this. In that regard, which is not the aim of our present study, some previous phylogenetic-based studies have reported temporal fluctuations in folding stability for diverse protein data (Arenas, et al. 2017; Olabode, et al. 2017; Arenas and Bastolla 2020; Ferreiro, et al. 2022).

      Finally, a discussion on the impact of structural templates - and whether the fixed template remains valid across divergent sequences - would be valuable. Addressing the possibility of structural remodeling or template switching during evolution would improve confidence in the model's applicability to more divergent evolutionary scenarios.

      This is an important point. For the datasets that required homology modeling (in several cases it was not necessary because the sequence was present in a protein structure of the PDB), the structural templates were selected using SWISS-MODEL, and we applied the best-fitting template. We have now included in a supplementary table details about the fitting of the structural templates. Indeed, our proposal assumes that the protein structure is maintained over the studied evolutionary time, which can be generally reasonable for short timescales where the structure is conserved (Illergard, et al. 2009; Pascual-Garcia, et al. 2010). Over longer evolutionary timescales, structural changes may occur and, in such cases, modeling the evolution of the protein structure would be necessary. To our knowledge, modeling the evolution of the protein structure remains a challenging task that requires substantial methodological developments. Recent advances in artificial intelligence, particularly in protein structure prediction from sequence, may offer promising tools for addressing this challenge. However, we believe that evaluating such approaches in the context of structural evolution would be difficult, especially given the limited availability of real data with known evolutionary trajectories involving structural change. In any case, this is probably an important direction for future research. We have now included this discussion in the manuscript.

      Reviewer #1 (Recommendations for the authors): 

      (1) Abstract: "expectedly, the errors grew up in the prediction of the corresponding sequences" <- Not entirely clear what is meant by "errors grew up" or what the errors grew with.

      This sentence refers to the accuracy of sequence prediction in comparison to that of folding stability prediction. We have now clarified this aspect in the manuscript.

      (2) Lines 162-165: "Alternatively, if the fitness is determined based on the similarity in folding stability between the modeled variant and a real variant, the birth rate is assumed to be 1 minus the root mean square deviation (RMSD) in folding stability." <- What is the biological motivation for using the RMSD? It seems like a more stable variant would always have higher fitness, at least according to Equation 1.

      RMSD is commonly used in molecular biology to compare proteins in terms of structural distance, folding stability, kinetics, and other properties. It offers advantages such as minimizing the influence of small deviations while amplifying larger differences, thereby enhancing the detection of remarkable molecular changes. Additionally, RMSD would facilitate the incorporation of other biophysical parameters, such as structural divergences from a wild-type variant or entropy, which could be informative for fitness in future versions of the method. We have now included this consideration in the manuscript.

      (3) Lines 165-166: "In both cases, the death rate (d) is considered as 1-b to allow a constant global (birth-death) rate" <- This would give a constant R = b / (1-b) over the entire phylogenetic tree. For applications to pathogens like viruses with epidemic dynamics, this is extremely implausible. Is there any need to make such a restrictive assumption? 

      Regarding technical considerations of the model, we refer to our answer to the first public review comment. Next, a constant global rate of evolution was observed in numerous genes and proteins of diverse organisms, including viruses (Gojobori, et al.1990; Leitner and Albert 1999; Shankarappa, et al. 1999; Liu, et al. 2004; Lu, et al. 2018; Zhou, et al. 2019). However, following the comment of the reviewer, and as we indicated in our answer to the first public review comment, we have now implemented and evaluated an additional birth-death model that allows for variation in the global birth-death rate among lineages. We have implemented this additional model in the framework and described it along with its results in the manuscript.

      (4) Lines 187-188: "As a consequence, since b+d=1 at each node, tn is consistent across all nodes, according to (Harmon, 2019)." <- This would also imply that all lineages have a growth rate r = b - d, which under a birth-death model is equivalent to saying all lineages have the same fitness! 

      We clarified this aspect in our answer to the first public review comment. In particular, in the model presented, protein variants with higher fitness have higher birth rates, leading to more birth events, while protein variants with lower fitness have lower birth rates leading to more extinction events, which presents biological meaning for the study system. In our model b and d can vary among lineages according to the corresponding fitness (i.e., a lineage may have b=0.9, d=0.1, r=0.8; while another one may have b=0.6, d=0.4, r=0.2). Since the reproductive success varies among lineages in our model, the statement “this is essentially assuming all lineages have the same absolute fitness” is incorrect, although it could be interpreted like that in certain models. Fitness affects reproductive success, but fitness and growth rate of evolution are different biological processes (despite a faster growth rate can sometimes be associated with higher fitness, a variant with a high fitness not necessarily has to accumulate substitutions at a higher rate). An example in molecular adaptation studies is the traditional nonsynonymous to synonymous substitution rates ratio (dN/dS), where dN/dS (that informs about selection derived from fitness) can be constant at different rates of evolution (dN and dS). In any case, we thank the reviewer for raising this point, which led us to incorporate an additional birth-death model and inspired some ideas.  Thus, following the comment of the reviewer and as indicated in the answer to the first public review comment, we have now implemented and evaluated an additional birthdeath model that allows for variation in the global birth-death rate among lineages. The results indicated that this model yields similar predictive accuracy compared to the previous birth-death model. We have now included these aspects, along with the results from the additional model, in the manuscript.

      (5) Line 321-322: "For the case of neutral evolution, all protein variants equally fit and are allowed, leading to only birth events," <- Why would there only be birth events? Lineages can die regardless of their fitness. 

      AU: In the neutral evolution model, all protein variants have the same fitness, resulting in a flat fitness landscape. Since variants are observed, we allowed birth events. However, it assumed the absence of death events as no information independent of fitness is available to support their inclusion and quantification, thereby avoiding the imposition of arbitrary death events based on an arbitrary death rate. We have now provided a justification of this assumption in the manuscript.

      Reviewer #2 (Recommendations for the authors): 

      (1) Clarify the purpose of the alternative fitness mode ("ΔG similarity to a target variant"): 

      The manuscript briefly introduces an alternative fitness function based on the similarity of a simulated protein's folding stability to that of a real protein variant, but does not provide a clear motivation, usage scenario, or results derived from it. 

      The presented model provides two approaches for deriving fitness from predicted folding stability. The simpler approach assumes that a more stable protein variant has higher fitness than a less stable one. The alternative approach assigns high fitness to protein variants whose stability closely matches observed stability, acknowledging that the real observed stability is derived from the real selection process, and this approach considers negative design by contrasting the prediction with real information. For the analyses of real data in this study, we used the second approach, guided by these considerations. We have now clarified this aspect in the manuscript.

      (2) Report structural template quality and modeling confidence: 

      Since folding stability (ΔG) estimates rely on structural models derived from homology templates, the accuracy of these predictions will be sensitive to the choice and quality of the template structure. I recommend that the authors report, for each protein modeled, the template's sequence identity, coverage, and modeling quality scores. This will help readers assess the confidence in the ΔG estimates and interpret how template quality might impact simulation outcomes. 

      We agree with the reviewer and we have now included additional information in a supplementary table regarding sequence identity, modeling quality and coverage of the structural templates for the proteins that required homology modeling. The selection of templates was performed using the well-established framework SWISS-MODEL and the best-fitting template was chosen. Next, a large number of protein structures are available in the PDB for the study proteins, which favors the accuracy of the homology modeling. For some datasets, homology modeling was not required, as the modeled sequence was already present in an available protein structure. We have now included this information in the manuscript and in a supplementary table.

      (3) Clarify whether structural remodeling occurs during simulation: 

      It appears that folding stability (ΔG) for all simulated protein variants is computed by mapping them onto a single initial homology model, without remodeling the structure as sequences evolve. If correct, this should be clearly stated, as it assumes that the structural fold remains valid across all simulated variants. A discussion on the potential impact of structural drift would be welcome.

      We agree with the reviewer. As indicated in our answer to a previous comment, our method assumes that the protein structure is maintained over the studied evolutionary time, which is generally acceptable for short timescales where the structure is conserved (Illergard, et al. 2009; Pascual-Garcia, et al. 2010). At longer timescales the protein structure could change, requiring the modeling of structural evolution over the evolutionary time. To our knowledge, modeling the evolution of the protein structure remains a challenging task that requires substantial methodological developments. Recent advances in artificial intelligence, particularly in protein structure prediction from sequence, can be promising tools for addressing this challenge. However, we believe that evaluating such approaches in the context of structural evolution would be difficult, especially given the limited availability of real datasets with known evolutionary trajectories involving structural change. In any case, this is probably an important direction for future research. We have now included this discussion in the manuscript.

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    The nanosd integral gene drive enables population modification of the malaria vector Anopheles gambiae
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    1. EMBOpress 04 Sep 2025

      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 #1

      Evidence, reproducibility and clarity

      In this study, the authors develop a complete integral drive system in Anopheles gambiae malaria mosquitoes. This type of gene drive is interesting, with special advantages and disadvantages compared to more common designs. Here, the authors develop the Cas9 element and combine it with a previously developed antimalaria effector element. The new element performs very well in terms of drive efficiency, but it has unintended fitness costs, and a higher than desirable rate of functional resistance allele formation. Nevertheless, this study represents a very good step forward toward developing effective gene drives and is thus of high impact.

      The format of the manuscript is a bit suboptimal for review. Please add line numbers next time for easy reference. It would also help to have spaces between paragraphs and to have figures (with legends) added to the text where they first appear.

      It might be useful to add subsections to the results, just like in the methods section. It could even be expanded a bit with some specific parts from the discussion, through this is optional.

      Abstract: The text says: "As a minimal genetic modification, nanosd does not induce widespread transcriptomic perturbations." However, it does seem to change things based on Figure 3c.

      Page 2: "drive technologies for public health and pest control applications" needs a period afterward.

      Page 2: "The fitness costs, homing efficiency, and resistance rate of the gene drive is" should be "The fitness costs, homing efficiency, and resistance rate of the gene drive are".

      Page 2: "When they target important mosquito genes, gene drives are designed to ensure that the nuclease activity window (germline) does not overlap with that of the target gene (somatic)." is note quite correct. This is, of course, sensible for suppression drives, but it's not a necessary requirement for modification drives with rescue elements in many situations.

      Page 2: "recessive somatic fitness cost phenotypes" is unclear. I think that you are trying to avoid the recessive fitness cost of null alleles becoming a dominant fitness cost from a gene drive allele (in drive-wild-type heterozygotes).

      Page 2: "This optimization approach has had only limited success, and suboptimal performance is commonly attributed to not capturing all the regulatory elements specific to the germline gene's expression9,12". I don't think this is correct. There are several examples where a new promoter helped a lot. The zpg promoter in Anopheles gambiae allowed success at the dsx site in suppression cage studies (Kyrou et al 2018), and nanos gave big improvement to modification drives at the cardinal locus (Carballer et al 2020). In flies, several promoters were tested, and one allowed success in cage experiments (Du et al 2024). In Aedes, the shu promoter allowed for high drive performance (Anderson et al 2023), though this last one hasn't been tested in more difficult situations. I think you could certainly argue in the general case that not all promoters will work the way their transcriptome says, but there are many examples where they seem to be pretty good.

      Page 2: "make it more likely that mutations that disrupt the drive components are selected against though loss of function of the host gene." I think that this needs a bit more explanation. You are referring to mutations in regulatory elements or frameshift mutations. This will make it more resistant to mutation. Also, these mutations would tend to have a minor effect expect perhaps in the cargo gene of a modification drive. By using a cargo gene in an integral drive, you could still keep it somewhat safer, but whether this is 1.2x or 10x safer is unclear.

      Page 3: "they can incur severe unintended fitness costs". This is central to integral drives and this manuscript. It's worth elaborating on.

      Page 3: "Regulatory elements from germline genes that have worked sub-optimally in traditional gene drive designs for the reasons outlined above may work well in an IDG design20." This is setting up the integral drive with nanos, but nanos DOES work well in traditional Anopheles gambiae gene drive designs. It is possible that you might end up with less somatic expression than Hammond et al 2020 (though the comparison is unclear due to batch effects in that study), but there is no direct comparison in this manuscript to that.

      Page 3: "This suggests an impact of maternal deposition on drive efficiency only in female drive carriers." This is quite strange. Usually, I would expect to see an equal reduction in efficiency between male and female progeny. Could this be due to limited sample size? Random idea: It's also possible that almost all maternal deposition was mosaic and wouldn't be enough to direct affect drive conversion. However, it could cause enough of a fitness cost TOGETHER with new drive expression in females that perhaps only tissues with randomly low expression rates properly developed and led to progeny, reducing drive inheritance? Another possibility: Could the drive/resistance males have impaired fertility, so these ones are underrepresented in the batch cross? If nanos is needed in males and a single drive copy is not quite enough for good fertility or mating competitiveness, they may be underrepresented in your crosses. They might have worse fertility than drive homozygous males, which at least have two partially working copies of nanos rather than just one (in many cells, at least). Maybe check the testis for abnormal phenotypes?

      Overall, it would be favorable if the drive allele was somewhere more fit than a nonfunctional resistance allele. This could already be achieved in this drive, but it doesn't get much mention.

      Page 3: There should be a comma after, "Interestingly, while many of the observed mutations were predicted to abolish nanos expression" and "This could indicate that in these experiments".

      Page 3 last sentence: Please improve the clarity.

      Removing the EGFP is supposed to restore the fitness, and this was helpful in some previous integral drive constructs. This could get a bit more mention (it is possible that I missed this somewhere in the manuscript).

      Page 4: The MM-CP line and it's association with the integral drive strategy could get a little more introduction. Maybe even a supplemental figure showing the general idea.

      Page 5: "cassette is predicted to disrupt the CP function entirely (Fig. 5d)" also lacks a period.

      Page 5: "The subsequent stabilization of the nanosd frequency and the lack of rapid loss suggests that any associated fitness cost is primarily recessive." This is not quite correct because by this point, drive/wild-type heterozygotes are rare, and this is where you'd find a potential dominant fitness cost. It should be correct in the end stages where it is a mix of drive and functional/nonfunctional resistance alleles (though the nonfunctional resistance alleles may cause greater fitness costs when together with a drive - see above).

      Page 6: "Maternal deposition of Cas9, or Cas9;gRNA, into the zygote can lead to cutting at stages when homing is not favoured, and has been commonly observed for canonical Anopheles nanos drives9,10,35." Reference 35 (which is more suitable for referencing an example of nanos in other Anopheles) found some resistance alleles by deep sequencing, but the timing that they formed was unclear (it's not certain if it was maternal deposition). This study may be a more suitable reference: Carballar-Lejarazú R, Tushar T, Pham TB, James AA. Cas9-mediated maternal-effect and derived resistance alleles in a gene-drive strain of the African malaria vector mosquito, Anopheles gambiae. Genetics, 2022.

      Page 8: "could further reduce the likelihood of resistance allele formation by increasing the frequency of HDR events." Multiple gRNAs would mostly help by reducing functional resistance allele formation, especially since drive conversion is already very high in Anopheles.

      Page 8, last paragraph: This conclusion is perhaps a little optimistic considering the functional resistance alleles, which should get a little more attention in the summary or elsewhere in the discussion section.

      Figure 1d: The vertical text saying "Non-WT" is confusing. The circles themselves show + and -. Also, "-" isn't necessarily a knockout allele, so I'm not sure if - is the best symbol for resistance.

      Figure 2e: The vertical scale is not the most intuitive. Consider rearranging it to "Transition from larvae to pupae" starting at zero and going to 1 when all the larvae become pupae.

      Figure 2e-f: For both of these, there are clear differences between males and females. Thus, when comparing drive homozygotes to wild-type, it would probably be better to have separate statistical comparisons for males and females.

      Figure 3: Can any of these transcription results in individual genes potentially explain the observed fitness cost?

      Figure 3b: The scale here also doesn't quite make sense. It's the fraction of underdeveloped ovaries, but the graph is also perhaps trying to show whether just 1-2 ovaries are present, or maybe how many ovaries are undeveloped, but then it would say "zero"? This should be clarified. Number of ovaries and how well-developed they are is separate (it can be put on the same graph, but needs to be more clear).

      Figure 4f: The vertical axis should say "ONNV."

      Figure 5c-d: These should be labeled as the most common resistance allele. Also, I'm not sure how relevant it is, but we also found an alternate start codon here: Hou S, Chen J, Feng R, Xu X, Liang N, Champer J. A homing rescue gene drive with multiplexed gRNAs reaches high frequency in cage populations but generates functional resistance. J Genet Genomics, 2024. Maybe this is a more common problem than one would expect?

      Figure 5cd,S4,S5: They have a bit of a weird plot. Why not make four line graphs for each? Also, some alleles use the  symbol. + is wild-type, which is well understood, but - as resistance is not always clear, and seeing them together may confuse readers. Additionally, the fact that you have the most common resistance allele in Figure 5cd might mean that you know more about the genotype? If so, it would be best to separate wild-type and resistance alleles in whatever the final figure looks like.

      Some supplemental raw data files would be useful if they were available, but the figures are through enough that this isn't essential.

      Review by:

      Jackson Champer, with major assistance from Ruobing Feng (essentially section B) and Jie Du

      Referee cross-commenting

      We don't have any cross-comments, other than supporting the idea of slightly more comparisons to the authors' previous construct.

      Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      A key innovation of the nanosd gene drive is its integral gene drive (IGD) design, which inserts the drive cassette directly into the A. gambiae nanos gene, incorporating only the minimal components necessary for drive function. The drive achieves high transmission rates, without causing widespread disruption of gene expression or increasing susceptibility to malaria parasites, and imposes an acceptable fitness cost-primarily a reduction in female fecundity when homozygous. The strong performance of nanosd can be attributed to its design: Cas9 is expressed in the correct cells and timing to induce efficient homing, effectively hijacking the nanos gene's natural expression profile. However, despite the careful design aimed at preserving nanos function, the rescue was incomplete: homozygous female drive carriers exhibited a clear reduction in ovarian function.

      In caged population trials, both the drive and a co-introduced anti-malaria effector gene reached high frequencies, even in the presence of emerging resistance alleles. Because the drive is inserted into an essential gene, nonfunctional resistance alleles are selected against and tend to be purged over time. Nonetheless, functional resistance remains a concern. The use of a single, though precisely positioned gRNA targeting the native nanos gene ATG site increases the likelihood of generating functional resistance alleles. Over the long term, if the drive imposes fitness costs, it may be outcompeted by such functional resistance alleles, potentially undermining the goal of sustained population modification.

      Overall, this study represent a notable advance in Anopheles mosquito gene drive development and can be considered as high impact. - Place the work in the context of the existing literature (provide references, where appropriate).

      Previous IGD efforts in Drosophila, mice and mosquitoes have demonstrated nearly super‐Mendelian inheritance but often at the expense of host fitness. For example, Nash et al. built an intronic‐gRNA Cas9 drive at the D. melanogaster rcd-1r locus that propagated efficiently through cage populations (Nash et al., 2022), and Gonzalez et al. reported that a Cas9 drive inserted at the germline zpg locus in Anopheles stephensi biased inheritance by ~99.8% (Gonzalez et al., 2025). However, these strong drives disrupted essential genes: in A. gambiae, inserting Cas9 into zpg produced efficient homing but rendered females largely sterile (Ellis et al., 2022). A similar germline Cas9 knock-in in Mus musculus enabled gene conversion in both sexes, albeit with only modest efficiency and potential fitness trade-offs (Weitzel et al., 2021). The current nanosd IGD is explicitly designed to overcome this limitation by selecting a more permissive gene target and using a minimal drive cassette, so as to preserve mosquito viability while maintaining robust drive efficiency, although still with reduced female drive homozygotes fertility.

      This nanosd gene drive like previous homing drives in Anopheles, is capable of achieving a high level of inheritance bias. Although it uses the endogenous nanos regulatory elements, which have less leaky somatic expression compared to using vasa (Gantz et al., 2015; Hammond et al., 2016; Hammond et al., 2017) or zpg promoters(Hammond et al., 2021; Kyrou et al., 2018), to drive Cas9 expression and thereby reduces somatic expression-induced female sterility, the incomplete rescue of nanos function still leads to reduced female fertility in drive homozygotes. - State what audience might be interested in and influenced by the reported findings.

      It's worth noting the broad audience that will find this work relevant. Gene drive developers and molecular geneticists will be impressed by the good drive performance and directly influenced by the design principles showcased here. The study's integral gene drive architecture that leverages the endogenous nanos regulatory elements, in-frame E2A peptide linkage for co-expression, and intronic insertion of gRNA and selectable markers addresses long-standing challenges in promoter leakage, somatic fitness costs, and resistance allele evolution. What's more, vector biologists and malaria researchers will be interested in the successful deployment of a gene drive in A. gambiae that actually carries a disease-blocking trait. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      We have worked on CRISPR gene drive development in both fruit flies and Anopheles mosquitoes and have experience with modeling their spread.

