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    1. Paraalém disso, deve cumprir os seguintes critérios:• Promover a formulação de questões que podem estar sujeitas a investigação;• Convidar para expressar, organizar e contrastar o conhecimento e a hipóteseinicial dos alunos sobre os objetos de estudo a serem investigados;• Promover o desenvolvimento de projetos de pesquisa para responder aproblemas;• Promover a exploração de novos conteúdos através de recursos digitais eoutras fontes de informação;• Estruturar as informações obtidas, incluindo tarefas como resumir, entender,relacionar, concluir, etc;• Estimular a comunicação, a discussão ou a colaboração com outrosparticipantes do curso online

      Aqui reforça-se o facto de que a realização de e-atividades constitui uma abordagem pedagógica muito rica, ao estimular o pensamento crítico, a autonomia dos alunos e o desenvolvimento de competências essenciais para uma aprendizagem ativa e significativa, especialmente em contextos de ensino online.

    2. Entre estes podemos encontrar e-atividades cujo objetivoseja o de socialização entre os participantes num determinado curso/módulo,até aquelas que permitem a transferência de conhecimentos para contextosdiferentes. Tal como se tem vindo a afirmar, as e-atividades podem revestir-se dediferentes formatos, consoante os objetivos a alcançar, o perfil de estudantes ea tecnologia que se tem ao dispor. Entre esses exemplos referimos, como formade ‘quebrar o gelo’ e de socialização online, a apresentação dos estudantesou o falarem sobre a sua motivação e a sua expectativa relativamente adeterminado módulo ou curso; a um nível de construção de conhecimento,a consulta de sites web, estudos de caso, a construção /utilização de wikis,mapas conceptuais, webquest ou weblogs, as simulações, o role play, entremuitos outros

      Pela minha experiência, os estudantes gostam das e-atividades por representarem uma novidade e por envolverem o uso de tecnologias, o que costuma ser motivador. No entanto, o meu receio prende-se com o facto de muitas dessas atividades poderem desviar-se do verdadeiro objetivo do curso. Concordo com a utilização destas estratégias como forma de "quebrar o gelo", criando oportunidades de socialização e interação entre os estudantes, ou até mesmo como elemento diferenciador. Contudo, é importante que não se tornem repetitivas ou desinteressantes, perdendo o seu impacto inicial.

    3. professor vê alargado/reformulado o seu quadro decompetência. Assim, para além dos conhecimentos inerentes às suas disciplinas– conhecimentos científicos, deverá adquirir competências ao nível tecnológico,para lidar com os instrumentos que tem à sua disposição

      Entendo que estamos em constante evolução e que, também a avaliação em ambientes digitais de aprendizagem está. Não podemos somente limitar-nos a implementar o modelo utilizado no regime presencial para o formato online. Verifica-se assim a necessidade do professor ver as suas competências sofrerem modificações conducentes, a esta evolução não apenas tecnológica como também de mediação, assumindo um papel de mediador/tutor. Vemos o professor a ter que desenvolver um conjunto alargado de novas práticas por forma a conseguir responder aos constantes desafios da educação digital, passando a carecer de um domínio sobre as novas ferramentas não só de transmissão de conhecimentos como de métodos de avaliação. Para tal, a formação nesta área é fundamental.

    4. diferentesestilos de aprendizagem

      Independentemente dos ritmos, destaco esta questão do estilo de aprendizagem, que é muito evidente por exemplo na aprendizagem de conceitos matemáticos. Explicando o mesmo conceito com recurso a várias modalidades complementares, nota-se que a cada modalidade há estudantes para quem aquela forma de contacto com o conceito é que finalmente fez o clique. Uns preferem explicações verbais, com analogias entre os novos conceitos e conceitos familiares; outros compreendem melhor quando conseguem visualizar e, melhor ainda, interagir com visualizações dos conceitos; outros ainda preferem o rigor de definições e demonstrações lógicas; e, finalmente, alguns só interiorizam a aprendizagem quando constroem algo, nomeadamente com recurso a programação. Independentemente da modalidade preferida, normalmente todos os alunos ganham um entendimento mais profundo graças à complementaridade. Os ambientes digitais prestam-se a uma integração das várias modalidades, com vantagens claras no que toca a visualizações, interacção e programação.

    5. As e-atividades podem ser concebidas de forma assíncrona ou deforma síncrona. No primeiro caso, os estudantes podem levá-las a cabo ao seupróprio ritmo, sem ser necessário estarem online ao mesmo tempo. No segundocaso, as mesmas só podem ser desenvolvidas quando todos os participantesestiverem online ao mesmo tempo, através de chats ou outro suporte quepermita a comunicação, em tempo real. Esta situação faz com que este tipode e-atividades seja menos flexível e pode levar a alguns constrangimentoscom estudantes mais introvertidos. Contrariamente, as e-atividades assíncronassão mais flexíveis, mas podem criar uma sensação de isolamento entre os seusparticipantes

      Para mim, esta é uma questão fulcral no desenho de e-atividades quando penso nos conteúdos e plano curricular do curso que lecciono e que se enquadra no domínio das ciências aplicadas. A criação de momentos síncronos e assíncronos e o seu equilibrio de forma a que haja uma fluidez de momentos interativos. Nesta microcredencial temos experiênciado que as próprias sessões síncronas não ocorrem nos mesmos dias da semana em função do desenho do curso/intuito do formador e isto por vezes cria alguns contrangimentos com os estudantes que necessitam de um curso onde haja uma previsibilidade de atividades e agenda prévia logo no início de cada semana de curso. Mas este aspeto, está mais relacionado com o tipo de estudante/formando e o seu ritmo e como as e-atividades podem ser alteradas ou redesenhadas ao longo de diferentes edições de um curso de forma a ir por um lado, ao encontro de um tipo de turma e por outro não perdendo o caráter dos conteúdos e aprendizagens que se pretendam alcançar.

    6. Na e-atividade devem estar indicados os recursos a utilizar para odesenvolvimento da mesma. Na área designada de Ações devem ser indicadoso tipo e os momentos em que se esperam as intervenções dos participantes –professores e estudantes. No caso dos professores pode ser indicado que cabeao mesmo abrir a e-atividade, formar os pares, encorajar e dar apoio e intervirregularmente nas discussões.

      Na e-atividade (online) deve ser incluídos os recursos necessários para a sua realização e indicar, de forma clara, o que se espera de cada participante/aluno. É relevante definir o que cada um faz e quando, tanto no caso dos alunos como dos professores. O docente tem um papel ativo, sendo responsável por colocar na plataforma a e-atividade, formar os grupos, incentivar a participação, dar apoio constante e fazer parte das discussões à medida que o trabalho decorre.

    7. Por outro, devem fomentar os diferentes tipos de interações. Isto é,através da e-atividades são colocadas em jogo competências diversas, quepossibilitam as aprendizagens, tanto a nível individual, como ao nível do trabalhocolaborativo, pois o contexto social e as interações com o meio são elementosessenciais à construção individual do conhecimento (Barberà, 2003)

      Ao ler este parágrafo, identifiquei, algo que enquanto professora tenho refletido, que a inserção da diversidade de metodologias de ensino aprendizagem e também no processo de avaliação, poderá ir muito mais encontro de cada aluno, na construção individual do conhecimento, e também desta forma potenciar a motivação de cada um para concretizar os seus objetivos. Tomei a liberdade de perguntar ao ChatGPT a relação entre este excerto do texto e diversidade de estratégias no processo de ensino aprendizagem. Resposta do ChatGPT "A citação de Barberà (2003) sublinha um aspeto essencial do processo educativo: o conhecimento constrói-se não apenas de forma individual, mas também através das interações sociais e do contacto com diferentes contextos. Esta visão reforça a necessidade de diversificar as estratégias de ensino-aprendizagem, especialmente em ambientes digitais ou híbridos, onde as e-atividades desempenham um papel central. Ao recorrer a diferentes tipos de atividades — como fóruns de discussão, trabalhos de grupo, projetos colaborativos, portfólios digitais ou simulações — os docentes potenciam diferentes formas de interação: aluno-conteúdo, aluno-aluno e aluno-professor. Cada uma destas interações contribui de maneira distinta para o desenvolvimento de competências cognitivas, sociais e metacognitivas. Além disso, a diversidade estratégica permite responder melhor às diferenças individuais, reconhecendo que os estudantes aprendem de maneiras variadas. Por exemplo, atividades que promovem o trabalho colaborativo favorecem o desenvolvimento de competências sociais e comunicacionais, enquanto tarefas individuais podem incentivar a reflexão crítica e a autonomia. Neste sentido, a utilização de metodologias ativas e colaborativas, apoiadas por tecnologias digitais, não só amplia as oportunidades de interação, como também torna o processo de aprendizagem mais significativo. Afinal, tal como destaca Barberà, o contexto social e a interação com o meio são determinantes para a construção individual do conhecimento — e quanto mais variadas forem essas interações, mais ricas e profundas tendem a ser as aprendizagens."

    8. A leitura do capítulo dedicado às e-atividades revela uma abordagem clara e prática sobre como desenhar boas atividades de aprendizagem em ambientes digitais. A proposta assenta numa perspetiva pedagógica centrada no estudante, com base na aprendizagem ativa, na colaboração e na construção do conhecimento através da interação com os outros e com os conteúdos. Criar boas atividades neste contexto implica, antes de tudo, uma intenção pedagógica clara: é essencial saber o que se pretende que o estudante aprenda, de que forma esse objetivo se liga ao percurso formativo e como pode ser alcançado num ambiente digital. Mais do que replicar o ensino presencial em formato online, trata-se de repensar as práticas, aproveitando o potencial da tecnologia para criar experiências de aprendizagem mais ricas e significativas. Uma boa atividade deve considerar o contexto dos alunos, as suas competências digitais, os seus interesses e diferentes ritmos de aprendizagem. Deve incluir orientações claras, tarefas bem estruturadas, prazos definidos, canais de comunicação acessíveis e critérios transparentes de avaliação. Ao mesmo tempo, deve estimular a autonomia, o pensamento crítico e a capacidade de trabalhar em colaboração. O papel do professor é essencial neste processo, não como transmissor de informação, mas como facilitador da aprendizagem. Cabe-lhe desenhar propostas desafiantes, acompanhar o progresso dos alunos, fornecer feedback regular e promover o diálogo entre todos os participantes.

    9. Este profissional deve aindater conhecimento dos princípios subjacentes ao processo de aprendizagem ecomo é que os estudantes aprendem. Ou seja, cabe ao conceptor encontraras estratégias mais adequadas para alcançar os seus objetivos, sendo que apanóplia de caminhos a selecionar é vasta.

      O impacto das tecnologias digitais no ensino e aprendizagem tem, na minha opinião, uma importância cada vez maior, sobretudo pela capacidade de flexibilizar o espaço, o tempo e o ritmo, o que é muito positivo para responder às necessidades individuais dos alunos. Estes ambientes digitais não só cativam os estudantes, incentivando a aprendizagem autónoma, como também promovem o trabalho colaborativo, uma componente fundamental no ensino atual. De acordo com Seymour Papert (1980), a tecnologia deve ser vista como uma ferramenta que apoia o estudante na construção ativa do seu conhecimento, reforçando o papel central do aluno no processo de aprendizagem. Esta perspetiva enfatiza a necessidade de ambientes que estimulem a autonomia e a criatividade. O professor assume, assim, um papel de mediador e facilitador, aproximando-se mais dos alunos para os apoiar no seu percurso. Diana Laurillard (2012) reforça esta ideia, defendendo que a tecnologia deve facilitar o diálogo entre professor e estudante, promovendo uma aprendizagem mais interativa e eficaz. É fundamental que o professor planeie cuidadosamente as atividades e escolha as estratégias pedagógicas mais adequadas ao público-alvo. Como salienta Merrill (2002), o sucesso do ensino depende do desenho instrucional e das atividades propostas, independentemente do suporte tecnológico. De igual modo, Salmon (2000) e Jonassen (1999) destacam que as estratégias devem respeitar os ritmos e estilos individuais dos alunos, promovendo a autonomia e o envolvimento ativo no processo. Desta forma, considero que, a integração das tecnologias digitais deve ser acompanhada de um planeamento pedagógico rigoroso que coloque o aluno no centro da aprendizagem e valorize a interação, a autonomia e a colaboração.

    10. No delineamento de uma e-atividade devemos começar tendo emmente o resultado da aprendizagem. Para tal, devemos procurar responder àquestão: O que é que os estudantes precisam de aprender?Tal como já referimos anteriormente, um outro elemento crucial é amotivação. Ou seja, o que faz mover para aprender? O que faz com que osestudantes queiram aprender?

      Não consigo passar sem reforçar este ponto. Parece-me ser o resumo "resumido": o que é que eu espero que os alunos aprendam; como é que eu os vou motivar para isso?

      Claro que podemos fazer outras perguntas, mas parecem-me acessórias. Onde o vou fazer, que materiais vou usar, que ambiente(s) vamos usar, etc... Mas, para mim estas questões extra apenas especificam os gostos e/ou competências particulares do professor e eveltualmente dos alunos.

    11. Tal como já referimos anteriormente, um outro elemento crucial é amotivação. Ou seja, o que faz mover para aprender? O que faz com que osestudantes queiram aprender?

      É esta para mim a pergunta que temos de fazer a toda a hora enquanto docentes de qualquer grau de ensino. O efeito da curiosidade do aluno faz mover a "montanha" da aprendizagem. Só assim conseguimos cativar os alunos a fazer esta caminhada.

    12. Elascentram-se no estudante, pois baseiam-se em pedagogias socio-construtivistas.

      Concordo em absoluto com a implementação de pedagogias socio-construtivistas porque considero a aprendizagem do aluno mas enriquecedora e significativa. Na minha perspetiva, um aluno que manifeste verdadeira motivação para aprender, consegue assimilar mais conhecimento fundamentado se for o “condutor” do seu próprio processo de aprendizagem. As e-atividades permitem que a aprendizagem se realize de forma “ativa” através do trabalho autónomo e da interação com o meio entre habitantes humanos (seja de forma síncrona ou assíncrona), assumindo o “humano professor” o papel de facilitador/orientador.

    13. Deverá ainda ter conhecimentos de didática adequada à especificidadedos contextos online. Ou seja, os conhecimentos de tipo didático utilizadospara uma aula presencial deverão ser reformulados tendo em vista estes novoscontextos (Goulão, 2012a; Barberà & Badia, 2004). A transição de um contextode ensino presencial para um contexto de ensino a distância online não é feitapela transposição ipsis verbis de um contexto para outro. Pelo contrário, cadaum implica uma didática específica, que vai além dos próprios conteúdosa lecionar. Por essa razão, é necessário que os professores que se queiramdedicar ao ensino a distância online, tenham consciência disso e procuremuma formação adequada ao desenvolvimento de competências especificas àdocência nestes ambientes

      (ANA ISABEL RODRIGUES) Como temos vindo a explorar e refletir ao longo desta formação, estamos perante uma mudança profunda no modelo didático, pedagógico e de aprendizagem. A transposição do ensino presencial para o online não pode, como bem referido no excerto, ser feita de forma “ipsis verbis”, trata-se, antes, de uma verdadeira transformação de paradigma.

      Neste sentido, a minha reflexão centra-se na necessidade de compreender que esta mudança não pode ser encarada apenas do ponto de vista do docente. Os alunos, também eles, terão de ajustar significativamente o seu perfil e postura enquanto alunos, de modo a acompanharem eficazmente este novo modelo.

      A participação no ensino online exige uma atitude mais proativa e imersiva por parte dos alunos, uma maior autonomia, envolvimento nos conteúdos e objetivos pedagógicos, capacidade de colaboração, partilha de ideias e gestão do seu próprio processo de aprendizagem. Trata-se de um compromisso mais exigente e, ao mesmo tempo, mais enriquecedor.

      Contudo, o que tenho vindo a observar é uma crescente dificuldade em identificar esse perfil nos alunos com os quais trabalho em contexto de ensino superior (em geral). Esta constatação levanta uma questão que me parece fundamental: como alinhar este novo modelo pedagógico com o perfil real dos alunos que temos diante de nós? Que estratégias devemos (re)pensar para promover essa transformação também do lado do discente?

    14. Estas atividades ou e-atividades (tratando-se de atividades desenvolvidasonline) são umas das variáveis críticas neste processo. Podemos encontrardiferentes tipologias de e-atividades e a escolha da mesma deve ser feita emfunção dos objetivos a alcançar.

      Este parágrafo chama a atenção para a importância de escolher cuidadosamente a tipologia das e-atividades em função dos objetivos de aprendizagem. Concordo que este é um ponto crítico, porque muitas vezes se começa por selecionar ferramentas ou formatos sem primeiro clarificar o que se pretende que os estudantes alcancem. Para mim, este princípio reforça que o design pedagógico deve ser orientado pelos resultados de aprendizagem esperados e não apenas pela tecnologia disponível.

    15. Cabreo e Román (2006) as e-atividades podem-se classificar, de uma formahierarquizada, tendo em conta as suas funções, em:• Socialização, são aquelas que permitem ‘quebrar o gelo’ entre osparticipantes e fomentar a noção de classe virtual – socialização online;• Aquisição de conceitos ou de vocabulário específico;• Aprofundamento de uma determinada matéria;• Transferência de conhecimentos para contextos diferentes dos utilizados;• Aplicação dos conteúdos/aprendizagens aos contextos de profissionais.

      A Socialização sem dúvida é muito importante no inicio da interação e colaboração entre estudantes. As atividades referenciadas como "quebrar o gelo" para que os estudantes tenham a noção de grupo são fundamentais como é referido. Que estratégias podemos implementar quando não funciona da forma que esperamos? Podemos reforçar? Ou simplesmente apelamos à participação? Claro, que teremos que ter sempre respeito pela individualidade e diversidade de cada estudante. E neste processo quando percebemos que alguns inscritos não participam ou não estão a conseguir acompanhar o que fazer? Claro, que no ensino de adultos a responsabilização e autonomia é fundamental, mas também sabemos que nem sempre é assim. Enquanto professor qual é realmente o meu papel? Principalmente quando percebo que não está a resultar? Mudo estratégia?....

    16. Contudo, a mediação tecnológica, per si, não é garantia do processode ensino e aprendizagem. A este propósito Ali (2004) refere que: “the delivery

      Esta ideia faz-me particular sentido: cada vez mais confirmo, na minha experiência, que não é a tecnologia a determinar o sucesso das aprendizagens, mas sim a forma como penso, planeio e dinamizo as atividades. A bibliografia da faormação (Ali, Goulão, Salmon,…) reforça o que sinto na prática: limitar-me a digitalizar rotinas presenciais raramente gera o envolvimento ou progresso real dos alunos. O verdadeiro desafio está em criar, no digital, ambientes vivos, intencionais e colaborativos, onde a tecnologia está ao serviço de relações, reflexão e crescimento, e não o contrário. Sinto que é neste compromisso pedagógico, muito mais do que nas ferramentas, que reside a qualidade do ensino online e do nosso potencial de sucesso como professores.

    17. Em suma, as e-atividades devem ter como função a estimulação dasaprendizagens profundas e do aprender a aprender. Devem ainda promover atransferência de conhecimentos entre diferentes contextos e a sua aplicação acontextos profissionais, onde os estudantes se venham a inserir

      É evidente que o envolvimento dos estudantes não depende apenas do formato, mas sobretudo da intencionalidade pedagógica. Só atividades motivadoras e bem orientadas para objetivos claros promovem aprendizagens significativas e duradouras, como tantas vezes sublinhado nas sessões síncronas.

    18. flexibilidade de escolha de recursos, que o digital propicia, remete paraum aumento de possibilidades de percursos de aprendizagem, que se devemcoadunar, não só com os objetivos, as tarefas, mas também com os diferentesestilos de aprendizagem ou ritmos dos estudantes

      Creio que a flexibilidade e a diferenciação são, de facto, dos maiores trunfos da educação digital. Ao termos como ajustar atividades, ritmos e desafios ao perfil de cada estudante, criamos condições para uma aprendizagem mais personalizada e inclusiva. No entanto, esta personalização exige bem mais de nós, professores, uma atenção permanente aos sinais da turma e a capacidade de ajustar os percursos de aprendizagem em tempo real. Como refere a literatura (Goulão, Dias & Freitas), a verdadeira personalização só acontece quando aliamos esta flexibilidade a uma planificação intencional e ao compromisso de promover o sucesso de todos os estudantes.

    19. Por último, para além destes aspetos, de acordo com Cabreo e Román(2006), a seleção de uma e-atividade deve refletir critérios que contemplem ascaracterísticas individuais dos estudantes, deve ser dada primazia às e-atividadesque potenciam o desenvolvimento de maiores competências dos estudantes eque mobilizem um maior número de capacidades, sejam mais motivadoras,não esquecendo as possibilidades que os contextos online propiciam para o seudesenvolvimento, quer em termos de recursos, quer de interações (síncronas e/ou assíncronas)

      Apesar do enorme leque de instrumentos digitais disponíveis é necessário compreender estas mesmas ferramentas e como é que elas podem contribuir para a aprendizagem dos formandos. Para tal é necessário que logo à partida o formador detenha bons conhecimentos sobre as ferramentas que irá utilizar e ter já previamente delineado os objetivos que pretende que os seus formandos atinjam para ajustar os mesmos às ferramentas disponíveis. Tem de ser tido em conta que a formação à distância pode gerar alguma dispersão pelo que é necessário compreender se os formandos se encontram motivados ou não e o que pode ser feito para os manter motivados.

    20. respeito ao impacto daintrodução das tecnologias digitais no plano de aprendizagem. Estes sistemaspermitem uma flexibilização de espaços, tempos e ritmos, que melhor respondamàs necessidades daqueles que a ele recorrem.

      Para além do respeito ao impacto das tecnologias no plano de aprendizagem, também o impacto das mesmas nos estudantes. Como disse o Prof. António Moreira em sessão síncrona: "Há quem leia 7 páginas em 10 minutos, e há quem leia em 15..." O mesmo vale para as tecnologias, e mais ainda se o corpo discente é composto por grande variabilidade etária. Para mim, a vantagem dos ambientes virtuais de aprendizagem é permitirem que adquiram não apenas o conhecimento do conteúdo da disciplina, mas também dos meios de se comunicarem nesses e em outros espaços.

    21. os estudantes e o que os motiva.

      Tendo larga experiência enquanto estudante e tendo bem reconhecidas as motivações para realizar cursos ou disciplinas, o que me intriga é como mobilizar estudantes para que sigam interessados ao longo de todo o processo de aprendizagem. Qual a medida das provocações para que a conexão não se perca, nem se torne por demais exigente?

    22. Tendo larga experiência enquanto estudante e percebendo quais as minhas motivações para a frequência em cursos e disciplinas, algo que sempre me intriga é descobrir as motivações dos estudantes para os quais estou a lecionar. Qual a medida para provocá-los até que se sintam instigados a permanecer participando até o final? Como manter seu interesse?

    23. A flexibilidade de escolha de recursos, que o digital propicia, remete paraum aumento de possibilidades de percursos de aprendizagem, que se devemcoadunar, não só com os objetivos, as tarefas, mas também com os diferentesestilos de aprendizagem ou ritmos dos estudantes, por exemplo (Martínez &Pérez, 2011; Goulão, 2012b). As e-atividades podem ter graus de complexidadecrescente e de adequação aos estudantes.

      O texto destaca a flexibilidade proporcionada pelo digital na aprendizagem; dito isto, é fundamental realçar o papel central da pessoa, a aprendizagem é para pessoas, sem elas é vazia; um som produzido numa floresta ou num deserto, que ninguém escute, nunca existiu; Do texto destaco, "estudantes" e "ritmos"; Ainda o reforço da ameaça de demasiadas ferramentas digitais, que aumentam o ruído e baixam a aprendizagem. Após a leitura do site do software hypothes.is, fico com a impressão, leitura pessoal apenas, que é apenas mais uma, mais uma árvore na florestas digital, e há o risco de desfocar do ritmo de estudo e do Estudante. A tecnologia é uma ferramenta ao serviço do humano, nunca um carril a obrigar o humano a respeitar o seu caminho.

    24. Okada, Meister e Barros (2013) destacam que aprender em colaboraçãoenvolve um processo de interação constante na resolução de problemas,desenvolvimento de projetos ou discussões sobre um determinado tópico, emque cada estudante tem definido o seu papel como um parceiro na realizaçãode aprendizagem e onde o professor participa como outro colaborador, mascom as funções de conselheiro e mediador, garantindo a efetividade da

      mantém-se fundamental repensar radicalmente o lugar da docência no nosso ensino superior. Se cada estudante é visto como pessoa parceira ativa no processo de ensino/aprendizagem, e cada docente como colaborador/a, conselheiro/ e mediador/a, que implicações reais estamos na disposição de assumir nas nossas práticas?

      Em muitas instituições, mantemos vivos modelos centrados na transmissão e no controlo, mesmo quando usamos tecnologias digitais. Chamamos-lhes “ambientes virtuais de aprendizagem”, mas quantas vezes não passam de repositórios de conteúdos ou plataformas de submissão de trabalhos?

      A ideia de “interação constante” e de “papéis definidos” exige mais do que fóruns abertos: exige intencionalidade pedagógica, responsabilização partilhada e mudança cultural — tanto em docentes como em estudantes. Mas quem está (realmente) disponível para essa mudança?

      Fica a provocação: estamos, no ensino superior, a formar estudantes para colaborarem e pensarem criticamente em rede — ou apenas a manter o hábito de seguir instruções e cumprir tarefas?

    25. “(...) As e-atividades deverão ajudar os estudantes a deixarem de ser passivos e setornarem ativos, pelo facto de que a aprendizagem não se refere exclusivamenteao armazenamento/memorização da informação, mas sim à sua reestruturaçãocognitiva; definitivamente, devemos realizar ações reais de e-learning e não dee-reading (...)” (Cabreo, 2006, p.8)

      A citação de Salmon (2002, 2019) representa um verdadeiro guia para o desenho de e-atividades significativas: aquelas que dão protagonismo ao aluno, estimulam a autonomia, a colaboração e a reflexão metacognitiva. Esta abordagem, profundamente enraizada em pedagogias socio-construtivistas, ganha ainda mais força quando cruzada com os princípios descritos por Moreira, Henriques e Barros (2020), que apontam a importância de conceber atividades digitais alinhadas com objetivos claros, motivadoras e adaptadas aos estilos de aprendizagem dos estudantes .Na minha prática como professora, sobretudo em projetos de educação STEAM, vejo esta perspetiva materializar-se quando os alunos participam ativamente em atividades como a construção de maquetes interativas do sistema solar com robôs programados em Scratch, ou quando realizam percursos matemáticos outdoor com recurso à app MathCityMap. Nestes contextos, os alunos deixam de ser meros recetores de informação e passam a ser investigadores, criadores e colaboradores. Um bom exemplo disto foi o projeto “Missão Estação Meteorológica” com o 8.º ano, em que os alunos construíram instrumentos meteorológicos, recolheram dados no terreno e discutiram os resultados em grupo. Esta atividade promoveu exatamente o que Salmon propõe: investigação, organização de conhecimento, exploração de novos recursos e partilha de ideias — sempre com o professor como orientador e facilitador do processo .É precisamente neste tipo de atividades que observo os alunos a formularem perguntas relevantes, a refletirem sobre os seus próprios erros e estratégias, e a aplicarem conceitos matemáticos ou científicos em contextos reais. Isto não seria possível se as e-atividades fossem meramente transmissivas ou desprovidas de propósito.

      Outro exemplo prático que coloco em prática com regularidade é o uso do Scratch para trabalhar conteúdos de Matemática, como as frações ou os movimentos geométricos. Quando os alunos criam um jogo ou uma animação que exige pensar logicamente, testar hipóteses e resolver problemas, estão a aplicar exatamente os critérios de qualidade apontados por Salmon: estruturam informação, tomam decisões e comunicam as suas ideias a pares. Contudo, esta abordagem também nos obriga, enquanto docentes, a repensar o nosso papel. Deixamos de ser a “fonte” e passamos a ser mediadores, o que exige mais tempo de planificação, feedback personalizado e atenção à diversidade. Mas a recompensa é visível: mais envolvimento, mais motivação, mais aprendizagens com sentido. Por isso, defendo que a qualidade de uma e-atividade não está na sofisticação tecnológica que envolve, mas na intencionalidade pedagógica com que é concebida. Tal como afirmam os autores da Universidade Aberta, “a tecnologia é o suporte, não o fim”. E cabe-nos a nós, professores, criar espaços digitais onde a curiosidade, a colaboração e a reflexão sejam cultivadas com propósito.

    26. Assim, as e-atividades devem ser concebidas e desenvolvidas de formaa garantir a motivação do estudante e, ao mesmo tempo, serem orientadaspara a prossecução de objetivos. De acordo com Salmon (2002) “The wholee-activity process should be geared towards engaging participants in activeonline learning that results in their achieving the outcomes that they and youdesire” (p. 87)

      O parágrafo apresentado sublinha um princípio essencial do design pedagógico em ambientes digitais: a necessidade de equilibrar a motivação dos estudantes com a intencionalidade dos objetivos de aprendizagem. Como refere Gilly Salmon (2002), todo o processo de e-atividade deve estar orientado para uma aprendizagem online ativa, com resultados visíveis e significativos tanto para os alunos como para os professores.

      Esta visão está profundamente alinhada com os princípios da educação digital em rede, tal como apresentados por Moreira, Henriques e Barros (2020) e desenvolvidos no capítulo 4 da obra Educação Digital em Rede (2020) . Nestes textos, as e-atividades são descritas não como simples tarefas transferidas do ensino presencial, mas como estruturas pedagógicas intencionais, desenhadas com base em pedagogias construtivistas e centradas no estudante. Refletindo enquanto professora, diria que esta abordagem exige de nós uma mudança de paradigma. Já não basta “transportar” as fichas ou os questionários para uma plataforma digital. Exige-se um novo olhar sobre o que é ensinar e aprender em rede. Como tal, é imperativo conhecer bem os nossos alunos — o que os motiva, como aprendem, com que ritmo — e usar as tecnologias como aliadas, não como fins em si mesmas. A citação de Salmon (2002) torna-se ainda mais pertinente quando consideramos que a motivação dos estudantes está intrinsecamente ligada ao sentimento de pertença e ao propósito. Quando percebem que as e-atividades têm um objetivo claro, que estão alinhadas com as suas necessidades e permitem aplicar o que aprendem, a sua participação torna-se mais ativa e significativa. Isto requer, da parte do professor, um cuidadoso trabalho de planificação, estruturação e acompanhamento — como se sublinha nos documentos analisados .Por isso, na minha prática, tento que cada e-atividade não seja apenas uma “tarefa”, mas uma oportunidade para os alunos refletirem, colaborarem e crescerem — tanto cognitivamente como pessoalmente. É esta dimensão mais humana e intencional do ensino online que acredito que devemos procurar, mesmo em ambientes mediados por tecnologia.

    27. que esperamos que os estudantes irão aprender através da realização daatividade;• como é que essa aprendizagem irá contribuir para alcançar os objetivos daunidade/tópico/tema em causa;• os estudantes e o que os motiva. Ou seja, elas devem ser concebidas tendocomo objetivo o desenvolvimento integral dos estudantes levando--os adesenvolver e a potenciar as suas competências;• as limitações decorrentes da formação e do manuseamento da tecnologiapor parte dos estudantes.

      Num tempo em que a educação se desenrola, cada vez mais, em múltiplos espaços digitais, torna-se urgente repensar a forma como concebemos e desenhamos atividades de aprendizagem em ambientes em rede. Não se trata apenas de transpor para o digital o que já se fazia em contexto presencial, mas de reinventar práticas pedagógicas, explorando o potencial colaborativo, multimodal e interativo que o online oferece. Desenhar uma boa e-atividade é, antes de tudo, um exercício de instructional thinking, em que cada decisão deve ser intencional e alinhada com os objetivos de aprendizagem. É preciso começar por uma pergunta essencial: “O que quero que os estudantes aprendam e como podem construir este conhecimento juntos, mesmo à distância?” A resposta a esta pergunta orienta a escolha de recursos, estratégias de interação e formatos de avaliação. Uma e-atividade eficaz deve assentar em três elementos essenciais. Primeiro, clareza e estrutura: o estudante precisa de perceber facilmente o que se espera dele, qual o percurso de trabalho, os prazos e critérios de sucesso. Para isso, a instrução deve ser inequívoca, organizada em etapas e apoiada em exemplos concretos. Em segundo lugar, a e-atividade deve promover uma interação significativa. Num ambiente digital em rede, aprender é tão social como em sala de aula. Fóruns de discussão bem moderados, tarefas de coautoria em documentos partilhados, ou até produções multimédia colaborativas fomentam o diálogo, o pensamento crítico e a negociação de “sentidos” entre pares. Por fim, uma boa e-atividade deve ser desafiante, envolvente e motivadora. O digital permite criar cenários autênticos, integrar recursos multimédia, gamificar processos e personalizar percursos de aprendizagem. É este potencial que torna a aprendizagem mais próxima da realidade dos estudantes, mais envolvente e, sobretudo, mais participativa. Na minha experiência, o grande segredo reside na capacidade de equilibrar autonomia e acompanhamento. O estudante deve sentir-se livre para explorar, criar e decidir, mas sabendo que o professor está presente, atento, pronto a dar feedback oportuno. É nesta presença, mesmo que mediada por ecrãs, que reside a alma de qualquer e-atividade bem concebida: garantir que ninguém fica para trás e que todos têm voz no processo.

    1. La procrastinación (del latín procrastinare: pro, 'adelante', y crastinus, 'mañana'),[2]​ postergación o posposición es la acción o hábito de retrasar actividades o situaciones que deben atenderse, sustituyéndolas por otras situaciones más irrelevantes o agradables por miedo a afrontarlas o pereza a realizarlas.

      Procrastinación es el acto de postergar ciertas actividades o tareas por tareas que nos parecen más agradable o miedo a afrontar así como por el simple hecho de tener flojera o pereza a realizarlas

    1. Jell-O was embedded in at least three major strands of Progressivism: therise of domestic science, attention to inequality, and pure food reform.

      Food reforms- making the byproducts (Jello-O) to maximize profits Reforms for women and equality Jell-O is made inexpensive and available to all --> support equality

    2. But as the scale of slaughter increased, those byproducts like gelatinwere present in sufficient quantity for the packers to find markets for theiruse, and indeed to earn the majority of their profit from the sale of these by-products.

      Jell-O was created as a by-product of the meatpacking industry to make more profits.

    3. Jell-O, the hard-to-define new dessert that emerged around 1900, was aproduct of some of the problems created by industrialization and a solution toothers. The same domestic scientists and food industry advertising executiveswho created Jell-O also molded and defined a new era in American history.

      Jell-O represents the changes and the Progression era of the American history.

    4. The rise of Jell-O is a story of the Progressive Era pure food movement inthe early twentieth century, of the development of domestic science, and ofthe emergence of food advertising and branding.

      Industrialization and progressions --> social change Pretty much summarize the theme for Jell-O

    1. Ultimately, the valuable work done inside companies is the work where human accountability is the primary bulwark against the O-ring problem. It acknowledges that complex decisions and accepted constraints need to have human owners, and that relationships between humans (notably, but obviously not exclusively, in reporting relationships) are ultimately the valuable stuff that companies are made of. Okay, so, what does this have to do with AI agents? Well, my suspicion is as follows. One, combinations of AI and Human work could be notably vulnerable to the O-Ring problem. Their utility in executing a chain of tasks is only as valuable as the weakest link, very obviously. (If there’s one hallucinatory or badly-interfaced step anywhere that isn’t caught, you have to throw out the whole thing.) Firms have self-selected for solving O-Ring problems, over decades. That’s just what firms are.  So I think you could construct a bear thesis that the closer a workflow is to core to of an average business, the less suitable agents are to help,for reasons that are readily explained by Coase’s theory to the firm. Maybe not for the very best companies, who truly master their internal agents, but for the average company to whom AI Agents doing real work is essentially a kind of outsourcing. I’m not quite as pessimistic as that, but I agree with Tyler Cowen’s post that any hope for 10% GDP growth or whatever to emerge out of existing firms (which, even in a very disruptive environment, make up most of GDP) is too hopeful.

      this part of this seems really good to me which is why it is wild it immediately goes off the rails to blockchain

    1. Reviewer #2 (Public review):

      Summary:

      The authors investigate EI balance in the CA3-CA1 projections, emphasizing synaptic depletion and the implied rebalancing of excitatory and inhibitory projections onto a single CA1 Pyramidal cell. They present physiological results with optical stimulation in CA3 and measuring various response features in CA1, showing signatures consistent with the adjustment of EI balance. In particular, the authors emphasize a transient effect where the neuron escapes from EI balance, which can be used for mismatch detection. They partially replicate these results in a computational model that looks at detailed properties of synaptic plasticity in CA1.

      Strengths:

      The authors provide compelling evidence that non-specific modulation of synaptic plasticity, combined with their differential effects on excitatory and inhibitory neurons, can be used by CA1 excitatory neurons to detect changes in the population activity of CA3 neurons. Indeed, they provide insight into the potential computational role of transient EI imbalance.

      Weaknesses:

      The authors observe that‬ "little‬‭ is‬‭ known‬‭ about‬‭ how‬‭ EI‬‭ balance‬ itself evolves dynamically due to activity-driven plasticity in sparsely active networks.‬" This is an overstatement, or better an understatement, given the extensive literature on EI balance (e.g. Wen W, Turrigiano GG. Keeping Your Brain in Balance: Homeostatic Regulation of Network Function. Ann Rev Neurosci. 2024. https://doi.org/10.1146/annurev-neuro-092523-110001 PMID:38382543). This way of framing the question does a disservice to the field and fails to contextualize the current research properly.

      The evidence is incomplete because the authors do not show a specific relationship between synaptic change in CA1 and EI balance adjustment, i.e., the alternative could be that this is an unspecific effect unrelated to the specific regulation of EI balance and its functional role in the hippocampus and the cortex. Indeed, the paper drifts from addressing EI balance to elucidating the mismatch detection. The second shortcoming is that they do not show that the stimulation of the CA3 neurons occurs in a physiologically realistic regime, nor do they analyze what the impact will be of the excitatory transient in "mismatch detection", and CA1, when this would occur at the level of the whole population, i.e., the physiological impossibility of triggering uncontrolled chaotic excitatory responses. In particular, when we consider CA3 as an attractor memory system, the range of deviations (mismatches) that a CA1 neuron can be exposed to and detect, given the model presented in this paper, might be below those generated due to CA3 pattern-completion dynamics. In addition, the match between the model and the physiological results is not fully quantified, leaving it to the reader to make a leap of faith.

      In addition, the manuscript suffers from poor analysis and presentation. The work could be improved by putting more effort into translating results into insightful metrics.

      Overall, the authors have not achieved their original aim to show that the observed phenomenon is relevant to computation in CA1 or the brain outside of a highly controlled in vitro setup and reductionist single cell model.

      The authors combine several techniques for in vitro whole-cell patch-clamp recordings with patterned optical stimulation of the CA3 network in the mouse hippocampus, which is consistent with the state-of-the-art.

      They introduce a metric of similarity between expected and observed response patterns, called gamma. The name is confusing given the wide use of the label gamma for oscillation frequencies above 20 Hz. Gamma is calculated as (E*O)/(E-O). This means that gamma approximates infinity as the difference goes to 0, to mention one of the problems. This metric is not interpretable, and it is not clear why the authors did not follow a standard approach, e.g., likelihood, correlation, or percent error.

      The authors aim to replicate the physiological results with an "abstract‬‭ model‬ of‬‭ the‬‭ hippocampal‬‭ FFEI‬‭ network. In practice, this is a conductance-based model of a single CA1 neuron, including chemical‬ kinetics-based‬‭ multi-step‬‭ neurotransmitter‬‭ vesicle‬‭ release‬‭. This is an abstraction from the FFEI network that the paper starts with. It raises the question whether this is the right level at which to model the computational impacts of EI imbalance on CA1 neurons. Given the highly reduced model they have elaborated, the generalization to the complete CA3-CA1 network that the authors suggest can be achieved in the discussion is overoptimistic. Network models of CA3 and C1 must be considered, together with afferents from the entorhinal cortex to accomplish this generalization.

      The authors reveal a potentially interesting physiological feature of CA1 excitatory neurons under very specific stimulus conditions. It could warrant follow-up studies to place EI imbalance in a physiologically realistic context.

    1. While my English skills were never judged as poor, compared tomath, English could not be considered my strong suit. In grade school I did moderately well, gettingperhaps B's, sometimes B-pluses, in English and scoring perhaps in the sixtieth or seventieth percentile o

      I think the inability of single-answer tests to capture her individuality. And this Multifaceted thinking caused her considerable distress.

    1. 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

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):*

      As stated by the authors in the introduction, the RNA-binding protein Sxl is foundational to understanding sex determination in Drosophila. Sxl has been extensively studied as the master regulator of female sex determination in the soma, where it is known to initiate an alternative splicing cascade leading to the expression of DsxF. Additionally, Sxl has been shown to be responsible for keeping X chromosome dosage compensation off in females, while males hyperactivate their X chromosome. While these roles have been well defined, the authors explore an aspect of Sxl that is quite separate from its role as master regulator of female fate. They describe Sxl-RAC, a Sxl isoform that is expressed in the male and female nervous system. Using several genomic techniques, the authors conclude that the Sxl-RAC isoform associates with chromatin in a similar pattern to the RNA polymerase II/III subunit, Polr3E, and Sxl depends on Polr3E for chromatin-association. Further, neuronal loss of Sxl causes changes in lifetime and geotaxis in a similar manner as loss of Polr3E. The work is thorough and significant and should be appropriate for publication if a few issues can be addressed.

      Major Concerns:*

      * 1) How physiological is the Sxl chromatin-association assay? As binding interactions are concentration-dependent, how similar is Sxl-DAM expression to wt Sxl expression in neurons? In addition, does the Sxl-DAM protein function as a wt Sxl protein? Does UAS-Sxl-DAM rescue any Sxl loss phenotypes?*

      Author response:

      As Reviewer 3 correctly notes, Targeted DamID relies on ribosomal re-initiation (codon slippage) to produce only trace amounts of the Dam-fusion protein. By design, this results in expression levels that are significantly lower than those of the endogenous protein. As such, the experiment can be interpreted within a near–wild-type context, rather than as an overexpression model. The primary aim of this experiment was to determine whether Sxl associates with chromatin, and our dataset provides clear evidence supporting such binding.

      2) Is Polr3E chromatin-association also dependent on Sxl? They should do the reciprocal experiment to their examination of Sxl chromatin-association in Polr3E knockdown. This might also help address point 1-if wt Sxl is normally required for aspects of Polr3E chromatin binding, then concerns about whether the Sxl-DAM chromatin-association is real or artifactual would be assuaged.

      Author response:

      This is an interesting thought, however, if Sxl were required for Polr3E recruitment to RNA Pol III, then, in most male Drosophila melanogaster cells, Polr3E would not be incorporated, and males would not be viable (as it is essential for Pol III activity). While it is possible that there could be a subtle effect on Polr3E recruitment, such an experiment, would not alter the central conclusion of our study - that Sxl is recruited to chromatin (accessory to the Pol III complex) via Polr3E.

      Minor concerns:

      * The observed Sxl loss of function phenotypes are somewhat subtle (although perhaps any behavior phenotype at all is a plus). Did they try any other behaviour assays-courtship, learning/memory, anything else at all to test nervous system function?*


      Author response:

      Given the exploratory nature of this study, we focused on broader behavioural and transcriptional assays.

      While well written, it is sometimes difficult to understand how the experiment was performed or what genotypes were used without looking into the methods sections. One example is they should describe the nature of the Sxl-DAM fusion protein clearly in the results.

      Author response:

      We will revise these sections to improve clarity and ensure there is no confusion.

      * Reviewer #1 (Significance (Required)):

      This manuscript represents a dramatic change in our thinking about the action of the Sex-lethal protein. Previously, Sxl was known as the master regulator of both sex determination and dosage compensation, and performed these roles as an RNA-binding protein affecting RNA splicing and translational regulation. Here, the authors describe a sex-non-specific role of Sxl in the male and female nervous system. Further, this activity appears independent of Sxl's RNA binding activity and instead Sxl functions as a chromatin-associating protein working with the RNA pol2/3 factor Polr3E to regulate gene expression. Thus, this represents a highly significant finding. *

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):*

      Summary: In this paper, the authors report on an unexpected activity for Sex lethal (Sxl) (a known splicing regulator that functions in sex determination and dosage compensation) in binding to chromatin. They show, using DamID, that Sxl binds to approximately the same chromatin regions as Polr3E (a subunit of RNA Pol III). They show that this binding to chromatin is unaffected by mutations in the RNA binding domains or by deletions of either N or C terminal regions of the Sxl protein. This leads the authors to conclude that Sxl must bind to chromatin through some interacting protein working through the central region of the Sxl protein. They show that Sxl binding is dependent on Polr3E function. They show that male-specific neuronal knockdown of Sxl gives similar phenotypes to knockdown of Polr3E in terms of lethality and improved negative geotaxis. They show gene expression changes with knockdown of Sxl in male adult neurons - mainly that metabolic and pigmentation genes go down in expression. They also show that expression of a previously discovered male adult specific form of Sxl (that does not have splicing activity) in the same neurons also leads to changes in gene expression, including more upregulated than downregulated tRNAs. But they don't see (or don't show) that the same tRNA genes are down with knockdown of Sxl. Nonetheless, based on these findings, they suggest that Sxl plays an important role in regulating Pol III activity through the Polr3E subunit.

      Major comments:

      *

      *To be honest, I'm not convinced that the conclusions drawn from this study are correct. The fact that every mutant form of Sxl shows the same result from the DamID labelling is a little concerning. I would like to see independent evidence of the SxlRac protein binding chromatin. *

      Do antibodies against this form (or any form) of Sxl bind chromatin in salivary gland polytene chromosomes, for example? Does Sxl from other insects where Sxl has no role in sex determination bind chromatin?


      __Author Response: __

      Regarding the reviewer’s overall concerns about the legitimacy of the Sxl binding data:

      1. i) The fold differences between Dam-Sxl-mutants and the Dam-only control are very robust (up to 9 log2 fold change (500-fold change)), which is higher than what we observe with most transcription factors using Targeted DamID.
      2. ii) We observed that Sxl binding was significantly reduced upon knockdown of Polr3E, confirming that the signal we observe is biologically specific and not due to technical noise or background. iii) If the concern relates to potential Sxl binding in non-neuronal tissues such as salivary glands, we would like to clarify that all DamID constructs were expressed under elav-GAL4, a pan-neuronal driver. Furthermore, dissections were performed to isolate larval brains, with salivary glands carefully removed. This ensures that chromatin profiles were derived from neuronal tissue exclusively.

      3. iv) Salivary gland polytene chromosome staining with a Sxl antibody in a closely related species (Drosophila virilis) show __binding of Sxl to chromatin __in both sexes (Bopp et al., 1996). We will include more text in the revised manuscript to emphasise these points.

      Do antibodies against this form (or any form) of Sxl bind chromatin in salivary gland polytene chromosomes, for example? Does Sxl from other insects where Sxl has no role in sex determination bind chromatin?

      Author Response:

      Prior work in Drosophila virilis (where Sxl is also required for sex determination and Sxl-RAC is conserved) has already demonstrated Sxl-chromatin association (using a full-length Sxl antibody) in salivary glands using polytene chromosome spreads (Bopp et al., 1996). Binding is observed in both sexes and across the genome, reflecting our observations. We will incorporate this into the revised discussion to support the chromatin-binding role of Sxl across species.

      There is a clear and long-overlooked precedent for Sxl's alternative, sex-independent roles, findings that have been largely overshadowed by the gene’s canonical function. Our study not only validates and extends these observations but also brings much-needed attention to this understudied aspect of fundamental biology.

      Bopp D, Calhoun G, Horabin JI, Samuels M, Schedl P. Sex-specific control of Sex-lethal is a conserved mechanism for sex determination in the genus Drosophila. Development. 1996 Mar;122(3):971-82. doi: 10.1242/dev.122.3.971. PMID: 8631274.

      I would like to see independent evidence of the SxlRac protein binding chromatin.

      * *__Author Response: __

      We do not believe this is necessary:

      1. i) Our data demonstrated that a large N-terminal truncation of Sxl (removing far more of the N-terminal region than is absent in Sxl-RAC) does not impair chromatin binding.
      2. ii) Our deletion experiments show that it is the central domain __of Sxl that is required for chromatin association (as removal of the N-or C-terminal domain has no effect). This central domain is __unaffected in Sxl-RAC. iii) Independent Y2H experiments have shown that it is exclusively the__ RBD-1 __(RNA binding domain 1) of the central domain of Sxl that interacts with Polr3E (Dong et al., 1999). Sxl-RAC contains this region, therefore will be recruited by Polr3E.

      iv) Review 3 also believes that this is not necessary (see cross-review below) and highlights the robustness of the Y2H experiments performed by Dong et al., 1999.

      • *

      Also, given that their DamID experiments reveal that Sxl binds half of the genes encoded in the Drosophila genome, finding that it binds around half of the tRNA genes is perhaps not surprising.


      __Author Response: __

      Our data show that Sxl binds to a range of Pol III-transcribed loci, and this binding pattern supports the proposed model that Sxl plays a broader regulatory role in Pol III activity. Within these Pol III targets, tRNA genes represent a specific and biologically relevant subset. The emphasis on tRNAs is not to suggest they are the exclusive or primary targets of Sxl, but rather to__ highlight a functionally important class of Pol III-transcribed elements__ that align with the model we are proposing. We will revise the text to better reflect this framing and avoid any confusion regarding the scope of Sxl’s binding profile.

      *I would like to see evidence beyond citing a 1999 yeast two-hybrid study that Sxl and Polr3E directly interact with one another. *


      Author response:

      We do not believe this is necessary (these points were also mentioned above):

      1. i) The Dong et al., 1999 study was highly comprehensive in its characterisation of Sxl binding to Polr3E.
      2. ii) Our DamID data provide strong complementary evidence for this interaction: knockdown of Polr3E robustly reduces Sxl’s recruitment to chromatin, strongly supporting the relevance of the interaction in vivo. iii) Review 3 highlights the robustness of the Y2H experiments performed by Dong et al., 1999.

      In my opinion, the differences in lethality observed with loss of Sxl versus control are unlikely to be meaningful given the different genetic backgrounds. The similar defects in negative geotaxis could be meaningful, but I'm unsure how often this phenotype is observed. What other class of genes affect negative geotaxis? It's a little unclear why having reduced expression of metabolic and pigment genes or of tRNAs would improve neuronal function.


      Author response:

      While the differences in survival were indeed subtle, they were statistically significant and thus warranted inclusion. Our primary aim in this section was to demonstrate that knockdown of Sxl or Polr3E results in comparable behavioural and transcriptional phenotypes, suggesting overlapping functional roles. In this context, we believe the data were presented transparently and effectively support our interpretation.

      Regarding the negative geotaxis phenotype, we appreciate the reviewer’s interest and agree that it is both intriguing and atypical. For this reason, we performed the assay multiple times, particularly in Polr3e knockdowns, to confirm the robustness of the result. To address potential confounding variables, we carefully selected control lines that account for genetic background and transgene insertion site, including KK controls and attP40-matched lines. We also employed multiple independent RNAi lines targeting Sxl to validate the phenotype across different genetic backgrounds.

      Although the observed improvement in climbing is unexpected, it is not without precedent in the RNA polymerase III field. Notably, Malik et al. (2024) demonstrated that heterozygous Polr3DEY/+ mutants exhibit a significantly delayed decline in climbing ability with age. We allude to this in the discussion and will revise the text to emphasise this connection more explicitly.

      Finally, while we recognise that negative geotaxis is a relatively broad assay and thus does not pinpoint the precise cellular mechanisms involved, we interpret the phenotype as suggesting a neural basis and a functional role for Sxl in the nervous system.

      One would expect that not just the same classes of genes would be affected by loss and overexpression of Sxl, but the same genes would be affected - are the same genes changing in opposite directions in the two experiments or just the same classes of genes. Likewise, are the same genes changing expression in the same direction with both Sxl and the Polr3E loss? Also, why are tRNA genes not also affected with Sxl loss. Finally, they describe the changes in gene expression as being in male adult neurons, but the sequencing was done of entire heads - so no way of knowing which cell type is showing differential gene expression.

      Author response:

      While we do examine gene classes, our approach also includes pairwise correlation analyses of gene expression changes between specific genotypes. Notably, we observed a significant positive correlation between Polr3e knockdowns and Sxl knockdowns, and a significant negative correlation between Sxl-RAC–expressing flies and Sxl knockdowns. Furthermore, we examined Sxl-DamID target genes within our RNA-seq datasets and found a consistent relationship between Sxl targets and genes differentially expressed in Polr3e knockdowns.

      Regarding the Pol III qPCR results, we note that tRNA expression changes may require a longer duration of RNAi induction (e.g., beyond 4 days) to become apparent, especially given that phenotypic effects such as changes in lifespan and negative geotaxis only emerge after 20 days or more. It is also plausible that Sxl knockdown leads to a partial reduction in Pol III efficiency, which may not be readily detectable through bulk Pol III qPCRs. We are willing to repeat Pol III qPCRs at later timepoints to further investigate this trend.

      Finally, we infer that gene expression changes observed in our RNA-seq data are of neuronal origin, as all knockdown and overexpression constructs used in this study were driven pan-neuronally using elav-/nSyb-GAL4. While we acknowledge that bulk RNA-seq does not provide cell-type resolution, tissue-specific assumptions are widely used in the field when driven by a relevant promoter.

      I'm also not sure what I'm supposed to be seeing in panel 5F (or in the related supplemental figure) and if it has any meaning - If they are using the Sxl-T2A-Gal4 to drive mCherry, I think one would expect to see expression since Sxl transcripts are made in both males and in females. Also, one would expect to see active protein expression (OPP staining) in most cells of the adult male brain and I think that is what is observed, but again, I'm not sure what I'm supposed to be looking at given the absence of any arrows or brackets in the figures.

      Author Response:

      Due to the presence of the T2A tag and the premature stop codon in exon 3 of early male Sxl transcripts, GAL4 expression is not expected in males unless the head-specific SxlRAC isoform is produced. The aim of panel 5F is to demonstrate the spatial overlap between SxlRAC expression (as we are examining male brains) and regions of elevated protein synthesis, as detected by OPP staining.

      To quantitatively assess this relationship, we performed colocalisation analysis using ImageJ, which showed a positive correlation between Sxl and OPP signal intensity, supporting this interpretation. It is also evident from our images that regions with lower levels of protein synthesis (such as the neuropil - as shown in independent studies Villalobos-Cantor et al., 2023) concurrently lack Sxl-related signal. We have highlighted regions in Fig. 5 exhibiting higher/lower levels of Sxl/OPP signal to better illustrate this relationship. We can also test the effects of knockdown/overexpression on general protein synthesis if required.

      Villalobos-Cantor S, Barrett RM, Condon AF, Arreola-Bustos A, Rodriguez KM, Cohen MS, Martin I. Rapid cell type-specific nascent proteome labeling in Drosophila. Elife. 2023 Apr 24;12:e83545. doi: 10.7554/eLife.83545. PMID: 37092974; PMCID: PMC10125018.

      Minor comments:

      * Line 223 - 225 - I believe that it is expected that Sxl transcripts would be broadly expressed in the male and female adult, given that it is only the spliced form of the transcript that is female specific in expression. *

      As explained above, the only isoform that will be ‘trapped’ by the T2A-GAL4 in males is the Sxl-RAC isoform (as the other isoforms contain premature stop codons). Our immunohistochemistry data indicate that Sxl-RAC is expressed in the male brain, specifically in neurons. Therefore, knockdown experiments in males will reduce all mRNA isoforms, of which, Sxl-RAC is the only one producing a protein.

      Line 236 - 238 - Sentence doesn't make sense.

      We have addressed and clarified this.

      Reviewer #2 (Significance (Required)):

      It would be significant to discover that a gene previously thought to function in only sex determination and dosage compensation also moonlights as a regulator of RNA polymerase III activity. Unfortunately, I am not convinced by the work presented in this study that this is the case.

      My expertise is in Drosophila biology, including development, transcription, sex determination, morphogenesis, genomics, transcriptomics, DNA binding

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):*

      Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Drosophila Sxl, widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species, is also a chromatin factor that can stimulate transcription by Pol III and Pol II of genes involved with metabolism and protein homeostasis, specifically some encoding tRNAs.

      The evidence for the tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments. I have a few specific comments below, all minor.*

      Scientific points: - The approach taken for the evaluation of Sxl DNA-binding activity in Fig2 is not entirely clear. I assume these are crosses of elav-Gal4 x different UAS- lines, then using males or females for UAS-Sxl-Full-Length. But what about the others? Were the experiments done in males only? This is hinted at in the main text but not explicitly indicated in the figure or the methods (at least, that I could easily find). And is this approach extended to all other experiments? Longevity? Climbing assays? Considering the role of Sxl, it may be helpful to be fastidiously systematic with this.


      Author Response:

      We have revised the wording to ensure greater clarity. Males were used for all survival and behavioural experiments (as only males can be leveraged for knocking down Sxl-RAC without affecting the canonical Sxl-F isoform).

      - In the discussion, lines 360-61, the authors say: Indeed, knockdown of Polr3E leads to a loss of Sxl binding to chromatin, suggesting a cooperative mechanism. Maybe I am misunderstanding the authors, but when I read "cooperation" in this context I think of biochemical cooperative binding. This is possible, but I do not think a simple 'requirement' test can suggest specifically that this mechanistic feature of biochemical binding is at play. I would expect, for starters, a reciprocal requirement for binding (which is not tested), and some quantitative features that would be difficult to evaluate in vivo. I do not think cooperative binding needs to be invoked anyway, as the authors do not make any specific point or prediction about it. But if they do think this is going on, I think it would need to be referred to as a speculation.


      Author Response:

      We appreciate that the original wording may have been unclear and will revise the text to more accurately reflect a functional relationship, rather than implying direct cooperation.

      - In lines 428-432, the authors discuss the ancestral role of Sxl and make a comparison with ELAV, in the context of an RNA-binding protein that has molecular functions beyond those of a splicing factor, considering the functions of ELAV in RNA stability and translation, and finishing with "suggesting that similar regulatory mechanisms may be at play". I do not understand this latter sentence. Which mechanisms are these? Are the authors referring to the molecular activities of ELAV and SXL? But what would be the similarity? SXL seems to have a dual capacity to bind RNA and protein interactors, which allows it to work both in chromatin-level regulation as well as post-transcriptionally in splicing; but ELAV seems rather to take advantage of its RNA binding function to make it work in multiple RNA-related contexts, all post-transcriptional. I do not see an obvious parallel beyond the fact that RNA binding proteins can function at different levels of gene expression regulation -- but I would not say this parallel are "similar regulatory mechanisms", so I find the whole comparison a bit confusing.


      Author Response:

      We have reduced this section, as it is largely speculative and intended to highlight potential, though indirect, links in higher organisms. Our goal was primarily to illustrate the possibility that Sxl may have an ancestral role distinct from its well-characterised function, and to suggest a potential avenue for future research into ELAV2’s involvement in chromatin or Pol III regulation.

      - One aspect of the work that I find is missing in the discussion is the possibility that the simultaneous capacity of Sxl for RNA binding and Polr3E binding: are these mutually exclusive? if so, are they competitive or hierarchical? how would they be coordinated anyway?


      Author Response:

      This is an interesting point, and we have expanded on it further in the Discussion section.

      - The only aspect of the paper where I found that one could make an experimental improvement is the claim that Sxl induces the expression of genes that have the overall effect of stimulating protein synthesis. The OPP experiment shows a correlation between the expression of Sxl and the rate of protein synthesis initiation. However, a more powerful experiment would be, rather obviously, to introduce Sxl knock-down in the same experiment, and observe whether in Sxl-expressing neurons the incorporation of OPP is reduced. I put this forth as a minor point because the tenet of the paper would not be affected by the results (though the perception of importance of the newly described function could be reinforced).

      • *

      Author Response:

      This could be a valid experiment and we are prepared to perform it if required.

      - In a similar way, it would be interesting to know whether the recruitment of Polr3E and Sxl to chromatin is co-dependent or Sxl follows Polr3E. This is also a minor point because this would possibly refine the mechanism of recruitment but does not alter the main discovery.

      Author Response:

      We have addressed a similar point for Reviewer 2 (see below) and will include a Discussion point for this:

      If Sxl were required for Polr3E recruitment to RNA Pol III, then, in most male Drosophila melanogaster cells, Polr3E would not be incorporated, and males would not be viable (as it is essential for Pol III activity). While it is possible that there could be a subtle effect on Polr3E recruitment, such an experiment, would not alter the central conclusion of our study - that Sxl is recruited to chromatin (accessory to the Pol III complex) via Polr3E.

      * Figures and reporting:

      • In Figure 2, it would be helpful to see the truncation coordinate for the N and C truncations.

      • In Figure 3D, genomic coordinates are missing.

      • In Figure 3E, the magnitude in the Y axis is not entirely clear (at least not to me). How is the amount of binding across the genome quantified? Is this the average amplitude of normalised TaDa signal across the genome? Or only within binding intervals?

      • Figure S3E-F: it would be interesting to show the degree of overlap between the downregulated genes that are also binding targets (regardless of the outcome).

      • Figure 5C-E: similarly to Figure S3, it would be interesting to know how the transcriptional effects compare with the binding targets.

      • Authors use Gehan-Breslow-Wilcoxon to test survival, which is a bit unusual, as it gives more weight to the early deaths (which are rare in most Drosophila longevity experiments). Is there any rationale behind this? It may be even favour their null hypothesis.*


      Author response:

      Thank you for the detailed feedback on our figures. We have__ incorporated__ the suggested changes.

      We agree that examining the overlap between Sxl binding sites and transcriptional changes is valuable, and we aimed to highlight this in the pie charts shown in Figures S3 and S5. If the reviewer is suggesting a more explicit quantification of the proportion of Sxl-Dam targets with significant transcriptomic changes, we are happy to include this analysis in the final version of the manuscript.

      As noted in the Methods, both Gehan–Breslow–Wilcoxon (GBW) and Kaplan–Meier tests were used. The significance in Figure 4a is specific to the GBW test, which we indicated by describing the effect as mild. Our focus here is not on the magnitude of survival differences, but on the consistent trends observed in both Polr3e and Sxl knockdowns.