      References

      Ellis, D.A., Avraam, G., Hoermann, A., Wyer, C.A.S., Ong, Y.X., Christophides, G.K., and Windbichler, N. (2022). Testing non-autonomous antimalarial gene drive effectors using self-eliminating drivers in the African mosquito vector Anopheles gambiae. PLOS Genetics 18, e1010244-e1010244.

      Gantz, V.M., Jasinskiene, N., Tatarenkova, O., Fazekas, A., Macias, V.M., Bier, E., and James, A.A. (2015). Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi. Proc Natl Acad Sci U S A 112, E6736-E6743.

      Gonzalez, E., Anderson, M.A.E., Ang, J.X.D., Nevard, K., Shackleford, L., Larrosa-Godall, M., Leftwich, P.T., and Alphey, L. (2025). Optimization of SgRNA expression with RNA pol III regulatory elements in Anopheles stephensi. Scientific Reports 15, 13408.

      Hammond, A., Galizi, R., Kyrou, K., Simoni, A., Siniscalchi, C., Katsanos, D., Gribble, M., Baker, D., Marois, E., Russell, S., et al. (2016). A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat Biotechnol 34, 78-83.

      Hammond, A., Karlsson, X., Morianou, I., Kyrou, K., Beaghton, A., Gribble, M., Kranjc, N., Galizi, R., Burt, A., Crisanti, A., et al. (2021). Regulating the expression of gene drives is key to increasing their invasive potential and the mitigation of resistance. PLOS Genetics 17, e1009321-e1009321.

      Hammond, A.M., Kyrou, K., Bruttini, M., North, A., Galizi, R., Karlsson, X., Kranjc, N., Carpi, F.M., D'Aurizio, R., Crisanti, A., et al. (2017). The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito. PLOS Genetics 13, e1007039-e1007039.

      Kyrou, K., Hammond, A.M., Galizi, R., Kranjc, N., Burt, A., Beaghton, A.K., Nolan, T., and Crisanti, A. (2018). A CRISPR-Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nature Biotechnology 36, 1062-1066.

      Nash, A., Capriotti, P., Hoermann, A., Papathanos, P.A., and Windbichler, N. (2022). Intronic gRNAs for the construction of minimal gene drive systems. Frontiers in Bioengineering and Biotechnology 0, 570-570. Weitzel, A.J., Grunwald, H.A., Ceri, W., Levina, R., Gantz, V.M., Hedrick, S.M., Bier, E., and Cooper, K.L. (2021). Meiotic Cas9 expression mediates gene conversion in the male and female mouse germline. Plos Biol 19, e3001478-e3001478.

      PeerReviewed
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    SLC4A1 mutations that cause distal renal tubular acidosis alter cytoplasmic pH and cellular autophagy
    1
    1. Public_Reviews 04 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      The authors have identified novel dRTA causing SLC4A1 mutations and studied the resulting kAE1 proteins to determine how they cause dRTA. Based on a previous study on mice expressing the dRTA kAE1 R607H variant, the authors hypothesize that kAE1 variants cause an increase in intracellular pH, which disrupts autophagic and degradative flux pathways. The authors clone these new kAE1 variants and study their transport function and subcellular localization in mIMCD cells. The authors show increased abundance of LC3B II in mIMCD cells expressing some of the kAE1 variants, as well as reduced autophagic flux using eGFP-RFP-LC3. These data, as well as the abundance of autophagosomes, serve as the key evidence that these kAE1 mutants disrupt autophagy. Furthermore, the authors demonstrate that decreasing the intracellular pH abrogates the expression of LC3B II in mIMCD cells expressing mutant SLC4A1. Lastly, the authors argue that mitochondrial function, and specifically ATP synthesis, is suppressed in mIMCD cells expressing dRTA variants and that mitochondria are less abundant in AICs from the kidney of R607H kAE1 mice. While the manuscript does reveal some interesting new results about novel dRTA causing kAE1 mutations, the quality of the data to support the hypothesis that these mutations cause a reduction in autophagic flux can be improved. In particular, the precise method of how the western blots and the immunofluorescence data were quantified, with included controls, would enhance the quality of the data and offer more supportive evidence of the authors' conclusions.

      Strengths:

      The authors cloned novel dRTA causing kAE1 mutants into expression vectors to study the subcellular localization and transport properties of the variants. The immunofluorescence images are generally of high quality, and the authors do well to include multiple samples for all of their western blots.

      Weaknesses:

      Inconsistent results are reported for some of the variants. For example, R295H causes intracellular alkalinization but also has no effect on intracellular pH when measured by BCECF. The authors also appear to have performed these in vitro studies on mIMCD cells that were not polarized, and therefore, the localization of kAE1 to the basolateral membrane seems unlikely, based upon images included in the manuscript. Additionally, there is no in vivo work to demonstrate that these kAE1 variants alter intracellular pH, including the R607H mouse, which is available to the authors. The western blots are of varying quality, and it is often unclear which of the bands are being quantified. For example, LAMP1 is reported at 100kDa, the authors show three bands, and it is unclear which one(s) are used to quantify protein abundance. Strikingly, the authors report a nonsensical value for their quantification of LCRB II in Figure 2, where the ratio of LCRB II to total LCRB (I + II) is greater than one. The control experiments with starvation and bafilomyocin are not supportive and significantly reduce enthusiasm for the authors' findings regarding autophagy. There are labeling errors between the manuscript and the figures, which suggest a lack of vigilance in the drafting process.

      Review 3
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    Ubiquitination-activated TAB–TAK1–IKK–NF-kB axis modulates gene expression for cell survival in the lysosomal damage response
    3
    1. Public_Reviews 04 Sep 2025

      eLife Assessment

      This study presents the important finding that lysosomal damage triggers inflammatory signaling through ubiquitination and the TAB-TAK1-IKK-NF-kB axis. The data obtained from the unbiased transcriptomic and proteomic analyses are convincing and provide invaluable information to the field. Although further experiments will be required to clarify how TAB2/3 are recruited after various types of lysosome damage, this work will be of interest to researchers in the fields of organelle biology and inflammation.

      Summary
    2. Public_Reviews 04 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      The response to lysosomal damage is a fast-moving and timely field. Besides repair and degradation pathways, increasing interest has been focusing on damaged-induced signaling. The authors conducted both transcriptomics and proteomics to characterize the cellular response to lysosomal damage. They identify a signaling pathway leading to activation of NFkappaB. Based on this and supported by Western blot and microscopy data, the authors nicely show that TAB2/3 and TAK1 are activated at damaged lysosomes and kick off the pathway to alter gene expression, which induces cytokines and protect from cell death. TAB2/3 activation is proposed to occur through K63 ubiquitin chain formation. Generally, this is a careful and well conducted study that nicely delineates the pathway under lysosomal stress. The "omics" data serves a valuable resource for the field. More work should be invested into how TAB2/3 are activated at the damaged lysosomes, also to increase novelty in light of previous reports.

      Strengths:

      Generally, this is a careful and well-conducted study that nicely delineates how the NFkB pathway is activated under lysosomal stress and modulates cell behavior. The "omics" data serves as a valuable resource for the field.

      Weaknesses:

      While activation of TAB2/3 by K63-linked Ub chains is convincing, more work needs to be done on how they are recruited by distinct damage types to probe relevance for different pathophysiological conditions."

      Comments on revisions:

      The authors have addressed much of my criticism. Specifically, they have put (with new experiments) the data on the TAB2/3-TAK1 pathway in perspective to the previously reported LUBAC-mediated activation of NFkB. They also addressed the question about the significance of K63-linked chains for TAB2/3 activation with new complementation experiments (a K63-specific NZF mutant failed to rescue).

      The third point (types of damage as triggers) raises more questions, though. The authors find that, in contrast to LLOMe, GPN or DC661-induced damage does not activate TAK1 (consistent with lower damage levels). However, the authors still observe K63 ubiquitylation. This goes along with their finding that TAB2 is recruited in the absence of any ubiquitylation (blocked by TAK-243). It argues that TAB2 is recruited by an unknown cue (that may be damage-specific) and then activated by K63. The authors need to clarify whether TAB2 is or is not recruited in the GPN/DC661 conditions (in which K63 occurs, but TAK1 is not activated). The point about the effects of other damage types was also raised by reviewer #1 and should be solved. The fact that TAB2 is recruited independently of K63 should also be visualized in the model. The manuscript will then be an important contribution to the field.

      Review 3
    3. Public_Reviews 04 Sep 2025

      Author response:

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

      Reviewer #1 (Public review):

      Summary:

      Lysosomal damage is commonly found in many diseases including normal aging and age-related disease. However, the transcriptional programs activated by lysosomal damage have not been thoroughly characterized. This study aimed to investigate lysosome damage-induced major transcriptional responses and the underlying signaling basis. The authors have convincingly shown that lysosomal damage activates a ubiquitination-dependent signaling axis involving TAB, TAK1, and IKK, which culminates in the activation of NF-kB and subsequent transcriptional upregulation of pro-inflammatory genes and pro-survival genes. Overall, the major aims of this study were successfully achieved.

      Strengths:

      This study is well-conceived and strictly executed, leading to clear and well-supported conclusions. Through unbiased transcriptomics and proteomics screens, the authors identified NF-kB as a major transcriptional program activated upon lysosome damage. TAK1 activation by lysosome damage-induced ubiquitination was found to be essential for NF-kB activation and MAP kinase signaling. The transcriptional and proteomic changes were shown to be largely driven by TAK1 signaling. Finally, the TAK1-IKK signaling was shown to provide resistance to apoptosis during lysosomal damage response. The main signaling axis of this pathway was convincingly demonstrated.

      Weaknesses:

      One weakness was the claim of K63-linked ubiquitination in lysosomal damage-induced NF-kB activation. While it was clear that K63 ubiquitin chains were present on damaged lysosomes, no evidence was shown in the current study to demonstrate the specific requirement of K63 ubiquitin chains in the signaling axis being studied. Clarifying the roles of K63-linked versus other types of ubiquitin chains in lysosomal damage-induced NF-kB activation may improve the mechanistic insights and overall impact of this study.

      Another weakness was that the main conclusions of this study were all dependent on an artificial lysosomal damage agent. It will be beneficial to confirm key findings in other contexts involving lysosomal damage.

      We would like to thank Reviewer #1 for the positive and constructive comments on our study. For a main concern regarding the molecular mechanism by which TAB proteins are activated in response to lysosomal damage, we have added the experimental results to support that the lysosomal accumulation of K63 ubiquitin chains serves as a trigger to activate the TAB-TAK1 pathway. We also investigated and discussed the role of LUBAC-mediated M1 ubiquitin chains in NF-kB activation and the effects of other lysosomal-damaging compounds. Please see the response to “Reviewer #3 (Public review): Suggestions:”.

      Reviewer #2 (Public review):

      Summary:

      Endo et al. investigate the novel role of ubiquitin response upon lysosomal damage in activating cellular signaling for cell survival. The authors provide a comprehensive transcriptome and proteome analysis of aging-related cells experiencing lysosomal damage, identifying transcription factors involved in transcriptome and proteome remodeling with a focus on the NF-κB signaling pathway. They further characterized the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis in controlling gene expression, inflammatory responses, and apoptotic processes.

      Strengths:

      In the aging-related model, the authors provide a comprehensive transcriptome and characterize the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis. Through compelling experiments and advanced tools, they elucidate its critical role in controlling gene expression, inflammatory responses, and apoptotic processes.

      Weaknesses:

      The study lacks deeper connections with previous research, particularly:

      • The established role of TAB-TAK1 in AMPK activation during lysosomal damage

      • The potential significance of TBK1 in NF-κB signaling pathways

      We would like to thank Reviewer #2 for the helpful comments on our study. To achieve a more comprehensive understanding of the signaling pathways involved in the lysosomal damage response, we investigated additional related signal mediators, such as TBK1 and LUBAC. The citations related to AMPK have been incorporated.

      Reviewer #3 (Public review):

      Summary:

      The response to lysosomal damage is a fast-moving and timely field. Besides repair and degradation pathways, increasing interest has been focusing on damaged-induced signaling. The authors conducted both transcriptomics and proteomics to characterize the cellular response to lysosomal damage. They identify a signaling pathway leading to activation of NFkappaB. Based on this and supported by Western blot and microscopy data, the authors nicely show that TAB2/3 and TAK1 are activated at damaged lysosomes and kick off the pathway to alter gene expression, which induces cytokines and protect from cell death. TAB2/3 activation is proposed to occur through K63 ubiquitin chain formation. Generally, this is a careful and well conducted study that nicely delineates the pathway under lysosomal stress. The "omics" data serves as a valuable resource for the field. More work should be invested into how TAB2/3 are activated at the damaged lysosomes, also to increase novelty in light of previous reports.

      Strengths:

      Generally, this is a careful and well-conducted study that nicely delineates the pathway under lysosomal stress. The "omics" data serves as a valuable resource for the field.

      Weaknesses:

      More work should be invested into how TAB2/3 are activated at the damaged lysosomes, also to increase novelty in light of previous reports. Moreover, different damage types should be tested to probe relevance for different pathophysiological conditions.

      We would like to thank Reviewer #3 for the valuable comments on our study. We have added the experimental results to address two concerns raised by Reviewer #3. Please see the response to “Reviewer #3 (Public review): Suggestions:”.

      Suggestions:

      (1) A recent paper claims that NFkappaB is activated by Otulin/M1 chains upon lysosome damage through TBK1 (PMID: 39744815). In contrast, Endo et al. nicely show that ubiquitylation is needed (shown by TAK-243) for NFkB activation but only have correlative data to link it specifically to K63 chains. On page 15, line 11, the authors even argue a "potential" involvement of K63. This point should be better dealt with. Can the authors specifically block K63 formation? K63R overexpression or swapping would be one way. Is the K63 ligase ITCH involved (PMID: 38503285) or any other NEDD4-like ligase? This could be compared to LUBAC inhibition. Also, the point needs to be dealt with more controversially in the discussion as these are alternative claims (M1 vs K63, TAB vs TBK1).

      It is well-characterized that the NZF domain of TAB proteins preferentially associates with K63-linked ubiquitin chains. Therefore, we performed the add-back experiment using siRNA-resistant TAB2 WT and mutants incapable of binding to K63-linked ubiquitin chains, dNZF and E685A, to elucidate the requirement of K63 ubiquitin chains for TAK1 activation. We investigated whether the add-back of TAB2 mutants rescues the activation of TAK1 in TAB2-depleted cells (Fig. 2E). TAB2 WT, but not dNZF and E685A, rescued TAK1 activation in response to LLOMe, suggesting that the specific interaction of TAB proteins and K63 ubiquitin chains is a key mechanism to activate TAK1. We also found that the treatment of an E1 inhibitor TAK-243 effectively prevented the lysosomal accumulation of K63 ubiquitin chains, but TAB2 was recruited to damaged lysosomes (Fig. S2B). This suggests that the recruitment of TAB proteins to damaged lysosomes is independent of the association with K63 ubiquitin chains. Collectively, it is postulated that TAB proteins require interaction with K63 ubiquitin chains for TAK1 activation, but not for recruitment to damaged lysosomes. We have added the sentences (p9, lines 7-20, and p10, lines 8-10).

      Next, we confirmed that LUBAC functions are essential for NF-kB activation in the lysosomal damage response. RNF31/HOIP is a component of LUBAC that catalyzes M1 ubiquitination. The depletion of RNF31 showed no significant effects on TAK1 activation, but abolished IKK activation (Fig. S4G). It is well-characterized that LUBAC-mediated M1 ubiquitin chains recruit IKK subunits and transduce the signaling to downstream in the canonical pathway. We assume that K63 ubiquitin chains in damaged lysosomes initially activate TAB-TAK1 and trigger LUBAC-mediated M1 ubiquitination, and subsequently, M1 ubiquitination functions to recruit the IKK complex. Consequently, activated TAK1 phosphorylates IKK subunits in damaged lysosomes, leading to NF-kB activation. We also examined whether TBK1 is involved in the activation of NF-kB. TBK1 was phosphorylated upon LLOMe, and depletion of TAB and TAK1 resulted in a slight reduction of TBK1 phosphorylation (Fig. S4D, E). The treatment of a TBK1 inhibitor BX-795 exhibited no or little effects on TAK1 activation, but abolished phosphorylation of IKK and IkBa (Fig. S4F). These suggest that TBK1 is required for the activation of NF-kB. We have added the sentences (p13, line 13-p14, line 10).

      As mentioned by Reviewer #3, it is important to identify the E3 ligase responsible for K63 ubiquitination in the lysosomal damage response. We have been aiming to identify such E3 ligase(s). However, depletions of ITCH and other E3 ligases that have been tested exhibited no or little effects on K63 ubiquitination and TAK1 activation.  We would like to explore E3 ligase(s) in future study.

      (2) It would be interesting to know what the trigger is that induces the pathway. Lipid perturbation by LLOMe is a good model, but does activation also occur with GPN (osmotic swelling) or lipid peroxidation (oxidative stress) that may be more broadly relevant in a pathophysiological way? Moreover, what damage threshold is needed? Does loss of protons suffice? Can activation be induced with a Ca2+ agonist in the absence of damage?

      To further clarify the initial trigger that induces TAB-TAK1 activation coupled with lysosomal damage, we examined other damage sources, GPN and DC661, which induce hyperosmotic stress and lipid peroxidation in lysosomes, respectively, thereby resulting in lysosomal membrane damage. Under our experimental conditions, the treatment of these compounds did not result in significant accumulation of Gal-3, indicating a reduced level of lysosomal membrane permeabilization compared with LLOMe (Fig. S2C, D), and no or little TAK1 activation was observed (Fig. S2E). TAB proteins require their association with K63 ubiquitin chains for TAK1 activation. It is therefore postulated that the severe lysosomal membrane permeabilization that triggers the formation and cytosolic exposure of K63 ubiquitin chains may be a determinant of TAB-TAK1 activation. In our future work, we would like to examine broad stimulation of lysosomal damage and further elucidate the initial mechanism of TAB-TAK1 activation. We have added the sentences (p9, line 21-p10, line 7).

      (3) The authors nicely define JNK and p38 activation. This should be emphasized more, possibly also in the abstract, as it may contribute to the claim of increased survival fitness.

      We further tested whether the inhibition of JNK affects the anti-apoptotic effect (Fig. S5B). The inhibition of JNK resulted in an increase in the cleaved caspase-3. This suggests that the anti-apoptotic action in the lysosomal damage response requires JNK as well as IKK. We have added the sentences in results to emphasize the pivotal role of stress-induced MAPKs (p15, lines 7-11).

      Reviewer #1 (Recommendations for the authors):

      (1) Although the ubiquitination-TAB-TAK1-IKK axis was previously characterized in other contexts, specific evidence supporting lysosomal recruitment of these components by ubiquitination during lysosome damage would be beneficial.

      We found that the treatment of an E1 inhibitor TAK-243 abolished the lysosomal accumulation of K63 ubiquitin chains, but TAB2 and TAK1 were recruited to damaged lysosomes (Fig. S2B). This suggests that the recruitment of TAB proteins to damaged lysosomes is independent of the association with K63-linked ubiquitin chains. Next, we investigated whether the add-back of TAB2 mutants incapable of binding K63 ubiquitin chains rescues the activation of TAK1 in TAB2-depleted cells (Fig. 2E). K63 ubiquitin binding of TAB2 was essential for TAK1 activation in response to LLOMe. Taken together, it is suggested that TAB proteins require their interaction with K63 ubiquitin chains for TAK1 activation, but not for recruitment to damaged lysosomes. We have added the sentences (p9, lines 7-20, and p10, lines 8-10). Please also see the response to “Reviewer #3 (Public review): Suggestions:”.

      (2) The activation of p38 and JNK by lysosomal damage does not fit well into the main conclusions of the paper, since IKK knockdown was sufficient to block cellular resistance to apoptosis (caspase cleavage in Fig. 5f). Are p38 and JNK also important for cell survival during lysosomal damage?

      We found that the inhibition of JNK resulted in an increase in the cleaved caspase-3, suggesting that the anti-apoptotic action in the lysosomal damage response requires both IKK and JNK (Fig. S5B). We have added the sentences (p15, lines 7-11).

      (3) Cell death tests are recommended to support the conclusions related to apoptosis.

      As suggested by Reviewer #1, we performed the cell death assay using propidium iodide (PI) and confirmed that HeLa cells co-treated with LLOMe and TAK-243 or HS-276 exhibited increased cell death (Fig. 5E). This indicates a direct correlation between the degree of caspase-3 cleavage and cell death, possibly apoptosis.

      (4) Page 8, line 19-21, gal3 is not exposed upon lysosomal damage. It is recruited from the cytosol by the exposed beta-galactoside-containing glycans on lysosomal membrane proteins.

      We have corrected the corresponding sentence (p7, lines 17-20).

      (5) Carefully checking grammar throughout the text is recommended. Below are a few examples:

      a) Page 4, line 10, remove "that".

      b) "K63 ubiquitin" shall be replaced with "K63 ubiquitination" or "K63 ubiquitin chains".

      c) Page 8, line 9, "remain" should be "remains".

      We have carefully checked the revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      Despite the novelty and significance of these findings in advancing the field, several technical and experimental limitations require further clarification:

      We have responded to each comment. Please see below.

      The manuscript should introduce or discuss previous research showing that TAB-TAK1 facilitates AMPK activation during lysosomal damage and TAK1's increased association with damaged lysosomes (PMID: 31995728).

      We have added the reference (PMID: 31995728) and the sentences (p17, lines 15-20).

      Figure 2A: The differential LAMP1 staining intensity between control and LLOMe-treated cells needs explanation. The weaker LAMP1 signal in control and puncta changes, especially during 5-minute LLOMe treatment, require detailed clarification

      We have added the explanation (p8, lines 17-21).

      Recent literature (PMID: 34585663) reports TBK1 activation during lysosomal damage. The authors should investigate or discuss whether TBK1 potentially contributes to NF-κB signaling in this context.

      We experimentally investigated whether TBK1 is involved in the TAB-TAK1 pathway. We confirmed that TBK1 was activated upon LLOMe (Fig. S4D). Depletions of TAB and TAK1 exhibited a modest decrease in TBK1 phosphorylation (Fig. S4E). The inhibition of TBK1 by BX-795 did not affect TAK1 activation, but abolished phosphorylation of IKK and IkBa (Fig. S4F). This suggests that TBK1 is required for NF-kB activation. We have added the reference (PMID: 34585663) and the sentences (p13, lines 13-21, p14, lines 8-10, and p18, lines 15-20).

      The introduction of lysosomal damage response lacks comprehensive mechanistic information. For example, while ESCRT is discussed, other critical mechanisms such as lipid transfer and stress granule formation in lysosomal repair should be incorporated. Moreover, mTOR and AMPK signaling pathways undergo significant changes upon lysosomal damage.

      We have added the sentences (p3, lines 16-18, and p3, line 21-p4, line 1).

      The statement "lysosomal permeabilization causes the dissociation of mTORC1 from lysosomes" should explicitly reference PMID: 29625033.

      We have added the suggested reference (PMID: 29625033, p4, line 19).

      The claim that "The elimination of damaged lysosomes through lysophagy requires a period of more than half a day" needs a specific publication citation.

      We have added the reference (PMID: 23921551) to claim the time-scale of lysosomal clearance (p4, line 21).

      Figure 1G: The label "WO after 2h" lacks explanation in the figure legend and requires detailed interpretation.

      To simplify the figures, we have deleted the label “WO after 2 h” (Fig. 1G, 3F, 5D, F-J, S4G, S5A). Instead, we have added the explanation in the figure legends (Fig. 1G).

      Reviewer #3 (Recommendations for the authors):

      (1) page 8, line 13: it is recommended to phrase colocalisation "at" damaged lysosomes rather than "in" damaged lysosomes as the resolution does not allow the claim of influx into lysosomes.

      We have corrected the word (p8, line 17).

      (2) page 11, line 22: why is "whereas" used to link two events driven by the same mechanism.

      We have corrected the word (p13, line 8).

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    RubyACRs Enable Red-Shifted Optogenetic Inhibition in Freely Behaving Drosophila
    4
    1. Public_Reviews 04 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      This study by Bushey et al., focuses on two newly released red-shifted anion-Channelrhodopsins (A1ACR and HfACR, referred as Ruby-ACRs) in Drosophila. Here, the authors use a combination of electrophysiology, calcium imaging, and behavioral analyses to demonstrate the advantages of Ruby-ACRs over previous optogenetic silencers like the green-shifted GtACR1 and the blue-shifted GtACR2: higher photocurrent, faster kinetics, and operating at a light spectrum range that prevents unwanted behavioral effects in the fly. The availability of these new red-shifted silencers constitutes a great addition to the Drosophila genetic toolkit.

      Strengths:

      (1) The authors generate both UAS and LexAop RubyACR reagents and test them in a variety of preparations (electrophysiological recordings, calcium imaging, different behavioral paradigms) that cover the breadth of the fly research environment.

      (2) The optical stimulation parameters are carefully measured and characterized. Especially impressive is that they managed to titrate over both wavelength and intensity across their various assays. This provides a comprehensive dataset to the community.

      (3) Tools are made available to the community through the stock center.

      Weaknesses:

      (1) The authors could better describe their construct and choice of parameters for the chosen construct. I am specifically wondering about the following points:

      a) Why use that particular backbone (not the most commonly used one across recent literature (pJFRC7 is more common).

      b) Why do the CsChrimson and GTACR1 have a Kir sequence in it, and why did the authors not put this in the RubyACRs? I would also prefer if authors don't refer to GtACR1 as GTACR-Kir in text (e.g., in line 72); instead, they should either refer to it as GtACR1 or GtACR1-kir-mVenus (based on the full genotype mentioned in their table at the end). Same for CsChrimson-kir. From what I understand, this is just a Kir trafficking sequence and not the entire Kir sequence, which can confuse the readers.

      c) Finally, I would also encourage authors to deposit plasmids on Addgene.

      (2) Figure 2 is interesting, but it is a bit unfortunate that there is a YFP baseline in most of the samples here (except Chrimson88; this should also be mentioned). I wonder how the YFP baseline impacts this data. Could the high intensity stimulation (red light) lead to bleaching of YFP or tdTomato that reduces the baseline in the green channel? All this also makes me wonder if authors tried tagging the RubyACRs with other fluorophores or non-fluorescent tags and how that impacted their functioning. Non-YFP-tagged versions would be more useful for applications involving GCaMP imaging.

      (3) Another point for Figure 2: Since RubyACRs seem to have such a broad activation range, I wonder how much the imaging light (920nm) impacts the baseline in these experiments. If there were plots without the red light stimulation and just varying imaging light intensity, that could be useful to the research community.

      (4) Also, for Figures 2C - D, in the methods authors indicate that the stimulation light intensities were progressively increased. Could this lead to desensitization of opsin? Wouldn't randomized intensities be a better way to do this? Perhaps it should be mentioned as a caveat.

      (5) In Figure 3E the bottom middle panel Vglut-Gal4,GtACR1 shows a major increase in walking at light onset. This seems very different than all other conditions, and I could not find any discussion of this. It would help if some explanation were provided for this.

      Review 1
    2. Public_Reviews 04 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      Bushey et al. investigate the feasibility of using RubyACRs, specifically A1ACR1 and HfACR1 (described previously in (Govorunova et al., 2020)) as red-shifted inhibitory opsins in Drosophila melanogaster. The study employs a wide range of techniques to demonstrate successful neuronal inhibition. Electrophysiology experiments established that HfACR1 was most effective at hyperpolarizing cells, compared to A1ACR1 and GtACR1; both RubyACRs also appeared to be more effective than GtACR1 when the latter was actuated by green light. The authors further demonstrate successful neuronal inhibition using calcium imaging. RubyACRs were also shown to be useful in in vivo behavioral setups, specifically in spontaneous locomotion, associative learning, and courtship paradigms. In the courtship assay, in particular, the authors test multiple wavelengths of light at various light intensities, thus providing a rigorous analysis of the RubyACRs' efficacy under different light conditions.

      Strengths:

      The work provides the Drosophila field with a promising new tool. Red-shifted opsins are particularly advantageous in behavioral assays as red light penetrates the cuticle better than green or blue light, and provides less visual stimulation to the fly. It is also ideal for imaging as it allows for simultaneous optogenetic stimulation and GCamp imaging. A particular strength of the paper is the direct demonstration of RubyACR's capacity to inhibit neurons via electrophysiology and calcium imaging. Furthermore, inhibition effects in the three behavioral assays are strong and convincing. Given the apparent efficacy of RubyACRs and the advantages of a red-sensitive anion channelrhodopsin, this tool has great potential.

      Weaknesses:

      This work convincingly demonstrates the efficacy and potential utility of RubyACRs in Drosophila for imaging and behavior. However, the lethality/toxicity of RubyACRs is a relevant concern that should be addressed in-depth rather than glossed over, as it may pose a major obstacle to use. Discussing this issue in the present study will also help guide potential users and will set the stage for potential future efforts to ameliorate RubyACRs as optogenetic inhibitors.

      Major concerns:

      (1) Table 1 demonstrates high lethality in the RubyACRs compared to GtACR1. For example, in the MI04979-VGlut driver, GtACR1 expression resulted in 32.9% lethality, while HfACR1 expression resulted in 98.7% lethality. This lethality presents an obstacle to the potential adoption of this tool, and should be discussed in detail, rather than in passing. The authors might like to present "% lethality" rather than "% survived", as the former is more relevant when discussing the relative yield and health of flies that can be used in experiments.

      (2) In Figure 3D, driver>opsin flies have lower locomotion during the baseline (i.e., dark) phase, compared to opsin-only controls or GtACR1 flies. For some comparisons, flies are walking around 10-fold slower. For example, in the case of VGlut-GAL4>HfACR1, test flies are walking at <1 mm/s, while "Empty" test flies are walking at ~10 mm/s. This suggests that, for these drivers, neuronal and/or network function is affected. It opens the possibility that the lethality and locomotor defects could be due to cell-autonomous toxicity. We ask the authors to provide a description of this effect in the Results and to discuss it in the Discussion. Relatedly, VGlut-GAL4>GtACR1 flies in red light exhibit a locomotion increase, but this data is not mentioned in the text. The use of differing scales for the Y-axes in these panels can be confusing when the reader is expected to compare velocity across different panels. It would be best if the y-axes were set to a single range, e.g., 0 to 12 mm/s.

      (3) Lethality in broad drivers could result from cell-autonomous toxicity or neuronal dysfunction resulting from RubyACR expression. Ideally, the authors would address or even investigate the possible mechanisms of toxicity of the RubyACRs. Do cells and/or synapses expressing RubyACRs have normal morphology and function? For example, the authors could compare cell survival between flies with RubyACR expression and flies with a fluorescent protein with no opsin. The authors may also want to present lethality data for other, less broad drivers (such as MB320C, which was used for the associative memory assay) in order to demonstrate whether this problem is confined to broad drivers such as VGlut-GAL4, or if this is a problem with narrow drivers as well. If new experiments are not possible, these issues should at least be mentioned in the Discussion.

      Minor concerns

      (1) The specific method used for quantifying lethality is mentioned briefly in Table 1 but is not detailed in the Methods. The authors derive lethality by comparing to a sibling control group with either the opsin or the driver alone, but the opsin alone or driver alone may cause some lethality by themselves. We suggest the use of a viability assay, e.g. (Rockwell et al., 2019), which would give potential users a clearer picture of which developmental stage is most affected by opsin expression, as well as allow opsin-only, driver-only and experimental groups to be assessed separately (lethality would then be reported as the % of embryos that reach each stage of development, and eventually enclosure).

      (2) For the calcium imaging analysis in Figure 2, the U-shaped curve observed for mean ΔF/F0 for A1ACR1 and HfACR1 may not be due to actual desensitization for the channels, as the authors suggest (lines 143-145), but may be due simply to a shifting baseline. The authors use the 5-s period preceding stimulation onset as F0, but in some cases (e.g., HfACR1 at 250 uW/mm2), calcium fluorescence rises above baseline and remains high post-stimulation (ΔF/F0 of +0.5, which we observe is the same magnitude as the ΔF/F0 of -0.5 observed during inhibition), thus affecting the ΔF/F0 for subsequent trials. The authors should discuss this incomplete recovery in the text, or (if available) use a static channel instead to provide a stable F0 for calculating ΔF/F0. Alternatively, if the authors wish to rigorously test the hypothesis that high light intensity indeed results in desensitization of these channels, they may consider using different flies for each light intensity or longer inter-stimulus intervals.

      (3) For Figure 3C (Flybowl assay), the authors mention that "simply expressing the opsins decreased baseline locomotor activity compared to empty driver lines". However, the "Empty" controls in 3C appear to refer to opsin-only controls, not driver-only controls. The driver-only controls are not presented in the figure. The use of "empty" differs between the text and the figure, as the text refers to "empty" driver lines, while the figure uses "empty" to apparently refer to opsin-only controls. We recommend changing the terminology across all figures to be unambiguous, e.g., by using "opsin-only" or "driver-only" as opposed to the ambiguous "empty". In addition, the fact that opsin-only controls move less than driver-only controls may suggest some toxicity as a result of the opsin-only construct; this should be discussed further.

      (4) Figures 4 and 5 lack the reporting of driver-only controls.

      (5) Figures 3 and 4 lack positive controls; that is, the benchmarking of the efficacy of RubyACRs in their respective behavioral paradigms against a known inhibitor, e.g., GtACR1 with green light. To confirm that this GtACR1 transgene is functional, the authors could include GtACR1 with green light as a positive control for these two figures, as they have done for Figure 5-supplement 2 and 3.

      (6) Several citations are missing. In their discussion, the authors highlight that shorter wavelengths of light are more attenuated by tissue (lines 278-281); this should be accompanied by the relevant citations (Inagaki et al., 2014). Similarly, the claim that behavioral experiments exhibit greater sensitivity to shorter wavelengths should be substantiated (lines 281-283).

      References:

      Govorunova EG, Sineshchekov OA, Li H, Wang Y, Brown LS, Spudich JL. 2020. RubyACRs, nonalgal anion channelrhodopsins with highly red-shifted absorption. Proc Natl Acad Sci U S A 117:22833-22840.

      Inagaki HK, Jung Y, Hoopfer ED, Wong AM, Mishra N, Lin JY, Tsien RY, Anderson DJ. 2014. Optogenetic control of Drosophila using a red-shifted channelrhodopsin reveals experience-dependent influences on courtship. Nat Methods 11:325-332.

      Rockwell AL, Beaver I, Hongay CF. 2019. A direct and simple method to assess Drosophila melanogaster's viability from embryo to adult. J Vis Exp e59996.

      Review 2
    3. Public_Reviews 04 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      This study by Bushey et al. adapts and evaluates two newly developed red-shifted optogenetic inhibitors, A1ACR1 and HfACR1, collectively referred to as RubyACRs, for neuronal silencing in Drosophila melanogaster. Traditional optogenetic inhibitors such as GtACR1 and GtACR2 are activated by green (~515 nm) and blue (~470 nm) light, respectively, which poses several limitations in Drosophila. Specifically, shorter-wavelength light suffers from reduced tissue penetration and increased absorption, and is visible to flies, potentially confounding behavioral assays, particularly those involving visual processing. In contrast, RubyACRs are activated by red light (~610-660 nm), which penetrates the cuticle more effectively and thus can be more potent in manipulating fly behavior. In the current manuscript, the authors first demonstrate that both A1ACR1 and HfACR1 can be robustly expressed in fly neurons and are properly trafficked to the plasma membrane. Upon red-light stimulation, both opsins produce strong and sustained hyperpolarization in larval motor neurons, outperforming GtACR1 in both magnitude and temporal dynamics. Next, using two-photon calcium imaging in the visual system, the authors further demonstrate that activation of RubyACRs significantly reduces GCaMP6s signal, indicating that they can reliably inhibit neuronal activity. Importantly, unlike reported in some mammalian studies, RubyACRs do not appear to trigger paradoxical depolarization at axon terminals in the fly visual system, as no evidence of aberrant depolarization is observed in motion-detecting Mi1 neurons.

      In the second part of the manuscript, the authors characterize the effects of RubyACRs on fly behavior (walking, learning, and courtship song). Using the inhibition of genetically labelled neurons that regulate these behaviors, the authors demonstrate that stimulation of RubyACRs leads to potent suppression of locomotion, courtship song, or dopamine-dependent associative learning.

      Strengths:

      Altogether, the experiments conducted in this manuscript demonstrate that RubyACRs are powerful tools for optogenetic inhibition in Drosophila, with advantages in spectral compatibility, behavioral specificity, and potential applications in vivo two-photon calcium imaging.

      Weaknesses:

      The manuscript is strong, but it can be further improved with a few additional analyses and minor revisions. Especially, a more detailed evaluation of RubyACRs with two-photon excitation will help clarify to what extent these opsins can be simultaneously used together with green GECIs, such as GCaMPs.

      Review 3
    4. Public_Reviews 04 Sep 2025

      Author response:

      We thank the reviewers for their thoughtful and thorough consideration of the work. We appreciate the positive reception they give the work, and plan to address several of the comments with further experiments. To outline that work (and ensure that we are on the right track to addressing those concerns), we summarize the core concerns that prompt new experiments:

      (1) Does the YFP tag on the ACRs interfere with simultaneous GCaMP imaging of RubyACR-expressing cells and could bleaching of the YFP complicate interpretation of the experiments here?

      We will test whether 920 nm (2p) and 650 nm (1p) excitation cause YFP bleaching that interferes with interpretation of inhibitory calcium (i.e. GCaMP) signals. Because the YFP tag enhances opsin sensitivity, we prioritized these tagged RubyACRs for initial characterization. FLAG-tagged ACRs are in progress, but will take time to fully characterize. Considering that the RubyACR-EYFP versions work very well, and in many cases people will want the YFP tag, either for visualizing expression or to maximize sensitivity, we feel the current work is a valuable contribution on its own. Indeed several labs have already requested these lines.

      (2) Are the ACRs activated by two-photon illumination?

      We will examine GCaMP signals at increasing 2p intensities to determine whether imaging unintentionally activates RubyACRs, as well as whether 2p illumination could be used for intentional opsin activation.

      (3) How toxic is the expression of these opsins?

      We will update the quantification of toxicity in Table 1 to include all the drivers we used in this study. In fact the toxicity we observed was primarily with the vGlut driver, which was why that was the only information in the table. The other drivers we used did not appreciably reduce survival rate, but showing the one case where it did have a big effect left a strong and understandably inaccurate impression that toxicity was a big pitfall. We note that the widely used CSChrimson has similar % survival to the RubyACRs when expressed with these vGlut drivers.

      We also plan to examine whether ACR expression leads to cell-autonomous perturbations. We will determine whether expression leads to some frequency of neuronal cell death, and we will evaluate whether any morphological effects occur.

      We will also clarify in the Discussion that potential toxicity may be driver-specific (as it is here) and should be evaluated case-by-case by investigators using the tool.

      (4) Use functional imaging to confirm inhibition of the neurons used only for behavioral experiments (pIP10 & PPL1-γ1pedc)

      We will perform these imaging experiments. One caveat is that inhibition may not be readily detectable with GCaMP, as the resting calcium levels in pIP10 and PPL1-γ1pedc neurons may already be quite low. This differs from the non-spiking Mi1 neurons, where inhibition was clearly observed with GCaMP. For this reason, we consider the behavioral results stronger evidence of efficacy, but we agree that imaging could provide useful supporting evidence, recognizing that a negative result would be difficult to interpret.

      (5) Confirm that the GtACR1 will inhibit locomotion in the flybowl when activated with green light, its spectral peak.

      We will perform this benchmark experiment. Please note that our intention with this study was to find an effective red-light activated opto-inhibitor because these wavelengths are much less perturbing to behavior. In that respect, regardless of GtACR1’s performance with green light, the RubyACRs clearly provide important new tools for Drosophila behavioral neuroscience.

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    biorxiv.org/content/10.1101/2025.06.13.659144v2
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    L13105
    1
    1. hvs 04 Sep 2025
      modificar
      • Informativo nº 806
      • CORTE ESPECIAL
      • Processo: EAREsp 1.766.665-RS, Rel. Ministro Francisco Falcão, Rel. para acórdão Ministro Ricardo Villas Bôas Cueva, Corte Especial, por maioria, julgado em 3/4/2024.

      Ramo do Direito DIREITO PROCESSUAL CIVIL

      TemaPaz, Justiça e Instituições Eficazes <br /> Multa cominatória. Valor exorbitante. Desproporcionalidade. Valor acumulado. Possiblidade de revisão. Exigência de postura ativa do devedor. Sucessivas revisões. Impossibilidade. Preclusão consumativa.

      DESTAQUE - Incide a preclusão consumativa sobre o montante acumulado da multa cominatória, de forma que, já tendo havido modificação, não é possível nova alteração, preservando-se as situações já consolidadas.