      Writing and language:*

      • Introduction finishes without providing an outline of the findings (which is fine by me if that is what the authors wanted).

      • In lines 361-5, the authors say "We speculate that this interaction not only facilitates Pol III transcription but may also influence chromatin architecture and RNA Pol II-driven transcription as observed with Pol III regulation in other organisms". "This interaction" refers to Polr3E-Sxl-DNA interaction and with "Pol III transcription" I presume the authors refer to transcription executed by Pol III. I am not clear about the meaning of the end of the sentence "as observed with Pol III regulation in other organisms". What is the observation, exactly? That Pol III modifies chromatin in Pol II regulated loci, or that Pol III interactors change chromatin architecture?

      • DPE abbreviation is not introduced (and only used once).

      • A few typos: Line 41 ...splicing of the Sxl[late] transcripts, which is [ARE?] constitutively transcribed (Keyes et al.,... Line 76 ...sexes but appears restricted to the nervous system [OF] male pupae and adults (Cline et Line 289 ...and S41). To assess any effect [ON]translational output, O-propargyl-puromycin (OPP)o Line 323 ...illustrating that the majority (72%) changes in tRNA levels [ARE] due to upregulation...hi Line 402 ...it was discovered [WE DISCOVERED] Line 792 ...Sxl across chromosomes X, 2 L/R, 3 L/R and 4. The y-axis represents the log[SYMBOL] ratio... This happens in other figure legends as well.*


      Author response:

      Thank you for the detailed feedback, we have clarified and incorporated the suggested changes.

      **Referee Cross-commenting***

      Reviewer 1 asks how physiological is the Sxl chromatin-association assay. I think the loss of association in Polr3E knock-down and the lack of association of other splicing factors goes a long way into answering this question. It is true that having positive binding data specifically for Sxl-RAC and negative binding data for a deletion mutant of the RMM domain would provide more robust conclusions (see below), but I am not sure it is completely necessary -- though this will depend on which journal the authors want to send the paper to.

      I think that the comment of reviewer 1 about the levels of expression of Sxl-DAM does not apply here because of the way TaDa works - it relies on codon slippage to produce minimal amounts of the DAM fusion protein, so by construction it will be expressed at much lower levels than the endogenous protein.

      Reviewer 1 also asks whether Polr3E chromatin-association is also dependent on Sxl, to round up the model and also as a way to address whether Sxl association to chromatin is real. While I agree with this on the former aim (this would be a nice-to-have), I think I disagree on the latter; there is no need for Polr3E recruitment to depend on Sxl for Sxl association to chromatin to be physiologically relevant. Polr3E is a peripheral component of Pol III and unlikely to depend on a factor of restricted expression like Sxl to interact with chromatin. The recruitment of Sxl could well be entirely 'hierarchical' and subject to Polr3E.

      Revewer 2 is concerned with the fact that every mutant form of Sxl shows the same result from the DamID labelling. I have to agree with this to a point. A deletion mutant of RMM domains would address this. Microscopy evidence in salivary glands would be nice, certainly, but the system may not lend itself to this particular interaction, which might be short-lived and/or weak. I do not immediately see the relevance of the chromatin binding capacity of non-Drosophilidae Sxl -- though it might indicate that the impact of the discovery is less likely to go beyond this group.

      Reviewer 2 does not find surprising that some tRNA genes (less than half) are regulated by Sxl. I think the value of that observation is just qualitative, as tRNAs are Pol III-produced transcripts, but their point is correct. A hypergeometric test could settle this.

      Reviewer 2 is concerned that the evidence of direct interaction between Sxl and Polr3E is a single 1999 two-hybrid study. But that paper contains also GST pull-downs that narrow down the specific domains that mediate binding, and perform the binding in competitive salt conditions. I think it is enough. The author team, I think, are not biochemists, so finding the right collaborators and performing these experiments would take time that I am not sure is warranted.

      Reviewer 2 is also concerned that the longevity assays may not be meaningful due to the difference in genetic backgrounds. This is a very reasonable concern (which I would extend to the climbing assays - any quantitative phenotype is sensitive to genetic background). However, I think the authors here may have already designed the experiment with this in mind - the controls express untargeted RNAi constructs, but I lose track of which one is control of which. This should be clarified in Methods.

      Other comments are in line, I think, with what I have pointed out and I generally agree with everything else that has been said.

      Reviewer #3 (Significance (Required)):

      Drosophila Sxl is widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species. It is a favourite example of how splicing factors and alternative can have profound influence in biology and used cleverly in the molecular circuitry of the cell to enact elegant regulatory decisions.

      In this work, Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Sxl is also a chromatin factor with an sex-independent, neuron-specific role in stimulating transcription by Pol III and Pol II, of genes involved with metabolism and protein homeostasis, including some encoding tRNAs.

      This opens a large number of interesting biological questions that range from biochemistry, gene regulation or neurobiology to evolution. How is the simultaneous capacity of binding RNA and chromatin (with the same protein domain, RRM) regulated/coordinated? How did this dual activity evolve and which one is the ancestral one? How many other RRM-containin RNA-binding proteins can also bind chromatin? How is Sxl recruited to chromatin to both Pol II and Pol III targets and are they functionally related? If so, how is the coordination of cellular functions activated through different RNA polymerases taking place and what is the role of Sxl in this? What are the functional consequences to neuronal biology? Does this affect similarly all Sxl-expressing neurons?

      The evidence for the central tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments.*

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      *The convincing analysis demonstrates a role for the Drosophila Sex determining gene sex lethal in controlling aspects of transcription in the nervous system independent of its role in splicing. Interaction with an RNA Pol III subunit mediating Sxl association with chromatin and similar knockdown phenotypes strongly support the role of Sxl in the regulation of neuronal metabolism. Given that Sxl is an evolutionary recent acquisition for sex determination, the study may reveal an ancestral role for Sxl.

      The conclusions are well justified by the datasets presented and I have no issues with the study or the interpretation. Throughout the work is well referenced, though perhaps the authors might take a look at Zhang et al (2014) (PMID: 24271947) for an interesting evolutionary perspective for the discussion.*

      Author Response:

      Thank you for the thoughtful suggestion. We will be sure to incorporate the findings from Zhang et al. regarding the evolution of the sex determination pathway.

      *I have some minor comments for clarification:

      There is no Figure 2b, should be labelled 2 or label TaDa plots as 2b

      Clarify if Fig 2 data are larval or adult *

      *Larval

      Fig 3d - are these replicates or female and male?

      Please elaborate on tub-GAL80[ts] developmental defects

      Fig 4e, are transcriptomics done with the VDRC RNAi line? The VDRC and BDSC RNAi lines exhibit different behaviours - former has "better" survival and Better negative geotaxis, the latter seems to have poorer survival but little geotaxis effect?*

      *Fig S3 - volcano plot for Polr3E?

      Fig S4a - legend says downregulated genes?

      The discussion should at least touch on the fact that Sxl amorphs (i.e. Sxl[fP7B0] are male viable and fertile, emphasising that the newly uncovered role is not essential.*

      Author Response:

      We agree with the suggestions outlined in the comments and have made the appropriate revisions.

      Reviewer #4 (Significance (Required)):*

      A nonessential role for Sxl in the nervous system independent of sex-determination contributes to better understanding a) the evolution of sex determining mechanisms, b) the role of RNA PolIII in neuronal homeostasis and c) more widely to the neuronal aging field. I think this well-focused study reveals a hitherto unsuspected role for Sxl.*

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      Referee #3

      Evidence, reproducibility and clarity

      Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Drosophila Sxl, widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species, is also a chromatin factor that can stimulate transcription by Pol III and Pol II of genes involved with metabolism and protein homeostasis, specifically some encoding tRNAs.

      The evidence for the tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments. I have a few specific comments below, all minor.

      Scientific points:

      • The approach taken for the evaluation of Sxl DNA-binding activity in Fig2 is not entirely clear. I assume these are crosses of elav-Gal4 x different UAS- lines, then using males or females for UAS-Sxl-Full-Length. But what about the others? Were the experiments done in males only? This is hinted at in the main text but not explicitly indicated in the figure or the methods (at least, that I could easily find). And is this approach extended to all other experiments? Longevity? Climbing assays? Considering the role of Sxl, it may be helpful to be fastidiously systematic with this.
      • In the discussion, lines 360-61, the authors say: Indeed, knockdown of Polr3E leads to a loss of Sxl binding to chromatin, suggesting a cooperative mechanism. Maybe I am misunderstanding the authors, but when I read "cooperation" in this context I think of biochemical cooperative binding. This is possible, but I do not think a simple 'requirement' test can suggest specifically that this mechanistic feature of biochemical binding is at play. I would expect, for starters, a reciprocal requirement for binding (which is not tested), and some quantitative features that would be difficult to evaluate in vivo. I do not think cooperative binding needs to be invoked anyway, as the authors do not make any specific point or prediction about it. But if they do think this is going on, I think it would need to be referred to as a speculation.
      • In lines 428-432, the authors discuss the ancestral role of Sxl and make a comparison with ELAV, in the context of an RNA-binding protein that has molecular functions beyond those of a splicing factor, considering the functions of ELAV in RNA stability and translation, and finishing with "suggesting that similar regulatory mechanisms may be at play". I do not understand this latter sentence. Which mechanisms are these? Are the authors referring to the molecular activities of ELAV and SXL? But what would be the similarity? SXL seems to have a dual capacity to bind RNA and protein interactors, which allows it to work both in chromatin-level regulation as well as post-transcriptionally in splicing; but ELAV seems rather to take advantage of its RNA binding function to make it work in multiple RNA-related contexts, all post-transcriptional. I do not see an obvious parallel beyond the fact that RNA binding proteins can function at different levels of gene expression regulation -- but I would not say this parallel are "similar regulatory mechanisms", so I find the whole comparison a bit confusing.
      • One aspect of the work that I find is missing in the discussion is the possibility that the simultaneous capacity of Sxl for RNA binding and Polr3E binding: are these mutually exclusive? if so, are they competitive or hierarchical? how would they be coordinated anyway?
      • The only aspect of the paper where I found that one could make an experimental improvement is the claim that Sxl induces the expression of genes that have the overall effect of stimulating protein synthesis. The OPP experiment shows a correlation between the expression of Sxl and the rate of protein synthesis initiation. However, a more powerful experiment would be, rather obviously, to introduce Sxl knock-down in the same experiment, and observe whether in Sxl-expressing neurons the incorporation of OPP is reduced. I put this forth as a minor point because the tenet of the paper would not be affected by the results (though the perception of importance of the newly described function could be reinforced).
      • In a similar way, it would be interesting to know whether the recruitment of Polr3E and Sxl to chromatin is co-dependent or Sxl follows Polr3E. This is also a minor point because this would possibly refine the mechanism of recruitment but does not alter the main discovery.

      Figures and reporting:

      • In Figure 2, it would be helpful to see the truncation coordinate for the N and C truncations.
      • In Figure 3D, genomic coordinates are missing.
      • In Figure 3E, the magnitude in the Y axis is not entirely clear (at least not to me). How is the amount of binding across the genome quantified? Is this the average amplitude of normalised TaDa signal across the genome? Or only within binding intervals?
      • Figure S3E-F: it would be interesting to show the degree of overlap between the downregulated genes that are also binding targets (regardless of the outcome).
      • Figure 5C-E: similarly to Figure S3, it would be interesting to know how the transcriptional effects compare with the binding targets.
      • Authors use Gehan-Breslow-Wilcoxon to test survival, which is a bit unusual, as it gives more weight to the early deaths (which are rare in most Drosophila longevity experiments). Is there any rationale behind this? It may be even favour their null hypothesis.

      Writing and language:

      • Introduction finishes without providing an outline of the findings (which is fine by me if that is what the authors wanted).
      • In lines 361-5, the authors say "We speculate that this interaction not only facilitates Pol III transcription but may also influence chromatin architecture and RNA Pol II-driven transcription as observed with Pol III regulation in other organisms". "This interaction" refers to Polr3E-Sxl-DNA interaction and with "Pol III transcription" I presume the authors refer to transcription executed by Pol III. I am not clear about the meaning of the end of the sentence "as observed with Pol III regulation in other organisms". What is the observation, exactly? That Pol III modifies chromatin in Pol II regulated loci, or that Pol III interactors change chromatin architecture?
      • DPE abbreviation is not introduced (and only used once).
      • A few typos: Line 41 ...splicing of the Sxl[late] transcripts, which is [ARE?] constitutively transcribed (Keyes et al.,... Line 76 ...sexes but appears restricted to the nervous system [OF] male pupae and adults (Cline et Line 289 ...and S41). To assess any effect [ON]translational output, O-propargyl-puromycin (OPP)o Line 323 ...illustrating that the majority (72%) changes in tRNA levels [ARE] due to upregulation...hi Line 402 ...it was discovered [WE DISCOVERED] Line 792 ...Sxl across chromosomes X, 2 L/R, 3 L/R and 4. The y-axis represents the log[SYMBOL] ratio... This happens in other figure legends as well.

      Referee Cross-commenting

      Reviewer 1 asks how physiological is the Sxl chromatin-association assay. I think the loss of association in Polr3E knock-down and the lack of association of other splicing factors goes a long way into answering this question. It is true that having positive binding data specifically for Sxl-RAC and negative binding data for a deletion mutant of the RMM domain would provide more robust conclusions (see below), but I am not sure it is completely necessary -- though this will depend on which journal the authors want to send the paper to.

      I think that the comment of reviewer 1 about the levels of expression of Sxl-DAM does not apply here because of the way TaDa works - it relies on codon slippage to produce minimal amounts of the DAM fusion protein, so by construction it will be expressed at much lower levels than the endogenous protein.

      Reviewer 1 also asks whether Polr3E chromatin-association is also dependent on Sxl, to round up the model and also as a way to address whether Sxl association to chromatin is real. While I agree with this on the former aim (this would be a nice-to-have), I think I disagree on the latter; there is no need for Polr3E recruitment to depend on Sxl for Sxl association to chromatin to be physiologically relevant. Polr3E is a peripheral component of Pol III and unlikely to depend on a factor of restricted expression like Sxl to interact with chromatin. The recruitment of Sxl could well be entirely 'hierarchical' and subject to Polr3E.

      Revewer 2 is concerned with the fact that every mutant form of Sxl shows the same result from the DamID labelling. I have to agree with this to a point. A deletion mutant of RMM domains would address this. Microscopy evidence in salivary glands would be nice, certainly, but the system may not lend itself to this particular interaction, which might be short-lived and/or weak. I do not immediately see the relevance of the chromatin binding capacity of non-Drosophilidae Sxl -- though it might indicate that the impact of the discovery is less likely to go beyond this group.

      Reviewer 2 does not find surprising that some tRNA genes (less than half) are regulated by Sxl. I think the value of that observation is just qualitative, as tRNAs are Pol III-produced transcripts, but their point is correct. A hypergeometric test could settle this.

      Reviewer 2 is concerned that the evidence of direct interaction between Sxl and Polr3E is a single 1999 two-hybrid study. But that paper contains also GST pull-downs that narrow down the specific domains that mediate binding, and perform the binding in competitive salt conditions. I think it is enough. The author team, I think, are not biochemists, so finding the right collaborators and performing these experiments would take time that I am not sure is warranted.

      Reviewer 2 is also concerned that the longevity assays may not be meaningful due to the difference in genetic backgrounds. This is a very reasonable concern (which I would extend to the climbing assays - any quantitative phenotype is sensitive to genetic background). However I think the authors here may have already designed the experiment with this in mind - the controls expres untargeted RNAi constructs, but I lose track of which one is control of which. This should be clarified in Methods.

      Other comments are in line, I think, with what I have pointed out and I generally agree with everything else that has been said.

      Significance

      Drosophila Sxl is widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species. It is a favourite example of how splicing factors and alternative can have profound influence in biology and used cleverly in the molecular circuitry of the cell to enact elegant regulatory decisions.

      In this work, Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Sxl is also a chromatin factor with an sex-independent, neuron-specific role in stimulating transcription by Pol III and Pol II, of genes involved with metabolism and protein homeostasis, including some encoding tRNAs.

      This opens a large number of interesting biological questions that range from biochemistry, gene regulation or neurobiology to evolution. How is the simultaneous capacity of binding RNA and chromatin (with the same protein domain, RRM) regulated/coordinated? How did this dual activity evolve and which one is the ancestral one? How many other RRM-containin RNA-binding proteins can also bind chromatin? How is Sxl recruited to chromatin to both Pol II and Pol III targets and are they functionally related? If so, how is the coordination of cellular functions activated through different RNA polymerases taking place and what is the role of Sxl in this? What are the functional consequences to neuronal biology? Does this affect similarly all Sxl-expressing neurons?

      The evidence for the central tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments.

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

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      Reply to the reviewers

      Reply to the Reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Reviewer #1

      1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of CTCF-binding motif sequence and RAD21-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-253, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

      __2. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. __

      Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 435 and 829: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      3. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). We found that the DNA-binding sites of the insulator-associated DBPs were statistically overrepresented in the 5 kb boundary sites more than other DBPs (Fig. 4d). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2. We also observed the directional biases of CTCF, RAD21, and SMC3 by using Micro-C chromatin interaction data as we estimated, but the directionality was more apparent to distinguish the differences between the four directions of FR, RF, FF, and RR using CTCF-mediated ChIA-pet chromatin interaction data (lines 287 and 288).

       I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. *Cell* 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay, and include less long-range interactions due to distance bias. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study.
      
       I discussed other causes in lines 614-622: Another reason for the difference may be that boundary sites are more closely associated with topologically associated domains (TADs) of chromosome than are insulator sites. Boundary sites are regions identified based on the separation of numerous chromatin interactions. On the other hand, we found that the multiple DNA-binding sites of insulator-associated DNA-binding proteins were located close to each other at insulator sites and were associated with distinct nested and focal chromatin interactions, as reported by Micro-C assay. These interactions may be transient and relatively weak, such as tissue/cell type, conditional or lineage-specific interactions.
      
       Furthermore, I have added the statistical summary of the analysis in lines 372-395 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.
      

      4. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 405 - 412: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      6. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

      Reviewer #2

      1. Introduction, line 95: CTCF appears two times, it seems redundant.

      On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

       I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.
      

      3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 501: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

       In Aljahani A et al. *Nature Communications* 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. *Nature Genetics* 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.
      
      I added the following sentence on lines 569-577: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.
      
       FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. *Molecular Cell* 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. *Nucleic acids research* 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 556: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.
      

      7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

       The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. *Nature Genetics* 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. *Proc Natl Acad Sci USA* 2021 ; Ortabozkoyun H et al. *Nature genetics* 2022 ; Wang R et al. *Nature communications* 2022). I have added the following sentences on lines 571-575: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 582-584: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.
      
       As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. *Nature* 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. *Nature Reviews Molecular Cell Biology* 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. *EMBO Journal* 2024). I have added the following sentences on lines 543-547: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 577-582: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.
      
       Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. *Elife* 2024) (Ke W et al. *Elife* 2024) (Fujioka M et al. *PLoS Genetics* 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 559-567: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.
      

      8. Do the authors think that the identified DBPs could work in that way as well?

      The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

       Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. *Nucleic Acids Research* 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. *Cell Reports* 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 554: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.
      

      9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 584-590: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 539 - 543 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

       To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the *Drosophila even skipped *locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. *PLoS Genetics* 2016).
      

      Reviewer #3

      Major Comments:

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 257 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 929 - 931 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 974 - 976 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 356 - 360: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 593-597: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      Minor Comments:

      1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., ____https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2____or ____https://pubmed.ncbi.nlm.nih.gov/37486787____/). The authors should discuss how that would impact their results.

      The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, although the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

       As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.
      

      2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

       Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 623-628: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.
      
       Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.
      
    1. Alas, alas! Help, help! my lady's dead! O, well-a-day, that ever I was born!

      The Nurse finds Juliet unresponsive and thinks she is really dead, not knowing that Juliet only took a sleeping potion.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1:

      (1) Developmental time series:

      It was not entirely clear how this experiment relates to the rest of the manuscript, as it does not compare any effects of transport within or across species.

      Implemented Changes:  

      The importance of species arrival timing for community assembly is addressed in both the introduction and discussion. To accommodate the reviewer’s concerns and further emphasize this point, we have added a clarifying sentence to the results section and included an illustrative example with supporting literature in the discussion.

      Results: Clarifying the timing of initial microbial colonization is essential for determining whether and how priority effects mediate community assembly of vertically transmitted microbes in early life, or whether these microbes arrive into an already established microbial landscape. We used non-sterile frogs of our captive laboratory colony (…)

      Discussion: For example, early microbial inoculation has been shown to increase the relative abundance of beneficial taxa such as Janthinobacterium lividum (Jones et al., 2024), whereas efforts to introduce the same probiotic into established adult communities have not led to long-term persistence (Bletz, 2013; Woodhams et al., 2016).  

      (2) Cross-foster experiment:

      The "heterospecific transport" tadpoles were manually brushed onto the back of the surrogate frog, while the "biological transport" tadpoles were picked up naturally by the parent. It is a little challenging to interpret the effect of caregiver species since it is conflated with the method of attachment to the parent. I noticed that the uptake of Os-associated microbes by Os-transported tadpoles seemed to be higher than the uptake of Rv-associated microbes by Rv-associated tadpoles (comparing the second box from the left to the rightmost boxplot in panel S2C). Perhaps this could be a technical artifact if manual attachment to Os frogs was more efficient than natural attachment to Rv frogs.

      I was also surprised to see so much of the tadpole microbiome attributed to Os in tadpoles that were not transported by Os frogs (25-50% in many cases). It suggests that SourceTracker may not be effectively classifying the taxa.

      Implemented Changes:  

      Methods (Study species, reproductive strategies and life history): Oophaga sylvatica (Os) (Funkhouser, 1956; CITES Appendix II, IUCN Conservation status: Near Threatened) is a large, diurnal poison frog (family Dendrobatidae) inhabiting lowland and submontane rainforests in Colombia and Ecuador. While male Os care for the clutch of up to seven eggs, females transport 1-2 tadpoles at a time to water-filled leaf axils where tadpoles complete their development (Pašukonis et al., 2022; Silverstone, 1973; Summers, 1992). Notably, females return regularly to these deposition sites to provision their offspring with unfertilized eggs.

      Discussion: Most poison frogs transport tadpoles on their backs, but the mechanism of adherence remains unclear. Similar to natural conditions, tadpoles that are experimentally placed onto a caregiver’s back also gradually adhere to the dorsal skin, where they remain firmly attached for several hours as the adult navigates dense terrain. Although transport durations were standardized, species-specific factors- such as microbial density at the contact site, microbial taxa identity, and skin physiology such as moisture -could influence microbial transmission between the transporting frog and the tadpole. While these differences may have contributed to varying transmission efficacies observed between the two frog species in our experiment, none of these factors should compromise the correct microbial source assignment. We thus conclude that transporting frogs serve as a source of microbiota for transported tadpoles. However, further studies on species-specific physiological traits and adherence mechanisms are needed to clarify what modulates the efficacy of microbial transmission during transport, both under experimental and natural conditions.  

      Methods (Vertical transmission): Cross-fostering tadpoles onto non-parental frogs has been used previously to study navigation in poison frogs (Pašukonis et al., 2017). According to our experience, successful adherence to both parent and heterospecific frogs depends on the developmental readiness of tadpoles, which must have retracted their gills and be capable of hatching from the vitelline envelope through vigorous movement. Another factor influencing cross-fostering success is the docility of the frog during initial attachment, as erratic movements easily dislodge tadpoles before adherence is established. Rv are small, jumpy frogs that are easily stressed by handling, making experimental fostering of tadpoles—even their own— impractical. Therefore, we favored an experimental design where tadpoles initiate natural transport and parental frogs pick them up with a 100% success rate. We chose the poison frog Os as foster frogs because adults are docile, parental care in this species involves transporting tadpoles, and skin microbial communities differ from Rv- a critical prerequisite for our SourceTracker analysis. The use of the docile Os as the foster species enabled a 100% cross-fostering success rate, with no notable differences in adherence strength after six hours.

      Methods (Sourcetracker Analysis): To assess training quality, we evaluated model selfassignment using source samples. We selected the model trained on a dataset rarefied to the read depth of the adult frog sample with the lowest read count (48162 reads), as it showed the best overall self-assignment performance, whereas models trained on datasets rarefied to the lowest overall read depth performed worse. Unlike studies using technical replicates, our source samples represent distinct biological individuals and sampling timepoints, where natural microbiome variability is expected within each source category. Consequently, we considered self-assignment rates above 70% acceptable. All source samples were correctly assigned to their respective categories (Rv, Os, or control), but with varying proportions of reads assigned as 'Unknown'. Adult frog sources were reliably selfidentified with high confidence (Os: 97.2% median, IQR = 1.4; Rv: 76.3% median, IQR = 38.1). Adult R. variabilis frogs displayed a higher proportion of 'Unknown' assignments compared to O. sylvatica, likely reflecting greater biological variability among individuals and/or a higher proportion of rare taxa not well captured in the training set. The control tadpole source showed lower self-assignment accuracy (median = 30.5%, IQR = 17.1), as expected given the low microbial biomass of these samples, which resulted in low read depth. Low readdepth limits the information available to inform the iterative updating steps in Gibbs sampling and reduces confidence in source assignments. We therefore verified the robustness of our results by performing the second Sourcetracker analysis as described above, training the model only on adult sources and assigning all tadpoles, including lowbiomass controls, as sinks (as described above). Self-assignment rates for the second training set varied (O. sylvatica: 79.2% median, IQR = 29; R. variabilis: 96.6% median, IQR = 3.7), while results remained consistent across analyses, supporting the reliability of our findings.

      (3) Cross-species analysis:

      Like the developmental time series, this analysis doesn't really address the central question of the manuscript. I don't think it is fair for the authors to attribute the difference in diversity to parental care behavior, since the comparison only includes n=2 transporting species and n=1 non-transporting species that differ in many other ways. I would also add that increased diversity is not necessarily an expectation of vertical transmission. The similarity between adults and tadpoles is likely a more relevant outcome for vertical transmission, but the authors did not find any evidence that tadpole-adult similarity was any higher in species with tadpole transport. In fact, tadpoles and adults were more similar in the non-transporting species than in one of the transporting species (lines 296-298), which seems to directly contradict the authors' hypothesis. I don't see this result explained or addressed in the Discussion.

      To address the reviewer’s concerns, we implemented the following changes:  

      Results:

      We rephrased the following sentence from the results part:  

      “These variations may therefore be linked to differing reproductive traits: Af and Rv lay terrestrial egg clutches and transport hatchlings to water, whereas Ll, a non-transporting species, lays eggs directly in water.”

      To read

      “These variations may therefore reflect differences in life history traits among the three species.”

      We moved the information on differing reproductive strategies into the Discussion, where it contributes to a broader context alongside other life history traits that may influence community diversity.

      Discussion (1): We added to our discussion that increased microbial diversity was not an expected outcome of vertical transmission.

      “However, increased microbial diversity is not a known outcome of vertical transmission, and further studies across a broader range of transporting and non-transporting species are needed to assess the role of transport in shaping diversity of tadpole-associated microbial communities.”

      Discussion (2): Likewise, communities associated with adults and tadpoles of transporting species were no more similar than those of non-transporting species. While poison frog tadpoles do acquire caregiver-specific microbes during transport, most of these microbes do not persist on the tadpoles' skin long-term. This pattern can likely be attributed to the capacity of tadpole skin- and gut microbiota to flexibly adapt to environmental changes (Emerson & Woodley, 2024; Santos et al., 2023; Scarberry et al., 2024). It may also reflect the limited compatibility of skin microbiota from terrestrial adults with aquatic habitats or tadpole skin, which differs structurally from that of adults (Faszewski et al., 2008). As a result, many transmitted microbes are probably outcompeted by microbial taxa continuously supplied by the aquatic environment. Interestingly, microbial communities of the non-transporting Ll were more similar to their adult counterparts than those of poison frogs. This pattern might reflect differences in life history among the species. While adult Ll commonly inhabit the rock pools where their tadpoles develop, adults of the two poison frog species visit tadpole nurseries only sporadically for deposition. These differences in habitat use may result in adult Ll hosting skin microbiota that are better adapted to aquatic environments as compared to Rv and Af. Additionally, their presence in the tadpoles’ habitat could make Ll a more consistent source of microbiota for developing tadpoles.

      (4) Field experiment: The rationale and interpretation of the genus-level network are not clear, and the figure is not legible. What does it mean to "visualize the microbial interconnectedness" or to be a "central part of the community"? The previous sentences in this paragraph (lines 337-343) seem to imply that transfer is parent-specific, but the genuslevel network is based on the current adult frogs, not the previous generation of parents that transported them. So it is not clear that the distribution or co-distribution of these taxa provides any insight into vertical transmission dynamics.

      Implemented Changes:  

      We appreciate the reviewer’s close reading and understand how the inclusion of the network visualization without further clarification may have led to confusion. To clarify, the network was constructed from all adult frogs in the population, including—but not limited to—the parental frogs examined in the field experiment. We do not make any claims about the origin of the microbial taxa found on parental frogs. Rather, our aim was to illustrate how genera retained on tadpoles (following potential vertical transmission) contribute to the skin microbial communities of adult frogs of this population beyond just the parental individuals. This finding supports the observation that these retained taxa are generally among the most abundant in adult frogs. However, since this information is already presented in Table S8 and the figure is not essential to the main conclusions, we have removed Supplementary Figure S5 and the accompanying sentence: “A genus-level network constructed from 44 adult frogs shows that the retained genera make up a central part of the community of adult Rv in wild populations (Fig. S5).” We have adjusted the Methods section accordingly.

      Reviewer #2:

      I did not find any major weaknesses in my review of this paper. The work here could potentially benefit from absolute abundance levels for shared ASVs between adults and tadpoles to more thoroughly understand the influences of vertical transmission that might be masked by relative abundance counts. This would only be a minor improvement as I think the conclusions from this work would likely remain the same, however.

      In response to the reviewer’s suggestion, we estimated the absolute abundance of specific ASVs for all samples of tadpoles in which Sourcetracker identified shared ASVs between adults and tadpoles. The resulting scaled absolute abundance values (in copies/μL and copies per tadpole) are provided in Table S10, and a description of the method has been incorporated into the revised Methods section of the manuscript. To support the robustness of this approach in our dataset, we additionally designed an ASV-specific system for ASV24902-Methylocella. Candidate primers were assessed for specificity by performing local BLASTn alignments against the full set of ASV sequences identified in the respective microbial communities of tadpoles. We optimized the annealing temperature via gradient PCR and confirmed primer specificity through Sanger sequencing of the PCR product (Forward: 5′–GAGCACGTAGGCGGATCT–3′ Reverse: 5′–GGACTACNVGGGTWTCTAAT–3′). Using this approach, we confirmed that the relative abundance of ASV24902 (18.05% in the amplicon sequencing data) closely matched its proportion of the absolute 16S rRNA copy number in transported tadpole 6 (18.01%). While we intended to quantify all shared ASVs, we were limited to this single target due to insufficient material for optimizing the assays. As this particular ASV was also detected in the water associated with the same tadpole, we chose not to include this confirmation in the manuscript. Nevertheless, the close match supports the reliability of our approach for scaling absolute abundances in this dataset.