      INFORMAÇÕES DO INTEIRO TEOR - A controvérsia diz respeito à ocorrência de preclusão sobre decisão que revisa o valor de astreintes. Sobre tema, a Corte Especial, no julgamento do EAREsp n. 650.536-RJ, firmou o entendimento de ser possível a redução quando o valor for exorbitante, levando-se em conta a razoabilidade e a proporcionalidade, e a fim de evitar o enriquecimento sem causa do credor.

      • No entanto, a questão demanda reflexões mais aprofundadas, especialmente porque essa decisão, muito embora tenha sido proferida sob a égide do CPC atual, baseou-se especialmente em jurisprudência majoritária construída à época em que vigia o CPC/1973, com destaque para o Tema Repetitivo n. 706: "A decisão que comina astreintes não preclui, não fazendo tampouco coisa julgada" (REsp n. 1.333.988/SP, Segunda Seção, Rel. Ministro Paulo de Tarso Sanseverino, DJe 11/4/2014).

      • Além disso, não se levou em consideração que o CPC/2015 alterou substancial e expressamente o regime jurídico das astreintes no tocante à possibilidade de modificação. Com efeito, de acordo com a premissa estabelecida no julgamento do EAREsp n. 650.536-RJ, a regra que permite ao magistrado alterar a multa cominatória estaria prevista no art. 461, § 6°, do CPC/1973 e no seu correspondente, art. 537, § 1°, do CPC/2015. Todavia, há uma diferença substancial entre essas duas regras, em particular no que diz respeito a quais valores podem ser modificados.

      • A partir da análise dessas regras supracitadas, percebe-se a nítida intenção do legislador de autorizar a revisão ou a exclusão apenas da "multa <u>vincenda</u>", ou seja, a decisão não pode ter eficácia retroativa para atingir o montante acumulado da multa. Por outro lado, há quem sustente a possibilidade de decisão com efeitos retroativos no caso de redução do montante da multa que já incidiu, pois a expressão "vincendas" diria respeito apenas à multa que está incidindo.

      • Contudo, não há motivo para submeter a modificação e a exclusão a regimes jurídicos diversos. A regra do art. 537, § 1°, do CPC deixa claro que o legislador optou por preservar as situações já consolidadas, independentemente de se tratar da multa que está incidindo ou do montante oriundo da sua incidência. Analisando a questão com mais profundidade, tem-se que a pendência de discussão acerca do montante da multa não guarda relação com o seu vencimento, mas, sim, com a sua definitividade.

      • Dessa forma, se a incidência da multa durante o período de inadimplência alcança valores exorbitantes, seja porque o devedor permaneceu inerte e não requereu a revisão ou exclusão, seja porque o magistrado não agiu de ofício, qualquer decisão que venha a ser proferida somente poderia provocar, em regra, efeitos <u>prospectivos</u>.

      • Percebe-se que o legislador do CPC/2015 optou por levar em consideração a postura do devedor, a fim de premiar aquele que, muito embora inadimplente num primeiro momento, acaba por cumprir a obrigação, ainda que parcialmente, ou que demonstra a impossibilidade de cumprimento. Significa dizer que somente tem direito à redução da multa aquele que abandona a recalcitrância.

      • Desse modo, a partir da regra expressa do art. 537, §1°, do CPC, somente seria possível alterar o valor acumulado das multas vincendas e, consoante disposto no inciso II, a redução exige postura <u>ativa</u> do devedor, consubstanciada no cumprimento parcial da obrigação ou na demonstração de sua impossibilidade.

      • De qualquer sorte, na hipótese, há outro óbice para a revisão pretendida, qual seja a preclusão pro judicato consumativa, pois já havia sido revisado o valor da multa diária.

      • O STJ sedimentou, por meio de recurso especial julgado na sistemática dos repetitivos, que "a decisão que comina astreintes não preclui, não fazendo tampouco coisa julgada" (Tema 706), conforme já anotado. Trata-se, no entanto, de não incidência de preclusão <u>temporal</u>, de forma que o valor da multa pode ser modificado a qualquer tempo. Não se trata de ausência de preclusão consumativa, sob pena de grave violação da segurança jurídica.

      • Dessa forma, uma vez fixada a multa, é possível alterá-la ou excluí-la a qualquer momento. No entanto, uma vez reduzido o valor, não serão lícitas sucessivas revisões, a bel prazer do inadimplente recalcitrante, sob pena de estimular e premiar a renitência sem justa causa. <u>Em outras palavras, é possível modificar a decisão que comina a multa, mas não é lícito modificar o que já foi modificado</u>.

      • Considerando que a multa cominatória é um importantíssimo instrumento para garantir a efetividade das decisões judiciais e pode ser fixada de ofício, trata-se de matéria de ordem pública. No caso, a multa fixada em sentença transitada em julgado pode ser alterada na fase de execução porque tem natureza de técnica processual, de modo que não é acobertada pela coisa julgada material. Uma vez fixada ou alterada no início da execução, mantém tal natureza e, portanto, pode ser modificada a qualquer momento, inclusive de ofício.

      • Todavia, o valor acumulado da multa deixa de ser técnica processual e passa a integrar o patrimônio do exequente como crédito de valor, perdendo a natureza de matéria de ordem pública. Com efeito, nos termos do art. 537, § 2°, do CPC, "o valor [acumulado] da multa será devido ao exequente".

      • Além disso, mesmo se considerada também a multa acumulada como matéria de ordem pública, deve incidir a preclusão pro judicato consumativa, de forma que, tendo havido modificação, não é possível nova alteração, preservando-se as situações já consolidadas, como deixa claro o art. 537, § 1°, do CPC ao se referir a "multa vincenda". Isso porque há preclusão consumativa em relação às questões de ordem pública, inclusive àquelas que estão fora da esfera de disponibilidade das partes, tais como os pressupostos processuais e as condições da ação, conforme entendimento sedimentado no STJ.

      • Assim sendo, e com maior razão, há preclusão consumativa no tocante ao montante acumulado da multa cominatória, pois ostenta natureza patrimonial e disponível.

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    Mycobacterial Metallophosphatase MmpE Acts as a Nucleomodulin to Regulate Host Gene Expression and Promote Intracellular Survival
    3
    1. Public_Reviews 04 Sep 2025

      Reviewer #1 (Public review):

      Summary:

      Review of the manuscript titled " Mycobacterial Metallophosphatase MmpE acts as a nucleomodulin to regulate host gene expression and promotes intracellular survival".

      The study provides an insightful characterization of the mycobacterial secreted effector protein MmpE, which translocates to the host nucleus and exhibits phosphatase activity. The study characterizes the nuclear localization signal sequences and residues critical for the phosphatase activity, both of which are required for intracellular survival.

      Strengths:

      (1) The study addresses the role of nucleomodulins, an understudied aspect in mycobacterial infections.

      (2) The authors employ a combination of biochemical and computational analyses along with in vitro and in vivo validations to characterize the role of MmpE.

      Weaknesses:

      (1) While the study establishes that the phosphatase activity of MmpE operates independently of its NLS, there is a clear gap in understanding how this phosphatase activity supports mycobacterial infection. The investigation lacks experimental data on specific substrates of MmpE or pathways influenced by this virulence factor.

      (2) The study does not explore whether the phosphatase activity of MmpE is dependent on the NLS within macrophages, which would provide critical insights into its biological relevance in host cells. Conducting experiments with double knockout/mutant strains and comparing their intracellular survival with single mutants could elucidate these dependencies and further validate the significance of MmpE's dual functions.

      (3) The study does not provide direct experimental validation of the MmpE deletion on lysosomal trafficking of the bacteria.

      (4) The role of MmpE as a mycobacterial effector would be more relevant using virulent mycobacterial strains such as H37Rv.

      Review 1
    2. Public_Reviews 04 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      In this manuscript titled "Mycobacterial Metallophosphatase MmpE Acts as a Nucleomodulin to Regulate Host Gene Expression and Promote Intracellular Survival", Chen et al describe biochemical characterisation, localisation and potential functions of the gene using a genetic approach in M. bovis BCG and perform macrophage and mice infections to understand the roles of this potentially secreted protein in the host cell nucleus. The findings demonstrate the role of a secreted phosphatase of M. bovis BCG in shaping the transcriptional profile of infected macrophages, potentially through nuclear localisation and direct binding to transcriptional start sites, thereby regulating the inflammatory response to infection.

      Strengths:

      The authors demonstrate using a transient transfection method that MmpE when expressed as a GFP-tagged protein in HEK293T cells, exhibits nuclear localisation. The authors identify two NLS motifs that together are required for nuclear localisation of the protein. A deletion of the gene in M. bovis BCG results in poorer survival compared to the wild-type parent strain, which is also killed by macrophages. Relative to the WT strain-infected macrophages, macrophages infected with the ∆mmpE strain exhibited differential gene expression. Overexpression of the gene in HEK293T led to occupancy of the transcription start site of several genes, including the Vitamin D Receptor. Expression of VDR in THP1 macrophages was lower in the case of ∆mmpE infection compared to WT infection. This data supports the utility of the overexpression system in identifying potential target loci of MmpE using the HEK293T transfection model. The authors also demonstrate that the protein is a phosphatase, and the phosphatase activity of the protein is partially required for bacterial survival but not for the regulation of the VDR gene expression.

      Weaknesses:

      (1) While the motifs can most certainly behave as NLSs, the overexpression of a mycobacterial protein in HEK293T cells can also result in artefacts of nuclear localisation. This is not unprecedented. Therefore, to prove that the protein is indeed secreted from BCG, and is able to elicit transcriptional changes during infection, I recommend that the authors (i) establish that the protein is indeed secreted into the host cell nucleus, and (ii) the NLS mutation prevents its localisation to the nucleus without disrupting its secretion.

      Demonstration that the protein is secreted: Supplementary Figure 3 - Immunoblotting should be performed for a cytosolic protein, also to rule out detection of proteins from lysis of dead cells. Also, for detecting proteins in the secreted fraction, it would be better to use Sauton's media without detergent, and grow the cultures without agitation or with gentle agitation. The method used by the authors is not a recommended protocol for obtaining the secreted fraction of mycobacteria.

      Demonstration that the protein localises to the host cell nucleus upon infection: Perform an infection followed by immunofluorescence to demonstrate that the endogenous protein of BCG can translocate to the host cell nucleus. This should be done for an NLS1-2 mutant expressing cell also.

      (2) In the RNA-seq analysis, the directionality of change of each of the reported pathways is not apparent in the way the data have been presented. For example, are genes in the cytokine-cytokine receptor interaction or TNF signalling pathway expressed more, or less in the ∆mmpE strain?

      (3) Several of these pathways are affected as a result of infection, while others are not induced by BCG infection. For example, BCG infection does not, on its own, produce changes in IL1β levels. As the authors did not compare the uninfected macrophages as a control, it is difficult to interpret whether ∆mmpE induced higher expression than the WT strain, or simply did not induce a gene while the WT strain suppressed expression of a gene. This is particularly important because the strain is attenuated. Does the attenuation have anything to do with the ability of the protein to induce lysosomal pathway genes? Does induction of this pathway lead to attenuation of the strain? Similarly, for pathways that seem to be downregulated in the ∆mmpE strain compared to the WT strain, these might have been induced upon infection with the WT strain but not sufficiently by the ∆mmpE strain due to its attenuation/ lower bacterial burden.

      (4) CHIP-seq should be performed in THP1 macrophages, and not in HEK293T. Overexpression of a nuclear-localised protein in a non-relevant line is likely to lead to several transcriptional changes that do not inform us of the role of the gene as a transcriptional regulator during infection.

      (5) I would not expect to see such large inflammatory reactions persisting 56 days post-infection with M. bovis BCG. Is this something peculiar for an intratracheal infection with 1x107 bacilli? For images of animal tissue, the authors should provide images of the entire lung lobe with the zoomed-in image indicated as an inset.

      (6) For the qRT-PCR based validation, infections should be performed with the MmpE-complemented strain in the same experiments as those for the WT and ∆mmpE strain so that they can be on the same graph, in the main manuscript file. Supplementary Figure 4 has three complementary strains. Again, the absence of the uninfected, WT, and ∆mmpE infected condition makes interpretation of these data very difficult.

      (7) The abstract mentions that MmpE represses the PI3K-Akt-mTOR pathway, which arrests phagosome maturation. There is not enough data in this manuscript in support of this claim. Supplementary Figure 5 does provide qRT-PCR validation of genes of this pathway, but the data do not indicate that higher expression of these pathways, whether by VDR repression or otherwise, is driving the growth restriction of the ∆mmpE strain.

      (8) The relevance of the NLS and the phosphatase activity is not completely clear in the CFU assays and in the gene expression data. Firstly, there needs to be immunoblot data provided for the expression and secretion of the NLS-deficient and phosphatase mutants. Secondly, CFU data in Figure 3A, C, and E must consistently include both the WT and ∆mmpE strain.

      Review 3
    3. Public_Reviews 04 Sep 2025

      Author response:

      Reviewer #1 (Public review):

      Summary:

      Review of the manuscript titled " Mycobacterial Metallophosphatase MmpE acts as a nucleomodulin to regulate host gene expression and promotes intracellular survival".

      The study provides an insightful characterization of the mycobacterial secreted effector protein MmpE, which translocates to the host nucleus and exhibits phosphatase activity. The study characterizes the nuclear localization signal sequences and residues critical for the phosphatase activity, both of which are required for intracellular survival.

      Strengths:

      (1) The study addresses the role of nucleomodulins, an understudied aspect in mycobacterial infections.

      (2) The authors employ a combination of biochemical and computational analyses along with in vitro and in vivo validations to characterize the role of MmpE.

      Weaknesses:

      (1) While the study establishes that the phosphatase activity of MmpE operates independently of its NLS, there is a clear gap in understanding how this phosphatase activity supports mycobacterial infection. The investigation lacks experimental data on specific substrates of MmpE or pathways influenced by this virulence factor.

      We thank the reviewer for this insightful comment and agree that identification of the substrate of MmpE is important to fully understand its role in mycobacterial infection.

      MmpE is a putative purple acid phosphatase (PAP) and a member of the metallophosphoesterase (MPE) superfamily. Enzymes in this family are known for their catalytic promiscuity and broad substrate specificity, acting on phosphomonoesters, phosphodiesters, and phosphotriesters (Matange et al., Biochem J., 2015). In bacteria, several characterized MPEs have been shown to hydrolyze substrates such as cyclic nucleotides (e.g., cAMP) (Keppetipola et al., J Biol Chem, 2008; Shenoy et al., J Mol Biol, 2007), nucleotide derivatives (e.g., AMP, UDP-glucose) (Innokentev et al., mBio, 2025), and pyrophosphate-containing compounds (e.g., Ap4A, UDP-DAGn) (Matange et al., Biochem J., 2015). Although the binding motif of MmpE has been identified, determining its physiological substrates remains challenging due to the low abundance and instability of potential metabolites, as well as the limited sensitivity and coverage of current metabolomic technologies in mycobacteria.

      (2) The study does not explore whether the phosphatase activity of MmpE is dependent on the NLS within macrophages, which would provide critical insights into its biological relevance in host cells. Conducting experiments with double knockout/mutant strains and comparing their intracellular survival with single mutants could elucidate these dependencies and further validate the significance of MmpE's dual functions.

      We thank the reviewer for the comment. In our study, we demonstrate that both the nuclear localization and phosphatase activity of MmpE are required for full virulence (Figure 3D–E). Importantly, deletion of the NLS motifs did not impair MmpE’s phosphatase activity in vitro (Figure 2F), indicating that its enzymatic function is structurally independent of its nuclear localization. These findings suggest that MmpE functions as a bifunctional protein, with distinct and non-overlapping roles for its nuclear trafficking and phosphatase activity. We have expanded on this point in the Discussion section “MmpE Functions as a Bifunctional Protein with Nuclear Localization and Phosphatase Activity”.

      (3) The study does not provide direct experimental validation of the MmpE deletion on lysosomal trafficking of the bacteria.

      We thank the reviewer for the comment. The role of Rv2577/MmpE in phagosome maturation has been demonstrated in M. tuberculosis, where its deletion increases colocalization with lysosomal markers such as LAMP-2 and LAMP-3 (Forrellad et al., Front Microbiol, 2020). In our study, we found that mmpE deletion in M. bovis BCG led to upregulation of lysosomal genes, including TFEB, LAMP1, LAMP2, and v-ATPase subunits, compared to the wild-type strain. These results suggest that MmpE may regulate lysosomal trafficking by interfering with phagosome–lysosome fusion.

      To further validate MmpE’s role in phagosome maturation, we will perform fluorescence colocalization assays in THP-1 macrophages infected with BCG/wt, ∆mmpE, complemented, and NLS-mutant strains. Co-staining with LAMP1 and LysoTracker will allow us to assess whether the ∆mmpE mutant is more efficiently trafficked to lysosomes.

      (4) The role of MmpE as a mycobacterial effector would be more relevant using virulent mycobacterial strains such as H37Rv.

      We thank the reviewer for the comment. Previously, the role of Rv2577/MmpE as a virulence factor has been demonstrated in M. tuberculosis CDC 1551, where its deletion significantly reduced bacterial replication in mouse lungs at 30 days post-infection (Forrellad et al., Front Microbiol, 2020). However, that study did not explore the underlying mechanism of MmpE function. In our work, we found that MmpE enhances M. bovis BCG survival in both macrophages (THP-1 and RAW264.7) and mice (Figure 2A-B, Figure 6A), consistent with its proposed role in virulence. To investigate the molecular mechanism by which MmpE promotes intracellular survival, we used M. bovis BCG as a biosafe surrogate and this model is widely accepted for studying mycobacterial pathogenesis (Wang et al., Nat Immunol, 2025; Wang et al., Nat Commun, 2017; Péan et al., Nat Commun, 2017).

      Reviewer #2 (Public review):

      Summary:

      In this paper, the authors have characterized Rv2577 as a Fe3+/Zn2+ -dependent metallophosphatase and a nucleomodulin protein. The authors have also identified His348 and Asn359 as critical residues for Fe3+ coordination. The authors show that the proteins encode for two nuclease localization signals. Using C-terminal Flag expression constructs, the authors have shown that the MmpE protein is secretory. The authors have prepared genetic deletion strains and show that MmpE is essential for intracellular survival of M. bovis BCG in THP-1 macrophages, RAW264.7 macrophages, and a mouse model of infection. The authors have also performed RNA-seq analysis to compare the transcriptional profiles of macrophages infected with wild-type and MmpE mutant strains. The relative levels of ~ 175 transcripts were altered in MmpE mutant-infected macrophages and the majority of these were associated with various immune and inflammatory signalling pathways. Using these deletion strains, the authors proposed that MmpE inhibits inflammatory gene expression by binding to the promoter region of a vitamin D receptor. The authors also showed that MmpE arrests phagosome maturation by regulating the expression of several lysosome-associated genes such as TFEB, LAMP1, LAMP2, etc. These findings reveal a sophisticated mechanism by which a bacterial effector protein manipulates gene transcription and promotes intracellular survival.

      Strength:

      The authors have used a combination of cell biology, microbiology, and transcriptomics to elucidate the mechanisms by which Rv2577 contributes to intracellular survival.

      Weakness:

      The authors should thoroughly check the mice data and show individual replicate values in bar graphs.

      We kindly appreciate the reviewer for the advice. We will update the relevant mice data in the revised manuscript.

      Reviewer #3 (Public review):

      Summary:

      In this manuscript titled "Mycobacterial Metallophosphatase MmpE Acts as a Nucleomodulin to Regulate Host Gene Expression and Promote Intracellular Survival", Chen et al describe biochemical characterisation, localisation and potential functions of the gene using a genetic approach in M. bovis BCG and perform macrophage and mice infections to understand the roles of this potentially secreted protein in the host cell nucleus. The findings demonstrate the role of a secreted phosphatase of M. bovis BCG in shaping the transcriptional profile of infected macrophages, potentially through nuclear localisation and direct binding to transcriptional start sites, thereby regulating the inflammatory response to infection.

      Strengths:

      The authors demonstrate using a transient transfection method that MmpE when expressed as a GFP-tagged protein in HEK293T cells, exhibits nuclear localisation. The authors identify two NLS motifs that together are required for nuclear localisation of the protein. A deletion of the gene in M. bovis BCG results in poorer survival compared to the wild-type parent strain, which is also killed by macrophages. Relative to the WT strain-infected macrophages, macrophages infected with the ∆mmpE strain exhibited differential gene expression. Overexpression of the gene in HEK293T led to occupancy of the transcription start site of several genes, including the Vitamin D Receptor. Expression of VDR in THP1 macrophages was lower in the case of ∆mmpE infection compared to WT infection. This data supports the utility of the overexpression system in identifying potential target loci of MmpE using the HEK293T transfection model. The authors also demonstrate that the protein is a phosphatase, and the phosphatase activity of the protein is partially required for bacterial survival but not for the regulation of the VDR gene expression.

      Weaknesses:

      (1)   While the motifs can most certainly behave as NLSs, the overexpression of a mycobacterial protein in HEK293T cells can also result in artefacts of nuclear localisation. This is not unprecedented. Therefore, to prove that the protein is indeed secreted from BCG, and is able to elicit transcriptional changes during infection, I recommend that the authors (i) establish that the protein is indeed secreted into the host cell nucleus, and (ii) the NLS mutation prevents its localisation to the nucleus without disrupting its secretion.

      We kindly appreciate the reviewer for the advice and will include the relevant experiments in the revised manuscript. The localization of WT MmpE and the NLS mutated MmpE will be tested in the BCG infected macrophages.

      Demonstration that the protein is secreted: Supplementary Figure 3 - Immunoblotting should be performed for a cytosolic protein, also to rule out detection of proteins from lysis of dead cells. Also, for detecting proteins in the secreted fraction, it would be better to use Sauton's media without detergent, and grow the cultures without agitation or with gentle agitation. The method used by the authors is not a recommended protocol for obtaining the secreted fraction of mycobacteria.

      We agree with the reviewer and we will further validate the secretion of MmpE using the tested protocol.

      Demonstration that the protein localises to the host cell nucleus upon infection: Perform an infection followed by immunofluorescence to demonstrate that the endogenous protein of BCG can translocate to the host cell nucleus. This should be done for an NLS1-2 mutant expressing cell also.

      We will add this experiment in the revised manuscript.

      (2) In the RNA-seq analysis, the directionality of change of each of the reported pathways is not apparent in the way the data have been presented. For example, are genes in the cytokine-cytokine receptor interaction or TNF signalling pathway expressed more, or less in the ∆mmpE strain?

      We thank the reviewer for pointing this out and fully agree that conventional KEGG pathway enrichment diagrams do not convey the directionality of individual gene expression changes within each pathway. While KEGG enrichment analysis identifies pathways that are statistically overrepresented among differentially expressed genes, it does not indicate whether individual genes within those pathways are upregulated or downregulated.

      To address this, we re-analyzed the expression trends of DEGs within each significantly enriched KEGG pathway. The results show that key immune-related pathways, including cytokine–cytokine receptor interaction, TNF signaling, NF-κB signaling, and chemokine signaling, are collectively upregulated in THP-1 macrophages infected with ∆mmpE strain compared to those infected with the wild-type BCG strain. The full list of DEGs will be provided in the supplementary materials. The complete RNA-seq dataset has been deposited in the GEO database, and the accession number will be included in the revised manuscript.

      (3) Several of these pathways are affected as a result of infection, while others are not induced by BCG infection. For example, BCG infection does not, on its own, produce changes in IL1β levels. As the author s did not compare the uninfected macrophages as a control, it is difficult to interpret whether ∆mmpE induced higher expression than the WT strain, or simply did not induce a gene while the WT strain suppressed expression of a gene. This is particularly important because the strain is attenuated. Does the attenuation have anything to do with the ability of the protein to induce lysosomal pathway genes? Does induction of this pathway lead to attenuation of the strain? Similarly, for pathways that seem to be downregulated in the ∆mmpE strain compared to the WT strain, these might have been induced upon infection with the WT strain but not sufficiently by the ∆mmpE strain due to its attenuation/ lower bacterial burden.

      We thank the reviewer for the comment. We will update qRT-PCR data with the uninfected macrophages as a control in the revised manuscript.

      Wild-type Mycobacterium bovis BCG strain still has the function of inhibiting phagosome maturation (Branzk et al., Nat Immunol, 2014; Weng et al., Nat Commun, 2022). Forrellad et al. previously identified Rv2577/MmpE as a virulence factor in M. tuberculosis and disruption of the MmpE gene impairs the ability of M. tuberculosis to arrest phagosome maturation (Forrellad et al., Front Microbiol, 2020). In our study, transcriptomic and qRTPCR data (Figures 4C and G, S4C) show that deletion of mmpE in M. bovis BCG leads to upregulation of lysosomal biogenesis and acidification genes, including TFEB, LAMP1, and vATPase. To further validate MmpE’s role in phagosome maturation, we will perform fluorescence colocalization assays in THP-1 macrophages infected with BCG/wt, ∆mmpE, complemented, and NLS-mutant strains. Co-staining with LAMP1 and LysoTracker will assess whether the ∆mmpE mutant is more efficiently trafficked to lysosomes.

      Furthermore, CFU assays demonstrated that the ∆mmpE strain exhibits markedly reduced bacterial survival in both human THP-1 and murine RAW264.7 macrophages, as well as in mice, compared to the wild-type strain (Figures 4A and C, 6A). These findings suggest that the loss of MmpE compromises bacterial survival, likely due to enhanced lysosomal trafficking and acidification. This supports previous studies showing that increased lysosomal activity promotes mycobacterial clearance (Gutierrez et al., Cell, 2004; Pilli et al., Immunity, 2012).

      (4) CHIP-seq should be performed in THP1 macrophages, and not in HEK293T. Overexpression of a nuclear-localised protein in a non-relevant line is likely to lead to several transcriptional changes that do not inform us of the role of the gene as a transcriptional regulator during infection.

      We thank the reviewer for the comment. We performed ChIP-seq in HEK293T cells is based on the fact that this cell line is widely used in ChIP-based assays due to its high transfection efficiency, robust nuclear protein expression, and well-annotated genome (Lampe et al., Nat Biotechnol, 2024; Marasco et al., Cell, 2022). These features make HEK293T an ideal system for the initial identification of genome wide chromatin binding profiles of novel nuclear effectors such as MmpE.

      Furthermore, we validated the major observations in THP-1 macrophages, including (i) RNAseq of THP-1 cells infected with either WT BCG or ∆mmpE strains revealed significant transcriptional changes in immune and lysosomal pathways (Figure 4A); (ii) Integrated analysis of CUT&Tag and RNA-seq data identified 298 genes in infected THP-1 cells that exhibited both MmpE binding and corresponding expression changes. Among these, VDR was validated as a direct transcriptional target of MmpE using EMSA and ChIP-PCR (Figures 5E-J, S5D-F). Notably, the signaling pathways associated with MmpE-bound genes, including PI3K-Akt-mTOR signaling and lysosomal function, substantially overlap with those transcriptionally modulated in infected THP-1 macrophages (Figures 4B-G, S4B-C, S5C-D), further supporting the biological relevance of the ChIP-seq data obtained from HEK293T cells.

      (5) I would not expect to see such large inflammatory reactions persisting 56 days postinfection with M. bovis BCG. Is this something peculiar for an intratracheal infection with 1x107 bacilli? For images of animal tissue, the authors should provide images of the entire lung lobe with the zoomed-in image indicated as an inset.

      We thank the reviewer for the comment. The lung inflammation peaked at days 21–28 and had clearly subsided by day 56 across all groups (Figure 6B), consistent with the expected resolution of immune responses to an attenuated strain like M. bovis BCG. This temporal pattern is in line with previous studies using intravenous or intratracheal BCG vaccination in mice and macaques, which also demonstrated robust early immune activation followed by resolution over time (Smith et al., Nat Microbiol, 2025; Darrah et al., Nature, 2020).

      In this study, the infectious dose (1×10⁷ CFU intratracheally) was selected based on previous studies in which intratracheal delivery of 1×10⁷CFU produced consistent and measurable lung immune responses and pathology without causing overt illness or mortality (Xu et al., Sci Rep, 2017; Niroula et al., Sci Rep, 2025). We will provide whole-lung lobe images with zoomed-in insets in the revised manuscript.

      (6) For the qRT-PCR based validation, infections should be performed with the MmpEcomplemented strain in the same experiments as those for the WT and ∆mmpE strain so that they can be on the same graph, in the main manuscript file. Supplementary Figure 4 has three complementary strains. Again, the absence of the uninfected, WT, and∆mmpE infected condition makes interpretation of these data very difficult.

      We thank the reviewer for the comment. As suggested, we will conduct the qRT-PCR experiment including the uninfected, WT, ∆mmpE, Comp-MmpE, and the three complementary strains infecting THP-1 cells. The updated data will be provided in the revised manuscript.

      (7) The abstract mentions that MmpE represses the PI3K-Akt-mTOR pathway, which arrests phagosome maturation. There is not enough data in this manuscript in support of this claim. Supplementary Figure 5 does provide qRT-PCR validation of genes of this pathway, but the data do not indicate that higher expression of these pathways, whether by VDR repression or otherwise, is driving the growth restriction of the ∆mmpE strain.

      We thank the reviewer for the comment. The role of MmpE in phagosome maturation was previously characterized. Disruption of mmpE impairs the ability of M. tuberculosis to arrest lysosomal trafficking (Forrellad et al., Front Microbiol, 2020). In this study, we further found that MmpE suppresses the expression of key lysosomal genes, including TFEB, LAMP1, LAMP2, and ATPase subunits (Figure 4G), suggesting MmpE is involved in arresting phagosome maturation. As noted, the genes in the PI3K–Akt–mTOR pathway are upregulated in ∆mmpE-infected macrophages (Figure S5C).

      To functionally validate this, we will conduct two complementary experimental approaches:

      (i) Immunofluorescence assays: We will assess phagosome maturation and lysosomal fusion in THP-1 cells infected with BCG/wt, ∆mmpE, Comp-MmpE, and NLS mutant strains. Colocalization of intracellular bacteria with LAMP1 and LysoTracker will be quantified to determine whether the ∆mmpE strain is more efficiently trafficked to lysosomes.

      (ii) CFU assays: We will perform CFU assays in THP-1 cells infected with BCG/wt or ∆mmpE in the presence or absence of PI3K-Akt-mTOR pathway inhibitors (e.g., Dactolisib), to assess whether activation of this pathway contributes to the intracellular growth restriction observed in the ∆mmpE strain.

      (8) The relevance of the NLS and the phosphatase activity is not completely clear in the CFU assays and in the gene expression data. Firstly, there needs to be immunoblot data provided for the expression and secretion of the NLS-deficient and phosphatase mutants. Secondly, CFU data in Figure 3A, C, and E must consistently include both the WT and ∆mmpE strain.

      We thank the reviewer for the comment. We will provide immunoblot data for the expression and secretion of the NLS-deficient and phosphatase mutants. Additionally, we will revise Figure 3A, 3C, and 3E to consistently include both the WT and ΔmmpE strains in the CFU assays.

      Reference

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      Pilli M, Arko-Mensah J, Ponpuak M, Roberts E, Master S, Mandell MA, Dupont N, Ornatowski W, Jiang S, Bradfute SB, Bruun JA, Hansen TE, Johansen T, Deretic V (2012) TBK-1 promotes autophagy-mediated antimicrobial defense by controlling autophagosome maturation Immunity 37:223-34.

      Shenoy AR, Capuder M, Draskovic P, Lamba D, Visweswariah SS, Podobnik M (2007) Structural and biochemical analysis of the Rv0805 cyclic nucleotide phosphodiesterase from Mycobacterium tuberculosis J Mol Biol 365:211-25.

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    Decision-making components and times revealed by the single-trial electro-encephalogram
    3
    1. Public_Reviews 04 Sep 2025

      Reviewer #1 (Public review):

      From my reading, this study aimed to achieve two things:

      (1) A neurally-informed account of how Pieron's and Fechner's laws can apply in concert at distinct processing levels.

      (2) A comprehensive map in time and space of all neural events intervening between stimulus and response in an immediately-reported perceptual decision.

      I believe that the authors achieved the first point, mainly owing to a clever contrast comparison paradigm, but with good help also from a new topographic parsing algorithm they created. With this, they found that the time intervening between an early initial sensory evoked potential and an "N2" type process associated with launching the decision process varies inversely with contrast according to Pieron's law. Meanwhile, the interval from that second event up to a neural event peaking just before response increases with contrast, fitting Fechner's law, and a very nice finding is that a diffusion model whose drift rates are scaled by Fechner's law, fit to RT, predicts the observed proportion of correct responses very well. These are all strengths of the study.

      The second, generally stated aim above is, in the opinion of this reviewer, unconvincing and ill-defined. Presumably, the full sequence of neural events is massively task-dependent, and surely it is more in number than just three. Even the sensory evoked potential typically observed for average ERPs, even for passive viewing, would include a series of 3 or more components - C1, P1, N1, etc. So are some events being missed? Perhaps the authors are identifying key events that impressively demarcate Pieron- and Fechner-adherent sections of the RT, but they might want to temper the claim that they are finding ALL events. In addition, the propensity for topographic parsing algorithms to potentially lump together distinct processes that partially co-evolve should be acknowledged.

      To take a salient example, the last neural event seems to blend the centroparietal positivity with a more frontal midline negativity, some of which would capture the CNV and some motor-execution related components that are more tightly time-locked to, of course, the response. If the authors plotted the traditional single-electrode ERP at the frontal focus and centroparietal focus separately, they are likely to see very different dynamics and contrast- and SAT-dependency. What does this mean for the validity of the multivariate method? If two or more components are being lumped into one neural event, wouldn't it mean that properties of one (e.g., frontal burstiness at response) are being misattributed to the other (centroparietal signal that also peaks but less sharply at response)?

      Also related to the method, why must the neural events all be 50 ms wide, and what happens if that is changed? Is it realistic that these neural events would be the same duration on every trial, even if their duration was a free parameter? This might be reasonable for sensory and motor components, but unlikely for cognitive.

      In general, I wonder about the analytic advantage of the parsing method - the paradigm itself is so well-designed that the story may be clear from standard average event-related potential analysis, and this might sidestep the doubts around whether the algorithm is correctly parsing all neural events.

      In particular, would the authors consider plotting CPP waveforms in the traditional way, across contrast levels? The elegant design is such that the C1 component (which has similar topography) will show up negative and early, giving way to the CPP, and these two components will show opposite amplitude variations (not just temporal intervals as is this paper's main focus), because the brighter the two gratings, the stronger the aggregate early sensory response but the weaker the decision evidence due to Fechner. I believe this would provide a simple, helpful corroborating analysis to back up the main functional interpretation in the paper.

      The first component is picking up on the C1 component (which is negative for these stimulus locations), not a "P100". Please consult any visual evoked potential study (e.g., Luck, Hillyard, etc).

      It is unexpected that this does not vary in latency with contrast - see, for example. Gebodh et al (2017, Brain Topography) - and there is little discussion of this. Could it be that nonlinear trends were not correctly tested for?

      There is very little analysis or discussion of the second stage linked to attention orientation - what would the role of attention orientation be in this task? Is it spatial attention directed to the higher contrast grating (and if so, should it lateralise accordingly?), or is it more of an alerting function the authors have in mind here?

      Review 1
    2. Public_Reviews 04 Sep 2025

      Reviewer #2 (Public review):

      Summary:

      The authors decomposed response times into component processes and manipulated the duration of these processes in opposing directions by varying contrast, and overall by manipulating speed-accuracy tradeoffs. They identify different processes and their durations by identifying neural states in time and validate their functional significance by showing that their properties vary selectively as expected with the predicted effects of the contrast manipulation. They identify 3 processes: stimulus encoding, attention orienting, and decision. These map onto classical event-related potentials. The decision-making component matched the CPP, and its properties varied with contrast and predicted decision-accuracy, while also exhibiting a burst not characteristic of evidence accumulation.

      Strengths:

      The design of the experiment is remarkable and offers crucial insights. The analysis techniques are beyond state-of-the-art, and the analyses are well motivated and offer clear insights.

      Weaknesses:

      It is not clear to me that the results confirm that there are only 3 processes, since e.g., motor preparation and execution were not captured. While the authors discuss this, this is a clear weakness of the approach, as other components may also have been missed. It is also unclear to what extent topographies map onto processes, since, e.g., different combinations of sources can lead to the same scalp topography.

      Review 2
    3. Public_Reviews 04 Sep 2025

      Reviewer #3 (Public review):

      Summary:

      In this manuscript, the authors examine the processing stages involved in perceptual decision-making using a new approach to analysing EEG data, combined with a critical stimulus manipulation. This new EEG analysis method enables single-trial estimates of the timing and amplitude of transient changes in EEG time-series, recurrent across trials in a behavioural task. The authors find evidence for three events between stimulus onset and the response in a two-spatial-interval visual discrimination task. By analysing the timing and amplitude of these events in relation to behaviour and the stimulus manipulation, the authors interpret these events as related to separable processing stages for stimulus encoding, attention orientation, and decision (deliberation). This is largely consistent with previous findings from both event-related potentials (across trials) and single-trial estimates using decoding techniques and neural network approaches.

      Strengths:

      This work is not only important for the conceptual advance, but also in promoting this new analysis technique, which will likely prove useful in future research. For the broader picture, this work is an excellent example of the utility of neural measures for mental chronometry.

      Weaknesses:

      The manuscript would benefit from some conceptual clarifications, which are important for readers to understand this manuscript as a stand-alone work. This includes clearer definitions of Piéron's and Fechner's laws, and a fuller description of the EEG analysis technique. The manuscript, broadly, but the introduction especially, may be improved by clearly delineating the multiple aims of this project: examining the processes for decision-making, obtaining single-trial estimates of meaningful EEG-events, and whether central parietal positivity reflects ramping activity or steps averaged across trials. A fuller discussion of the limitations of the work, in particular, the absence of motor contributions to reaction time, would also be appreciated.

      At times, the novelty of the work is perhaps overstated. Rather, readers may appreciate a more comprehensive discussion of the distinctions between the current work and previous techniques to gauge single-trial estimates of decision-related activity, as well as previous findings concerning distinct processing stages in decision-making. Moreover, a discussion of how the events described in this study might generalise to different decision-making tasks in different contexts (for example, in auditory perception, or even value-based decision-making) would also be appreciated.

      Review 3
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      Individual is unemployed, employed at a large firm or employed at a small firm at time tDummy (1: Individual is unemployed, 2 if he is employed at a large firm, and 3 if he is employed at a small firm at time t)Employed, unemployed or inactive (individual)Dummy (0 or 1)16Individual is unemployed, employed at a large firm or employed at a small firm at time t+2 (the last year in each panel)Dummy (1: Individual is unemployed, 2 if he is employed at a large firm, and 3 if he is employed at a small firm at time t + 2)Employed, unemployed or inactive (individual)Dummy (0 or 1), interaction20Job to non-employmentDummy (Current job: 0, New job: 1, Nonemployment: 2)Employed, unemployed or inactive (individual)Dummy (0 or 1)14

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    A conserved Arf-GEF modulates axonal integrity through RAB-35 by altering neuron-epidermal attachment
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    1. EMBOpress 04 Sep 2025

      Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons

      Reply to the reviewers

      Manuscript number: RC-2025-03064

      Corresponding author(s): Massimo, Hilliard; Sean, Coakley

      1. General Statements

      We are grateful to the reviewers for taking time to review our manuscript and for providing such clear, insightful and actionable suggestions. The consensus between 4 independent reviewers that this story is of general interest to cell biologists, neurobiologists and clinical researchers is remarkable. In addition to our mechanistic insights into the regulation of GTPase activity, we think that the experimental systems we have developed will be of great value to study how GTPases their associated GAPs and GEFs function to maintain the nervous system, especially due to the demonstrated conservation of these molecules. We believe that our data provides a powerful and tractable model to study such molecules in a physiological context.

      We agree with the reviewers' concerns and propose the following plan below to address them.

      2. Description of the planned revisions

      Reviewer #1(Evidence, reproducibility and clarity (Required)):

      __Summary Stability of the PLM axon in C. elegans is maintained through interactions with the epidermis. Previous studies by this group found that loss of the tbc-10 Rab GTPase Activating Protein strongly enhanced the PLM axon break phenotype of unc-70/beta-spectrin mutants. TBC-10 is a GAP for RAB-35 and thus loss of rab-35 suppresses the tbc-10 phenotype. Of the two RAB-35 GEFs, loss of RME-4 partially suppressed the tbc-10 phenotype and FLCN-1 was not involved suggesting that there may be an additional GEF involved. Here Bonacossa-Pereira et al identify a point mutation in agef-1a (vd92) as a suppressor of tbc-10 PLM axon break phenotype (all experiments also have a dominant allele of unc-70) and confirm that point mutation is causative by replicating the mutation via genome editing (vd123). Rescue experiments demonstrate that AGEF-1a is required in the epidermis and not PLM as previous demonstrated with tbc-10 and unc-70. Rescue is dependent on a functional SEC7/GEF activity. AGEF-1a is a functional ortholog to human BIG2/ArfGEF2 as its expression fully rescues tbc-10. AGEF-1a functions upstream of RAB-35 as expression of activated RAB-35 can suppress loss of agef-1. AGEF-1a functions in parallel to RME-4 as the double has stronger suppression of tbc-10. AGEF-1a is an ARF GEF, however it functions independently of ARF-1.2 as loss of arf-1.2 does not suppress tbc-10. They demonstrate that AGEF-1a interacts with RAB-35 through colocalization experiments suggesting that AGEF-1a could directly activate RAB-35. Finally, they demonstrate that AGEF-1a regulates the localization of the LET-805 epidermal attached complex component as it restores localization in a tbc-10 mutant.