      Results: Absolute abundances of shared ASVs likely originating from the parental source pool (as identified by Sourcetracker) after one month of growth ranged from 7804 to 172326 copies per tadpole (Table S10).

      Methods: Quantitative analysis of 16S rRNA copy numbers with digital PCR (dPCR)

      Absolute abundances were estimated for ASVs that were shared between tadpoles after a one-month growth period and their respective caregivers, and for which Sourcetracker analysis identified the caregiver as a likely source of microbiota. We followed the quantitative sequencing framework described by Barlow et al. (2020), measuring total microbial load via digital PCR (dPCR) with the same universal 16S rRNA primers used to amplify the v4 region in our sequencing dataset. Absolute 16S rRNA copy numbers obtained from dPCR were then multiplied by the relative abundances from our amplicon sequencing dataset to calculate ASV-specific scaled absolute abundances. All dPCR reactions were carried out on a QIAcuity Digital PCR System (Qiagen) using Nanoplates with a 8.5K partition configuration, using the following cycling program: 95°C for 2 minutes, 40 cycles of 95°C for 30 seconds and 52°C for 30 seconds and 72°C for 1 minute, followed by 1 cycle of 40°C for 5 minutes. Reactions were prepared using the QIAcuity EvaGreen PCR Kit (Qiagen, Cat. No. 250111) with 2 µL of DNA template per reaction, following the manufacturer's protocol, and included a negative no-template control and a cleaned and sequenced PCR product as positive control. Samples were measured in triplicates and serial dilutions were performed to ensure accurate quantification. Data were processed with the QIAcuity Software Suite (v3.1.0.0). The threshold was set based on the negative and positive controls in 1D scatterplots. We report mean copy numbers per microliter with standard deviations, correcting for template input, dPCR reaction volume, and dilution factor. Mean copy numbers per tadpole were additionally calculated by accounting for the DNA extraction (elution) volume.  

      Recommendations for the authors:

      Reviewer #1:

      (1) Figure 1b summarizes the ddPCR data as a binary (detected/not detected), but this contradicts the main text associated with this figure, which describes bacteria as present, albeit in low abundances, in unhatched embryos (lines 145-147). Could the authors keep the diagram of tadpole development, which I find very useful, but add the ddPCR data from Figure S1c instead of simply binarizing it as present/absent?

      We appreciate the reviewer’s positive feedback on the clarity of the figure. We agree that presenting the ddPCR data in a more quantitative manner provides a more accurate representation of bacterial abundance across developmental stages. In response, we have retained the developmental diagram, as suggested, and replaced the binary (detected/not detected) information in Figure 1B with rounded mean values for each stage. To complement this, we have included mean values and standard deviations in Table S1. The corresponding text in the main manuscript and legends has been revised accordingly to reflect these changes.  

      (2) More information about the foster species, Oophaga sylvatica, would be helpful. Are they sympatric with Rv? Is their transporting behavior similar to that of Rv?

      We thank the reviewer for this helpful comment. In response, we have added further details on the biology and parental care behavior of Oophaga sylvatica, including information on its distribution range. The species does not overlap with Ranitomeya variabilis at the specific study site where the field work was conducted, although the species are sympatric in other countries. These additions have been incorporated into the Methods section under "Study species, reproductive strategies, and life history."  

      (3) Plotting the proportion of each tadpole microbiome attributed to R. variabilis and the proportion attributed to O. sylvatica on the same plot is confusing, as these points are nonindependent and there is no way for the reader to figure out which points originated from the same tadpole. I would suggest replacing Figure 1D with Figure S2C, which (if I understand correctly) displays the same data, but is separated according to source.

      We agree with the reviewer that Figure S2C allows for clearer interpretation of our results. In response, we implemented the suggested change and replaced Figure 1D with the alternative visualization previously shown in Figure S2C, which displays the same data separated by source. To provide readers with a complementary overview of the full dataset, we have retained the original combined plot in the supplementary material as Figure S2D.

      (4) On the first read, I found the use of "transport" in the cross-fostering experiment confusing until I understood that they weren't being transported "to" anywhere in particular, just carried for 6 hours. A change of phrasing might help readers here.

      We acknowledge the reviewer’s concern and have replaced “transported” with “carried” to avoid confusion for readers who may be unfamiliar with the behavioral terminology. However, because “transport” is the term widely used by specialists to describe this behavior, we now introduce it in the context of the experimental design with the following phrasing:

      “For this design, sequence-based surveys of amplified 16S rRNA genes were used to assess the composition of skin-associated microbial communities on tadpoles and their adult caregivers (i.e., the frogs carrying the tadpoles, typically referred to as ‘transporting’ frogs).”

      (5) "Horizontal transfer" typically refers to bacteria acquired from other hosts, not environmental source pools (line 394).

      We addressed this concern by rephrasing the sentence in the Discussion to avoid potential confusion. The revised text now reads:

      “Across species, newborns might acquire bacteria not only through transfer from environmental source pools and other hosts (…)”  

      (6) The authors suggest that tadpole transport may have evolved in Rv and Af to promote microbial diversity because "increased microbial diversity is linked to better health outcomes" (lines 477-479). It is often tempting to assume that more diversity is always better/more adaptive, but this is not universally true. The fact that the Ll frogs seem to be doing fine in the same environment despite their lower microbiome diversity suggests that this interpretation might be too far of a reach based on the data here.

      We appreciate the reviewer’s concern, agree that increased microbial diversity is not inherently advantageous and have revised the paragraph to make this clearer.  

      “While increased microbial diversity is not inherently advantageous, it has been associated with beneficial outcomes such as improved immune function, lower disease risk, and enhanced fitness in multiple other vertebrate systems.”

      However, rather than claiming that greater diversity is always advantageous, we suggest that this possibility should not be excluded and consider it a relevant aspect of a comprehensive discussion. We also note that whether poison frog tadpoles perform equally well with lower microbial diversity remains an open question. Drawing such conclusions would require experimental validation and cannot be inferred from comparisons with an evolutionarily distant species that differs in life history.

      Reviewer #2:

      (1) Figure 2: Are the data points in C a subset (just the tadpoles for each species) of B? The numbers look a little different between them. The number of observed ASVs in panel B for Rv look a bit higher than the observed ASVs in panel C.

      The data shown in panel C are indeed a subset of the samples presented in panel B, focusing specifically on tadpoles of each species. The slight differences in the number of observed ASVs between panels result from differences in rarefaction depth between comparisons: due to variation in sequencing depth across species and life stages, we performed rarefaction separately for each comparison in order to retain the highest number of taxa while ensuring comparability within each group. Although we acknowledge that this is not a standard approach, we found that results were consistent when rarefying across the full dataset, but chose the presented approach to better accommodate variation in our sample structure. This methodological detail is described in the Methods section:

      “All alpha diversity analyses were conducted with datasets rarefied to 90% of the read number of the sample with the fewest reads in each comparison and visualized with boxplots.”

      It is also noted in the figure legend: “The dataset was separately rarefied to the lowest read depth f each comparison.” We hope this clarification adequately addresses the reviewer’s concern and therefore have not made additional changes.

      (2) Lines 304-305: in the Figure 4B plot, there appear to be 12 transported tadpoles and 8 non-transported tadpoles.

      Thank you for catching this. We have corrected the plot and the associated statistics (alpha and beta diversity) in the results section as well as in the figure. Importantly, the correction did not affect any other results, and the overall findings and interpretations remain unchanged.  

      (3) Line 311: I think this should be Figure 4B.

      (4) Line 430: tadpole transport.

      (5) Line 431: I believe commas need to surround this phrase "which range from a few hours to several days depending on the species (Lötters et al., 2007; McDiarmid & Altig, 1999; Pašukonis et al., 2019)".

      We thank the reviewer for the thorough review and have corrected all typographical and formatting errors noted in comments (3) – (5).

    1. La fuerza total sobre una superficie plana sumergida vertical o inclinada es igual al área de la superficie multiplicada por la profundidad del centroide.

      multiplicada también por ρ g

    1. "Control your data, control your destiny."

      Peergos

      is building the next web - **the private web, where end users are in control. ** your private online space -

      Peergos

      motto

    1. The best way to understand Punjabi weddings is to realize that every single thing that's happening fulfills one of three goals.Meeting the religious obligations of marriage, as defined by the Vedas.Merging two families together and building a joint community.Showing off as much as humanly possible.You do your vows by walking around a sacred fire seven times while a pandit recites prayers in Sanskrit. That's category one. But you take those vows while dressed in the most ornate clothing you will ever see, decked out in jewelry fit for royalty. That's all category three. O, and while those vows are going on, there's a low key capture-the-flag game happening with the groom's shoes, with the bride's family members attempting to steal them from the groom's family members. That's, of course, category two.Every ritual, event, and tradition is like this, multiplied over at least 5 days.

      Wry introspection, elaborate detail

    1. Author Response:

      The following is the authors response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      The authors report four cryoEM structures (2.99 to 3.65 Å resolution) of the 180 kDa, full-length, glycosylated, soluble Angiotensin-I converting enzyme (sACE) dimer, with two homologous catalytic domains at the N- and C-terminal ends (ACE-N and ACE-C). ACE is a protease capable of effectively degrading Aβ. The four structures are C2 pseudo-symmetric homodimers and provide insight into sACE dimerization. These structures were obtained using discrete classification in cryoSPARC and show different combinations of open, intermediate, and closed states of the catalytic domains, resulting in varying degrees of solvent accessibility to the active sites. 

      To deepen the understanding of the gradient of heterogeneity (from closed to open states) observed with discrete classification, the authors performed all-atom MD simulations and continuous conformational analysis of cryo-EM data using cryoSPARC 3DVA, cryoDRGN, and RECOVAR. cryoDRGN and cryoSPARC 3DVA revealed coordinated open-closed transitions across four catalytic domains, whereas RECOVAR revealed independent motion of two ACE-N domains, also observed with cryoSPARC-focused classification. The authors suggest that the discrepancy in the results of the different methods for continuous conformational analysis in cryo-EM could result from different approaches used for dimensionality reduction and trajectory generation in these methods. 

      Strengths: 

      This is an important study that shows, for the first time, the structure and the snapshots of the dynamics of the full-length sACE dimer. Moreover, the study highlights the importance of combining insights from different cryo-EM methods that address questions difficult or impossible to tackle experimentally while lacking ground truth for validation. 

      Weaknesses: 

      The open, closed, and intermediate states of ACE-N and ACE-C in the four cryo-EM structures from discrete classification were designated quantitatively (based on measured atomic distances on the models fitted into cryo-EM maps, Figure 2D). Unfortunately, atomic models were not fitted into cryo-EM maps obtained with cryoSPARC 3DVA, cryoDRGN, and RECOVAR, and the open/closed states in these cases were designated based on qualitative analysis. As the authors clearly pointed out, there are many other methods for continuous conformational heterogeneity analysis in cryo-EM. Among these methods, some allow analyzing particle images in terms of atomic models, like MDSPACE (Vuillemot et al., J. Mol. Biol. 2023, 435:167951), which result in one atomic model per particle image and can help in analyzing cooperativity of domain motions through measuring atomic distances or angular differences between different domains (Valimehr et al., Int. J. Mol. Sci. 2024, 25: 3371). This could be discussed in the article. 

      Reviewer #2 (Public review): 

      Summary: 

      The manuscript presents a valuable contribution to the field of ACE structural biology and dynamics by providing the first complete full-length dimeric ACE structure in four distinct states. The study integrates cryo-EM and molecular dynamics simulations to offer important insights into ACE dynamics. The depth of analysis is commendable, and the combination of structural and computational approaches enhances our understanding of the protein's conformational landscape. However, the strength of evidence supporting the conclusions needs refinement, particularly in defining key terms, improving structural validation, and ensuring consistency in data analysis. Addressing these points through major revisions will significantly improve the clarity, rigor, and accessibility of the study to a broader audience, allowing it to make a stronger impact in the field. 

      Strengths: 

      The integration of cryo-EM and MD simulations provides valuable insights into ACE dynamics, showcasing the authors' commitment to exploring complex aspects of protein structure and function. This is a commendable effort, and the depth of analysis is appreciated. 

      Weaknesses: 

      Several aspects of the manuscript require further refinement to improve clarity and scientific rigor as detailed in my recommendations for the authors. 

      Reviewer #3 (Public review): 

      Summary: 

      Mancl et al. report four Cryo-EM structures of glycosylated and soluble Angiotensin-I converting enzyme (sACE) dimer. This moves forward the structural understanding of ACE, as previous analysis yielded partially denatured or individual ACE domains. By performing a heterogeneity analysis, the authors identify three structural conformations (open, intermediate open, and closed) that define the openness of the catalytic chamber and structural features governing the dimerization interface. They show that the dimer interface of soluble ACE consists of an N-terminal glycan and protein-protein interaction region, as well as C-terminal protein-protein interactions. Further heterogeneity mining and all-atom molecular dynamic simulations show structural rearrangements that lead to the opening and closing of the catalytic pocket, which could explain how ACE binds its substrate. These studies could contribute to future drug design targeting the active site or dimerization interface of ACE. 

      Strengths: 

      The authors make significant efforts to address ACE denaturation on cryo-EM grids, testing various buffers and grid preparation techniques. These strategies successfully reduce denaturation and greatly enhance the quality of the structural analysis. The integration of cryoDRGN, 3DVA, RECOVAR, and all-atom simulations for heterogeneity analysis proves to be a powerful approach, further strengthening the overall experimental methodology. 

      Weaknesses: 

      In general, the findings are supported by experimental data, but some experimental details and approaches could be improved. For example, CryoDRGN analysis is limited to the top 5 PCA components for ease of comparison with cryoSPARC 3DVA, but wouldn't an expansion to more components with CryoDRGN potentially identify further conformational states? The authors also say that they performed heterogeneity analysis on both datasets but only show data for one. The results for the first dataset should be shown and can be included in supplementary figures. In addition, the authors mention that they were not successful in performing cryoSPARC 3DFLex analysis, but they do not show their data or describe the conditions they used in the methods section. These data should be added and clearly described in the experimental section. 

      Some cryo-EM data processing details are missing. Please add local resolution maps, box sizes, and Euler angle distributions and reference the initial PDB model used for model building. 

      Reviewer #1 (Recommendations for the authors): <br /> Major point: 

      The authors could discuss the use of continuous conformational heterogeneity analysis methods that analyze particle images in terms of atomic models, based on MD simulations, like MDSPACE (Vuillemot et al., J. Mol. Biol. 2023, 435:167951). MDSPACE can be used on a dataset preprocessed with cryoSPARC or Relion by discrete classification to reduce compositional heterogeneity and obtain initial particle poses. It results in one atomic model per particle image and can help in analyzing the cooperativity of domain motions by measuring atomic distances or angular differences between different domains (Valimehr et al., Int. J. Mol. Sci. 2024, 25: 3371). 

      We agree that MDSPACE is a promising and useful tool for analysis, and are excited to implement such a method. Prior to manuscript submission, we have had discussions with the primary author, Slavica Jonic, about how we may employ her software in our analysis. Unfortunately, we were unable to overcome significant computational issues, notably MDSPACE’s lack of GPU functionality, which prevent us from employing MDSPACE in a reasonable manner for our dataset. We hope to employ MDSPACE in future work, once the computational issues have been addressed, and have added a section on MDSPACE to the discussion in an effort to increase the visibility of MDSPACE, as we feel it is an exciting approach that deserves more visibility. We have added a substantial discussion on this point, specifically on MDspace as follows:

      line 565-574

      Similarly, MDSPACE holds tremendous promise as a method for investigating conformational dynamics from cryo-EM data (61). MDSPACE integrates cryo-EM particle data with short MD simulations to fit atomic models into each particle image through an iterative process which extracts dynamic information. However, the lack of GPU-enabled processing for MDSPACE requires either a dedicated a computational setup that diverges from most other cryo-EM software, or access to a CPU-based supercomputer, which severely limits the accessibility of such software. Despite these challenges, both 3DFlex and MDSPACE use promising approaches to study protein conformational dynamics. We look forward to exploring effective methods to incorporate these strategies into our future research.

      Minor points: 

      (1) Lines 348-350: "The discrepancy in population size between these clusters is likely due to bias in the initial particle poses, rather than a subunit-specific preference for the open state." Which bias? The cluster size is related to conformations, not to poses. 

      We hope to emphasize that the assignment of particles to either the OC or CO cluster is likely due to the particle orientation within the complete dimer refinement, and the discrepancy in size between OC and CO clusters does not necessarily indicate a domain specific preference for one state or another, which would carry allosteric implications. This remains a possibility, but we hope to avoid over-interpretation of our results with the statement above.

      The statement was altered to now read:

      Line 418-423

      “The discrepancy in population size between these clusters is likely due to bias in the initial particle orientation, rather than a subunit-specific preference for the open state. As the O/C state and the C/O state are 180 degree rotations of each other, particle assignment to either cluster is likely influenced by the initial particle orientation of the complete dimer, and we currently lack the data to discern any allosteric implication to the orientation assignment.”

      (2) Line 519: "Micrographs with a max CTF value worse than 4Å were removed from the dataset,..." (also, lines 822-823 in supplementary material). <br /> Do you want to say that micrographs with a resolution worse than 4 A were removed? 

      Max CTF value was replaced with CTF fit resolution to properly match the parameter used in Cryosparc.

      (3) Figure 2C: The black lines are barely visible. Can you make them thicker and in red color? 

      The figure has been amended.

      (4) Figure 2D: The values for Chain A and Chain B in the second row (ACE-C) of sACE-3.05 columns are 17.9 (I) (Chain A) and 13.9 (C) (Chain B). Shouldn't they be reversed (13.9 (C) (Chain A) and 17.9 (I) (Chain B))? 

      The values are now correct. sACE-3.65 chains were flipped in the table, and the updated color scheme should make it easier to map the values from the table to their corresponding structure.

      Reviewer #2 (Recommendations for the authors): 

      The manuscript presents the first complete full-length dimeric ACE structure. The integration of cryo-EM and MD simulations provides valuable insights into ACE dynamics, showcasing the authors' commitment to exploring complex aspects of protein structure and function. This is a commendable effort, and the depth of analysis is appreciated. However, several aspects of the manuscript require further refinement to improve clarity and scientific rigor. In the view of this reviewer, a major revision is necessary. Please see the detailed comments below: 

      (1) Definition of "Conformational Heterogeneity": The term "conformational heterogeneity" should be clearly defined when citing references 27-29. <br /> References 27 and 29 use MD simulations, which reveal "conformational flexibility" rather than "conformational heterogeneity" as observed in cryo-EM data. A more precise distinction should be made. 

      We have changed the term “conformational heterogeneity” to the broader “conformational dynamics

      (2) Figure Adjustments for Clarity: <br /> Figure 1B: A scale bar is needed for accurate representation. 

      A 100 Angstrom scale bar was added to figure 1B.

      Figure 2A, B: Using a Cα trace representation would improve clarity and make structural differences more apparent. 

      We found using a Cα trace representation makes the figure too confusing and impossible to determine individual structural elements. Everything just becomes a jumble of lines.

      Additionally, a Cα displacement vs. residue index plot (with Figure 1A placed along the x-axis) should be included alongside Figures 2A and B to provide quantitative insight into structural variations. 

      This analysis has been combined with several other suggestions and now comprises a new figure 4.

      (3) Structural Resolution and Validation: <br /> Euler angle distribution and 3D-FSC analysis should be provided to help the audience assess how these factors influence the resolution of each structure. <br /> Local resolution analysis in Relion should be included to determine if there are dynamic differences among the four structures. <br /> To enhance structural interpretation, the manuscript would benefit from showcasing examples of bulky side-chain densities (e.g., Trp, Phe, Tyr) for each of the four structures. 

      Information is included in Figure S3 and S5.

      (4) Glycan Modeling Considerations: <br /> Since the resolution of cryo-EM does not allow for precise glycan composition determination, additional experimental validation (e.g., Glyco-MS) would strengthen the modeling. If experimental support is unavailable, appropriate references should be cited to justify the modeled glycans. 

      Minimal glycan modeling was performed with the goal of demonstrating that the protein is glycosylated. We have highlighted that we chose 12 N-linked glycosylation sites that have the observed extra density, an indication that glycan should be present and modeled them with complex glycans in the manuscript.  

      (5) Advanced Cryo-EM and MD Analyses: 3DFlex Analysis: <br /> It is recommended that the authors explore 3DFlex to better capture conformational variability. CryoSPARC's community support can assist in proper implementation. 

      We have incorporated our 3Dflex analysis in our discussion as follows:

      Line 553-565

      Surprisingly, we did not observe such motion using cryoSPARC 3DFlex, a neural network-based method analyzing our cryo-EM data of sACE (54). Central to the working of cryoSPARC 3DFlex is the generation of a tetrahedral mesh used to calculate deformations within the particle population. Proper generation of the mesh is critical for obtaining useful results and must often be determined empirically. Despite several attempts, we were unable to obtain results from 3DFlex comparable to what we observed with our other methods. Even using the results from our 3DVA as prior input to 3DFlex, the largest conformational change we observed was a slight wiggling at the bottom of the D3a subdomain (Movie S12). The authors of 3DFlex note that 3DFlex struggles to model intricate motions, and the implementation of custom tetrahedral meshes currently requires a non-cyclical fusion strategy between mesh segments. Given these limitations, and the complexity of sACE conformational dynamics, it appears that sACE, as a system, is not well-suited to analysis via 3DFlex in its current implementation.

      (6) Movie Consistency: <br /> The MD simulation movies should use the same color coding as the first four movies for consistency. Similarly, the 3DVar analysis map should be color-coded to enhance interpretability. 

      MD simulation movies are re-colored.

      (7) MD Simulations - Data Extraction and Validation: <br /> The manuscript includes several long-timescale MD simulations, but further analysis is needed to extract meaningful dynamic information. Suggested analyses include: <br /> a. RMSF (Root Mean Square Fluctuation) Analysis: Calculate RMSF from MD trajectories and compare it with local resolution variations in cryo-EM maps. 

      RMSF values were included in the new figure 4 along with structural depictions colored by RMSF value to localize variation to the structure.

      b. Assess whether regions exhibiting lower dynamics correspond to higher resolution in cryo-EM. 

      Information is added to Figure 4, Figure S3, S5, S6.

      c. Compare RMSF between simulations with and without glycans to identify potential effects. 

      This has been done in Figure 4.

      d. Clustering Analysis: Use the four solved structures as reference states to cluster MD simulation trajectories. Determine if the population states observed in MD simulations align with cryo-EM findings. 

      This has been done in supplementary figure S10.

      e. Principal Component Analysis (PCA): Perform PCA on MD trajectories and compare with dynamics inferred from cryo-EM analyses (3DVar, cryoDRGN, and RECOVAR) to ensure consistency. 

      This has been done in supplementary figure S11.

      f. Correction of RMSF Analysis or the y-axis label in Figure S9: The RMSF values cannot be negative by definition. The authors should carefully review the code used for this calculation or explicitly define the metric being measured. 

      The Y-axis label has been corrected to clarify that the plot depicts the change in RMSF values when comparing the glycosylated and non-glycosylated MD simulations.

      (8) Discussion on Coordinated Motion and Allostery: <br /> The discussion of coordinated motion and allosteric regulation between sACE-N domains should be explicitly connected to experimental evidence mentioned in the introduction: <br /> "Enzyme kinetics analysis suggests negative cooperativity between two catalytic domains (31-33). However, ACE also exhibits positive synergy toward Ab cleavage and allostery to enhance the activity of its binding partner, the bradykinin receptor (11, 34)." 

      (9) The authors should elaborate on how their new insights provide a mechanistic explanation for these experimental observations. 

      (10) Connection to Therapeutic Implications: <br /> The discussion section should more explicitly connect the structural findings to potential therapeutic applications, which would significantly enhance the impact of the study. 

      These three points (8-10) were addressed in a significant overhaul to the discussion section.

      In summary, this study makes a valuable contribution to the field of ACE structural biology and dynamics. The combination of cryo-EM and MD simulations is particularly powerful, and with major revisions, this manuscript has the potential to make a strong impact. Addressing the points outlined above will significantly improve clarity, strengthen the scientific claims, and enhance the manuscript's accessibility to a broader audience. I appreciate the authors' rigorous approach to this complex topic and encourage them to refine their work to fully highlight the significance of their findings. 

      Reviewer #3 (Recommendations for the authors): 

      (1) The authors incorrectly refer to their ACE construct as full-length throughout the manuscript. Given that they are purifying the soluble region (aa 1-1231), saying full-length ACE is not the correct nomenclature. I suggest removing full-length and using soluble ACE (sACE) throughout the text. 

      We utilize the term full-length to highlight the fact that our structures contain both the N and C domains for both subunits in the dimer, in contrast to the previously published ACE cryo-EM structure. We have clarified in the text that we refer to the full-length soluble region of ACE (sACE), and sACE is used to specifically refer to our construct throughout the text, except when referring to ACE in a more generalized biological context in the introduction and discussion.

      (2) The authors could show differences between the different structural states by measuring and displaying the alpha carbon distances. For example, in Figures 2A, B, 3A, and 4B and C. 

      Alpha carbon displacements for each residue have been added to the new figure 4.

      (3) Most figures, with a few exceptions (Figures 2 and S11), are of low quality. Perhaps they are not saved in the same format. In addition, the color schemes used throughout the figures and movies are not consistent. For example, in Figure 1 D2 domains are in green, while they appear yellow in Figure 2 and later. Please double-check all coloring schemes and keep them consistent throughout the manuscript. In addition, it would be good to keep the labeling of the domains in the subsequent figures, as it is difficult to remember which domain is which throughout the manuscript. 

      We are unsure of how to address the low quality issue, our files and the online versions appear to be of suitable high quality. We will work with editorial staff to ensure all files are of suitable quality. The color scheme has been revised throughout the manuscript to ensure consistency and better differentiate between domains and chains.

      (4) Figure 1. Indicate exactly where in panel A ACE-N ends and ACE-C starts. Also, the pink and magenta, as well as aqua vs. light blue, are hard to distinguish. 

      We have updated coloring scheme.

      (5) Figure 2. In the figure legend, the use of brackets for defining closed, intermediate, and open states is confusing, given that the panels are also described with brackets, and some letters match between them. Using a hyphen or bolding the abbreviations could help. Also, define chains A and B, make the black lines that I assume indicate distances in C bold or thicker as they are very hard to see in the figure, and add to the legend what those lines mean. 

      The abbreviations have been changed from parentheses to quotes, and suggestions have been implemented.

      (6) Figure 4 is confusing as shown. Since the authors mention the general range of motion in sACE-N first in the text, wouldn't it make more sense to show panel B first and then panel A? Also, can you point and label the "tip connecting the two long helices of the D1a subdomain" in the figure? It is not clear to me where this region is in B. In addition, add a description of the arrows in B and C to the figure legend. 

      Most changes incorporated. The order should make more sense now in light of other changes.

      (7) Figure 5. Can the authors add a description to the legend as to what the arrows indicate and their thickness? 

      Done

      (8) Add a scale bar to the micrograph images in the supplementary figures. 

      Figure S2 and S4 need the scale bar.

      (9) Provide a more comprehensive description of buffers used in the DF analysis, as this information could be useful to others. 

      We have included the data in Table S1.<br /> (10) Line 51: Reference format not consistent with other references: (Wu et al., 2023). 

      Fixed

      (11) Line 66: Define "ADAM". 

      The definition has been added.

      (12) Line 90: The authors say: Recent open state structures of sACE-N, sACE monomer, and a sACE-N dimer, along with molecular dynamics (MD) simulations of sACE-C, have begun to reveal the conformational heterogeneity, though it remains under-studied (27-29)." Can the authors clarify what "it" refers to? The full-length ACE, sACE, or its specific domains? 

      The sentence now reads: Recent open state structures of sACE-N, sACE monomer, and a sACE-N dimer, along with molecular dynamics (MD) simulations of sACE-C, have begun to reveal ACE conformational dynamics, though they remain under-studied (29-31).

      (13) Line 204: "The comparison of our dimeric sACE cryoEM structures of reveals the conformational dynamics of sACE catalytic domains." The second "of" should be removed. 

      Fixed<br /> (14) Line 268: "From room mean square fluctuation (RMSF) analysis..." "room" should be replaced with "root."

      Fixed

    1. ecografía
      • Modificación de la imagen para incluir las ventanas del extendido (bases pulmonares y deslizamiento)
      • Generar dos supuestos, el de "ecografía en paciente estable" y el de "FAST en inestable para la toma de decisiones".
      • En qué pacientes repetir la exploración a los pocos minutos (ej pacientes con tiempo prehospitalario corto en el cuál el primero es negativo o con poco líquido para la inestabilidad objetivada)
    1. Mercutio. O here's a wit of cheveril, that stretches from an inch narrow to an ell broad!

      Mercutio is joking that Romeo's no smart (wit) is like soft leather ("cheveril") that can stretch easily,

    2. More than prince of cats, I can tell you. O, he is the courageous captain of compliments. He fights as you sing prick-song, keeps time, distance, and proportion; rests me his minim rest, one, two, and 1180the third in your bosom: the very butcher of a silk button, a duellist, a duellist; a gentleman of the very first house, of the first and second cause: ah, the immortal passado! the punto reverso! the hai! 1185 Benvolio. The what? Mercutio. The pox of such antic, lisping, affecting fantasticoes; these new tuners of accents! 'By Jesu, a very good blade! a very tall man! a very good whore!' Why, is not this a lamentable thing, 1190grandsire, that we should be thus afflicted with these strange flies, these fashion-mongers, these perdona-mi's, who stand so much on the new form, that they cannot at ease on the old bench? O, their bones, their bones!

      Mercutio describes Tybalt as if he's some sort of legendary Capulet family member. Tall, quick, fights perfectly, charming.

    3. O Romeo, Romeo! wherefore art thou Romeo? 880Deny thy father and refuse thy name; Or, if thou wilt not, be but sworn my love, And I'll no longer be a Capulet.

      Juliet is saying that if she falls in love with Romeo that she would be denying her families name.

    1. This special issue gestures towards anOceanic Filipinx studies in Hawai‘i that empowers Filipinx to intimatelyknow and meditate on their social position in relation to the waves ofcultural knowledges, languages, and stories that reside here in Filipinx/a/o Hawai‘i. We can then strategically mobilize these cultural energiestowards collective liberation for Filipinx, Kānaka Maoli, and all otherdifferently displaced diasporic, local, Black, and Brown communitieswho call Hawai‘i land, seas, and skies home in ways that do not drawfalse equivalences in search of commonalities

      it's a call to familiarize oneself with one's own history, and strengths to come together for others in solidarity

    Annotators

    1. Reviewer #3 (Public review):

      Summary:

      Wang et al. reported a new role of LRRK2-GS mutant in astrocyte morphology and synapse maintenance and a potential mechanism that acts through phosphorylation of ERM, which binds to ATG7. In both human LRRK2-GS patients and LRRK2-GS KI mouse brain cortex, they found increased ERM phosphorylation levels. LRRK2-GS alters excitatory and inhibitory synapse densities and functions in the cortex, which can be restored by p-ERM-dead mutant. They further demonstrated that LRRK2 regulates astrocyte morphological complexity in vivo through ERM phosphorylation. Proteomic and biochemistry approaches found that ATG7 interacts with Ezrin, which is inhibited by Ezrin phosphorylation. This provides a potential mechanism by which LRRK2-GS impairs the astrocyte morphology.