      Major comments

      The manuscript is well written and easy to understand.

      The experiments are well done and controlled.

      I enjoyed reading this paper. However...

      Some of the claims are not supported by the data.__

      __1) The claim that AGEF-1a directly interacts with RAB-35 was not demonstrated. The evidence provided to support a direct interaction are colocalization experiments in Figure 3. AGEF-1a does partially colocalize with RAB-35 in the epidermis. However, colocalization does not indicate a physical interaction direct or indirect. A simple fix would be to change the claim to that they partially colocalize. Optional, a physical interaction could be done with the split-GFP since they already have the AGEF-1 strain or they could perform co-IP experiments, though neither of those are proof of direct interactions.

      __

      We agree that the biochemical co-IP experiment could provide some answers, however, using a full length AGEF-1a would not only represent a significant technical challenge but will also not prove a direct interaction in a physiological context. To overcome this limitation, and to directly test their interaction in vivo, we propose to use a split-GFP approach as suggested by the reviewer. In this experiment, we will generate an endogenously tagged GFP1-10::rab-35 allele and combine it with the previously generated and available tagged agef-1a::GFP11x7. If AGEF-1 and RAB-35 closely interact, we should observe the reconstitution of full length GFP. It is possible that the endogenously tagged versions only provide a very weak GFP signal that will be difficult to detect. As an alternative approach, we will generate the same tagged molecules as overexpressed transgenes under epidermal-specific promoters (such as Pdpy-7). If the results are still negative, we agree to temper our claim that these molecules physically interact and rephrase the manuscript to reflect the new data.

      • *

      2) The claim that AGEF-1a facilitates RAB-35 activation is not supported. While it is likely that AGEF-1a facilitates RAB-35 activation based on the epistasis experiments as well as studies in mammalian cells there were no experiments to demonstrate that modulating AGEF-1a activity resulted in a change in RAB-35 activity. I would suggest tempering this claim to something along the line that the data are consistent with AGEF-1a regulating RAB-35 activity as shown in mammalian cells. An optional experiment would be to look at the colocalization of RAB-35 with a known effector in wild type and agef-1(vd92) with the expectation that there would be a higher level of colocalization in agef-1 mutants. Effector pull-down experiments or perhaps a cell based GEF assay could be used (PMID: 35196081).

      We welcome this suggestion and acknowledge the limitations of these experiments. While we might be able to determine if AGEF-1 and RAB-35 physically interact in vivo with the experiments proposed above, screening for the relevant rab-35 effector in this context and/or doing effector pull-down/cell based GEF assays would be a significant technical challenge. We propose to temper our claim as suggested.

      3) The claim that AGEF-1a functions independently of ARF-1.2 is not well supported. The fact that the ARF-1.2 mutant does not suppress tbc-10 suggests that ARF-1.2 may not be involved but does not eliminate the possibility that ARF-1.2 functions redundantly with ARF-5 or WARF-1/ARF-1.1. This can be resolved by toning down the claim. Alternatively, this can be tested by RNAi of arf-5 and warf-1 in tbc-10 and arf-1.2; tbc-10 mutants.

      We agree that warf-1 and arf-5 could be functioning redundantly with arf-1.2. We have attempted to generate an AID::arf-5 allele to test the effect of cell-specific degradation, but homozygous AID::arf-5 animals were lethal. We have not yet examined warf-1. We believe the best way to test these two molecules is through RNAi knockdown, and we propose to do this experiment and adjust our interpretation and discussion according to the new data.

      Minor comments

      Figure 1C the CRISPR generated allele (vd123) is referred to as [S784L] and then in 1E vd92 is referred to as [S784L]. Perhaps it would be clearer if the allele name was used instead of the amino acid change.

      We will reformat the manuscript to include the allele names instead of amino acid change.

      Page 6 "We reasoned that if the S784L mutation we isolated causes a similar loss of the GTPase activation function, then SKIN::AGEF-1a[E608K] would not have the capacity to restore the rate of PLM axon breaks to background levels in agef-1[S784L]; tbc-10; vdSi2 animals." It was unclear to me whether you were testing if the S784L mutation could be disrupting a GEF independent function or might disrupt the nucleotide exchange activity as might be tested in a biochemical assay. There are many reasons this change could cause a loss of function phenotype (ie. Improper folding, mislocalization, etc.). The most clear explanation would be that you were testing if GEF function was required for rescue rather than testing if the S784L mutation disrupted GEF activity.

      Indeed, this experiment reveals that reducing the activation of the AGEF-1 target phenocopies the effect of S784L and does not further enhance the effect of S784L. However, it does not answer if, specifically, the GEF function is affected by S784L. We propose to rewrite the quoted sentence as follows: "We asked whether the GEF function is required for axonal damage. If that is the case, then SKIN::AGEF-1a[E608K] overexpression should phenocopy the effect of AGEF-1a[S784L]."

      • *

      Page 13. It was unclear how testing if AGEF-1, RME-4, ARF-5 and RAB-35 form complexes in vivo (I assume you are suggesting colocalize based on figure 3 interpretation) would resolve how AGEF-1 was regulating RAB-35.

      We apologize that our phrasing was not clear. We will rewrite this section to better reflect the following idea. Given literature data showing an allosteric interaction between RME-4/DENND1 and ARF-5/Arf5, and our own data showing that AGEF-1 regulates RAB-35, we believe these molecules could form a complex. Considering that we do not have data to support this notion, mostly due to the inability to test the effect of ARF-5, we will present this possibility in the discussion section.

      __**Cross-commenting**

      I agree with the comments made by the other reviewers and I stand by my own as well. I will echo that it is important to know the nature of their agef-1 allele.

      Reviewer #1 (Significance (Required)):

      Bonacossa-Pereira et al identify AGEF-1 as a regulator of axon integrity that functions in a pathway with RAB-35 in the epidermis is an exciting finding. As pointed out in the discussion, mutations in the human ortholog cause neurodevelopmental defects which leads to obvious characterization of BIG2/ArfGEF2 in neurons while this study indicates that this protein can have cell non-autonomous roles in regulating neurons. These findings could have important implications for understanding the etiology of these defects that would be of interest to neurobiologists and clinical researchers.

      The finding of this paper would also be of interest to cell biologists and particularly those studying the roles of Rab and Arf GTPases in membrane trafficking, such as myself. The idea that AGEF-1 might function as a Rab35 GEF is provocative and would generate a lot of interest and skepticism from the field. However, there is no data to support that AGEF-1 would be a direct regulator of Rab35 over the previously demonstrated cross regulation of Rab35 by Arf GTPases. Therefore, it would be fine to speculate in the discussion a direct interaction, but I would refrain from suggesting this as a model and elsewhere in the manuscript.

      __

      Although we agree that current evidence is not sufficient to support the model where AGEF-1 is a direct regulator of RAB-35, our data points to the direction where there is an important genetic relationship between these molecules in a physiological context in a living animal, with a defined phenotype relevant to the nervous system maintenance. We think that the proposed revision experiments will provide a better understanding of how AGEF-1 functions with RAB-35 and we agree with the suggestion to rephrase our manuscript to reflect the limitations of our results.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This interesting manuscript reports the outcome of a fruitful C. elegans genetic screen with a complex but clever design. Through it, the authors identify AGEF-1 as a GEF that likely regulates the active state of the GTPase RAB-35 in the skin to protect touch receptor axons from mechanical breakage.

      Major points: 1. Based on localization experiments, the authors claim "AGEF-1a interacts with RAB-35 in the epidermis" (Results heading) and state "these data demonstrate that AGEF-1a interacts with a subset of RAB-35 molecules in the epidermis." In general, localization studies cannot be used to conclude physical interaction (with some exceptions such as single-molecule kinetics). In this case, the data in my view do not even make a compelling argument for co-localization. There is a lot of AGEF-1 and RAB-35 signal everywhere and it may not be meaningful that the signals sometimes overlap. A more quantitative approach with controls would be needed to conclude meaningful co-localization. Importantly, this would still not demonstrate interaction.__

      We thank the reviewer for the comment. Indeed, co-localization does prove a physical interaction, and we appreciate the concern about our imaging data not making a compelling argument. To address this notion, we plan to perform an experiment using a more robust, quantitative and physiologically relevant strategy. We will generate an endogenously tagged mScarlet3::rab-35 allele for precise endogenous localization. In addition, as a positive control, we will generate an endogenous rme-4::GFP11x7 allele to cell-specifically demonstrate the level of colocalization of RME-4 with mScarlet3::RAB-35 within the epidermis. To address the possible interaction between AGEF-1a and RAB-35 we will leverage a split-GFP approach to assess their interaction in vivo, in the context relevant to the phenotypes we observed (see reply to reviewer #1 point 1).

      __2. The effect of the AGEF-1(S784L) mutation is not clear to me. Naively, as the S784L mutation lies in the auto-inhibitory domain, I would have expected AGEF-1 to become constitutively active, not inactive as the authors seem to suggest. Is the idea that it is constitutively auto-inhibited? The main evidence for a loss of function effect seems to be that a putative dominant negative mutation AGEF-1(E608K) does not further supress axon breakage when co-expressed in trans to AGEF(S784L), but in my view this only shows that, once the defect is suppressed, it cannot be suppressed any further. Defining the nature of the S784L allele is important. Some suggestions, although the authors may come up with different approaches: use of an inducible or cell-specific depletion system like AID/TIR1, Cre/lox, or FLP/FRT to circumvent the lethality of agef-1(0) and reveal what a true loss-of-function looks like; testing if deletion of the auto-inhibitory domain phenocopies S784L to test if this mutation impairs autoinhibition.

      __

      This is an very insightful comment. To address this point, we will follow the reviewer's suggestion and deplete AGEF-1 cell-specifically in the epidermis using the auxin-inducible degron system. Specifically, we will generate an agef-1::AID allele to degrade this molecule in a spatially and temporally controlled fashion, which will allow to circumvent the lethality of agef-1(0) and determine whether the S784L allele mimics the depletion of AGEF-1.

      Although it would be interesting to further dissect the effect of this mutation on AGEF-1 activity, we believe that this falls outside of the scope of this manuscript. As an alternative, we propose to elaborate more in the discussion the implications of the possible roles for the S784L mutation to clarify our model of its function. Our data supports a model in which this mutation reduces AGEF-1 function leading to a reduction in the activity of its downstream target GTPases. It is possible that this is due to AGEF-1 becoming constitutively autoinhibited, or that this mutation affects the structure of the molecule in a way that it reduces its affinity towards its downstream effectors.

      Minor points: 1. I am not able to see the "vesicle-like structures with a clear luminal space" or RAB-35 being "notably enriched at the membrane near the epidermal furrow" in Fig. 3. The "3D surface rendering" in Fig. 3e is grossly oversampled and should not be included.

      We will rectify this section and include new super-resolved images using Airyscan confocal microscopy. We hope these will yield a better-quality representation of these concepts. __ 2. As the agef-1a isoform is specifically referenced throughout, please describe the different agef-1 isoforms somewhere to save readers from having to look this up.__

      Yes, we will include a description of the isoforms. In C. elegans there are two: AGEF-1a which has been confirmed by cDNA and AGEF-1b which is predicted and partially confirmed by cDNA. The mutation we isolated exclusively affects AGEF-1a.

      3. The authors include an interesting speculation in the Discussion: "Future investigations of BIG2-associated neurological disorders should consider... hyper-activity of BIG2 as a driver of neuropathology." If the authors have the tools to test the effect of hyperactive BIG2 in this system, it could be an exciting addition.

      This is an exciting idea that we would like to keep in the Discussion. The biology of BIG2 activity regulation is a nascent field of research and we believe that to accurately generate and characterise a hyperactive BIG2 would be beyond the scope of this manuscript.

      __ On a personal note, since GEFs act oppositely to GTPase Activating Proteins (GAPs), I had to stop and re-read carefully whenever the authors referred to a GEF "activating" a GTPase. I understand their meaning (i.e., putting the GTPase in its active GTP-bound state, not activating its GTPase function) but I wanted to point out this potential confusion in case there is a way to better define terms in the Introduction or change word choice. I realize this may be a standard jargon in the field.__

      Indeed, this is confusing nomenclature and a difficult concept to deliver in an accurate and succinct manner. We propose to include a clearer, more didactic explanation of their function. In a simple explanation, GTPases perform cellular functions when bound to GTP. GAPs terminate GTPase activity by catalysing GTP hydrolysis, generating GDP. GEFs initiate GTPase activity by catalysing the release of GDP and allowing GTP binding.

      __ Please check the correct nomenclature for CRISPR/Cas9.__

      We will rectify where appropriate.

      __6. p.7 "these molecules act in synergy", consider replacing with "redundantly".

      __

      We will rectify where appropriate.

      __Reviewer #2 (Significance (Required)):

      The significance of this story is to show that GEF-GTPases pairing can be highly context-dependent. Previous studies have identified GEFs that pair with RAB-35 and GTPases that pair with AGEF-1, but the authors find that these factors have at best a modest role in the context of skin-axon interactions. Instead, the authors suggest a novel GTPase-GEF pairing of RAB-35 with AGEF-1 and provide evidence that this relationship is conserved in the human homolog of AGEF-1. These results suggest that GTPase-GEF pairings depend not only on chemical affinity but also cellular context.

      The main strength of the study is its clever genetics. For the screen, the authors looked for suppressors of a synthetic defect in axon integrity caused in part by elevated activity of RAB-35 due to loss of its GAP TBC-10. It is satisfying that this screen isolated a mutation in a GEF that in principle could counterbalance the loss of a GAP.

      The main weakness of the study is the lack of direct evidence for an AGEF-1/RAB-35 interaction. While not necessary for publication, the inclusion of biochemical data to support the role of AGEF-1 as a GEF for RAB-35 and the effect of the S784L mutation on this activity would strongly elevate the study. The genetic data for this interaction are consistent with the model but not conclusive, and in my view the colocalization data are not compelling. Nevertheless this is a solid genetic story with a clever screen.__

      __ __We appreciate the feedback and are grateful for the positive comments on the significance of our study. As explained in the significance section related to Reviewer 1, if we find evidence of a direct interaction between AGEF-1 and RAB-35 in the proposed new experiments, we will include it in the manuscript; alternatively, we will present it as a possibility in the discussion section, as suggested. We agree that a more nuanced understanding of the effect of the S784L is interesting and that our colocalization data can be improved, and we have proposed experiments to address these concerns.

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      This paper investigates the mechanism by which molecular pathways in the skin protect the processes of nerves that innervate them from damage. The authors previously showed that spectrin and the small GTPase RAB-35 act in the epidermis of C. elegans to protect mechanosensory axons from breaking. In this paper they used a suppression screen to identify another gene involved in this process, an ARF-GEF called AGEF-1. Partial loss-of-function mutations in agef-1 suppress the axon-breakage phenotype of spectrin mutations, and genetic experiments by the authors are consistent with the possibility that AGEF-1 could act directly as an exchange factor for RAB-35. Consistent with this model, they show that AGEF-1 and RAB-35 colocalise in the skin.

      Major comments: The experiments in this paper are well-designed and well-controlled, and the interpretations of the results are all reasonable. On the other hand, I don't think the authors' hypothesis that AGEF-1 acts directly as an exchange factor for RAB-35, or that these two proteins directly interact, is definitively proven. This is not an issue of the authors overinterpreting their data--the paper is very carefully and thoughtfully written. However, the most interesting and counterintuitive finding--that an ARF-GEF could also be a RAB-GEF--might be strengthened with more experiments (for example, could they more directly show protein-protein interaction through co-IP or mass spec?).__

      We thank the reviewer for the suggestion. We propose to further investigate the notion that AGEF-1a might be a direct interactor of RAB-35 using a split-GFP approach to assess whether these molecules closely interact, in vivo, in the physiological context that is relevant for the maintenance of the touch sensing neurons (please see reply to reviewer #1 major point 1 and reviewer #2 major point 1 for more details).

      Minor comments: There are also two places where the fact that null mutations are lethal (for agef-1 and arf-5) prevented the authors from addressing the effect of agef-1 loss of function in the skin, and addressing whether ARF-5 could be an AGEF-1 target, respectively. In principle, they could have tried to make a CRISPR line in which these genes could be cell-specifically deleted in the skin (using a dpy-7-driven recombinase). I don't think either of these experiments are essential, but if it is feasible to make these lines it would tie up a couple of loose ends.

      We agree to explore the roles of agef-1 and arf-5 loss-of-function. We propose to tissue-specifically degrade agef-1 using an auxin-inducible degradation strategy (please see reviewer #2 major point 2 reply for more details). For arf-5, we propose knocking-down its function using RNAi to overcome lethality (please see reviewer #1 major point 3 reply for more details).

      __Reviewer #3 (Significance (Required)):

      Overall I think this is an interesting paper on a topic of general interest. The most interesting finding is that an exchange factor for an ARF (a small GRPase involved in vesicle coating/uncoating) could also be an exchange factor for a RAB (a small GTPase involved in vesicle tethering). The evidence presented is suggestive and intriguing, though as noted above not completely definitive. In summary, I think it is an interesting paper in its current form, and anything it could do to more firmly establish a direct interaction between AGEF-1 and RAB-35 would increase its impact and importance.

      __

      We thank the reviewer for the positive evaluation of the significance of our study.

      __ Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      Summary: In this study Bonacossa-Pereira et al. identify AGEF-1a, an Arf-GEF, as a factor that functions in the epidermis through RAB-35 to regulate axonal integrity of the PLM mechanosensory neurons in C. elegans. Specifically, epidermal attachment sites are regulated by these genes form the epidermis and compromising these attachment sites results in axonal degeneration. The study provides some evidence that that RAB-35 and AGEF-1 at least partially colocalize in the skin. Finally, the authors provide evidence that the human orthologue BIG2 is capable of functionally replacing AGEF-1a in C. elegans. Overall, the experiments are well designed and the paper is clear and succinct. The conclusions are supported by the findings and provide an important extension of the author's findings a few back, when they identified the role of rab-35 in mediating the epidermal-neuronal attachment sites.

      Major comments: 1. AGEF-1/BIG2 are known to regulate other GTPases such as ARF-5 or ARF-2. The authors exclude a non-redundant function for ARF-2, but are unable to establish a role for ARF-5 because of the lethality associated with the mutation. Alternative approaches, such as cell specific knock out or knock down experiment. In addition, studies to test potentially physical interaction such as pull-down assays, co-IP experiments and FRET could be used to test whether AGEF-can bind RAB-35 or ARF-5.__

      We thank the reviewer for this suggestion. We propose addressing these concerns using a tissue-specific degradation for AGEF-1a (please see reviewer #1 major point 2 for details). To establish a role for ARF-5 we propose to do an RNAi mediated knock-down to overcome lethality (please see reviewer #1 major point 3 for details). Finally, we plan to use a split-GFP approach to test the physical interaction between agef-1a and rab-35 in vivo (please see reviewer #1 major point 1 for details)

      __ Phenotypic readout has been limited to only axon breaks. It may be interesting to also test other aspects such as axonal deformities including swellings and vesiculation in other parts of the nervous system. Moreover, behavioral or functional experiments such as response to gentle touch or synaptic integrity could be informative.__

      We have not observed any obvious touch receptor neurons axonal phenotypes other than axonal breaks in these mutants, and we will include a statement that reflects this concept. In relation to the behavior, we have not tested it as the results will be difficult to interpret for two reasons: first, the breaks are not always bilateral and one neuron is sufficient to provide mechanical response; second, the mixed identity of the PLM neurite allows it to retain some function despite being severed. However, if deemed essential, we will perform these experiments.

      __ Overexpression constructs such as SKIN::RAB-35[Q69L], SKIN::BIG2, SKIN::AGEF-1a[E608K] in extrachromosomal transgenes could lead to non-physiological localization or effects. Single copy expression using MosSCI or CRISPR insertions are generally considered better approaches (other than endogenous reporters) to provide accurate insights at the physiological level. While the authors tacitly acknowledge this by conducting the experiments in a rab-35 mutant background and very low transgene concentration, at the very least this caveat regarding the localization should be discussed.__

      This is an important remark, and we appreciate the comment. We acknowledge that experiments using extrachromosomal arrays have inherent caveats, especially for localization studies. To address the RAB-35 localization concern we plan to repeat the localization studies using an endogenously tagged RAB-35 using CRISPR to overcome the possible artifacts caused by extrachromosomal array driven expression (please see reviewer #1 point 1 for more details). For the cell-specific rescues or dominant-negative constructs expression, we believe that using extrachromosomal arrays is sufficient, since this allows us to compare genetically identical transgenic vs non-transgenic siblings of independent lines. Moreover, given these constructs are already driven by a tissue-specific promoter that is inherently stronger than their respective endogenous promoters, even a single-copy insertion would have the same caveats.

      __4. The study does not address clearly whether AGEF-1a acts in parallel to spectrin or upstream/ downstream to it. Epistasis experiments could help to figure out the signaling pathway involved.

      __

      Indeed, this is a concept that we need to communicate more clearly. We have data showing that a mutation in agef-1 does not cause axonal damage on its own, and that it has no effect on the axonal damage caused by unc-70 dominant negative mutation alone. We only detect an effect of agef-1 when tbc-10 is mutated together with unc-70 (Fig. 1a of manuscript). Together, these data indicate that agef-1 functions upstream of rab-35, thus acting in parallel to unc-70 (see schematic below) to ensure the mechanical stability of neuron epidermal attachment. We plan to include this data and the following schematic as a supplement to better convey the idea and discuss the results appropriately.

      __ The finding that BIG2 rescues the mutant defect is an important finding and rightfully finds its place in the abstract. I wonder whether a reference to the human diseases caused by loss of BIG2 in the abstract and introduction would not increase interest/impact for the study, rather than burying this potentially interesting connection in the discussion.

      __

      We appreciate the reviewer's comment, and welcome the suggestion. We propose to include relevant background about BIG2-related human diseases in the abstract and introduction as suggested and expand the discussion regarding BIG2 mutations.

      __Minor comments:

      1. Some explanation about how mutating the autoinhibitory domain could impact the catalytic activity of a GEF might be helpful.__

      2. *

      We acknowledge that this notion was not well communicated. We propose to elaborate more about why we think a mutation in the autoinhibitory domain might be affecting the GEF activity and we plan to do further experiments to dissect how this might be happening. Please see reviewer #2 major point 2 for a more detailed explanation.

      __ The paper refers to rme-4(b1001) as a null allele while wormbase refers to the same as a missense allele. It would be more accurate to refer rme-4(b1001) as a strong loss of function or putative null.__

      We agree and will refer to b1001 as a strong loss-of-function.

      __ The paper does not clearly discuss limitations of the hypomorphic agef-1[S784L] and that the observed phenotypes in this hypomorph might underestimate the complete role of AGEF-1a.__

      • *

      We thank the reviewer for this suggestion. We propose to elaborate more on these limitations, especially considering the possible new results from the experiments suggested in reply to reviewer #2 major comment point 2.

      __ In figure 1, where there really only one extrachromosomal transgenic line for some of the construct tested? __

      • *

      For the Pdpy-7::AGEF-1a lines we have scored 3 transgenic lines (data not included) and only one yielded a full rescue. For all extrachromosomal lines presented, we tested 3 independent transgenic lines. For brevity, we only included the result for the positive rescues (1 for BIG2 and 1 for AGEF-1a), except for the Pmec-4 lines, of which none rescued the phenotype (data included in Table S2). We will update Table S2 to include all the lines tested.

      __ The concentrations of transgenes vary in different transgenes. Is there a rationale behind this? __

      Yes, we have attempted multiple concentrations of injections for each transgene and there was some variability for each construct injected, thus we only included the ones where we observed an effect. As mentioned in point 4 above, we will update Table S2 to include details of all lines tested.

      __ In Fig.1e: I may be useful to also show the "WT" phenotype, i.e. the strong defects to get a visual comparison for the degree of rescue. __

      • *

      We think this suggestion will help the readers. We will include this as a representative dashed line showing the WT phenotype.

      __Reviewer #4 (Significance (Required)):

      The study has identified AGEF-1a as a regulator of axonal maintenance, functioning to protect neurons against mechanical stress by acting through RAB-35. Additionally, this epidermal GEF, AGEF-1a is functionally conserved as its human orthologue BIG2 can replace AGEF-1a in C. elegans for axonal protection. Important points here are that the findings extend prior work by the authors of non-autonomous mechanism that regulates epidermal-neuronal attachment. In my humble opinion, the human disease connection, in particular with regard to the unexplained neuronal phenotypes in patients could be better developed in the manuscript. It may also increase impact/interest of a wonderful story that right now reads a bit 'wormy'.__

      This is an important remark and we are grateful for the positive comments. The fact that human BIG2 is also conserved in C. elegans points to a fundamental role of this molecule in multicellular life, and it provides a tractable model to investigate the function of this molecule in a physiological context. We welcome the suggestion to elaborate more the connection with the unexplained neuronal phenotypes in patients and use a more accessible language to convey our findings to a wider audience.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      N/A

      4. Description of analyses that authors prefer not to carry out

      __Reviewer #1 __

      "...studies to test potentially physical interaction such as pull-down assays, co-IP experiments and FRET could be used to test whether AGEF-can bind RAB-35 or ARF-5."

      While pull-down assays, co-IP and FRET would reveal whether AGEF-1a can form a complex with RAB-35, we believe that using a full length AGEF-1a would not only represent a significant technical challenge but will also not prove a direct interaction in a physiological context.

      "...An optional experiment would be to look at the colocalization of RAB-35 with a known effector in wild type and agef-1(vd92) with the expectation that there would be a higher level of colocalization in agef-1 mutants. Effector pull-down experiments or perhaps a cell based GEF assay could be used (PMID: 35196081)."

      We think that screening for the relevant rab-35 effector in this context and/or doing effector pull-down/cell based GEF assays would be a significant technical challenge. We propose to address this concern by tempering our claim as suggested by the reviewer.

      "...It may be interesting to also test other aspects such as axonal deformities including swellings and vesiculation in other parts of the nervous system. Moreover, behavioral or functional experiments such as response to gentle touch or synaptic integrity could be informative."

      As indicated above in major point 2 of reviewer 4, these are interesting ideas that might answer how the function of these neurons might be affected. However, in addition to the challenges indicated above, they will not provide further insights into how their integrity is maintained. We believe these will fall outside the scope of the manuscript, but if deemed essential we will perform behavioral analysis.

      PeerReviewed
    2. EMBOpress 04 Sep 2025

      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 #4

      Evidence, reproducibility and clarity

      Summary:

      In this study Bonacossa-Pereira et al. identify AGEF-1a, an Arf-GEF, as a factor that functions in the epidermis through RAB-35 to regulate axonal integrity of the PLM mechanosensory neurons in C. elegans. Specifically, epidermal attachment sites are regulated by these genes form the epidermis and compromising these attachment sites results in axonal degeneration. The study provides some evidence that that RAB-35 and AGEF-1 at least partially colocalize in the skin. Finally, the authors provide evidence that the human orthologue BIG2 is capable of functionally replacing AGEF-1a in C. elegans. Overall, the experiments are well designed and the paper is clear and succinct. The conclusions are supported by the findings and provide an important extension of the author's findings a few back, when they identified the role of rab-35 in mediating the epidermal-neuronal attachment sites.

      Major comments:

      1. AGEF-1/BIG2 are known to regulate other GTPases such as ARF-5 or ARF-2. The authors exclude a non-redundant function for ARF-2, but are unable to establish a role for ARF-5 because of the lethality associated with the mutation. Alternative approaches, such as cell specific knock out or knock down experiment. In addition, studies to test potentially physical interaction such as pull-down assays, co-IP experiments and FRET could be used to test whether AGEF-can bind RAB-35 or ARF-5.
      2. Phenotypic readout has been limited to only axon breaks. It may be interesting to also test other aspects such as axonal deformities including swellings and vesiculation in other parts of the nervous system. Moreover, behavioral or functional experiments such as response to gentle touch orsynaptic integrity could be informative.
      3. Overexpression constructs such as SKIN::RAB-35[Q69L], SKIN::BIG2, SKIN::AGEF-1a[E608K] in extrachromosomal transgenes could lead to non-physiological localization or effects. Single copy expression using MosSCI or CRISPR insertions are generally considered better approaches (other than endogenous reporters) to provide accurate insights at the physiological level. While the authors tacitly acknowledge this by conducting the experiments in a rab-35 mutant background and very low transgene concentration, at the very least this caveat regarding the localization should be discussed.
      4. The study does not address clearly whether AGEF-1a acts in parallel to spectrin or upstream/ downstream to it. Epistasis experiments could help to figure out the signaling pathway involved.
      5. The finding that BIG2 rescues the mutant defect is an important finding and rightfully finds its place in the abstract. I wonder whether a reference to the human diseases caused by loss of BIG2 in the abstract and introduction would not increase interest/impact for the study, rather than burying this potentially interesting connection in the discussion.

      Minor comments:

      1. Some explanation about how mutating the autoinhibitory domain could impact the catalytic activity of a GEF might be helpful.
      2. The paper refers to rme-4(b1001) as a null allele while wormbase refers to the same as a missense allele. It would be more accurate to refer rme-4(b1001) as a strong loss of function or putative null.
      3. The paper does not clearly discuss limitations of the hypomorphic agef-1[S784L] and that the observed phenotypes in this hypomorph might underestimate the complete role of AGEF-1a.
      4. In figure 1, where there really only one extrachromosomal transgenic line for some of the construct tested?
      5. The concentrations of transgenes vary in different transgenes. Is there a rationale behind this?
      6. In Fig.1e: I may be useful to also show the "WT" phenotype, i.e. the strong defects to get a visual comparison for the degree of rescue.

      Significance

      The study has identified AGEF-1a as a regulator of axonal maintenance, functioning to protect neurons against mechanical stress by acting through RAB-35. Additionally, this epidermal GEF, AGEF-1a is functionally conserved as its human orthologue BIG2 can replace AGEF-1a in C. elegans for axonal protection. Important points here are that the findings extend prior work by the authors of non-autonomous mechanism that regulates epidermal-neuronal attachment. In my humble opinion, the human disease connection, in particular with regard to the unexplained neuronal phenotypes in patients could be better developed in the manuscript. It may also increase impact/interest of a wonderful story that right now reads a bit 'wormy'.

      PeerReviewed
    3. EMBOpress 04 Sep 2025

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      Referee #3

      Evidence, reproducibility and clarity

      This paper investigates the mechanism by which molecular pathways in the skin protect the processes of nerves that innervate them from damage. The authors previously showed that spectrin and the small GTPase RAB-35 act in the epidermis of C. elegans to protect mechanosensory axons from breaking. In this paper they used a suppression screen to identify another gene involved in this process, an ARF-GEF called AGEF-1. Partial loss-of-function mutations in agef-1 suppress the axon-breakage phenotype of spectrin mutations, and genetic experiments by the authors are consistent with the possibility that AGEF-1 could act directly as an exchange factor for RAB-35. Consistent with this model, they show that AGEF-1 and RAB-35 colocalise in the skin.

      Major comments: The experiments in this paper are well-designed and well-controlled, and the interpretations of the results are all reasonable. On the other hand, I don't think the authors' hypothesis that AGEF-1 acts directly as an exchange factor for RAB-35, or that these two proteins directly interact, is definitively proven. This is not an issue of the authors overinterpreting their data--the paper is very carefully and thoughtfully written. However, the most interesting and counterintuitive finding--that an ARF-GEF could also be a RAB-GEF--might be strengthened with more experiments (for example, could they more directly show protein-protein interaction through co-IP or mass spec?).

      Minor comments: There are also two places where the fact that null mutations are lethal (for agef-1 and arf-5) prevented the authors from addressing the effect of agef-1 loss of function in the skin, and addressing whether ARF-5 could be an AGEF-1 target, respectively. In principle, they could have tried to make a CRISPR line in which these genes could be cell-specifically deleted in the skin (using a dpy-7-driven recombinase). I don't think either of these experiments are essential, but if it is feasible to make these lines it would tie up a couple of loose ends.

      Significance

      Overall I think this is an interesting paper on a topic of general interest. The most interesting finding is that an exchange factor for an ARF (a small GRPase involved in vesicle coating/uncoating) could also be an exchange factor for a RAB (a small GTPase involved in vesicle tethering). The evidence presented is suggestive and intriguing, though as noted above not completely definitive. In summary, I think it is an interesting paper in its current form, and anything it could do to more firmly establish a direct interaction between AGEF-1 and RAB-35 would increase its impact and importance.

      PeerReviewed
    4. EMBOpress 04 Sep 2025

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      Referee #2

      Evidence, reproducibility and clarity

      This interesting manuscript reports the outcome of a fruitful C. elegans genetic screen with a complex but clever design. Through it, the authors identify AGEF-1 as a GEF that likely regulates the active state of the GTPase RAB-35 in the skin to protect touch receptor axons from mechanical breakage.

      Major points:

      1. Based on localization experiments, the authors claim "AGEF-1a interacts with RAB-35 in the epidermis" (Results heading) and state "these data demonstrate that AGEF-1a interacts with a subset of RAB-35 molecules in the epidermis." In general, localization studies cannot be used to conclude physical interaction (with some exceptions such as single-molecule kinetics). In this case, the data in my view do not even make a compelling argument for co-localization. There is a lot of AGEF-1 and RAB-35 signal everywhere and it may not be meaningful that the signals sometimes overlap. A more quantitative approach with controls would be needed to conclude meaningful co-localization. Importantly, this would still not demonstrate interaction.
      2. The effect of the AGEF-1(S784L) mutation is not clear to me. Naively, as the S784L mutation lies in the auto-inhibitory domain, I would have expected AGEF-1 to become constitutively active, not inactive as the authors seem to suggest. Is the idea that it is constitutively auto-inhibited? The main evidence for a loss of function effect seems to be that a putative dominant negative mutation AGEF-1(E608K) does not further supress axon breakage when co-expressed in trans to AGEF(S784L), but in my view this only shows that, once the defect is suppressed, it cannot be suppressed any further. Defining the nature of the S784L allele is important. Some suggestions, although the authors may come up with different approaches: use of an inducible or cell-specific depletion system like AID/TIR1, Cre/lox, or FLP/FRT to circumvent the lethality of agef-1(0) and reveal what a true loss-of-function looks like; testing if deletion of the auto-inhibitory domain phenocopies S784L to test if this mutation impairs autoinhibition.

      Minor points:

      1. I am not able to see the "vesicle-like structures with a clear luminal space" or RAB-35 being "notably enriched at the membrane near the epidermal furrow" in Fig. 3. The "3D surface rendering" in Fig. 3e is grossly oversampled and should not be included.
      2. As the agef-1a isoform is specifically referenced throughout, please describe the different agef-1 isoforms somewhere to save readers from having to look this up.
      3. The authors include an interesting speculation in the Discussion: "Future investigations of BIG2-associated neurological disorders should consider... hyper-activity of BIG2 as a driver of neuropathology." If the authors have the tools to test the effect of hyperactive BIG2 in this system, it could be an exciting addition.
      4. On a personal note, since GEFs act oppositely to GTPase Activating Proteins (GAPs), I had to stop and re-read carefully whenever the authors referred to a GEF "activating" a GTPase. I understand their meaning (i.e., putting the GTPase in its active GTP-bound state, not activating its GTPase function) but I wanted to point out this potential confusion in case there is a way to better define terms in the Introduction or change word choice. I realize this may be a standard jargon in the field.
      5. Please check the correct nomenclature for CRISPR/Cas9.
      6. p.7 "these molecules act in synergy", consider replacing with "redundantly".

      Significance

      The significance of this story is to show that GEF-GTPases pairing can be highly context-dependent. Previous studies have identified GEFs that pair with RAB-35 and GTPases that pair with AGEF-1, but the authors find that these factors have at best a modest role in the context of skin-axon interactions. Instead, the authors suggest a novel GTPase-GEF pairing of RAB-35 with AGEF-1 and provide evidence that this relationship is conserved in the human homolog of AGEF-1. These results suggest that GTPase-GEF pairings depend not only on chemical affinity but also cellular context.

      The main strength of the study is its clever genetics. For the screen, the authors looked for suppressors of a synthetic defect in axon integrity caused in part by elevated activity of RAB-35 due to loss of its GAP TBC-10. It is satisfying that this screen isolated a mutation in a GEF that in principle could counterbalance the loss of a GAP.

      The main weakness of the study is the lack of direct evidence for an AGEF-1/RAB-35 interaction. While not necessary for publication, the inclusion of biochemical data to support the role of AGEF-1 as a GEF for RAB-35 and the effect of the S784L mutation on this activity would strongly elevate the study. The genetic data for this interaction are consistent with the model but not conclusive, and in my view the colocalization data are not compelling. Nevertheless this is a solid genetic story with a clever screen.

      PeerReviewed
    5. EMBOpress 04 Sep 2025

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      Stability of the PLM axon in C. elegans is maintained through interactions with the epidermis. Previous studies by this group found that loss of the tbc-10 Rab GTPase Activating Protein strongly enhanced the PLM axon break phenotype of unc-70/beta-spectrin mutants. TBC-10 is a GAP for RAB-35 and thus loss of rab-35 suppresses the tbc-10 phenotype. Of the two RAB-35 GEFs, loss of RME-4 partially suppressed the tbc-10 phenotype and FLCN-1 was not involved suggesting that there may be an additional GEF involved. Here Bonacossa-Pereira et al identify a point mutation in agef-1a (vd92) as a suppressor of tbc-10 PLM axon break phenotype (all experiments also have a dominant allele of unc-70) and confirm that point mutation is causative by replicating the mutation via genome editing (vd123). Rescue experiments demonstrate that AGEF-1a is required in the epidermis and not PLM as previous demonstrated with tbc-10 and unc-70. Rescue is dependent on a functional SEC7/GEF activity. AGEF-1a is a functional ortholog to human BIG2/ArfGEF2 as its expression fully rescues tbc-10. AGEF-1a functions upstream of RAB-35 as expression of activated RAB-35 can suppress loss of agef-1. AGEF-1a functions in parallel to RME-4 as the double has stronger suppression of tbc-10. AGEF-1a is an ARF GEF, however it functions independently of ARF-1.2 as loss of arf-1.2 does not suppress tbc-10. They demonstrate that AGEF-1a interacts with RAB-35 through colocalization experiments suggesting that AGEF-1a could directly activate RAB-35. Finally, they demonstrate that AGEF-1a regulates the localization of the LET-805 epidermal attached complex component as it restores localization in a tbc-10 mutant.

      Major comments

      The manuscript is well written and easy to understand.

      The experiments are well done and controlled.

      I enjoyed reading this paper. However...

      Some of the claims are not supported by the data.

      1. The claim that AGEF-1a directly interacts with RAB-35 was not demonstrated. The evidence provided to support a direct interaction are colocalization experiments in Figure 3. AGEF-1a does partially colocalize with RAB-35 in the epidermis. However, colocalization does not indicate a physical interaction direct or indirect. A simple fix would be to change the claim to that they partially colocalize. Optional, a physical interaction could be done with the split-GFP since they already have the AGEF-1 strain or they could perform co-IP experiments, though neither of those are proof of direct interactions.
      2. The claim that AGEF-1a facilitates RAB-35 activation is not supported. While it is likely that AGEF-1a facilitates RAB-35 activation based on the epistasis experiments as well as studies in mammalian cells there were no experiments to demonstrate that modulating AGEF-1a activity resulted in a change in RAB-35 activity. I would suggest tempering this claim to something along the line that the data are consistent with AGEF-1a regulating RAB-35 activity as shown in mammalian cells. An optional experiment would be to look at the colocalization of RAB-35 with a known effector in wild type and agef-1(vd92) with the expectation that there would be a higher level of colocalization in agef-1 mutants. Effector pull-down experiments or perhaps a cell based GEF assay could be used (PMID: 35196081).
      3. The claim that AGEF-1a functions independently of ARF-1.2 is not well supported. The fact that the ARF-1.2 mutant does not suppress tbc-10 suggests that ARF-1.2 may not be involved but does not eliminate the possibility that ARF-1.2 functions redundantly with ARF-5 or WARF-1/ARF-1.1. This can be resolved by toning down the claim. Alternatively, this can be tested by RNAi of arf-5 and warf-1 in tbc-10 and arf-1.2; tbc-10 mutants.

      Minor comments

      Figure 1C the CRISPR generated allele (vd123) is referred to as [S784L] and then in 1E vd92 is referred to as [S784L]. Perhaps it would be clearer if the allele name was used instead of the amino acid change.