      Strengths:

      (1) Data in human PD patients (Figure 1B, C) is impressive, showing a clear increase of p-ERM in LRRK2-GS samples.

      (2) Both LRRK2-GS and siLRRK2 show similar phenotypes, supporting both GOF and LOF decrease astrocyte complexity and size.

      (3) Using p-ERM-dead and mimic mutants is elegant. The data is striking that the p-ERM-dead mutant can restore LRRK2-GS-induced excitatory synapse density in the ACC and astrocyte territory volume and complexity, while the p-ERM-mimic mutant can restore the siLRRK2 phenotype.

      (4) ATG7 binding to Ezrin provides a potential mechanism. It is compelling that siATG7 shows a similar decrease in astrocyte territory volume and complexity, and siATG7 in LRRK2-GS does not enhance the astrocyte phenotype.

      Weaknesses:

      (1) The authors claim that p-ERM colocalizes with astrocyte marker ALDH1L1, e.g., Figure 1E, F, G, H, J, K. It is hard to tell from the representative images. Given that this is critical for this paper, it would be appreciated if the authors could improve the images and show clear colocalization. The same concern for Figures S1, 2, 3. Validation of the p-ERM antibody is critical. Figure S4, using λ-PPase to eliminate the phosphorylation signal in general, is very helpful. Additional validation of the p-ERM antibody specific to ERM would be appreciated.

      (2) Does the total ERM level change /increase in LRRK2-GS samples? The increased p-ERM levels could be because the total ERM level increases. Then, the follow-up question is whether the total ERM level matters to the astrocyte phenotypes seen in the paper.

      (3) WT mice carry WT-LRRK2, which also has kinase activity to phosphorylate ERM. So, what are the effects of overexpression of the p-ERM mutants (dead or mimic) on the excitatory and inhibitory synapse densities and functions in WT mouse samples? In Figure 4, statistics should be done comparing WT+Ezrin O/E vs WT+phosphor-dead Ezrin O/E. From what is shown in the graphs, it looks like phosphor-dead Ezrin worsens the phenotype in WT mice, which is opposite to the GS mice. How to explain? The same question for the graphs in Figure 5.

      (4) Rab10 is not a robust substrate for the LRRK2-G2019S mutant, and p-Rab10 is very difficult to detect in mouse brains. The specificity of the pRab10 immunostaining signal in Fig. S8 is not certain.

      (5) Would ATG7, Ezrin, and LRRK2 form a complex?

    1. Reviewer #3 (Public review):

      Summary:

      The authors describe an interesting study of arm movements carried out in weightlessness after a prolonged exposure to the so-called microgravity conditions of orbital spaceflight. Subjects performed radial point-to-point motions of the fingertip on a touch pad. The authors note a reduction in movement speed in weightlessness, which they hypothesize could be due to either an overall strategy of lowering movement speed to better accommodate the instability of the body in weightlessness or an underestimation of body mass. They conclude for the latter, mainly based on two effects. One, slowing in weightlessness is greater for movement directions with higher effective mass at the end effector of the arm. Two, they present evidence for an increased number of corrective submovements in weightlessness. They contend that this provides conclusive evidence to accept the hypothesis of an underestimation of body mass.

      Strengths:

      In my opinion, the study provides a valuable contribution, the theoretical aspects are well presented through simulations, the statistical analyses are meticulous, the applicable literature is comprehensively considered and cited, and the manuscript is well written.

      Weaknesses:

      Nevertheless, I am of the opinion that the interpretation of the observations leaves room for other possible explanations of the observed phenomenon, thus weakening the strength of the arguments.

      First, I would like to point out an apparent (at least to me) divergence between the predictions and the observed data. Figures 1 and S1 show that the difference between predicted values for the 3 movement directions is almost linear, with predictions for 90º midway between predictions for 45º and 135º. The effective mass at 90º appears to be much closer to that of 45º than to that of 135º (Figure S1A). But the data shown in Figure 2 and Figure 3 indicate that movements at 90º and 135º are grouped together in terms of reaction time, movement duration, and peak acceleration, while both differ significantly from those values for movements at 45º.

      Furthermore, in Figure 4, the change in peak acceleration time and relative time to peak acceleration between 1g and 0g appears to be greater for 90º than for 135º, which appears to me to be at least superficially in contradiction with the predictions from Figure S1. If the effective mass is the key parameter, wouldn't one expect as much difference between 90º and 135º as between 90º and 45º? It is true that peak speed (Figure 3B) and peak speed time (Figure 4B) appear to follow the ordering according to effective mass, but is there a mathematical explanation as to why the ordering is respected for velocity but not acceleration? These inconsistencies weaken the author's conclusions and should be addressed.

      Then, to strengthen the conclusions, I feel that the following points would need to be addressed:

      (1) The authors model the movement control through equations that derive the input control variable in terms of the force acting on the hand and treat the arm as a second-order low-pass filter (Equation 13). Underestimation of the mass in the computation of a feedforward command would lead to a lower-than-expected displacement to that command. But it is not clear if and how the authors account for a potential modification of the time constants of the 2nd order system. The CNS does not effectuate movements with pure torque generators. Muscles have elastic properties that depend on their tonic excitation level, reflex feedback, and other parameters. Indeed, Fisk et al.* showed variations of movement characteristics consistent with lower muscle tone, lower bandwidth, and lower damping ratio in 0g compared to 1g. Could the variations in the response to the initial feedforward command be explained by a misrepresentation of the limbs' damping and natural frequency, leading to greater uncertainty about the consequences of the initial command? This would still be an argument for unadapted feedforward control of the movement, leading to the need for more corrective movements. But it would not necessarily reflect an underestimation of body mass.

      *Fisk, J. O. H. N., Lackner, J. R., & DiZio, P. A. U. L. (1993). Gravitoinertial force level influences arm movement control. Journal of neurophysiology, 69(2), 504-511.

      (2) The movements were measured by having the subjects slide their finger on the surface of a touch screen. In weightlessness, the implications of this contact are expected to be quite different than those on the ground. In weightlessness, the taikonauts would need to actively press downward to maintain contact with the screen, while on Earth, gravity will do the work. The tangential forces that resist movement due to friction might therefore be different in 0g. This could be particularly relevant given that the effect of friction would interact with the limb in a direction-dependent fashion, given the anisotropy of the equivalent mass at the fingertip evoked by the authors. Is there some way to discount or control for these potential effects?

      (3) The carefully crafted modelling of the limb neglects, nevertheless, the potential instability of the base of the arm. While the taikonauts were able to use their left arm to stabilize their bodies, it is not clear to what extent active stabilization with the contralateral limb can reproduce the stability of the human body seated in a chair in Earth gravity. Unintended motion of the shoulder could account for a smaller-than-expected displacement of the hand in response to the initial feedforward command and/or greater propensity for errors (with a greater need for corrective submovements) in 0g. The direction of movement with respect to the anchoring point could lead to the dependence of the observed effects on movement direction. Could this be tested in some way, e.g., by testing subjects on the ground while standing on an unstable base of support or sitting on a swing, with the same requirement to stabilize the torso using the contralateral arm?

      The arguments for an underestimation of body mass would be strengthened if the authors could address these points in some way.

  3. accessmedicina-mhmedical-com.biblioteca.unimagdalena.edu.co accessmedicina-mhmedical-com.biblioteca.unimagdalena.edu.co
    1. se prescribe doxiciclina (100 mg por VO cada 12 h durante siete días) o amoxicilina (50 mg/kg, cada 8 h).

      Doxiciclina 100 mg VO c/12h por 7 días Amoxicilina, 50 mg/kg VO c/8h por 7 días

    1. This is not to say that coverage metrics should take into account code paths inexternal libraries (they shouldn’t), but rather to show you that you can’t rely onthose metrics to see how good or bad your unit tests are.

      Cuando estas haciendo un test, tambien hay que tomar en consideración que por mas que testeemos librerias o modulos externos, hay ciertos hidden cases que no están en ocnsideración.

      Por ejemplo, un metodo .parse() que tienen ciertos paths que no se ven externamente. Por ejemplo, edge cases tipo: que pasa si lega un null, un string, vacio o un string muy largo

    1. Goals, outcomes, and requirements can be aggregated in plateaus.

      I would add explanation what does it means. I am personally confused by the aggregation realization here. In most cases I think people would like o express that some goal will be realized by some plateau. Using aggregation means something different - possibly that the goal is valid for only that time period but not necesarilly realized by that. On the other hand it would mean that aggregation is weaker then realization in this case.

    1. Don't worry about finding exactly the right spot to branch off into a new follower note. Usually the right spot is whatever you were looking at when you got a new idea – other relevant notes can always be linked to explicitly, or placed in some shared structure note. If you didn't get the idea while looking at your Zettelkasten, just find some vaguely related note and start there. It doesn't have to be the best possible spot, or even be that relevant at all; the important part is that you pick some location for it.

      En esto me ayudó mucho la bitácora para disminuir los costos de pre-organización (o dónde debería ir este liga/información). Siguiendo el método de LogSeq, si no sé donde poner algo, empiezo colocando un enlace o info en una bitácora del día en que organizo dicho info (incluso días después de haberla encontrado). De este modo, el proceso se vuelve fluído:

      • Si sé donde debería ir un enlace, edito el tiddler donde debería ir.
      • Si no lo sé, la coloco en la bitácora del día en que organizo dicha info.

      El etiquetado y cierto orden emergente de cosas que encuentro regularmente harán el resto.

    2. Don't rely on an inbox, comprehensive indexes, or backlinks When writing notes for your Zettelkasten, it's natural to feel some anxiety about whether you're just writing into the void, or whether you'll actually stumble across those notes again in the future. To relieve this anxiety, there are at least three tempting solutions that seem to make sense on the surface, but which tend to fall apart in the long term.

      Mis blikis me permiten reencontrar la información fácilmente y los consulto/referencio con frecuencia. En particular para ofrecer información que solicitan una y otra vez, como hojas de vida y portafolios de proyectos.

      Si bien a veces creo índices con otros tiddlers, en general confío y uso los sistemas de etiquetados y de búsqueda que ya vienen incorporados en la herramienta para reencontrar la información y el sistema de filtros y operadores para ofrecer vistas específicas de la misma a terceros, como mi portafolio o mi perfil.

      No reorganizo la información en publicaciones más grandes y lineales como artículos o libros, como lo haría Luhmann, básicamente por dos cosas:

      1. me gusta estar lejos de las mafias de publicación académica clásica y sus circuitos y
      2. quisiera que las publicaciones vivas, tipo wiki, blikis, datasets y repositorios de código tuvieran un estatus epistémico en sí mismo, sin tener que tomar la forma de las publicaciones académicas clásicas que han sido tan gentrificadas.

      Lo anterior requerirá otras métricas, en las cuales vamos trabajando lento.

    3. The result of this is that a note can be renamed without changing its filename and without updating existing links to that file. You can also use special characters in titles without any issues. As for why this actually matters, well, it's mostly a matter of aesthetics. A Zettelkasten is supposed to be a simple system for building knowledge out of small notes in an organic way. The simplest possible Zettelkasten is implemented in terms of slips of paper which are assigned identifiers for linking between them. In a digital system, the most straight-forward emulation of such a system is with files whose filenames are unique IDs, and links that are simply the IDs themselves. Many note-taking applications assume a model that is fundamentally incompatible with such an approach, usually because they conflate titles with filenames (as Denote and zk.el do), or even with IDs, as Obsidian does! zt, in contrast, takes these simple, plain-text foundations as its base, and then adds extra digital-only functionality on top to make it an actually useful system.

      Me enfrenté a la duda sobre los identificadores únicos para documentos en Grafoscopio y finalmente opté por NanoID, debido a la baja posibilidad de colisión con tan sólo 11 caracteres, lo que es más corto que una fecha única (pues hay que capturar los milisegundos para hacer un indicador único), funciona incluso cuando se producen muchos trozos de información rápidamente (por ejemplo en una importación de información donde hasta los milisegundos podrían coincidir) y, de todos modos, la fecha es un metadato que se puede capturar en otro lado y por alguna razón para mí no es tan memorable. Si bien el NanoID es un indicador no memorable, los videos en YouTube se hacen virales por otros datos (como su título/autor o los algoritmos de recomendación) y no por la memorabilidad de su enlace (todos acá son no memorables).

      Para lidiar con la memorabilidad y organización para humanos, el identificador único es un sufijo en lugar de un prefijo, que va al final del nombre del archivo, separado por doble guiones y antes de la extensión, por ejemplo, cartofonias--e45j7.md.html). Dado que los navegadores web recuerdan partes del enlace, incluso si el nombre se cambiara, pero recordáramos (en parte) el identificador único (en e ejemplo e45j7), nos sugeriría diferentes títulos que incluyen esa secuencia de caracteres.

    1. Los fármacos se eliminan del cuerpo sin modificaciones o como metabolitos. Los órganos excretores, excluidos los pulmones, eliminan los compuestos polares de manera más eficiente que las sustancias con alta liposolubilidad. Por lo tanto, los fármacos liposolubles no se eliminan con facilidad hasta que se metabolizan en compuestos más polares. El riñón es el órgano más importante para excretar fármacos y sus metabolitos.

      Los fármacos se eliminan sin cambios o transformados en metabolitos. Los fármacos liposolubles primero se vuelven más polares (hidrosolubles) para poder ser eliminados. NOTA: El riñón es el principal órgano de excreción.

    2. un fármaco se distribuye en líquidos intersticiales e intracelulares en función de las propiedades fisicoquímicas del mismo, la frecuencia de administración de fármacos a órganos y compartimentos individuales y las diferentes capacidades de esas regiones para interactuar con el fármaco.

      Un fármaco se reparte entre líquidos y tejidos del cuerpo según:

      *Sus propiedades fisicoquímicas

      *Cuánta sangre llega a cada órgano

      *Y cómo cada tejido interactúa con él

      Es como si quisiéramos repartir comida en un campamento, depende de qué tan fácil sea transportarla, a dónde la lleves y si la gente del lugar la acepta o la usa.

    3. La biodisponibilidad describe el grado fraccional en que una dosis administrada del fármaco alcanza su sitio de acción o un líquido biológico (por lo general, la circulación sistémica) desde el cual el fármaco tiene acceso a su sitio de acción.

      O sea, que tanto fármaco útil llega hasta el sitio de acción y es capaz de actuar. Como si mandas 4 soldados a una misión y solo llegan 2 al objetivo.

    1. within the internal perspective. They are first-order claims about what is right or wrong in specific counterfactual conditions, and can thus be glossed in expressivist terms. This is underpinned by the fact that our moral attitudes respond to natural features of the world. We judge that kicking dogs is wrong because of the pain they suffer when kicked, not because we happen to disapprove of such behaviour. Quasi-realists can therefore hold that kicking dogs remains wrong in worlds at which our counterparts approve of it, for o

      .kjgjjhk

    1. Author Response:

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

      Reviewer #1 (Recommendations for the authors): 

      Overall, the manuscript could be clearer and more beneficial to the readers with the following suggested revisions:  

      (1) The abstract should include information on the comparative performance of 89Zr 64Cu and 18F labeled nanobodies, especially noting the challenges with DFO-89Zr and NOTA-64Cu. 

      (2) The abstract should explicitly note the types of transplants assessed and the specific PET findings.

      (3) The abstract should note the negative results in terms of brain PET findings. 

      We thank reviewer 1 for these three suggestions. We have now included this information in the abstract.

      (4)  Based on the data shown in Fig. 1 and Table 1, it seems that the nanobodies bind to quite a few proteins other than TfR. This should be discussed frankly as a limitation. 

      The presence of multiple other bands and proteins identified by LC/MS in Figure 1 is typical for immunoprecipitation experiments, as performed under the conditions used: all proteins other than TfR that are identified in Table 1 are abundant cytoplasmic (cytoskeletal) and/or nuclear proteins.  More rigorous washing would perhaps have removed some of these contaminants at the risk of losing some of the specific signal as well. We have added a comment to this effect.  In an in vivo setting, this would be of minor concern, as these proteins would be inaccessible to our nanobodies. In fact, when VHH123 radioconjugates are injected in huTfr+/+ mice (or VHH188 in C57BL/6), we observe no specific signal – which supports this conclusion. 

      We therefore state: “We show that both V<sub>H</sub>Hs bind only to the appropriate TfR, with no obvious cross-reactivity to other surface-expressed proteins by immunoblot, LC/MSMS analysis of immunoprecipitates, SDS-PAGE of <sup>35</sup>S-labelled proteins and flow cytometry (Fig 1;Table 1).”. We have added some clarification to make this clearer, and we also include the full LC/MSMS data tables are also added in supplemental materials, as supplementary Table 1. We have included subcellular localization information for each protein identified through LC/MSMS in Table 1 as well.

      (5)  Why did the authors use DFO, which is well known to leak Zr, rather than the current standard for 89Zr PET, DFO* (DFO-star)? 

      We used DFO rather than DFO-star for several reasons: 1) because we had already conducted and published numerous other studies using DFO-conjugated nanobodies and not observed any release of <sup>89</sup>Zr, 2) commercially sourced clickchemistry enabled DFO-star (such as DFO*-DBCO) was not available at the time of the study. 

      (6) Figure 2B appears to show complex structures, more complex than just GGG-DFOazide, and GGG-NOTA-azide. This should be explained in detail. 

      We have added two supplemental figures and methods that recapitulate how we generated what we have termed as GGG-DFO-Azide and GGG-NOTA-Azide. We have updated the legend of Figure 2B. 

      (7) Why is there a double band in Suppl. Fig 9 for VHH123-NOTA-Azide? 

      Under optimal conditions, sortase A-mediated transpeptidation is efficient,  resulting in the formation of a peptide bond between the C-terminally LPETG-tagged protein and the GGG-probe. However, extended reaction times or suboptimal concentrations of modified GGG-probes (which are often in limited supply) in the reaction mixture, allow hydrolysis of the sortase A-LPET-protein intermediate. The hydrolysis product can no longer participate in a sortase A reaction. This is what explains the doublet in the reaction used to generate VHH123-NOTA-N<sub>3</sub> – the upper band is VHH123-NOTA-N<sub>3</sub> and the lower band is the hydrolysis product.  VHH123-LPET, is unable to react with PEG<sub>20kDa</sub>-DBCO (the lower band that appears at the same position of migration in the next lane on the gel). We noticed that an adjacent lane was mislabelled as ‘VHH188-NOTA-PEG<sub>20kDa</sub>’ when in fact it was ‘VHH123-NOTA-PEG<sub>20kDa</sub>’. This has been corrected.

      The hydrolysis product, VHH123-LPET, has a short circulatory half-life and obviously lacks the PEG moiety as well as the chelator. It therefore cannot chelate <sup>64</sup>Cu. Its presence should not interfere with PET imaging.  Since all animals were injected with the same measured dose of <sup>64</sup>Cu labeled-conjugate, the presence of an unlabeled TfRbinding competitor in the form of VHH123-LPET - at a << 1:1 molar ratio to the labelled nanobody – would be of no consequence.

      (8) More details should be provided about the tetrazine-TCO click chemistry for 18F labeling. 

      We have added supplementary methods and figures that detail how <sup>18</sup>F-TCO was generated. For the principle of TCO-tetrazine click-chemistry, a brief description was added in the text, as well as a reference to a review on the subject.

      (9) For the data shown in Figure 3H, the authors should state whether the brain tissues were capillary depleted, and if so, how this was performed and how complete the procedure was. 

      No capillary depletion of the brain tissues was performed, as this was challenging to perform in compliance with the radiosafety protocols in place at our institution. We have updated the legend of figure 3H and methods to include this important detail. Whole blood gamma-counting did not show any obvious di  erence of activity across the 4 groups in figure 3G (same mice as in figure 3H), which would go against the interpretation that activity di  erences in the brain (figure 3H) are solely attributable to residual activity from blood in the capillaries. 

      (10) The authors should experimentally test the hypotheses that the PEG adduct reduced BBB transcytosis. 

      Reviewer 1 is correct to point out that we have not tested un-PEGylated conjugates of <sup>64</sup>Cu and <sup>89</sup>Zr with the anti-TfR nanobodies and we currently do not have the means to perform additional experiments. However, the <sup>18</sup>F conjugates were not PEGylated, and these also fail to show any detectable signal in the CNS by PET/CT (see figure 4A). PEGylation alone cannot be the sole factor that limits transcytosis across the BBB.

      (11) It was interesting to note that the Cu appears to dissociate from the NOTA chelator. The authors should provide more information about the kinetics of this process.  

      We have not tested the kinetics of dissociation between <sup>64</sup>Cu and the NOTA conjugates in vitro, like we have done for <sup>89</sup>Zr and DFO (supplemental figure 2), because previous work (see references 35 and 36 by Dearling JL and Mirick GR and colleagues) has shown that NOTA and other copper chelators tend to release free copper radioisotopes in the liver, a commonly reported artifact. We have also included a new set of images that show the biodistribution of VHH123-NOTA-<sup>64</sup>Cu in huTfR+/+ mice, where we still observe a substantial signal in the liver, indicating release of <sup>64</sup>Cu from NOTA, in the absence of the anti-TfR VHH binding to its target. This was clearly not seen using the DFO-<sup>89</sup>Zr conjugates.  Binding of the VHH to TfR, followed by internalization, appears to be required for the release of <sup>89</sup>Zr from DFO, prompting us to investigate this phenomenon further.

      (12) The authors should increase the sample size, and test two different radiolabels for the transplant imaging results (Figs. 5 and 6), since these seem to be the ones they feel are the most important, based on the title and abstract. 

      We agree with reviewer 1 that more repeats would increase the significance of our findings, but we unfortunately do not have the means of performing additional experiments at this time (the lab at Boston Children’s Hospital has closed as Dr. Ploegh has retired). We believe that the results are compelling and will be of use to the in vivo imaging community.

      (13) Fig. 6G appears to show a false positive result for the kidney imaging. Is this real, or an artifact of small sample size?

      We agree with reviewer 1 that the kidney signals in figure 6 are somewhat puzzling. The difference between the tumor-bearing mice that received VHH123 and VHHEnh conjugates is not significant – with the obvious caveat that the VHHEnh group is comprised of only 2 mice, so sample size may well be a factor here. If we compare the signals of the VHH123 conjugate in tumor-bearing mice vs. tumor-free mice, the VHH123 conjugates would have cleared much faster in the tumor-free mice over 24 hours (since no epitope is present for VHH123 to bind to), thus weakening the kidney signal observed after 24 hours. The same would be true for all the other tissues – except for the liver (where free <sup>64</sup>Cu that leaks from NOTA accumulates). VHHEnh conjugates in tumor-bearing mice show a significant kidney signal – although no VHH123 target epitope is present in these mice. B16.F10 tumors at 4 weeks of growth tend to be necrotic and can passively retain any radiotracer – this generates the weak lung signal visible in Fig 6D – thus the radiotracer would clear at a slower rate than VHH123 conjugates in tumor-free mice giving a higher kidney signal at 24 hours. 

      No tumors were found in the kidneys post-necropsy. We attribute the differences in kidney signals to di erent kinetics of clearance of the radioconjugates. We have added this explanation to the results and discussion.

      (14) Are the results shown in Fig. 7 generalizable? The authors should the constructs with 18F labeling and without the PEG adduct. 

      We agree with reviewer 1 that it would be very interesting to confirm these observations using 18F radioconjugates. The results should be generalizable, as the difference between signals can only be attributed to the presence of the recognized epitope in the placenta– which is in fact the only variable that differs between the two groups. At the time of conducting the study, we had not planned to perform the same experiments with 18F radioconjugates – partly because synthesis of 18F radioconjugates is more challenging (and costly) than the production of 89Zr-labeled nanobodies.  

      (15) The authors should discuss the relative safety of 89Zr and 64Cu. It is likely to be quite a bit worse than for 18F, since the 89Zr and 64Cu have longer half-lives, dissociate from their chelators, and lodge in off-target tissues. An alternative interpretation of the authors' data could be that 89Zr and 64Cu labeling in this context are unsuitable for the stated purposes of PET imaging. In this case, the key experiments shown in Figs. 5-7 should be repeated with the 18F labeled nanobody constructs. 

      Our vision was to o er a tool to the scientific community interested in in vivo tracking of cells in di erent preclinical disease models. The question of safety regarding 89Zr and 64Cu for clinical use was therefore not a factor we then considered. However, we have now included a section in the discussion about the potential safety issue of <sup>89</sup>Zr release and bone accumulation in clinical settings, especially for radioconjugates that target an internalizing surface protein. 

      (16) The authors should remark on the somewhat surprisingly modest amount of BBB transcytosis in the discussion. What were the a inities of the nanobodies? 

      The a inities and binding kinetics of both nanobodies was described in a separate work that is referenced in the introduction (references 21 and 22 by Wouters Y and colleagues). Through other methods that rely on a highly sensitive bio-assay, it was shown that both VHH123 and VHH188 are capable of transcytosis: both nanobodies coupled to a neurotensin peptide induced a drop of temperature after i.v. injection in matching mouse strains (VHH123 in C57BL/6 and VHH188 in huTfr +/+). The lack of any compelling CNS signal by PET/CT is discussed in the manuscript.

      (17) More details of the methods should be provided in the supplement. 

      a.  What was the source of the penta-mutant Sortase A-His6? 

      Sortase A pentamutant is produced in-house, by cytoplasmic expression in E.coli (BL21 strain), using a plasmid vector encoding a truncated and mutated version of Sortase A. References were added, as well as the Addgene repository number (51140).

      b.  What was the yield of the sortase reactions? 

      For small proteins, such as nanobodies/ V<sub>H</sub>Hs, we find that the yield of a sortase A reaction typically is > 75%. This is what we observed for all our conjugations. The methods section was updated to include this information.

      c.  What was the source of the GGG-Azide-DFO and GGG-Azide NOTA? Based on the structures shown in Fig. 2, these appear to be more complex that was noted in the text. 

      We have now detailed the synthesis of GGG-DFO-Azide and GGG-NOTA-Azide in the supplementary methods.

      d.  More details about the source and purity of the tetrazine and TCO labeling reagents should be provided. 

      We have included information on the synthesis of GGG-tetrazine in the supplementary methods. Concerning the synthesis of <sup>18</sup>F-TCO, we have also included a detailed description of the compound in supplementary methods. The reaction between GGG-tetrazine and <sup>18</sup>F-TCO is now further detailed in the manuscript. 

      e.  The TCO-agarose slurry purification should be explained in more detail, and the results should be shown. 

      We have included a detailed procedure of how the TCO-agarose slurry purification was performed in the methods sections. We had already included the Radio-Thin Layer Chromatography QC data of the final VHH123-18F and VHH188-18F purifications in the supplementary figures – which are obtained immediately after TCOagarose slurry purification. The detailed yields of the TCO-agarose slurry purification in terms of activity of each collected fraction is now detailed in the methods section.

      f.   The CT parameters should be provided.  

      We have now added more information about the PET/CT imaging procedure in the methods section of the manuscript.

      Reviewer #2 (Recommendations for the authors): 

      Authors should discuss the possibility of the TfR as a rejection antigen. Murine TfR is foreign for hTfR+/+ mice and vice versa. 

      We have not discussed this possibility, as we believe the risk of rejection of huTfR+ cells in moTfR+ mice (or vice versa) is negligible. The cells and mice are of the same genetic background – save for the coding region of ectodomain of the TfR (spanning amino acids ~194 to 390 of the full length TfR, which is 763 AA). The pairwise identity of both human and mouse TfR ectodomains is of 73% after alignment of both AA sequences using Clustal Omega. We agree that we cannot formally exclude the possibility of an immune rejection, and have now mentioned this possibility in the discussion.

      Is there any clinical use of the anti-human TfR receptor PET tracer? 

      We do not currently envision an application for the anti-human TfR VHH in PET/CT in a clinical setting.  

      Why is the in vivo anti-mouse TfR uptake level in C57BL/6 mice consistently higher than the anti-human TfR receptor PET tracer in hTfR+/+ mice? Is this due to differences in characteristics of the VHH's (e.g. a inity, internalization properties), or rather due to a different biological behavior of the hTfR-transgene (e.g. reduced internalization properties)? 

      We indeed observed that VHH123 uptake and binding appears to be more robust than that of VHH188 to their respective targets. Moreover, after later times post-injection (> 48h), VHH188 appears to display a very low reactivity to C57BL/6 (moTfR+) cells (see Figure 3B). We attribute this to the respective affinities and specificities of both VHHs. We have not investigated the VHH binding kinetics of the mouse versus humanectodomain TfR proteins in vitro. Internalization should be mildly different at best, as <sup>89</sup>Zr release from DFO occurs with both VHHs in both C57BL/6 and huTfR +/+ mouse models (when injected in a matched configuration). The huTfR +/+ mice rely exclusively on the huTfr for their iron supply. They are healthy with no obvious pathological features. The behavior of the huTfr is therefore presumably similar, if not identical to that of the mouse Tfr, bearing in mind that the huTfr and the mouse Tfr are both reliant on mouse Tf as their ligand

      The anti-TfR VHHs were initially developed as a carrier for BBB-transport of VHH-based drug conjugates (previous publications). The data shown here reduces enthusiasm towards this application. Uptake in the brain is several log-factors lower than physiological uptake elsewhere. Potential consequences of off-brain uptake on potential toxicity of VHH-based drug-conjugates could be better emphasized in the discussion. 

      We did not observe a significant presence of the anti-TfR VHHs in the CNS by PET/CT. We have addressed several possibilities: longer circulation times post-injection may favor transcytosis of the VHHs through the BBB. However, because transcytosis requires endocytosis –<sup>89</sup>Zr may be released by their chelating moiety at this step. The only radiotracers with a covalent bond between the radio-isotope and the VHHs in our work are the <sup>18</sup>F VHHs, but the signal acquisition window may have been too short to observe transcytosis and accumulation in the CNS. Another possible caveat is that PEGylation of the radiotracers may be an obstacle to transcytosis. The circulatory halflife of unpegylated VHHs is too low to allow adequate visualization after 24 hours postinjection, as the conjugates rapidly clear from the circulation (t ½ = 30 minutes or less). We have updated the discussion to address these points.

      In several locations (I have counted 5) a space is missing between words, please double-check. 

      We carefully checked the manuscript to remove any remaining typos.

      It is unclear to me why for the melanoma-tracking experiment the tracer is switched from the 89Zr-labeled variant to the 64Cu-labeled variant. 

      The decision to switch to the <sup>64</sup>Cu labeled VHHs for the melanoma experiment stemmed from a wish to 1) evaluate the performance of the <sup>64</sup>Cu-radioconjugates in detecting transplanted cells as we had done with the <sup>89</sup>Zr conjugates and 2) assess how the (non-specific) liver signal seen with <sup>64</sup>Cu contrasts with a specific signal.  

      typo in discussion: C57BL/6 instead of C57B/6         

      We have corrected the typo.

      It is unclear to me why in FIG1B cells are labeled with 35S. Is it correct that the signals seen are due to staining membranes with anti-TfR mAbs? Or is this an autoradiography of the gel? 

      In Figure 1B cells were labeled with 35S-Met/Cys, while the images shown are indeed those of Western Blots, using an anti-TfR monoclonal antibody as the primary antibody to detect human and mouse TfR retrieved by the anti Tfr VHHs. Autoradiography using the same lysates showed the presence of contaminants in the VHH eluates, as commonly seen in immunoprecipitates from metabolically labeled cells (as distinct from IP/Westerns). For this reason, we performed a Western Blot on the same samples to confirm TfR pull-down. As written in the results section, we also performed LCMS analysis of the immunoprecipitated proteins to better characterize contaminating proteins (Table 1). To clarify this, we have now added the autoradiographs in supplementary data (supplementary figure 15) and added a reference to these observation in the results. 

      ROI quantifications in all figures: these should be expressed as %ID/cc instead of %ID/g. Ex vivo tissue counts should be in %ID/g instead of cpm. 

      We have converted all ROI quantification figures as %ID/cc based on the assumption that 1mL (1cc) = 1g. For ex vivo tissue counts, %ID/g has been calculated based on injected dose (except for figure 3G, where the comparisons in %ID/G are not possible due to the uncertain nature of bone marrow and whole blood). All figures have now been updated.

      Fig4: it would be good to also see respective mouse controls (C57BL6 vs hTfR+/+) for the 64Cu- and 18F-labeled VHH123 tracers. Each radiolabeling methodology changes in vivo biodistribution and specificity, which can be better assessed by using appropriate controls. 

      We had performed these controls but they were not included in the manuscript as deemed redundant with the results of Figure 3. We have now separated Figure 4 in two panels (Figure 4A and 4B) with figure 4A showing the 1h timepoint post-injection of VHH123 radiotracers in C57BL/6 vs huTfr<sup>+/+</sup> and Figure 4B showing the 24h timepoint in the same configuration. ROI analyses were also done on the huTfR<sup>+/+</sup> controls and were included in Figure 4C as well.

      Fig7: is it correct that mouse imaging is performed at 24h p.i. and dissected embryo's at 72h p.i.? Why are there 2 days between each procedure of the same animals? 

      We acquired images at di erent timepoints, specifically at 1h, 24h, 48h and 72 hours after radio-tracer injection. As 72 h was the last timepoint, the mice were sacrificed the same day and embryo dissection performed thereafter, at 72 hours post radiotracer injection. We decided to show the 24h timepoint images as they were the most representative of the series, o ering the best signal-to-noise ratio. The signal pattern did not change over the course from 24h to 72h. We have now added those timepoints in the supplementary data.

    1. Escolha do material escolar: uma questão politica. Pensar na importância da escolha do material e como o seu conteúdo pode refletir no processo de aprendizagem dos estudantes e pensar também, se esse material didático está contemplado com fatos verdadeiros e uma abordagem na qual julgamos correta e saudável.

    1. Příběh se odehrává ve Francii 14. století a je o posledním, oficiálním, Francií posvěceném duelu mezi dvěma muži. Jean de Carrouges (Matt Damon) a Jacques Le Gris (Adam Driver) jsou přátelé, ze kterých se stanou rivalové. Středobodem jejich pře je Marguerite (Jodie Comer), žena Carrougese. Tu Le Gris dle jejích slov napadne, ona ho veřejně obviní, jenže on tvrdí, že je nevinný. Carrouges začne bránit čest své ženy a výsledkem je duel s tím nejvyšším rizikem – smrtí. V případě prokázání nevinny Le Grise navíc Marguerite hrozí ještě upálení za živa. (Falcon)

      .

    1. In 1969, Chicana/o/x activists came together at the University of California, Santa Barbara (UCSB) and published El Plan de Santa Barbara, a document that united diverse activists from around the state of California and laid out a roadmap for Chicana/Chicano Studies, as well as programs to increase the retention, engagement, and success of students from minoritized backgrounds

      hmmm

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      This work integrates two timepoints from the Adolescent Brain Cognitive Development (ABCD) Study to understand how neuroimaging, genetic, and environmental data contribute to the predictive power of mental health variables in predicting cognition in a large early adolescent sample. Their multimodal and multivariate prediction framework involves a novel opportunistic stacking model to handle complex types of information to predict variables that are important in understanding mental health-cognitive performance associations. 

      Strengths: 

      The authors are commended for incorporating and directly comparing the contribution of multiple imaging modalities (task fMRI, resting state fMRI, diffusion MRI, structural MRI), neurodevelopmental markers, environmental factors, and polygenic risk scores in a novel multivariate framework (via opportunistic stacking), as well as interpreting mental health-cognition associations with latent factors derived from partial least squares. The authors also use a large well-characterized and diverse cohort of adolescents from the ABCD Study. The paper is also strengthened by commonality analyses to understand the shared and unique contribution of different categories of factors (e.g., neuroimaging vs mental health vs polygenic scores vs sociodemographic and adverse developmental events) in explaining variance in cognitive performance 

      Weaknesses: 

      The paper is framed with an over-reliance on the RDoC framework in the introduction, despite deviations from the RDoC framework in the methods. The field is also learning more about RDoC's limitations when mapping cognitive performance to biology. The authors also focus on a single general factor of cognition as the core outcome of interest as opposed to different domains of cognition. The authors could consider predicting mental health rather than cognition. Using mental health as a predictor could be limited by the included 9-11 year age range at baseline (where many mental health concerns are likely to be low or not well captured), as well as the nature of how the data was collected, i.e., either by self-report or from parent/caregiver report. 

      Thank you so much for your encouragement.

      We appreciate your comments on the strengths of our manuscript.

      Regarding the weaknesses, the reliance on the RDoC framework is by design. Even with its limitations, following RDoC allows us to investigate mental health holistically. In our case, RDoC enabled us to focus on a) a functional domain (i.e., cognitive ability), b) the biological units of analysis of this functional domain (i.e., neuroimaging and polygenic scores), c) potential contribution of environments, and d) the continuous individual deviation in this domain (as opposed to distinct categories). We are unaware of any framework with all these four features.

      Focusing on modelling biological units of analysis of a functional domain, as opposed to mental health per se, has some empirical support from the literature. For instance, in Marek and colleagues’ (2022) study, as mentioned by a previous reviewer, fMRI is shown to have a more robust prediction for cognitive ability than mental health. Accordingly, our reasons for predicting cognitive ability instead of mental health in this study are motivated theoretically (i.e., through RDoC) and empirically (i.e., through fMRI findings). We have clarified this reason in the introduction of the manuscript.

      We are aware of the debates surrounding the actual structure of functional domains where the originally proposed RDoC’s specific constructs might not fit the data as well as the data-driven approach (Beam et al., 2021; Quah et al., 2025). However, we consider this debate as an attempt to improve the characterisation of functional domains of RDoC, not an effort to invalidate its holistic, neurobiological and basicfunctioning approach. Our use of a latent-variable modelling approach through factor analyses moves towards a data-driven direction. We made the changes to the second-to-last paragraph in the introduction to make this point clear:

      “In this study, inspired by RDoC, we a) focused on cognitive abilities as a functional domain, b) created predictive models to capture the continuous individual variation (as opposed to distinct categories) in cognitive abilities, c) computed two neurobiological units of analysis of cognitive abilities: multimodal neuroimaging and PGS, and d) investigated the potential contributions of environmental factors. To operationalise cognitive abilities, we estimated a latent variable representing behavioural performance across various cognitive tasks, commonly referred to as general cognitive ability or the gfactor (Deary, 2012). The g-factor was computed from various cognitive tasks pertinent to RDoC constructs, including attention, working memory, declarative memory, language, and cognitive control. However, using the g-factor to operationalise cognitive abilities caused this study to diverge from the original conceptualisation of RDoC, which emphasises studying separate constructs within cognitive abilities (Morris et al., 2022; Morris & Cuthbert, 2012). Recent studies suggest an improvement to the structure of functional domains by including a general factor, such as the g-factor, in the model, rather than treating each construct separately (Beam et al., 2021; Quah et al., 2025). The g-factor in children is also longitudinally stable and can forecast future health outcomes (Calvin et al., 2017; Deary et al., 2013). Notably, our previous research found that neuroimaging predicts the g-factor more accurately than predicting performance from separate individual cognitive tasks (Pat et al., 2023). Accordingly, we decided to conduct predictive models on the g-factor while keeping the RDoC’s holistic, neurobiological, and basic-functioning characteristics.”

      Reviewer #2 (Public review):

      Summary: 

      This paper by Wang et al. uses rich brain, behaviour, and genetics data from the ABCD cohort to ask how well cognitive abilities can be predicted from mental-health-related measures, and how brain and genetics influence that prediction. They obtain an out-ofsample correlation of 0.4, with neuroimaging (in particular task fMRI) proving the key mediator. Polygenic scores contributed less. 

      Strengths: 

      This paper is characterized by the intelligent use of a superb sample (ABCD) alongside strong statistical learning methods and a clear set of questions. The outcome - the moderate level of prediction between the brain, cognition, genetics, and mental health - is interesting. Particularly important is the dissection of which features best mediate that prediction and how developmental and lifestyle factors play a role. 

      Thank you so much for the encouragement. 

      Weaknesses: 

      There are relatively few weaknesses to this paper. It has already undergone review at a different journal, and the authors clearly took the original set of comments into account in revising their paper. Overall, while the ABCD sample is superb for the questions asked, it would have been highly informative to extend the analyses to datasets containing more participants with neurological/psychiatric diagnoses (e.g. HBN, POND) or extend it into adolescent/early adult onset psychopathology cohorts. But it is fair enough that the authors want to leave that for future work. 

      Thank you very much for providing this valuable comment and for your flexibility.

      For the current manuscript, we have drawn inspiration from the RDoC framework, which emphasises the variation from normal to abnormal in normative samples (Morris et al., 2022). The ABCD samples align well with this framework.

      We hope to extend this framework to include participants with neurological and psychiatric diagnoses in the future. We have begun applying neurobiological units of analysis for cognitive abilities, assessed through multimodal neuroimaging and polygenic scores (PGS), to other datasets containing more participants with neurological and psychiatric diagnoses. However, this is beyond the scope of the current manuscript. We have listed this as one of the limitations in the discussion section:

      “Similarly, our ABCD samples were young and community-based, likely limiting the severity of their psychopathological issues (Kessler et al., 2007). Future work needs to test if the results found here are generalisable to adults and participants with stronger severity.”

      In terms of more practical concerns, much of the paper relies on comparing r or R2 measures between different tests. These are always presented as point estimates without uncertainty. There would be some value, I think, in incorporating uncertainty from repeated sampling to better understand the improvements/differences between the reported correlations. 

      This is a good suggestion. We have now included bootstrapped 95% confidence intervals in all of our scatter plots, showing the uncertainty of predictive performance.

      The focus on mental health in a largely normative sample leads to the predictions being largely based on the normal range. It would be interesting to subsample the data and ask how well the extremes are predicted. 

      We appreciate this comment. Similar to our response to Reviewer 2’s Weakness #1, our approach has drawn inspiration from the RDoC framework, which emphasises the variation from normal to abnormal in normative samples (Morris et al., 2022). Subsampling the data would make us deviate from our original motivation. 

      Moreover, we used 17 mental healh variables in our predictive models: 8 CBCL subscales, 4 BIS/BAS subscales and 5 UPSS subscales. It is difficult to subsample them. Perhaps a better approach is to test the applicability of our neurobiological units of analysis for cognitive abilities (multimodal neuroimaging and PGS) in other datasets that include more extreme samples. We are working on this line of studies at the moment, and hope to show that in our future work. 

      Reviewer 2’s Weakness #4

      A minor query - why are only cortical features shown in Figure 3? 

      We presented both cortical and subcortical features in Figure 3. The cortical features are shown on the surface space, while the subcortical features are displayed on the coronal plane. Below is an example of these cortical and subcortical features from the ENBack contrast. The subcortical features are presented in the far-right coronal image.

      We separated the presentation of cortical and subcortical features because the ABCD uses the CIFTI format (https://www.humanconnectome.org/software/workbenchcommand/-cifti-help). CIFTI-format images combine cortical surface (in vertices) with subcortical volume (in voxels). For task fMRI, the ABCD parcellated cortical vertices using Freesurfer’s Destrieux atlas and subcortical voxels using Freesurfer’s automatically segmented brain volume (ASEG).

      Due to the size of the images in Figure 3, it may have been difficult for Reviewer 2 to see the subcortical features clearly. We have now added zoomed-in versions of this figure as Supplementary Figures 4–13.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the autors):

      (1) In the abstract, could the authors mention which imaging modalities contribute most to the prediction of cognitive abilities (e.g., working memory-related task fMRI)? 

      Thank you for the suggestion. Following this advice, we now mention which imaging modalities led to the highest predictive performance. Please see the abstract below.

      “Cognitive abilities are often linked to mental health across various disorders, a pattern observed even in childhood. However, the extent to which this relationship is represented by different neurobiological units of analysis, such as multimodal neuroimaging and polygenic scores (PGS), remains unclear. 

      Using large-scale data from the Adolescent Brain Cognitive Development (ABCD) Study, we first quantified the relationship between cognitive abilities and mental health by applying multivariate models to predict cognitive abilities from mental health in children aged 9-10, finding an out-of-sample r\=.36 . We then applied similar multivariate models to predict cognitive abilities from multimodal neuroimaging, polygenic scores (PGS) and environmental factors. Multimodal neuroimaging was based on 45 types of brain MRI (e.g., task fMRI contrasts, resting-state fMRI, structural MRI, and diffusion tensor imaging). Among these MRI types, the fMRI contrast, 2-Back vs. 0-Back, from the ENBack task provided the highest predictive performance (r\=.4). Combining information across all 45 types of brain MRI led to the predictive performance of r\=.54. The PGS, based on previous genome-wide association studies on cognitive abilities, achieved a predictive performance of r\=.25. Environmental factors, including socio-demographics (e.g., parent’s income and education), lifestyles (e.g., extracurricular activities, sleep) and developmental adverse events (e.g., parental use of alcohol/tobacco, pregnancy complications), led to a predictive performance of r\=.49. 

      In a series of separate commonality analyses, we found that the relationship between cognitive abilities and mental health was primarily represented by multimodal neuroimaging (66%) and, to a lesser extent, by PGS (21%). Additionally, environmental factors accounted for 63% of the variance in the relationship between cognitive abilities and mental health. The multimodal neuroimaging and PGS then explained 58% and 21% of the variance due to environmental factors, respectively. Notably, these patterns remained stable over two years. 

      Our findings underscore the significance of neurobiological units of analysis for cognitive abilities, as measured by multimodal neuroimaging and PGS, in understanding both a) the relationship between cognitive abilities and mental health and b) the variance in this relationship shared with environmental factors.”

      (2) Could the authors clarify what they mean by "completing the transdiagnostic aetiology of mental health" in the introduction? (Second paragraph). 

      Thank you. 

      We intended to convey that understanding the transdiagnostic aetiology of mental health would be enhanced by knowing how neurobiological units of cognitive abilities, from the brain to genes, capture variations due to environmental factors. We realise this sentence might be confusing. Removing it does not alter the intended meaning of the paragraph, as we clarified this point later. The paragraph now reads:

      “According to the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework (Insel et al., 2010), cognitive abilities should be investigated not only behaviourally but also neurobiologically, from the brain to genes. It remains unclear to what extent the relationship between cognitive abilities and mental health is represented in part by different neurobiological units of analysis -- such as neural and genetic levels measured by multimodal neuroimaging and polygenic scores (PGS). To fully comprehend the role of neurobiology in the relationship between cognitive abilities and mental health, we must also consider how these neurobiological units capture variations due to environmental factors, such as sociodemographics, lifestyles, and childhood developmental adverse events (Morris et al., 2022). Our study investigated the extent to which a) environmental factors explain the relationship between cognitive abilities and mental health, and b) cognitive abilities at the neural and genetic levels capture these associations due to environmental factors. Specifically, we conducted these investigations in a large normative group of children from the ABCD study (Casey et al., 2018). We chose to examine children because, while their emotional and behavioural problems might not meet full diagnostic criteria (Kessler et al., 2007), issues at a young age often forecast adult psychopathology (Reef et al., 2010; Roza et al., 2003). Moreover, the associations among different emotional and behavioural problems in children reflect transdiagnostic dimensions of psychopathology (Michelini et al., 2019; Pat et al., 2022), making children an appropriate population to study the transdiagnostic aetiology of mental health, especially within a framework that emphasises normative variation from normal to abnormal, such as the RDoC (Morris et al., 2022).“

      (3) It is unclear to me what the authors mean by this statement in the introduction: "Note that using the word 'proxy measure' does not necessarily mean that the predictive model for a particular measure has a high predictive performance - some proxy measures have better predictive performance than others". 

      We added this sentence to address a previous reviewer’s comment: “The authors use the phrasing throughout 'proxy measures of cognitive abilities' when they discuss PRS, neuroimaging, sociodemographics/lifestyle, and developmental factors. Indeed, the authors are able to explain a large proportion of variance with different combinations of these measures, but I think it may be a leap to call all of these proxy measures of cognition. I would suggest keeping the language more objective and stating these measures are associated with cognition.” 

      Because of this comment, we assumed that the reviewers wanted us to avoid the misinterpretation that a proxy measure implies high predictive performance. This term is used in machine learning literature (for instance, Dadi et al., 2021). We added the aforementioned sentence to ensure readers that using the term 'proxy measure' does not necessarily mean that the predictive model for a particular measure has high predictive performance. However, it seems that our intention led to an even more confusing message. Therefore, we decided to delete that sentence but keep an earlier sentence that explains the meaning of a proxy measure (see below).

      “With opportunistic stacking, we created a ‘proxy’ measure of cognitive abilities (i.e., predicted value from the model) at the neural unit of analysis using multimodal neuroimaging.”

      (4) Overall, despite comments from reviewers at another journal, I think the authors still refer to RDoC more than needed in the intro given the restructuring of the manuscript. For instance, at the end of page 4 and top of page 5, it becomes a bit confusing when the authors mention how they deviated from the RDoC framework, but their choice of cognitive domains is still motivated by RDoC. I think the chosen cognitive constructs are consistent with what is in ABCD and what other studies have incorporated into the g factor and do not require the authors to further justify their choice through RDoC. Also, there is emerging work showing that RDoC is limited in its ability to parse apart meaningful neuroimaging-based patterns; see for instance, Quah et al., Nature 2025 (https://doi.org/10.1038/s41467-025-55831-z). 

      Thank you very much for your comment. We have addressed it in our Response to Reviewer 1’s summary, strengths, and weaknesses above. We have rewritten the paragraph to clarify the relevance of our work to the RDoC framework and to recent studies aiming to improve RDoC constructs (including that from Quah and colleagues).

      (5) I am still on the fence about the use of 'proxy measures of cognitive abilities' given that it is defined as the predictive performance of mental health measures in predicting cognition - what about just calling these mental health predictors? Also, it would be easier to follow this train of thought throughout the manuscript. But I leave it to the authors if they decide to keep their current language of 'proxy measure of cognition'. 

      Thank you so much for your flexibility. As we explained previously, this ‘proxy measures’ term is used in machine learning literature (for instance, Dadi et al., 2021). We thought about other terms, such as “score”, which is used in genetics, i.e., polygenic scores (Choi et al., 2020). and has recently been used in neuroimaging, i.e., neuroscore (Rodrigue et al., 2024). However, using a ‘score’ is a bit awkward for mental health and socio-demographics, lifestyle and developmental adverse events. Accordingly, we decided to keep the term ‘proxy measures’.

      (6) It is unclear which cognitive abilities are being predicted in Figure 1, given the various domains that authors describe in their intro. Is it the g-factor from CFA? This should be clarified in all figure captions. 

      Yes, cognitive abilities are operationalised using a second-order latent variable, the g-factor from a CFA. We now added the following sentence to Figure 1, 2, 4 to make this point clearer. Thank you for the suggestion:

      “Cognitive abilities are based on the second-order latent variable, the g-factor, based on a confirmatory factor analysis of six cognitive tasks.”

      (7) I think it may also be worthwhile to showcase the explanatory power cognitive abilities have in predicting mental health or at least comment on this in the discussion. Certainly, there may be a bidirectional relationship here. The prediction direction from cognition to mental health may be an altogether different objective than what the paper currently presents, but many researchers working in psychiatry may take the stance (with support from the literature) that cognitive performance may serve as premorbid markers for later mental health concerns, particularly given the age range that the authors are working with in ABCD. 

      Thank you for this comment. 

      It is important to note that we do not make a directional claim in these cross-sectional analyses. The term "prediction" is used in a machine learning sense, implying only that we made an out-of-sample prediction (Yarkoni & Westfall, 2017). Specifically, we built predictive models on some samples (i.e., training participants) and applied our models to test participants who were not part of the model-building process. Accordingly, our predictive models cannot determine whether mental health “causes” cognitive abilities or vice versa, regardless of whether we treat mental health or cognitive abilities as feature/explanatory/independent variables or as target/response/outcome variables in the models. To demonstrate directionality, we would need to conduct a longitudinal analysis with many more repeated samples and use appropriate techniques, such as a cross-lagged panel model. It is beyond the scope of this manuscript and will need future releases of the ABCD data.

      We decided to use cognitive abilities as a target variable here, rather than a feature variable, mainly for theoretical reasons. This work was inspired by the RDoC framework, which emphasises functional domains. Cognitive abilities is the functional domain in the current study. We created predictive models to predict cognitive abilities based on a) mental health, b) multimodal neuroimaging, c) polygenic scores, and d) environmental factors. We could not treat cognitive abilities as a functional domain if we used them as a feature variable. For instance, if we predicted mental health (instead of cognitive abilities) from multimodal neuroimaging and polygenic scores, we would no longer capture the neurobiological units of analysis for cognitive abilities.

      We now made it clearer in the discussion that our use of predictive models cannot provide the directional of the effects

      “Our predictive modelling revealed a medium-sized predictive relationship between cognitive abilities and mental health. This finding aligns with recent meta-analyses of case-control studies that link cognitive abilities and mental disorders across various psychiatric conditions (Abramovitch et al., 2021; East-Richard et al., 2020). Unlike previous studies, we estimated the predictive, out-of-sample relationship between cognitive abilities and mental disorders in a large normative sample of children. Although our predictive models, like other cross-sectional models, cannot determine the directionality of the effects, the strength of the relationship between cognitive abilities and mental health estimated here should be more robust than when calculated using the same sample as the model itself, known as in-sample prediction/association (Marek et al., 2022; Yarkoni & Westfall, 2017). Examining the PLS loadings of our predictive models revealed that the relationship was driven by various aspects of mental health, including thought and externalising symptoms, as well as motivation. This suggests that there are multiple pathways—encompassing a broad range of emotional and behavioural problems and temperaments—through which cognitive abilities and mental health are linked.”

      (8) There is a lot of information packed into Figure 3 in the brain maps; I understand the authors wanted to fit this onto one page, and perhaps a higher resolution figure would resolve this, but the brain maps are very hard to read and/or compare, particularly the coronal sections. 

      Thank you for this suggestion. We agree with Reviewer 1 that we need to have a better visualisation of the feature-importance brain maps. To ensure that readers can clearly see the feature importance, we added a Zoom-in version of the feature-importance brain maps as Supplementary Figures 4 – 13.

      (9) It would be helpful for authors to cluster features in the resting state functional connectivity correlation matrices, and perhaps use shorter names/acronyms for the labels. 

      Thank you for this suggestion. 

      We have now added a zoomed-in version of the feature importance for rs-fmri as Supplementary Figure 7 (for baseline) and 12 (for follow-up).

      (10) Figures 4a) and 4b): please elaborate on "developmental adverse" in the title. I am assuming this is referring to childhood adverse events, or "developmental adversities". 

      Thank you so much for pointing this out. We meant ‘developmental adverse events’. We have made changes to this figure in the current manuscript.

      (11) For the "follow-up" analyses, I would recommend the authors present this using only the features that are indeed available at follow-up, even if the list of features is lower, otherwise it becomes a bit confusing with the mix of baseline and follow-up features. Or perhaps the authors could make this more clear in the figures by perhaps having a different color for baseline vs follow-up features along the y-axis labels. 

      Thank you for this advice. We have now added an indicator in the plot to show whether the features were collected in the baseline or follow-up. We also added colours to indicate which type of environmental factors they were. It is now clear that the majority of the features that were collected at baseline, but were used for the followup predictive model, were developmental adverse events.

      (12) Minor: Makowski et al 2023 reference can be updated to Makowski et al 2024, published in Cerebral Cortex. 

      Thank you for pointing this out. We have updated the citation accordingly. 

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    1. Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Shin et al. conduct extensive electrophysiological and behavioral experiments to study the mechanisms of short-term synaptic plasticity at excitatory synapses in layer 2/3 of the rat medial prefrontal cortex. The authors interestingly find that short-term facilitation is driven by progressive overfilling of the readily releasable pool, and that this process is mediated by phospholipase C/diacylglycerol signaling and synaptotagmin-7 (Syt7). Specifically, knockdown of Syt7 not only abolishes the refilling rate of vesicles with high fusion probability, but it also impairs the acquisition of trace fear memory.

      Overall, the authors offer novel insight to the field of synaptic plasticity through well-designed experiments that incorporate a range of techniques.

      Comments on revisions:

      The authors have adequately addressed my earlier comments and questions.

      Reviewer #2 (Public review):

      All the comments from Reviewer #2 are the same as her/his comments to our original manuscript. Therefore, we have already responded to all the following comments in the first revision. Here we described our additional responses to the same comments.

      Summary:

      Shin et al aim to identify in a very extensive piece of work a mechanism that contributes to dynamic regulation of synaptic output in the rat cortex at the second time scale. This mechanism is related to a new powerful model and is well versed to test if the pool of SV ready for fusion is dynamically scaled to adjust supply demand aspects. The methods applied are state-of-the-art and both address quantitative aspects with high signal to noise. In addition, the authors examine both excitatory output onto glutamatergic and GABAergic neurons, which provides important information on how general the observed signals are in neural networks. The results are compellingly clear and show that pool regulation may be predominantly responsible. Their results suggests that a regulation of release probability, the alternative contender for regulation, is unlikely to be involved in the observed short term plasticity behavior (but see below). Besides providing a clear analysis of the underlying physiology, they test two molecular contenders for the observed mechanism by showing that loss of Synaptotagmin7 function and the role of the Ca dependent phospholipase activity seems critical for the short term plasticity behavior. The authors go on to test the in vivo role of the mechanism by modulating Syt7 function and examining working memory tasks as well as overall changes in network activity using immediate early gene activity. Finally, they model their data, providing strong support for their interpretation of TS pool occupancy regulation.

      Strengths:

      This is a very thorough study, addressing the research question from many different angles and the experimental execution is superb. The impact of the work is high, as it applies recent models of short term plasticity behavior to in vivo circuits further providing insights how synapses provide dynamic control to enable working memory related behavior through non-permanent changes in synaptic output.

      Weaknesses:

      While this work is carefully examined and the results are presented and discussed in a detailed manner, the reviewer is still not fully convinced that regulation of release probability is not a putative contributor to the observed behavior. No additional work is needed, but in the moment, I am not convinced that changes in release probability are not in play. One solution may be to extend the discussion of changes in rules probability as an alternative.

      As the Reviewer #3 suggested, we examined the dependence of EPSC amplitude on extracellular [Ca<sup>2+</sup>] ([Ca<sup>2+</sup>]<sub>o</sub>) in order to test our assertion that vesicular release probability (p<sub>v</sub>) is already saturated in resting conditions at L2/3 recurrent synapses. A three-fold increase is expected according to Dodge and Rahamimoff (1967), if resting p<sub>v</sub> has enough room to increase, when [Ca<sup>2+</sup>]<sub>o</sub> is elevated from 1.3 to 2.5 mM. We found an increase in the baseline EPSC amplitude only by 23%, and this change was not statistically significant, supporting our assertion.

      Fig 3. I am confused about the interpretation of the Mean Variance analysis outcome. Since the data points follow the curve during induction of short term plasticity, doesn't these suggests that release probability and not the pool size increases?

      We separated the conventional release probability into a multiplication of p<sub>v</sub> and p<sub>occ</sub>, in which p<sub>v</sub> = probability of TS vesicles and p<sub>occ</sub> = occupancy of release sites by TS vesicles. In this regard, the abscissa of V-M plot represents the conventional release probability. Because p<sub>v</sub> is close to unity, we interpreted a change along the abscissa as a change of p<sub>occ</sub>.

      Related, to measure the absolute release probability and failure rate using the optogenetic stimulation technique is not trivial as the experimental paradigm bias the experiment to a given output strength, and therefore a change in release probability cannot be excluded.

      We agree to this concern. Because EPSC data were obtained by optogenetic stimulation, it cannot be ruled out a possibility that optogenetic stimulation biased the release probability. Although we found that STP obtained by dual patch experiment was not different from that by optogenetic stimulation, it needs to confirm our conclusion using dual patch or other methods.

      Fig. 4B interprets the phorbol ester stimulation to be the result of pool overfilling, however, phorbol ester stimulation has also been shown to increase release probability without changing the size of the readily releasable pool. The high frequency of stimulation may occlude an increased paired pulse depression in presence of OAG, that others have interpreted in mammalian synapses as an increase in release probability.

      Provided that pv of TS vesicles is very high, the OAG-induced increase in EPSC1 and low STF and PTA are consistent with higher baseline p<sub>occ</sub> in PDBu conditions, while the number of docking sites is limited. It should be noted that previous PDBu-induced invariance of the RRP size is based on measuring the RRP size using hypertonic solution (Basu et al., 2007). Given that this sucrose method releases not only TS but also LS vesicles, the sucrose-based RRP size may not be affected by PDBu or OAG at L2/3 synapses too. Therefore, PDBu or OAG-induced increase in p<sub>occ</sub> (proportion of TS vesicles over LS+TS vesicles) would result in an increase in release probability without a change in the RRP size.

      The literature on Syt7 function is still quite controversial. An observation in the literature that loss of Syt7 function in the fly synapse leads to an increase of release probability. Thus the observed changes in short term plasticity characteristics in the Syt7 KD experiments may contain a release probability component. Can the authors really exclude this possibility? Figure 5 shows for the Syt7 KD group a very prominent depression of the EPSC/IPSC with the second stimulus, particularly for the short interpulse intervals, usually a strong sign of increased release probability, as lack of pool refilling can unlikely explain the strong drop in synaptic output.

      Comments on revisions:

      I am satisfied with the reply of the authors and I do not have any further points of concern.

      Reviewer #3 (Public review):

      The results are consistent with the main claim that facilitation is caused by overfilling a readily releasable pool, but alternative interpretations continue to seem more likely, especially when the current results are taken together with previous studies. Key doubts could be resolved with a single straightforward experiment (see below).

      The central issue is the interpretation of paired pulse depression that occurs when the interval between action potentials is 25 ms, but not when 50. To summarize: a similar phenomenon was observed at Schaffer collateral synapses (Dobrunz and Stevens, 1997), but was interpreted as evidence for a decrease in pv. Ca2+-channel inactivation was proposed as the mechanism, but this was not proven. The key point for evaluating the current study is that Dobrunz and Stevens specifically ruled out the kind of decrease in pocc that is the keystone premise of the current study because the depression occurred independently of whether or not the first action potential elicited exocytosis. Of course, the mechanism might be different at layer 2/3 cortical synapses. But, it seems reasonable to hope that the older hypothesis would be ruled out for the cortical synapses before concluding that the new hypothesis must be correct.