      Page 6 "We reasoned that if the S784L mutation we isolated causes a similar loss of the GTPase activation function, then SKIN::AGEF-1a[E608K] would not have the capacity to restore the rate of PLM axon breaks to background levels in agef-1[S784L]; tbc-10; vdSi2 animals." It was unclear to me whether you were testing if the S784L mutation could be disrupting a GEF independent function or might disrupt the nucleotide exchange activity as might be tested in a biochemical assay. There are many reasons this change could cause a loss of function phenotype (ie. Improper folding, mislocalization, etc.). The most clear explanation would be that you were testing if GEF function was required for rescue rather than testing if the S784L mutation disrupted GEF activity.

      Page 13. It was unclear how testing if AGEF-1, RME-4, ARF-5 and RAB-35 form complexes in vivo (I assume you are suggesting colocalize based on figure 3 interpretation) would resolve how AGEF-1 was regulating RAB-35.

      Cross-commenting

      I agree with the comments made by the other reviewers and I stand by my own as well. I will echo that it is important to know the nature of their agef-1 allele.

      Significance

      Bonacossa-Pereira et al identify AGEF-1 as a regulator of axon integrity that functions in a pathway with RAB-35 in the epidermis is an exciting finding. As pointed out in the discussion, mutations in the human ortholog cause neurodevelopmental defects which leads to obvious characterization of BIG2/ArfGEF2 in neurons while this study indicates that this protein can have cell non-autonomous roles in regulating neurons. These findings could have important implications for understanding the etiology of these defects that would be of interest to neurobiologists and clinical researchers.

      The finding of this paper would also be of interest to cell biologists and particularly those studying the roles of Rab and Arf GTPases in membrane trafficking, such as myself. The idea that AGEF-1 might function as a Rab35 GEF is provocative and would generate a lot of interest and skepticism from the field. However, there is no data to support that AGEF-1 would be a direct regulator of Rab35 over the previously demonstrated cross regulation of Rab35 by Arf GTPases. Therefore, it would be fine to speculate in the discussion a direct interaction, but I would refrain from suggesting this as a model and elsewhere in the manuscript.

      PeerReviewed
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    1. tanemika 04 Sep 2025

      DOCUMENT DE SYNTHÈSE : Les Politiques d'Accompagnement à la Parentalité en France

      Source : Rapport d’information N° 1638, Assemblée Nationale, Délégation aux droits des femmes et à l’égalité des chances entre les hommes et les femmes, sur les politiques d’accompagnement à la parentalité, présenté par Mme Sarah Legrain et Mme Delphine Lingemann, enregistré le 24 juin 2025.

      Synthèse Exécutive

      Ce rapport de la Délégation aux droits des femmes et à l’égalité des chances entre les hommes et les femmes met en lumière les inégalités persistantes dans la répartition des charges domestiques et parentales en France, majoritairement assumées par les femmes.

      Il révèle que la parentalité, loin d'être neutre en matière de genre, est une cause majeure des inégalités économiques, professionnelles et sociales entre les hommes et les femmes. La "pénalité parentale" affecte de manière significative la carrière et les revenus des femmes, tandis que les hommes en sont largement épargnés.

      Les rapporteures identifient plusieurs axes clés pour favoriser une répartition plus égalitaire des tâches parentales et promouvoir une vision positive et égalitaire de la parentalité, formulant 44 recommandations pour y parvenir.

      Ces recommandations couvrent l'éducation et l'information, la prise en compte de la parentalité au travail, l'accompagnement des parents dès le désir d'enfant, la refonte des systèmes de congés parentaux et des modes d'accueil, le soutien aux parents d'adolescents et l'accompagnement des familles monoparentales.

      Thèmes Principaux et Idées Clés

      1. La Charge Domestique et Parentale Inégalitaire : Un Frein à l'Égalité des Femmes

      • Division Sexuée Persistante : Malgré une impression d'égalité, les femmes continuent d'assumer la majeure partie des responsabilités domestiques et parentales. En moyenne, elles réalisent 71% des tâches domestiques et 65% des tâches parentales du foyer. Cette division est profondément enracinée dans un héritage historique et des stéréotypes de genre tenaces.
      • Stéréotypes de Genre : L'idée que "les mères savent mieux répondre aux besoins et attentes des enfants que les pères" est très présente chez les adultes (60% y adhèrent) et se perpétue chez les jeunes (54% des 18-24 ans). Ces stéréotypes contribuent à une dévalorisation sociale des tâches considérées comme féminines.
      • "Double Journée" des Mères : L'arrivée des enfants aggrave cette inégalité. Pour les femmes, cela représente environ "cinq heures de travail supplémentaire", tandis que pour les hommes, cela "réduit leur temps domestique et parental de deux heures". Les femmes salariées cumulent travail professionnel, domestique et parental, totalisant "onze heures par jour contre moins de dix heures pour les hommes".
      • Impact du Système de Congés : La différence de durée entre le congé maternité (16 semaines) et le congé paternité (28 jours) renforce la dynamique d'une "mère 'parent principal' et d'un père 'auxiliaire'". Le congé parental est également majoritairement pris par les mères (94% des cas), ce qui pénalise leur carrière.
      • Difficultés des Modes d'Accueil : Le manque et la répartition inégale des places en crèche et chez les assistantes maternelles obligent souvent les mères à compenser les dysfonctionnements du système. "Près de 20% des parents n’obtiennent pas de mode d’accueil, plus de 160 000 ne reprennent pas le travail faute de solution de garde pour leur enfant", les mères étant la "variable d'ajustement".

      2. Conséquences Lourdes pour les Mères : Coût Humain, Économique et Social

      • Pénalité Parentale au Travail : La parentalité a un "impact négatif de la parentalité sur le parcours professionnel des femmes", alors qu'elle n'a "aucun effet ou presque sur l’évolution professionnelle des hommes". "90% des inégalités de revenu entre les femmes et les hommes sont directement dues à la 'pénalité parentale' que subissent les femmes". Dix ans après l'arrivée du premier enfant, le revenu moyen des femmes chute d'environ 38%.
      • Discrimination : Plus de six femmes sur dix estiment qu’être mère est un frein à la carrière. 27% des femmes déclarant être discriminées au travail estiment que cette discrimination est liée à la grossesse ou au congé maternité.
      • Vulnérabilité des Mères Solos : Les mères solos (82% des familles monoparentales) sont particulièrement touchées. Elles subissent une "triple pénalité croisée : leur genre, leur situation professionnelle […], leur situation familiale", les exposant aux emplois précaires et mal rémunérés, et augmentant leur risque de pauvreté. "Près d’une mère seule sur cinq est pauvre alors qu’elle a un emploi".
      • Risque d'Épuisement et Santé Mentale : La charge disproportionnée entraîne un "risque réel d’épuisement pour les mères". L'isolement peut favoriser la dépression post-partum, qui touche environ 20% des femmes et est la "première cause de la mortalité maternelle dans l’année qui suit la naissance de l’enfant".
      • Coût Économique Élevé : Outre la perte de revenus due aux congés maternité et à la réduction d'activité, la séparation a un "lourd coût pour les mères". Une femme séparée sur trois "bascule sous le seuil de pauvreté l’année de la séparation", son niveau de vie baissant d'environ 20% (contre 7% pour les hommes). 39% des enfants vivant en famille monoparentale sont en situation de pauvreté.

      3. Propositions pour une Parentalité Égalitaire

      Les rapporteures formulent 44 recommandations pour transformer les politiques d'accompagnement à la parentalité, axées sur l'égalité :

      Éducation et Information :

      • Mettre en place des "cours d’activités domestiques" à l'école ou au collège pour inculquer des compétences à tous les enfants.
      • Lancer des "campagnes nationales contre les stéréotypes de genre" sur la parentalité.
      • Adopter une "terminologie neutre" (ex: "école pré-élémentaire" au lieu d'"école maternelle", "prestation pour naissance et soin du mineur" au lieu de "congé maternité/paternité").
      • Renforcer l'information des parents sur les dispositifs d'accompagnement.

      Prise en Compte au Travail :

      • Intégrer la parentalité dans la "responsabilité sociétale des entreprises (RSE)" et généraliser la "Charte de la parentalité" aux entreprises de plus de 50 salariés.
      • Modifier le Code du travail pour inclure explicitement la parentalité dans les "négociations d’entreprises relatives à l’égalité professionnelle".
      • Intégrer des critères sur la parentalité dans le futur "index égalité professionnelle".
      • Accorder des "autorisations d’absence" (4 demi-journées/an) aux parents pour les moments clés de la scolarité de leurs enfants.
      • Accompagnement dès le Désir d'Enfant et Post-Partum :
      • Élargir les "consultations pré-conceptionnelles" au projet parental et permettre au second parent d'assister à tous les rendez-vous médicaux obligatoires de la grossesse.
      • Consacrer une séance de préparation à la naissance au "projet parental".
      • Renforcer le dispositif d'arrêt en cas d'interruption de grossesse et l'étendre aux interruptions volontaires, avec une autorisation d'absence pour le conjoint.
      • Faciliter l'accès aux "consultations sur l’allaitement" et renforcer la "formation des praticiens sur la dépression post-partum".
      • Prévoir une "consultation facultative et remboursée à 100% avec un psychologue" pour les mères dans les trois mois après la naissance.
      • Étendre le "congé de 'proche aidant'" au second parent en soutien à la mère souffrant de dépression post-partum.
      • Lutter contre l'isolement des mères en proposant aux parents "d’être mis en relation avec d’autres parents accueillant leur enfant au même moment".

      Réforme des Congés et Modes d'Accueil :

      • Congé Paternité : Porter "progressivement le congé paternité à seize semaines, soit à égalité avec le congé maternité". Huit semaines seraient obligatoires (4 à la naissance, 4 après le congé maternité de la mère) et huit facultatives et fractionnables. Cette mesure est un "levier clé pour l’égalité entre les parents" et répond à l'aspiration des pères à s'investir davantage.
      • Congé Parental : Réformer le congé parental en "renforçant son attractivité financière sans réduire sa portée pour les ménages modestes", et réfléchir à une "reprise progressive" après le congé.
      • Modes d'Accueil : Garantir la "lisibilité et la transparence" des modes de garde, "investir pour augmenter et harmoniser l’offre de crèches sur le territoire", et "revaloriser les métiers de la petite enfance".
      • Soutien aux Parents d'Adolescents :
      • Élargir les missions des "lieux d’accueil enfants-parents" pour qu’ils puissent "recevoir des adolescents".
      • Mettre en place des "politiques publiques ciblant spécifiquement les parents d’adolescents".
      • Renforcer l’offre en "pédopsychiatrie" et la "médecine scolaire" face à la dégradation de la santé mentale des jeunes.
      • Lancer une "campagne d’information nationale sur la santé mentale des enfants et des adolescents".

      Accompagnement des Familles Monoparentales :

      • Repenser le "mode de calcul des pensions alimentaires" pour prendre en compte le coût réel de l’entretien d’un enfant et permettre au parent bénéficiaire de "défiscaliser la pension alimentaire".
      • Instaurer un "abattement sur le montant de la pension alimentaire pris en compte dans les bases ressources des prestations familiales et des aides au logement, à hauteur de l’allocation de soutien familial (ASF)".
      • "Déconjugaliser le versement de l’ASF" et "ouvrir les allocations logement (APL) aux deux parents" pour faciliter l’accueil des enfants.
      • Ouvrir aux mères solos la "possibilité de transférer des droits de congés vers un proche de leur choix" et "doubler les jours 'enfant malade'".
      • Étudier la création d'un "statut des familles monoparentales" avec des droits spécifiques.

      Conclusion des Rapporteures

      Les rapporteures affirment que malgré des évolutions, les mères restent le "parent principal", ce qui a des conséquences négatives sur leur santé et leur vie professionnelle.

      Une "réforme ambitieuse du système des congés", en particulier du congé second parent, est un "moteur d'égalité" essentiel.

      S'inspirant des modèles scandinaves et espagnols, la France peut avancer vers une parentalité égalitaire, non seulement pour l'émancipation des femmes, mais aussi comme réponse aux inquiétudes démographiques.

      Assemblée nationale rapport parentalité 2025 REAAP
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  65. nap.nationalacademies.org nap.nationalacademies.org
    Read "Communities in Action: Pathways to Health Equity" at NAP.edu
    1
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  66. chem.libretexts.org chem.libretexts.org
    12.2: Structures and Formulas of Ionic Solids
    1
    1. savmellis1 04 Sep 2025
      Step 1: 1/2 bond dissociation energy of Cl2(g) -Known (122 kJ/mol) Step 2: Enthalpy of sublimation of Na(s) - Known (107.3 kJ/mol) Step 3: Ionization energy of Na(g) - Known (496 kJ/mol) Step 4: Electron affinity of Cl(g) - Known (-349 kJ/mol) Step 5: Lattice formation energy of NaCl - Unkown Step 6: Enthalpy of formation of NaCl - Known (-411 kJ/mol)

      this is specific to transition of NaCl but keep the steps in mind

      memorize
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  67. x.com x.com
    Jason Wei on X: "Reflecting back, these were the biggest technical lessons for me in AI in the past five years: 2020: you can cast any language task as sequence prediction and learn it via pretrain + finetune 2021: scaling to GPT-3 size enables doing arbitrary tasks specified via instructions" / X
    1
    1. tiendat 04 Sep 2025
      2020: you can cast any language task as sequence prediction and learn it via pretrain + finetune 2021: scaling to GPT-3 size enables doing arbitrary tasks specified via instructions 2022: scaling to GPT-3.5/PaLM size unlocks reasoning via chain of thought 2023: LLMs themselves can be a product a lot of people will use 2024: to push capabilities past GPT-4, scale test-time compute

      there is a dominant trend and scaffolding structure here

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  68. socialsci.libretexts.org socialsci.libretexts.org
    3.3: Ethical Speaking
    1
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    socialsci.libretexts.org/Bookshelves/Communication/Public_Speaking/Public_Speaking_(The_Public_Speaking_Project)/03:_Ethics_in_Public_Speaking/3.03:_Ethical_Speaking
  69. ecampusontario.pressbooks.pub ecampusontario.pressbooks.pub
    1.3 – Homeostasis
    1
    1. mesamzaidi_ 04 Sep 2025

      Answer to Question 1.5: Pyrogens cause body temperature to rise by acting on the hypothalamus, which controls the body’s thermostat. They trigger:

      Vasoconstriction – blood vessels narrow, reducing heat loss.

      Shivering – muscles generate more heat.

      Reduced sweating – less heat is lost through evaporation.

      Together, these responses raise body temperature, producing a fever.

      Answer to Question 1.6: Homeostasis is the maintenance of a stable internal environment despite changes in external conditions.

      Answer to Question 1.7:

      1. Stimulus: Body temperature rises or falls.
      2. Sensor: Thermoreceptors detect the change.
      3. Control center: Hypothalamus processes the information.
      4. Effectors: Sweat glands activate and blood vessels dilate (cooling) or muscles shiver and vessels constrict (warming).
      5. Response: Body temperature returns to normal.

      Answer to Question 1.8:

      1. Stimulus: Blood osmolarity increases (too concentrated) or decreases (too diluted).
      2. Sensor: Osmoreceptors in the hypothalamus detect the change.
      3. Control center: Hypothalamus signals the pituitary gland.
      4. Effectors: Kidneys retain or excrete water; thirst may be triggered.
      5. Response: Blood osmolarity returns to normal.

      Answer to Question 1.9: d

      Answer to Question 1.10: c

      Answer to Question 1.11: b

      Answer to Question 1.12:

      1. Stimulus – a change in the internal environment
      2. Sensor/receptor – detects the change
      3. Control center – processes the information and signals effectors
      4. Effector – acts to reverse the change

      If secretion of a body chemical became too great: The negative feedback loop would attempt to reduce or stop secretion to bring levels back to normal. If the system fails, it could lead to imbalances or disease.

      Answer to Question 1.13:

      1. Vasoconstriction – reduces heat loss from skin
      2. Shivering – generates heat through muscle activity
      3. Behavioral responses – seeking shelter, adding insulation, huddling
      4. Hormonal responses – thyroid hormone increases metabolic heat production
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  70. www.biorxiv.org www.biorxiv.org
    Uncovering Developmental Lineages from Single-cell Data with Contrastive Poincaré Maps
    1
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  71. drive.google.com drive.google.com
    Sutton-Smith, B. The Ambiguity of Play, pp. 1-7.pdf
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  72. ecampusontario.pressbooks.pub ecampusontario.pressbooks.pub
    1.2 – Limits of Life
    1
    1. mesamzaidi_ 03 Sep 2025

      Answer to Question 1.2: d

      Possible Answer to Question 1.3:

      Fairyflies (tiny insects <0.2 mm) rely on diffusion for oxygen because their bodies are too small for complex respiratory organs.

      Why size limits life: Too small → not enough cells for organs; too large → diffusion alone can’t supply oxygen efficiently.

      Possible Answer(s) for Question 1.4: 1. Oxygen Availability - Limited oxygen in water or soil can restrict survival. 2. Temperature - Extreme heat or cold can denature proteins and disrupt metabolism. 3. Water availability - Deserts and dry habitats limit hydration and cellular function. 4. Nutrient availability - Scarcity of essential minerals or food limits growth. 5. Size - Small or large body size can restrict diffusion, movement, or organ development.

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  73. socialsci.libretexts.org socialsci.libretexts.org
    2.3: Organizing Your Time
    2
    1. kierstencollier 03 Sep 2025

      Fixed activities: 1. 15 hours 2. 30 hours 3. 0 hours 4. 0 hours 5. 56 hours 6. 17.5 hours 7. 10.5 hours 8. 7.5 hours 9. 50 minutes 10. 150 minutes Total: Discretionary Activities: 1. 0 hours 2. 60.5 hours 3. 7 hours 4. 7 hours 5. 7 hours 6. 7 hours 7. 150 minutes

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  74. hsppr.org hsppr.org
    HSPPR Found Pet Page Prototype
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  75. pressbooks.pub pressbooks.pub
    Beowulf
    2
    1. Chapper101 03 Sep 2025
      Then Hrothgar departed, his earl-throng attending him, Hrothgar retires. Folk-lord of Scyldings, forth from the building; The war-chieftain wished then Wealhtheow to look for, The queen for a bedmate. To keep away Grendel 5 The Glory of Kings had given a hall-watch, God has provided a watch for the hall. As men heard recounted: for the king of the Danemen He did special service, gave the giant a watcher: And the prince of the Geatmen implicitly trusted His warlike strength and the Wielder’s protection. Beowulf is selfconfident; he prepares for rest. 10 His armor of iron off him he did then, His helmet from his head, to his henchman committed His chased-handled chain-sword, choicest of weapons, And bade him bide with his battle-equipments. The good one then uttered words of defiance, 15 Beowulf Geatman, ere his bed he upmounted: “I hold me no meaner in matters of prowess, In warlike achievements, than Grendel does himself; Beowulf boasts of his ability to cope with Grendel. Hence I seek not with sword-edge to sooth him to slumber, Of life to bereave him, though well I am able.

      Then Hrothgar left with his friend, the lord of Scyldings, from the building. With the War-Chieftan wishing them good fortune in their journey. The Kings had created a watcher, as the people had recalled, the king of the Danemen did a special service and gave the watcher his trust because of his warlike strength. The Watchers armor was then taken off, he gave his helmet to his henchman and his chain sword and thanked him for holding his items, then someone said words of Defiance Beowulf Geatman stood from his bed and said”I am no meaner in prowess, or in war time achievements than Grendel. So I seek to not defeat Grendel with a sword even though I could.

      Paraphrase
    2. katelyn_januse 03 Sep 2025
      Who valuable gift-gems of the Geatmen carried As peace-offering thither, that he thirty men’s grapple Beowulf is reported to have the strength of 10 Has in his hand, the hero-in-battle. 30 men. The holy Creator usward sent him, To West-Dane warriors, I ween, for to render ’Gainst Grendel’s grimness gracious assistance: I shall give to the good one gift-gems for courage. 15 Hasten to bid them hither to speed them, To see assembled this circle of kinsmen; Tell them expressly they’re welcome in sooth to

      The Geats arrived with treasures as a symbol or gift of peace. The leader is a warrior who has the strength of 30 men. This is Beowulf. God sent him to help out the Danes. He is there to save them all from Grendel's attacks. The king of this land promises to reward him with treasures for his bravery in saving everyone. The king then orders a messenger to welcome them and bring them inside.

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  76. watermark02.silverchair.com watermark02.silverchair.com
    swz028.pdf
    1
    1. acd 03 Sep 2025
      Social workers in Australia (Mays & Marston,2016) and South Africa (Sewpaul, 2005) arrived atsimilar conclusions. Mays and Marston (2016) ar-gued that the rise of neoliberalism “has created apunitive approach to governing poverty” (p. 3)

      Look these people up

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