      The old and new hypotheses could be distinguished from each other cleanly with a straightforward experiment. Most/maybe all central synapses strengthen a great amount when extracellular Ca2+ is increased from 1.3 to 2 mM, even when intracellular Ca2+ is buffered with EGTA. According to the authors' model, this is only possible when pv is low, and so could not occur at synapses between layer 2/3 neurons. Because of this, confirmation that increasing extracellular Ca2+ does not change synaptic strength would support the hypothesis that baseline pv is high, as the authors claim, and the support would be impressive because large changes have been seen at every other type of synapse where this has been studied (to my knowledge at least). In contrast, the Ca2+ imaging experiment that has been added to the new version of the manuscript does not address the central issue because a wide range of mechanisms could, in principle, decrease release without involving prior exocytosis or altering bulk Ca2+ signals, including: a small decrease in nano-domain Ca2+, which wouldn't be detected because nano-domains contribute a minuscule amount to the bulk signal during Ca2+-imaging; or even very fast activity-dependent undocking of synaptic vesicles, which was reported in the same Kusick et al, 2020 study that is central to the LS/TS terminology adopted by the authors.

      Additional points:

      (1) A new section in the Discussion (lines 458-475) suggests that previous techniques employed to show that augmentation and facilitation are caused by increases in pv did not have the resolution to distinguish between pv and pocc, but this is misleading. The confusion might be because the terminology has changed, but this is all the more reason to clarify this section. The previous evidence for increases in pv - and against increases in pocc - is as follows: The residual Ca2+ that drives augmentation decreases the latency between the onset of hypertonic solution and onset of the postsynaptic response by about 150 ms, which is large compared to the rise time of the response. The decrease indicates that the residual Ca2+ drives a decrease in the energy barrier that must be overcome before readily releasable vesicles can undergo exocytosis, which is precisely the type of mechanism that would enhance pv. In contrast, an increase in pocc could change the rise time, but not the latency. There is a small change in the rise time, but this could be caused by changes in either pv or pocc, and one of the studies (Garcia-Perez and Wesseling, 2008) showed that augmentation occluded facilitation, even at times when pocc was reduced by a factor of 3, which would seem to argue against parallel increases in both pv and pocc.

      We greatly appreciate for pointing out our mis-understanding. We acknowledge that the post-tetanic acceleration of the latency in the hypertonicity-induced vesicle release may reflect a decrease in the activation energy barrier (ΔEa) for vesicle fusion resulting in an increase in fusion probability of TS vesicles (Stevens and Wesseling, 1999; Garcia-Perez and Wesseling, 2008). We agree that such latency changes are not easily explained by increases in p<sub>occ</sub> alone. Indeed, Taschenberger et al (2016) concluded that PTP is similar to the PDBu-induced increase in baseline EPSCs. Subsequently, Lin et al (2025) estimated PDBu-induced changes of TS vesicle pool size and p_fusion of TS vesicles (these correspond to p<sub>occ</sub> and p<sub>v</sub> in this study, respectively), and found that PDBu increases majorly the former (2 folds) and minorly the latter (1.3 folds). Although it has not been directly tested, it is possible that PTP increases p<sub>v</sub>. Accordingly, we corrected the first statement of the paragraph, and mentioned the possibility for a post-tetanic increase in p<sub>v</sub> of TS vesicles.

      It should be noted, however, it is still puzzling what is represented by the acceleration of the latency in the hypertonicity-induced vesicle release. Schotten et al (2015) simulated how vesicle release rate is affected by reducing ΔEa for vesicle fusion. They found that a reduction of ΔEa resulted in increases in the peak amplitude and shorter time-to-peak of vesicle fusion, but did not accelerate the latency. Therefore, it remains to be clarified whether shorter latency can be regarded as lower activation barrier.  Moreover, the sucrose-induced release rate is comparable with the vesicle recruitment rate (1-2/s; Neher, Neuron, 2008). This slowness of sucrose-induced vesicle release rate makes it difficult to distinguish the vesicle fusion rate from their priming rate.

      (2) Similar evidence from hypertonic stimulation indicates that Phorbol esters increase pv, but I am not aware of evidence ruling out a parallel increase in pocc.

      As noted above, none of known mechanisms can clearly explain the PDBu-induced shorter latency to hypertonicity-induced vesicle fusion (Schotten et al, 2015). Even if shorter latency reflects higher p<sub>v</sub>, it does not rule out a concurrent change in p<sub>occ</sub>. Supporting this notion, Lin et al. (2025) showed in the framework of the two state vesicle fusion model that PDBu application leads to a substantial increase in the number of TS vesicles (vesicles having high fusion propensity), with a moderate change in fusion probability (p<sub>fusion</sub>). In light of previous observation that high tonicity (500 or 1000 mOsm) did not alter the RRP size (Basu et al., 2007), the results of Lin et al. (2025) can be interpreted as an increase of ‘p<sub>occ</sub>’ in terms of the present study.

      Reference:

      Schotten et al. (2015). Additive effects on the energy barrier for synaptic vesicle fusion cause supralinear effects on the vesicle fusion rate. eLife 4:e05531.

      Lin, K.-H., Ranjan, M., Lipstein, N., Brose, N., Neher, E., & Taschenberger, H. (2025). Number and relative abundance of synaptic vesicles in functionally distinct priming states determine synaptic strength and short-term plasticity. J. Physiology.

      Comments on revisions:

      There are at least two straightforward ways to address the main concern.

      The first would be experiments analogous to those in Dobrunz and Stevens that show that - unlike at Schaffer collateral synapses - paired pulse depression at L2/3 synapses requires neurotransmitter release. I proposed this in the first round, but realized since that a simpler and more powerful strategy would be to test directly that pv is/is-not near 1.0 in 1.2 mM Ca2+ simply by increasing to 2 mM Ca2+ (and showing that synaptic strength does-not/does change). This would be powerful because the increase in Ca2+ greatly increases synaptic strength at Schaffer collaterals by about 2.5-fold. Concerns about a confounding elevation in the basal intracellular Ca2+ concentration could be easily neutralized by pre-treating with EGTA-AM, which the authors have already done for other experiments.

      We thank to Reviewer #3 for suggesting an experiment for testing our assertion that the vesicular release probability (p<sub>v</sub>) is very high at layer 2/3 recurrent excitatory synapses. As the Reviewer recommended, we assessed EPSC changes induced by an increase in extracellular calcium concentration ([Ca<sup>2+</sup>]<sub>o</sub>). The results are added as Figure 3—figure supplement 3 to the revised manuscript.

      Dodge and Rahamimoff (1967) discovered a fourth-power relationship between end-plate potential (EPP) and [Ca<sup>2+</sup>]<sub>o</sub> at a neuromuscular junction. More specifically they found

      EPP amplitude µ  ([Ca<sup>2+</sup>]<sub>o</sub> / (1 + [Ca<sup>2+</sup>]<sub>o</sub> /1.1 mM + [Ma<sup>2+</sup>]<sub>o</sub> /2.97 mM))<sup>4</sup>.

      This equation nicely predicts the effects of high external calcium on EPSC amplitudes observed at the calyx synapses: a 2.6-fold increase of EPSC by changing [Ca<sup>2+</sup>]<sub>o</sub> from 1.25 to 2 mM  (Thanawala and Regehr, 2013; predicted as 2.57);  a 2.36-fold increase by changing [Ca<sup>2+</sup>] from 1.5 to 2 mM (Lin and Taschenberger, 2025; predicted as 2.16). In the framework of two-step priming model, Lin et al. (2015) estimated a 1.9-fold increase (from 0.22 to 0.42) in p<sub>v</sub> of TS vesicles and a 1.23-fold increase in the number of TS vesicles. It is clear that the increase in p<sub>v</sub> would be possible only if p<sub>v</sub> is not saturated, while the increase in the number of TS vesicles is still possible regardless of baseline p<sub>v</sub> of TS vesicles.

      The Dodge and Rahamimoff’s equation predicts a 3.24-fold increase in baseline EPSC amplitude by elevating [Ca Ca<sup>2+</sup>]<sub>o</sub> from 1.3 mM to 2.5 mM at L2/3 synapses. Contrary to this prediction, our recordings revealed a 1.23 fold increase in baseline EPSC amplitude, and this change was not statistically significant.

      Given the steep dependence of vesicle release on [Ca<sup>2+</sup>]<sub>o</sub>, this minimal increase strongly suggests that p<sub>v</sub> at L2/3 recurrent synapses is already near maximal at rest, limiting the dynamic range for further enhancement through increased calcium influx. Accordingly, we observed a small but statistically significant decrease in the paired-pulse ratio (PPR) at higher [Ca<sup>2+</sup>]<sub>o</sub>. Although this reduction in PPR might be indicative of increased p<sub>v</sub>, it is more consistent with a slight increase in p<sub>occ</sub> rather than a substantive increase in p<sub>v</sub> under the context of very high p<sub>v</sub>. Accordingly, Lin et al. (2025) recently estimated an increase in the TS vesicle subpool size as 1.23-fold by elevating [Ca<sup>2+</sup>]<sub>o</sub> under the framework of the two-step vesicle priming mode. Taken together, these findings suggest that an increase in the number of TS vesicles or p<sub>occ</sub> may contribute to both an increase in baseline EPSC amplitudes and a decrease in PPR.

      Overall, our central claim that baseline p<sub>v</sub> is near maximal at L2/3 recurrent synapses is supported by 1) high baseline PPR; 2) insensitivity to EGTA-AM; 3) high double failure rate; 4) insensitivity to elevating [Ca<sup>2+</sup>]<sub>o</sub>. These data are difficult to reconcile with a model in which facilitation is mediated by Ca<sup>2+</sup>-dependent increases in p<sub>v</sub>. Instead, our results support a mechanism in which facilitation arises from changes in release site occupancy.

      References

      Dodge, F.A., & Rahamimoff, R. (1967). Co-operative action of calcium ions in transmitter release at the neuromuscular junction. J Physiol, 193(2), 419–432. 

      Thanawala, M.S., & Regehr, W.G. (2013). Presynaptic calcium influx controls neurotransmitter release in part by regulating the effective size of the readily releasable pool. J Neurosci, 33(11), 4625–4633.

      Lin, K.-H., Ranjan, M., Lipstein, N., Brose, N., Neher, E., & Taschenberger, H. (2025). Number and relative abundance of synaptic vesicles in functionally distinct priming states determine synaptic strength and short-term plasticity. J. Physiology.

      Neher E, Sakaba T (2008) Multiple Roles of Calcium Ions in the Regulation of Neurotransmitter Release. Neuron 59:861-872.

    1. hases of the Event

      1/3

      Új eseményfázisok bevezetése • Új fázisok: o Early Setup (előzetes felépítés) o Extended Teardown (meghosszabbított bontás) • Ezek részletes kezelése, a folyamatba való integrálása szükséges.

    Annotators

    1. Cuan- do se recurre explícitamente a fuentes de segunda mano, el pro- blema es que se ha de verificar más de una y ver si cierta cita o referencia a un hecho u opinión es confirmada por varios auto- res.

      Hacer comentarios. P. ej. citar a Sullivan y hacer notas al pie comparando con otro autor.

    1. EL HABITUS Y EL ESPACIO DE LOS ESTILOS DE VID

      O objetivo deste exercício é ler atentamente o texto proposto e identificar os conceitos que considerar mais relevantes para a análise da arquitectura e do urbanismo. Cada conceito selecionado deve ser comentado de forma crítica, justificando a sua escolha e explicando qual a relação que estabelece com a prática arquitectónica e urbanística. Reflicta sobre a forma como a compreensão desse conceito pode contribuir para o desenvolvimento do pensamento projectual, para a interpretação do espaço construído ou para a abordagem de problemas concretos no exercício da profissão.

    1. De 25 alumnos, 23 ya tenían móvil desde el año pasado. Les pregunté quién les había enseñado a usarlo. Solo una chica dijo que su hermana mayor. El resto, con 10 o 11 años, ya estaban completamente inmersos en total soledad.

      Honetan ere ados: ezin zaie eman erabiltzen irakatsi gabe. Berak irakastea aurreratzea proposatzen du; beste batzuk, ematea atzeratzea, irakasteko eta ikaskitakoa abian jartzeko heldutasuna garatzeko denbora egon dadin.

    1. intelligence and Consciousness go together we solve problems based on our feelings our feelings are not something that kind of evolution decoration it's the core system through which marals make decisions and solve problems is based on our feelings

    1. The Point of a College Education by [[C-SPAN]]

      Orson Welles quote about so many of him and so few of you at a lecture to 3-4 people in a snow storm.

      statistics about the drops in humanities (~42:00)

      consumerist spirit in higher education (45:00)

      student evaluations (47:00)

      education is a buyer's market now instead of a seller's as it had been in past generations

      grade inflation

      consumerism with respect to feminism and women's studies, gay and lesbian studies, multiculturalism in higher education

      radical education as "going to the root"

      in short, "let us entertain you" as consumerist education

      "The job o education is never finished."

      The hidden point of a University of Chicago education: Be an artist, be a scientist, be a statesman, be a teacher of artists, scientists, or statesmen.

    1. Zbuduj wiarygodność rynkową i zapewnij klientów i partnerów o jakości i bezpieczeństwie swojej marki dzięki certyfikacji Benchmark.

      Zbuduj wiarygodność rynkową i zapewnij klientów oraz partnerów o jakości i bezpieczeństwie swojej marki dzięki certyfikacji Benchmark.

    1. 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

      Reviewer #1 (Evidence, Reproducibility, and Clarity)

      Reviewer comment: This is a very well conceived study of responses to plasma membrane stresses in yeast that signal through the conserved TORC2 complex. Physical stress through small molecular intercalators in the plasma membrane is shown to be independent of their biochemistry and then studies for its effect on plasma membrane morphology and the distribution of free ergosterol (the yeast equivalent of cholesterol), with free being the pool of cholesterol that is available to probes and/or sterol transfer proteins. Experiments nicely demonstrate a negative feedback loop consisting of: stress -> increased free sterol and TORC2 inhibition -> activation of LAM proteins (as demonstrated by Relents and co-workers previously) -> removal of free sterol -> return to unstressed state of PM and TORC2.

      Author response: We thank the reviewer for their positive and encouraging feedback. We are pleased to submit our revised manuscript and have addressed all points raised below.

      Comment: Fig 2A: Is detection of PIP/PIP2/PS linear for target, or possibly just showing availability that is increased due to local positive curvature?

      Response: This is an excellent and fundamental question. While FLARE signal likely reflects lipid availability, its detection is indeed influenced by factors such as membrane curvature and lipid composition, due to varying insertion depths of the lipid-binding domains. For example, studies using NMR suggest that the PLCδ PH domain partially inserts into membranes, potentially conferring curvature sensitivity (Flesch et al., 2005; Uekama et al., 2009). Similarly, curvature influences lactadherin binding, though it's unclear if this extends to its isolated C2 domain (Otzen et al., 2012; Shao et al., 2008; Shi et al., 2004). We could not find direct evidence for curvature sensitivity of P4C(SidC), but assume some influence exists.

      To avoid overinterpreting these limitations, we now describe our data based solely on the FLAREs used, rather than inferring enrichment of specific lipid species. We refer to these PM structures as "PI(4,5)P₂-containing", consistent with prior literature (Riggi et al., 2018) and have revised our manuscript accordingly.

      Comment: Can any marker be identified for the D4H spots at 2 minutes? In particular, are they early endosomes (shown by brief pre-incubation with FM4-64)?

      Response: We appreciate the reviewer's suggestion and have now added new data (Fig. S2E-H). We tested colocalization of D4H spots with FM4-64 (early endosomes), GFP-VPS21 (early endosome marker), and LipidSpot{trade mark, serif} 488 (lipid droplets), but found no overlap. This later observation was not unexpected given that D4H does not recognize Sterol esters. D4H foci also did not overlap with ER (dsRED-HDEL), though they were frequently adjacent to it. While their exact identity remains unknown, we agree this is an intriguing direction for future investigation.

      Comment: Is there any functional (& direct) link between Arp inhibition (as in the Pombe study of LAMs by the lab of Sophie Martin) and PM disturbance by amphipathic molecules?

      Response: We have explored this connection and now present new data (see final paragraph of Results). Briefly, we show that CK-666 induces internalization of PM sterols in a Lam2/4-dependent manner, and that TORC2 activity is more strongly reduced in lam2Δ lam4Δ cells compared to WT. These findings support the idea that, like PalmC, Arp2/3 inhibition triggers a PM stress that is counteracted by sterol internalization.

      Minor Comment: Fig 2A: Labels not clear. Say for each part what FP is used for pip2.

      Response: As noted above, we revised image labels to clarify which FLAREs were used, and refer to data accordingly throughout.

      Minor Comment: Move fig s2d to main ms. The 1 min and 2 min data are integral to the story.

      Response: We agree and have incorporated the 1-min and 2-min data into the main figures. Vehicle-treated controls were moved to Fig. S2.

      Minor Comment: The role of Lam2 and Lam4 in retrograde sterol transport has in vivo only been linked to one of their two StART domains not both, as mentioned in the text.

      Response: Thank you for pointing this out. We have corrected the text to:

      "[...]Lam2 and Lam4[...] contain two START domains, of which at least one has been demonstrated to facilitate sterol transport between membranes (Gatta et al., 2015; Jentsch et al., 2018; Tong et al., 2018)."

      Minor Comment: Throughout, images of tagged D4H should be labelled as such, not as "Ergosterol".

      Response: We have updated all relevant figure labels and text to refer to "D4H" rather than "Ergosterol", in line with this recommendation.

      Reviewer #1 (Significance):

      These results in budding yeast are likely to be directly applicable to a wide range of eukaryotic cells, if not all of them. I expect this paper to be a significant guide of research in this area. The paper specifically points out that the current experiments do not distinguish the precise causation among the two outcomes of stress: increased free sterol and TORC2 inhibition. Of these two outcomes which causes which is not yet known. If data were added that shed light on this causation that would make this work much more signifiant, but I can understand 100% that this extra step lies beyond - for a later study for which the current one forms the bedrock.

      Response:

      We thank the reviewer for their generous assessment. We agree that understanding the causality between increased free sterol and TORC2 inhibition is a critical next step.

      Based on our current data, we believe the increase in free ergosterol precedes TORC2 inhibition. For example, TORC2 inhibition alone (e.g., via pharmacological means) does not initially increase free sterol, while it does enhance Lam2/4 activity, promoting sterol internalization (Fig. 3A). Baseline TORC2 activity also inversely correlates with free PM sterol levels in lam2Δ lam4Δ versus LAM2T518A LAM4S401A cells (Figs. 2D, S2C).

      Additionally, during sterol depletion, we observe an initial increase in TORC2 activity before growth inhibition occurs, after which activity declines-likely due to compromised PM integrity (Fig. S2M). We now also show that adaptation to several other stresses (e.g., osmotic shock, heat shock, CK-666) partially depends on sterol internalization, which correlates with TORC2 activation (Fig. 4, S4B).

      While these findings strengthen the model that PM stress perturbs sterol availability and secondarily impacts TORC2, we cannot yet definitively demonstrate causality. As suggested by Reviewer 3, we tested cholesterol-producing yeast (Souza et al., 2011), but found their response to PalmC indistinguishable from WT, making it difficult to draw mechanistic conclusions (Rebuttal Fig. 2).

      Taken together, we favour a model where sterols affect PM properties sensed by TORC2, probably lipid-packing, rather than acting as direct effectors. We hope our revised manuscript more clearly conveys this model and serves as a strong foundation for future mechanistic studies.

      Reviewer #2 (Evidence, Reproducibility, and Clarity)

      Reviewer comment: This manuscript describes multiple effects of positively-charged membrane-intercalating amphipaths (palmitoylcarnitine, PalmC, in particular) on TORC2 in yeast plasma membranes. It is a "next step" in the Loewith laboratory's characterization of the effect of this agent on this system. The study confirms the findings of Riggi et al.(2018) that PalmC inhibits TORC2 and drives the formation of membrane invaginations that contain phosphatidylinositol-bis-phosphate (PIP2) and other anionic phospholipids. It also demonstrates that PalmC intercalates into the membrane, acts directly (rather than through secondary metabolism) and is representative of a class of cationic amphipaths. The interesting finding here is that PalmC causes a rapid initial increase in the plasma membrane ergosterol accessible to the DH4 sterol probe followed by a decrease caused by its transfer to the cytoplasm through its transporter, LAM2/4. TORC2 is implicated in these processes. Loewith et al. have pioneered in this area and this study clearly shows their expertise. Several of the findings reported here are novel. However, I am concerned that PalmC may not be revealing the physiology of the system but rather adding tangential complexity. (This concern applies to the precursor studies using PalmC to probe the TORC2 system.) In particular, I am not confident that the data justify the authors' conclusions "...that TORC2 acts in a feedback loop to control active sterol levels at the PM and [the results] introduce sterols as possible TORC2 signalling modulators."

      Author response:

      We thank Reviewer #2 for the constructive and critical evaluation of our work. We appreciate the acknowledgment of the novelty and technical strength of several of our findings, and we understand the concern that PalmC could be eliciting non-physiological effects. Our study was designed precisely to use PalmC and similar membrane-active amphipaths as tools to strongly perturb the plasma membrane (PM) in a controlled and tractable way. We now state this intention explicitly in both the Introduction and Discussion sections. To address concerns about the specificity and physiological relevance of PalmC, we have expanded our dataset to include additional PM stressors (hyperosmotic shock, Arp2/3 inhibition, and heat shock), all of which reproduce key features observed with PalmC-namely, TORC2 inhibition, PM invaginations, and retrograde sterol transport (Fig. 4, S4).

      We hope this more comprehensive dataset, along with revised discussion and clarified claims, addresses the reviewer's concerns regarding physiological interpretation and artifact.

      Major issues 1 and 2: 1. The invaginations induced by PalmC may not be physiologic but simply the result of the well-known "bilayer couple" bending of the bilayer due to the accumulation of cationic amphipaths in the inner leaflet of the plasma membrane bilayer which is rich in anionic phospholipids. Such unphysiological effects make the observed correlation of invagination with TORC2 inhibition etc. hard to interpret.

      Electrostatic/hydrophobic association of PIP2 with PalmC could sequester the anionic phospholipid(s). Such associations could also drive the accumulation of PIP2 in the invaginations. This could explain PalmC inhibition of TORC2 through a simple physical rather than biological process. So, it is difficult to draw any physiological conclusion about PIP2 from these experiments.

      Response to major issues 1 and 2:

      We agree that amphipath-induced bilayer stress, including via the bilayer-couple mechanism, may contribute to PM curvature changes. However, the reviewer's assumption that PalmC inserts preferentially into the inner leaflet appears inconsistent with both literature and our observations. PalmC is zwitterionic, not cationic, and is unlikely to electrostatically sequester anionic lipids such as PIP2. For clarification, we included a short summary of our proposed mechanism of PalmC in the context of the current literature in our Discussion:

      "[...] study it was also demonstrated that addition of phospholipids to the outer PM leaflet causes an excess of free sterol at the inner PM leaflet, and its subsequent retrograde transport to lipid droplets (Doktorova et al., 2025). Although we cannot exclude that it is the substrate of a flippase or scramblase, PalmC is not a metabolite found in yeast, nor, given its charged headgroup, is it likely to spontaneously flip to the inner leaflet (Goñi, Requero and Alonso, 1996). Thus, we propose that PalmC accumulates in the outer leaflet, disrupts the lipid balance with the inner leaflet which is, similarly to the mammalian cell model (Doktorova et al., 2025), rectified by sterol mobilization, flipping and internalization (Fig. 5B)."

      While we agree that PM invaginations per se are not the central focus of this study, they are indeed a reproducible and biologically intriguing phenomenon. We emphasize that similar invaginations occur not only during PalmC treatment but also in response to other physiological stresses, such as hyperosmotic shock and Arp2/3 inhibition (Fig. 4), and have been reported independently by others (Phan et al., 2025). Furthermore, related structures have been documented in yeast mutants with altered PIP2 metabolism or TORC2 hyperactivity (Rodríguez-Escudero et al., 2018; Sakata et al., 2022; Stefan et al., 2002), and even in mammalian neurons with SJ1 phosphatase mutations (Stefan et al., 2002). These observations support our interpretation that the observed invaginations represent an exaggerated manifestation of a physiologically relevant stress-adaptive process. In our previous study we indeed proposed that PI(4,5)P2 enrichment in PM invaginations was important for PalmC-induced TORC2 inactivation, using the heat sensitive PI(4,5)P2 kinase allele mss4ts - a rather blunt tool (Riggi et al., 2018). We have now come to the conclusion that different mechanisms other than, or in addition to, PIP2 changes drive TORC2 inhibition in our system. In this study, we use the 2xPH(PLC) FLARE exclusively as a generic PM marker, not as a readout of PIP2 biology. Rather, we propose that sterol redistribution and/or the biophysical impact that this has on the PM are central drivers, with TORC2 acting as a signaling node that senses and adjusts PM composition accordingly.

      We now clarify these arguments in the revised Discussion and have reframed our use of PalmC as a probe to explore the capacity of the PM to adapt to acute stress via dynamic lipid rearrangements.

      Major issue 3:

      As the authors point out, a large number of intercalated amphipaths displace sterols from their association with bilayer phospholipids. This unphysiologic mechanism can explain how PalmC causes the transient increase in the availability of plasma membrane ergosterol to the D4H probe and its subsequent removal from the plasma membrane via LAM2/4. TORC2 regulation may not be involved. In fact, the authors say that "TORC2 inhibition, and thereby Lam2/4 activation, cannot be the only trigger for PalmC induced sterol removal." Furthermore, the subsequent recovery of plasma membrane ergosterol could simply reflect homeostatic responses independent of the components studied here.

      Response:

      We agree that increased free sterols in the inner leaflet likely initiate retrograde transport. Our results suggest that TORC2 inhibition facilitates this process by disinhibiting Lam2/4, allowing more efficient clearance of ergosterol from the PM (Fig. 3A, S2C). However, the process is not exclusively dependent on TORC2, and we state this explicitly.

      We do not observe recovery of PM ergosterol on the timescales measured, while TORC2 activity recovers, suggesting that restoration likely occurs later via biosynthetic or anterograde trafficking pathways, which are outside the scope of this study. These points are clarified in the revised Discussion.

      Major issue 3a:

      The data suggest that LAM2/4 mediates the return of cytoplasmic ergosterol to the plasma membrane. To my knowledge, this is a nice finding that not been reported previously and is worth confirming more directly.

      Response:

      We thank the reviewer for this observation but would like to clarify a misunderstanding: our data do not suggest that Lam2/4 mediates anterograde sterol transport. Our results and prior work (Gatta et al., 2015; Roelants et al., 2018) show that Lam2/4 mediate retrograde transport from the PM to the ER, and TORC2 inhibits this process. We now clarify this point in the revised manuscript, stating:

      "In vivo, Lam2/4 seem to predominantly transport sterols from the PM to the ER, following the concentration gradient (Gatta et al., 2015; Jentsch et al., 2018; Tong et al., 2018)."

      Major issue 4:

      I agree with the authors that "It is unclear if the excess of free sterols itself is part of the inhibitory signal to TORC2..." Instead, the inhibition of TORC2 by PalmC may simply result from its artifactual aggregation of the anionic phospholipids (especially, PIP2) needed for TORC2 activity. This would not be biologically meaningful. If the authors wish to show that accessible ergosterol inhibits TORC2 activity or vice versa, they should use more direct methods. For example, neutral amphipaths that do not cause the aforementioned PalmC perturbations should still increase plasma membrane ergosterol and send it through LAM2/4 to the ER.

      Response:

      We now provide evidence that three orthologous treatments (hyperosmotic shock, heat shock and Arp2/3 inhibition) similarly cause sterol mobilization and, in the absence of sterol clearance from the PM, prolonged TORC2 inhibition. These results do not support the reviewer's contention that the inhibition of TORC2 by PalmC is simply resulting from its artifactual aggregation of the anionic phospholipids. Furthermore, PalmC is zwitterionic, and its interaction with anionic lipids should be somewhat limited.

      In our experimental setup, neutral amphipaths did not trigger TORC2 inhibition or D4H redistribution While this differs from prior in vitro work (Lange et al., 2009), we attribute this in part to a discrepancy to experimental setup differences, including flow chamber artifacts that we discuss in the methods section.

      Importantly, only amphipaths with a charged headgroup, including zwitterionic (PalmC) and positively charged analogs, produced robust effects. A negatively charged derivative also seemed to have a minor effect on TORC2 activity and PM sterol internalization (Palmitoylglycine (Fig. 1D, Rebuttal Fig. 1). This suggests that in vivo, charge-based membrane perturbation is required to alter PM sterol distribution and TORC2 activity.

      Major issue 5.:

      The mechanistic relationship between TORC2 activity and ergosterol suggested in the title, abstract, and discussion is not secure. I agree with the concluding section of the manuscript called "Limitations of the study". It highlights the need for a better approach to the interplay between TORC2 and ergosterol.

      Response:

      This may have been true of the previous submission, but we now demonstrate that provoking PM stress in four orthogonal ways triggers mobilization of sterols, which left uncleared, prevents normal (re)activation of TORC2 activity. We thus conclude that free sterols, directly or more likely indirectly, inhibit TORC2. The role that TORC2 plays in sterol retrotranslocation has been demonstrated previously (Roelants et al., 2018). We believe our expanded data and clarified framework make a compelling case for a stress-adaptive role of sterol retrograde transport that is supervised and modulated-but not fully driven-by TORC2 activity.

      Thus, we feel in the present version of this manuscript that the title is now justified.

      Minor issue: Based on earlier work using the reporter fliptR, the authors claim that PalmC reduces membrane tension. They should consider that this intercalated dye senses many variables including membrane tension but also lipid packing. I suspect that, by intercalating into and thereby altering the bilayer, PalmC is affecting the latter rather than the former.

      Response:

      We thank the reviewer for this important point regarding the multifactorial sensitivity of intercalating dyes such as Flipper-TR®, including to membrane tension and lipid packing.

      We respectfully note, however, that our current study does not include any new data generated using Flipper-TR®. We referred to earlier work (Riggi et al., 2018) for context, where Flipper-TR® was used as a membrane tension reporter.

      We fully agree that the response of such "smart" membrane probes integrates multiple biophysical parameters-including tension, packing, and hydration-which are themselves interrelated as consequences of membrane composition (Colom et al., 2018; Ragaller et al., 2024; Torra et al., 2024). Indeed, this interconnectedness is central to our interpretation of PalmC's pleiotropic effects on the plasma membrane (PM). In our previous study, we observed that PalmC treatment not only reduced apparent PM tension (as measured by Flipper-TR®) but also increased membrane order ((Riggi et al., 2018); see laurdan GP, Fig. 6C), and here we show that it promotes the redistribution of free sterol away from the PM.

      Furthermore, PalmC's effect on membrane tension was supported by orthogonal in vitro data: its addition to giant unilamellar vesicles (GUVs) led to a measurable increase in membrane surface area and decreased tension, as shown by pipette aspiration ((Riggi et al., 2018), Fig. 3F). This provides complementary evidence that the membrane tension reduction is not merely an artifact of Flipper-TR® reporting.

      That said, we agree with the reviewer that in the case of TORC2 inhibition or hyperactivation, the observed changes in PM tension are based solely on Flipper-TR® data, without additional orthogonal validation. To address this concern, we have revised the relevant text in the manuscript to more cautiously reflect this complexity. The revised sentence now reads:

      "Consistent with this role, data generated with the lipid packing reporter dye Flipper-TR® suggest that acute chemical inhibition of TORC2 increases PM tension, while Ypk1 hyperactivation decreases it."

      This revised phrasing acknowledges both the utility and the limitations of Flipper-TR® as a probe of membrane biophysics.

      Reviewer #2 Significance:

      This is an interesting topic. However, use of the exogenous probe, palmitoylcarnitine, could be causing multiple changes that complicate the interpretation of the data.

      Reviewers #1 and #3 were much more impressed by this study than I was. I am not a yeast expert and so I may have missed or confused something. I would therefore welcome their expert feedback regarding my comments (#2). Ted Steck

      Response:

      Thank you for your constructive feedback.

      We believe that the manuscript is now much improved, and we hope to have convinced you that the mechanisms that we've elucidated using PalmC represent a general adaptation response to physiological PM stressors.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Reviewer comment: The authors describe the effects of surfactant-like molecules on the plasma membrane (PM) and its associated TORC2 complex. Addition of the surfactants with a positively-charged headgroup and a hydro-carbon tail of at least 16 caused the rapid clustering of PI-4,5P2 together with PI-4P and phosphatidylserine in large membrane invaginations. The authors convincingly demonstrate that this effect of the surfactants on the PM is likely caused by a direct disturbance of the PM organization and/or lipid composition. Interestingly, upon PalmC treatment, free ergosterol of the PM was found to first concentrate in the clusters, but within The kinetics of the changes in free ergosterol levels and the changes in TORC2 activity do not match. Ergosterol is rapidly depleted after PalmC treatment (The Lam2/4 data support the idea that ergosterol transport plays a role in the TORC2 recovery, but what role this is, is not clear to me. I think the data fit better with a model in which PalmC causes low tension of the PM which in turn disrupts normal lipid organization and thus causes TORC2 to shut down, maybe not by changes in free ergosterol but by changes, for instance, in lipid raft formation (which is in part effected by ergosterol levels). The transport of ergosterol is only one mechanism that is involved in restoring PM tension and TORC2 activity. However, sensing free ergosterol alone is most likely not the mechanism explaining how TORC2 senses PM tension.

      Therefore, I recommend that the model is revised (or supported by more data), reflecting the fact that free ergosterol levels do not directly correlate with the TORC2 activity, but instead might be only one of the PM parameters that regulate TORC2.

      Author response:

      We thank the reviewer for their thoughtful assessment and constructive suggestions. As described in more detail above, we have included in our revised version of this manuscript a variety of new data, including the sterol-internalization dependent adaptation of the PM and regulation of TORC2 during additional stresses. We think that these data vastly improve on our previous manuscript version. We have addressed each point risen by the reviewer below and revised the manuscript accordingly, including a rewritten discussion and updated model to better reflect the limitations of our current understanding of how TORC2 senses changes in the plasma membrane (PM). It is true that the appearance of PM invaginations tracks well with TORC2 inhibition, but it is not clear to us if they are upstream of this inhibition or merely another symptom of the preceding PM perturbation (PalmC-induced free sterol increase can be observed after 10s (Fig. S2A), but PM invaginations become visible only after ~1 min - meanwhile we can observe near complete TORC2 inhibition after 30s). In this study, we are mostly interested in the role of PM sterol redistribution in stress response. Indeed we think that the role of free sterol clearance during stresses is to adapt the PM to these stresses - thus restoring PM parameters which in turn reactivates TORC2. This can be seen for hyperosmotic stress and the newly introduced PM stressors, Arp2/3 inhibition and heat shock response (Fig. 4). We have therefore softened our model and updated discussion and final figure (Fig. 5) to reflect that TORC2 likely responds to broader changes in PM organization or tension, with sterol redistribution representing one of several contributing factors rather than the sole signal.

      Comment: - If TORC2 is indeed inhibited by free ergosterol, the addition of ergosterol to the growth medium should be able to trigger similar effects as PalmC. If this detection of free ergosterol is very specific (e.g. if TORC2 has a binding pocket for ergosterol) we would expect that addition of other sterols such a cholesterol or ergosterol precursors should not inhibit TORC2.

      Response:

      We appreciate this suggestion and agree that testing whether exogenous ergosterol can mimic PalmC effects would help assess specificity. However, yeast do not readily take up sterols under aerobic conditions, which renders artificial sterol enrichment at the yeast PM rather difficult. We have now included additional data characterizing our Lam2/4 mutants (see below), and pharmacological sterol synthesis inhibition, showing that a depletion of free sterols from the PM correlates with lower TORC2 activity (Fig. 2D, S2C). Additionally, as suggested, we tried to probe if ergosterol directly interacts with TORC2 through a specific binding pocket, by treating a yeast strain expressing cholesterol rather than ergosterol (Souza et al., 2011) with PalmC. However, the response of TORC2 activity in these cells was very similar to that of WT cells (Rebuttal Fig. 2). In conclusion, we agree that at present we do not know mechanistically how sterols affect TORC2 activity, although it does indeed seem more likely to be through an indirect mechanism linked to changes in PM parameters. The nature of such a mechanism will be subject to further studies. We hope that the introduced changes to the manuscript adequately reflect these considerations.

      Rebuttal Fig. 2: WT yeast cells which produce ergosterol as main sterol, and mutant cells which produce cholesterol instead were treated with 5 µM PalmC, and TORC2 activity was assessed by relative phosphorylation of Ypk1 on WB. One representative experiment out of two replicates.

      Comment: - The experiment in Figure 1C is not controlled for differences in membrane intercalation of the different compounds. For instance, does C16 choline and C16 glycine accumulate at the same rate in the PM (measure similar to experiment in Figure 1B). Maybe the positive charge at the headgroup of the surfactants increases the local concentration at the PM and therefore can explain the difference in effect on the PM.

      Response:

      We agree with the reviewer that the effects of the various PalmC derivatives are not directly controlled for differences in membrane intercalation. Our structure-activity screen was intended to demonstrate the general biophysical mode of action of PalmC-like compounds and to define minimal structural requirements for activity.

      We now note in the manuscript that differential membrane insertion could contribute to the observed variation in efficacy, particularly in relation to tail length. While we considered this additional suggested experiment, it was ultimately judged to be outside the scope of this study due to its complexity and limited impact on the central conclusions.

      A clarifying sentence has been added to the relevant results section to explicitly acknowledge this limitation:

      "We did not control for differences in PM intercalation efficiency."

      We also include a discussion here to further clarify our interpretation. Prior in vitro studies have shown that while intercalation is necessary, it is not sufficient for PM perturbation. For example, palmitoyl-CoA intercalates into membranes but does not induce the same biophysical effects as PalmC (Goñi et al., 1996; Ho et al., 2002). Thus, we believe that intercalation is only part of the story, and that the intrinsic propensity of different headgroups to perturb the PM plays a key role in the disruption of PM lipid organization.

      Comment: - Are the intracellular ergosterol structures associated (or in close proximity) with lipid droplets (ergosterol being modified and delivered into a lipid droplet)?

      Response:

      We thank the reviewer for raising this point. We now include additional data (Fig. S2H) showing that intracellular D4H-positive structures do not reside near or colocalize with lipid droplets. The latter is not entirely unexpected as D4H does not recognize esterified sterols. However, we do observe an increase in overall LD volume following PalmC treatment, consistent with the idea that internalized PM sterols may be stored in LDs as sterol esters over time - although we did not test if this increase in LD volume is Lam2/4 dependent. This increase is mentioned in the revised results text. An increase in cellular LDs has also been recently reported during hyperosmotic shock (Phan et al., 2025).

      For more attempts to identify a marker for intracellular D4H foci, see reply to reviewer 1.

      Comment:

      • How does the AA and DD mutations in Lam2/4 change the localization of the ergosterol sensor (before and after PalmC treatment).

      Response:

      We thank the reviewer for this question, as in the course of generating these data we realized that our "inhibited" DD mutant was in fact not phosphomimetic but displayed the same D4H distribution as the "hyperactive" AA mutant, i.e. a marked inwards shift of D4H signal away from the PM to internal structures due to increased PM-ER retrograde transport of sterols (Fig. S2C). This led us to critically re-evaluate and ultimately repeat our TORC2 activity WB experiments for PalmC treatment in LAM2/4 mutants. In this new set of experiments, the faster TORC2 recovery after PalmC treatment in the LAM2T518A LAM4S401A mutant did unfortunately not repeat robustly. It is possible that such differences can be observed under specific conditions. Nevertheless, the improved overall quality of the Western blot data allowed us to make the observation that baseline activity was already slightly different in these strains. The Lam2/4 centered part of the results section has subsequently been updated in the manuscript:

      "Using a phosphospecific antibody, we did not observe an increase in baseline TORC2 activity in lam2Δ lam4Δ cells, which had been previously reported by electrophoretic mobility shift (Murley et al., 2017). Instead, baseline TORC2 activity was consistently slightly decreased in these cells (Fig. 2D). Ypk1, activated directly by TORC2, inhibits Lam2 and Lam4 through phosphorylation on Thr518 and Ser401, respectively (Roelants et al., 2018; Topolska et al., 2020). We substituted these residues with alanine, generating a strain in which Lam2/4 were no longer inhibited by phosphorylation (Roelants et al., 2018). In these cells, yeGFP-D4H showed that free sterols were constitutively shifted away from the PM to intracellular structures (Fig. S2C, bottom panel). Intriguingly, in opposition to lam2Δ lam4Δ cells, basal TORC2 activity was increased in LAM2T518A LAM4S401A cells (Fig. 2D). This suggests that a decrease in free PM sterols stimulates TORC2 activity [...]"

      "In LAM2T518A LAM4S401A cells, TORC2 activity recovers with similar kinetics as the WT (Fig. 2D, bottom blot), suggesting that Lam2/4 release from TORC2 dependent inhibition during PalmC treatment is a fast and efficient process in WT cells, not further expedited by these constitutively active Lams."

      As suggested, we also observed D4H localization in LAM2T518A LAM4S401A after PalmC treatment, and implemented these data to further demonstrate that PalmC causes an increase in the fraction of free ergosterol at the PM, which is subsequently removed:

      "PalmC addition to LAM2T518A LAM4S401A cells likewise resulted first in a transient increase and then a further decrease in PM yeGFP-D4H signal (Fig. 3C, S3D)."

      Comment: - Does Lam2/4 localize to ER-PM contact sites near the large PM invaginations, which could allow for efficient transport of the free ergosterol that accumulates in these structures.

      Response:

      We were curious about this too, and have now added the requested data in our supplementary material and added a sentence in our results:

      "Indeed, in cells expressing GFP-Lam2 we observed that PalmC induced PM invaginations often formed at sites with preexisting GFP-Lam2 foci (Fig. S2K, cyan arrow), although GFP-Lam2 foci did not always colocalize with invaginations (Fig. S2K, yellow arrow) and vice versa. "

      Additionally, in the effort to characterize intracellular D4H foci during PalmC as requested by reviewer 1, we also looked at the localization of these foci relative to ER, and found that

      "During early timepoints, intracellular foci are usually in close vicinity to ER (Fig. S2E)"

      Reviewer #3 (Significance (Required)): The manuscript describes the effects of small molecule surfactants on the PM organization and on TORC2 activity. This is an important set of observation that helps understanding the response of cells to environmental stressors that affect the PM. This field of study is very challenging because of the limited tools available to directly observe lipids and their movements. I consider the data and most of its interpretations of high importance, but I am not convinced of the larger model that tries to link the ergosterol data with TORC2 activity. With adjustments of the model or additional experimental support, this manuscript will be of general interest for cell biologists, especially for researchers studying membrane stress response pathways.

      Response:

      We thank the reviewer for highlighting the importance of studying PM stress responses and acknowledging the technical challenges involved. We hope the applied changes and additional data succeed in softening our claims about TORC2 regulation while convincing the reviewer that free sterol levels at the PM are one of several contributing factors that correlate with changes in TORC2 activity.

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    1. ACUDIR AL SERVICIO DE PREVENCIÓN DE RIESGOS LABORALES (SPRL) LO ANTES POSIBLE.

      Incluir el procesamiento de muestras serológicas (cuál sacamos, lo llevamos en mano o se procesan?) cumpliendo con el protocolo de riesgos laborales del hospital

    1. Goals, outcomes, and requirements can be aggregated in plateaus.

      I would add explanation what does it means. I am personally confused by the aggregation realization here. In most cases I think people would like o express that some goal will be realized by some plateau. Using aggregation means something different - possibly that the goal is valid for only that time period but not necesarilly realized by that. On the other hand it would mean that aggregation is weaker then realization in this case.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In the present study, Chen et al. investigate the role of Endophilin A1 in regulating GABAergic synapse formation and function. To this end, the authors use constitutive or conditional knockout of Endophilin A1 (EEN1) to assess the consequences on GABAergic synapse composition and function, as well as the outcome for PTZ-induced seizure susceptibility. The authors show that EEN1 KO mice show a higher susceptibility to PTZ-induced seizures, accompanied by a reduction in the GABAergic synaptic scaffolding protein gephyrin as well as specific GABAAR subunits and eIPSCs. The authors then investigate the underlying mechanisms, demonstrating that Endophilin A1 binds directly to gephyrin and GABAAR subunits, and identifying the subdomains of Endophilin A1 that contribute to this effect. Overall, the authors state that their study places Endophilin A1 as a new regulator of GABAergic synapse function.

      Strengths:

      Overall, the topic of this manuscript is very timely, since there has been substantial recent interest in describing the mechanisms governing inhibitory synaptic transmission at GABAergic synapses. The study will therefore be of interest to a wide audience of neuroscientists studying synaptic transmission and its role in disease. The manuscript is well-written and contains a substantial quantity of data.

      Weaknesses:

      A number of questions remain to be answered in order to be able to fully evaluate the quality and conclusions of the study. In particular, a key concern throughout the manuscript regards the way that the number of samples for statistical analysis is defined, which may affect the validity of the data analysed. Addressing this weakness will be essential to providing conclusive results that support the authors' claims.

      We would like to thank the reviewer for appreciation of the value of our study and careful critics to help us improve the manuscript. We will correct the way that the number of samples for statistical analysis is defined throughout the manuscript as suggested and update figures, figure legends, and Materials and Methods accordingly. For example, we will average the values for all dendritic segments from one neuron, so that each data point represents one neuron in the graphs.

      Reviewer #2 (Public review):

      Summary:

      The function of neural circuits relies heavily on the balance of excitatory and inhibitory inputs. Particularly, inhibitory inputs are understudied when compared to their excitatory counterparts due to the diversity of inhibitory neurons, their synaptic molecular heterogeneity, and their elusive signature. Thus, insights into these aspects of inhibitory inputs can inform us largely on the functions of neural circuits and the brain.

      Endophilin A1, an endocytic protein heavily expressed in neurons, has been implicated in numerous pre- and postsynaptic functions, however largely at excitatory synapses. Thus, whether this crucial protein plays any role in inhibitory synapse, and whether this regulates functions at the synaptic, circuit, or brain level remains to be determined.

      New Findings:

      (1) Endophilin A1 interacts with the postsynaptic scaffolding protein gephyrin at inhibitory postsynaptic densities within excitatory neurons.

      (2) Endophilin A1 promotes the organization of the inhibitory postsynaptic density and the subsequent recruitment/stabilization of GABA A receptors via Endophilin A1's membrane binding and actin polymerization activities.

      (3) Loss of Endophilin A1 in CA1 mouse hippocampal pyramidal neurons weakens inhibitory input and leads to susceptibility to epilepsy.

      (4) Thus the authors propose that via its role as a component of the inhibitory postsynaptic density within excitatory neurons, Endophilin A1 supports the organization, stability, and efficacy of inhibitory input to maintain the excitatory/inhibitory balance critical for brain function.

      (5) The conclusion of the manuscript is well supported by the data but will be strengthened by addressing our list of concerns and experiment suggestions.

      We would like to thank the reviewer for their favorable impression of manuscript. We also appreciate the great experiment suggestions to help us improve the manuscript.

      Weaknesses:

      Technical concerns:

      (1) Figure 1F and Figure 1H, Figures 7H,J:

      Can the authors justify using a paired-pulse interval of 50 ms for eEPSCs and an interval of 200 ms for eIPSCs? Otherwise, experiments should be repeated using the same paired pulse interval.

      We apologize for the confusion. As illustrated by the schematic current traces, the decay time constants of eEPSCs and eIPSCs in hippocampal CA1 neurons are different. The eEPSCs exhibit a faster channel closing rate, corresponding to a smaller time constant Tau. Thus, a shorter inter-stimulus interval (50 ms) was chosen for paired-pulse ratio recordings. In contrast, the eIPSCs display a slower channel closing rate, with a Tau value larger than that of eEPSCs, so a longer inter-stimulus interval (200 ms) was used for PPR. This protocol has been long-established and adopted in previous studies (please see below for examples).

      Contractor, A., Swanson, G. & Heinemann, S. F. Kainate receptors are involved in short- and long-term plasticity at mossy fiber synapses in the hippocampus. Neuron 29, 209-216, doi:10.1016/s0896-6273(01)00191-x (2001).

      Babiec, W. E., Jami, S. A., Guglietta, R., Chen, P. B. & O'Dell, T. J. Differential Regulation of NMDA Receptor-Mediated Transmission by SK Channels Underlies Dorsal-Ventral Differences in Dynamics of Schaffer Collateral Synaptic Function. Journal of neuroscience 37, 1950-1964, doi:10.1523/JNEUROSCI.3196-16.2017 (2017).

      (2) Figures 3G,H,I:

      While 3D representations of proteins of interest bolster claims made by superresolution microscopy, SIM resolution is unreliable when deciphering the localization of proteins at the subsynaptic level given the small size of these structures (<1 micrometer). In order to determine the actual location of Endophilin A1, especially given the known presynaptic localization of this protein, the authors should complete SIM experiments with a presynaptic marker, perhaps an active zone protein, so that the relative localization of Endophilin A1 can be gleaned. Currently, overlapping signals could stem from the presynapse given the poor resolution of SIM in this context.

      Thanks for your suggestions. It is certainly preferable to investigate the relative localization of endophilin A1 using both presynaptic and postsynaptic markers. For SIM imaging in Figure 3G-I, to visualize neuronal morphology, we immunostained GFP as cell fill, leaving two other channels for detection of immunofluorescent signals of endophilin A1 and another protein. We will try co-immunostaining of endophilin A1, the active zone protein bassoon (presynaptic marker) and gephyrin without morphology labeling. Alternatively, we will do co-staining of endophilin A1 and bassoon in GFP-expressing neurons. We agree that overlapping signals or proximal localization of presynaptic endophilin A1 with gephyrin or GABA<sub>A</sub>R γ2 could not be ruled out. To note, if image resolution is improved with the use of a more advanced imaging system, the overlap between two proteins will become smaller or even disappear. With the ~110 nm lateral resolution of SIM microscopy, the degree of overlap between the two proteins of interest is much lower than in confocal microscopy. Given the presynaptic localization of endophilin, most likely we will observe a small overlap (presynatpic) or proximal localization (postsynaptic) of endophilin A1 with bassoon. Nevertheless, we will complete the SIM experiments as suggested to improve the manuscript.

      Manuscript consistency:

      (1) Figure 2:

      The authors looked at VGAT and noticed a reduction of signals in hippocampal regions in their P21 slices, indicating that the proposed postsynaptic organization/stabilization functions of Endophilin A1 extend to the inhibitory presynapse, perhaps via Neuroligin 2-Neurexin. Simultaneously, hippocampal regions in P21 slices showed a reduction in PSD-95 signals, indicating that excitatory synapses are also affected. It would be crucial to also look at excitatory presynapses, via VGLUT staining, to assess whether EndoA1 -/- also affects presynapses. Given the extensive roles of Endophilin A1 in presynapses, especially in excitatory presynapses, this should be investigated.

      Thanks for the thoughtful comments. Given that the both VGAT and PSD95 signals are reduced in hippocampal regions in P21 slices, it is conceivable that the proposed postsynaptic organization/stabilization functions of endophilin A1 extend to the inhibitory presynapse via Neuroligin-2-Neurexin and the excitatory presynapse as well during development. Of note, endophilin A1 knockout did not impair the distribution of Neuroligin-2 in inhibitory postsynapses (immunoisolated with anti-GABA<sub>A</sub>R α1) in mature mice (Figure 3K), and endophilin A1 did not bind to Neuroligin-2 (Figure 4D), suggesting that endophilin A1 might function via other mechanisms. Nevertheless, as functions of endophilin A family members at the presynaptic site are well-established, the reduction of presynaptic signals in developmental hippocampal regions of EndoA<sup>-/-</sup> mice might result from the depletion of presynaptic endophilin A1. The presynaptic deficits can be compensatory by other mechanisms as neurons mature. Certainly, we will do VGLUT staining of EndoA1<sup>-/-</sup> brain slices as suggested to assess the role of endophilin A1 in excitatory presynapses in vivo.

      (2) Figure 7C:

      The authors do not assess whether p140Cap overexpression rescues GABAAR receptor loss exhibited in Endophilin A1 KO, as they did for Gephryin. This would be an important data point to show, as p140Cap may somehow rescue receptor loss by another pathway. In fact, it is mentioned in the text that this experiment was done, "Consistently, neither p140Cap nor the endophilin A1 loss-of-function mutants could rescue the GABAAR clustering phenotype in EEN1 KO neurons (Figure 7C, D)" yet the data for p140Cap overexpression seem to be missing. This should be remedied.

      Thanks a lot for the thoughtful comment. We will determine whether p140Cap overexpression also rescues the GABA<sub>A</sub>R clustering phenotype in EndoA1<sup>-/-</sup> neurons by surface GABA<sub>A</sub>R γ2 staining in our revised manuscript.

      Reviewer #3 (Public review):

      Summary:

      Chen et al. identify endophilin A1 as a novel component of the inhibitory postsynaptic scaffold. Their data show impaired evoked inhibitory synaptic transmission in CA1 neurons of mice lacking endophilin A1, and an increased susceptibility to seizures. Endophilin can interact with the postsynaptic scaffold protein gephyrin and promote assembly of the inhibitory postsynaptic element. Endophilin A1 is known to play a role in presynaptic terminals and in dendritic spines, but a role for endophilin A1 at inhibitory postsynaptic densities has not yet been described.

      Strengths:

      The authors used a broad array of experimental approaches to investigate this, including tests of seizure susceptibility, electrophysiology, biochemistry, neuronal culture, and image analysis.

      Weaknesses:

      Many results are difficult to interpret, and the data quality is not always convincing, unfortunately. The basic premise of the study, that gephyrin and endophilin A1 interact, requires a more robust analysis to be convincing.

      We greatly appreciate the positive comment on our study and the very valuable feedback for us to improve the manuscript. We will conduct additional experiments to improve our data quality and strengthen our evidences according to these great constructive suggestions. To gain strong evidence for the interaction between endophilin A1 and gephyrin, we will perform in vitro pull-down assay with recombinant proteins from bacterial expression system.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) For all of the electrophysiology experiments, only the number of neurons recorded is stated, but not the number of independent animals that these neurons were obtained from. The number of independent animals used should be stated for each panel. At least 3 independent animals should be used in each group, otherwise, more data needs to be added.

      We apologize for missing the information in the original manuscript. For all electrophysiological experiments, data were obtained from more than 3 experimental animals. The figure legends were updated to include the number of independent animals used for each panel.

      (2) For the cell culture experiments analyzing dendritic puncta at GABAergic synapses, the number of data points analysed appears to be the number of dendritic segments quantified, regardless of whether they originate from the same neuron or not. This analysis method is not valid, since dendritic segments from the same neuron cannot be counted as statistically independent samples. The authors need to average the values for all dendritic segments from one neuron, such that one neuron equals one data point. This alteration should be made for Figures 2B, 2D, 4H, 4J, 5B, 5C, 5E, 5J, 5L, 6B, 6D, 6F, 6H, 6J, 6K,7B, and 7D. In addition, the number of independent cultures from which the neurons were obtained should be stated for each panel. At least 3 independent cultures should be used in each group, otherwise, more data need to be added.

      Thanks for the criticism. We reanalyzed the data throughout the manuscript as suggested and updated the figure legends accordingly. Moreover, we increased the number of neurons from independent experiments to further confirm the results in our revised manuscript.

      In the revised manuscript, we averaged the values for all dendritic segments from a single neuron and updated the data in Figure 3B, 3D, 4H, 4J, 5B, 5C, 5E, 5K, 5M, 6B, 6D, 6F, 6H, 6J, 6K,7B, and 7D.

      Neurons analyzed in each group were derived from at least 3 independent cultures. Due to very low efficiency of sparse transfection in primary cultured hippocampal neurons, multiple experimental repetitions were necessary to obtain the sufficient number of neurons for analysis. We described statistical analysis in “Material and Methods” section in the original manuscript as follows:

      “For all biochemical, cell biological and electrophysiological recordings, at least three independent experiments were performed (independent cultures, transfections or different mice).”

      (3) Individual data points should be shown on all graphs, particularly in Figures 2C, 2F, 2I, 3F, 3K, and 3L.

      Thank you for the suggestion. We replaced the original graphs with scatterplots and mean ± S.E.M. in new Figures.

      (4) For each experiment, the authors should state explicitly in the methods section whether that experiment was conducted blind to genotype.

      Thank you for the suggestion. We have modified the description of blind analysis for each experiment in methods section to “Seizure susceptibility was measured blindly by rating seizures on a scale of 0 to 7 as follows…”, “Quantification of immunostaining were carried out blindly…” in our revised manuscript.

      (5) For each experiment, the authors should state whether they used male or female mice, and what age the mice were at the time of the experiment

      Thanks a lot for the suggestion. We usually use male and female mice for neuron culture and behavioral test. We observed no sex-related differences in PTZ-induced behaviors, so the results were pooled together.

      For mice ages, P0 pups were used for hippocampal neuron cultures and virus injection in electrophysiological recording assays or FingR probes assays. P14-21 mice were used for electrophysiological recording, immunofluorescent staining and FingR probes detection in brain slice, while adult mice (P60) for behavioral tests, immunofluorescent staining in brain slice and biochemical assays. We have modified the description in genders and ages of mice in methods section to “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1<sup>+/+</sup> or EndoA1<sup>-/-</sup> littermates or EndoA1<sup>fl/fl</sup> littermates were intraperitoneally administered… ”, “For virus injection, 8-9-week-old naive male and female littermates were anesthetized…”, “Male and female littermates (P21 or P60) were anesthetized and immediately perfused…”, “Hippocampi of female or male pups (P0) were rapidly dissected under sterile conditions…”, “PSD fractions from adult mouse brain were prepared as previously described…”, “Newborn EndoA1<sup>fl/fl</sup> littermates (male or female) were anesthetized on ice for 4-5 min…” in our revised manuscript.

      (6) For each experiment involving WT and KO mice, please state whether WTs and KOs were bred as littermates from heterozygous breeders

      Sorry for the confusion. In our study, EndoA1<sup>+/+</sup> and EndoA1<sup>-/-</sup> mice were bred as littermates from heterozygous breeders. We added the information in methods section as follows in our revised manuscript, “EndoA1<sup>+/+</sup> and EndoA1<sup>-/-</sup> mice were bred as littermates from heterozygous breeders…”, “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1<sup>+/+</sup> or EndoA1<sup>-/-</sup> littermates or EndoA1<sup>fl/fl</sup> littermates…”, “For virus injection, 8-9-week-old naive male and female littermates were anesthetized…”, “Male and female littermates (P21 or P60) were anesthetized and immediately perfused…”, “For co-IP from brain lysates, the whole brain from 8-10-week-old WT and KO littermates were dissected…”, “Newborn EndoA1<sup>fl/fl</sup> littermates (male or female) were anesthetized on ice for 4-5 min…”.

      (7) For experiments comparing three or more groups, the authors claim in the methods section to have used a one-way ANOVA for statistical analysis. However, no ANOVA values are given, only the post-hoc tests. Please add the ANOVA values for each experiment before stating the values of the post-hoc analysis.

      Sorry for the missing information. We used one-way ANOVA for comparing three or more groups in the original manuscript and have changed to two-way ANOVA for behavior data analysis in our revised manuscript as suggested in Recommendations (18). We added the ANOVA values (F & p values) for each experiment in new figures. For example, see Figure 1C.

      (8) In Figure 1A-C, seizure susceptibility was compared in EEN+/+ and EEN-/- mice, but the methods section states that seizure susceptibility was evaluated in 8-10-week-old male C57BL/6N mice (line 513). Was this meant to indicate that the EEN+/+ and EEN-/- mice were on a C57BL/6N background? How does this match with the statement that EEN1 -/- mice were generated on a C57BL/6J background (line 467)?

      We apologize for the mistake. In our study, EEN1<sup>-/-</sup> mice were generated on a C57BL/6J background, as stated in our previously published papers (Yang et al., 2021; Yang et al., 2018) and in “Animals” in Material and Methods of our original manuscript. We had corrected the statement to “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1<sup>+/+</sup> or EndoA1<sup>-/-</sup> littermates…” in Material and Methods of the revised manuscript.

      (9) In the electrophysiology experiments in Figure 1E-O, it is not clear to me which neurons were recorded in the control group. The methods section states that "Whole-cell recordings were performed on an AAV-infected neuron and a neighboring uninfected neuron" (line 736). However, the figure legends states that recordings were obtained from "10 control (Ctrl, mCherry alone) and 10 EEN1 KO (mCherry and Cre) pyramidal neurons" (line 1079), which would indicate that the controls are not uninfected neurons from the same animal, but AAV-mCherry infected neurons from a different animal. Please clarify which of the two descriptions is accurate.

      Thanks for catching the error! In all electrophysiological experiments, a neighboring uninfected neuron was used as the control in Figure 1E-O. This was incorrectly stated in the figure legend of the original manuscript. In the revised manuscript, the information has been corrected in figure legends of new Figure 1 (E-F).

      (10) The authors show that in Endophilin A1 KO animals, eIPSCs are reduced, but mIPSC frequency and amplitude are unaltered. How do they explain this finding in the context of the fact that gephyrin and GABAAR1.

      We apologize for the confusion about the data of electrophysiological recording. Compared with eIPSC, which are recorded in the presence of electrically evoked action potential that elicited a substantial release of neurotransmitter, mIPSCs are small, spontaneous currents recorded in the presence of TTX during patch-clamp experiments, resulting from the release of neurotransmitters from presynaptic terminals in the absence of action potential. The amplitude of mIPSCs typically reflects the quantal release of neurotransmitters, while their frequency can vary depending on synaptic activity and the state of the neuron.

      A number of molecules fine-tune presynaptic neurotransmitter release and functions of inhibitory postsynaptic receptors. In our study, inhibitory postsynapses were partially affected in endophilin A1 knockout neurons, while presynaptic endophilin A1 remained intact during electrophysiological recordings. Conceivably, the observed deficits in endophilin A1 knockout mice were mild. Following endophilin A1 depletion, inhibitory postsynaptic receptors appeared sufficient to respond to spontaneous neurotransmitter release but may be inadequate to large amounts of neurotransmitter release evoked by action potential. Meanwhile, spontaneous synaptic activity and the state of the neuron were not obviously affected under basic state by endophilin A1 depletion during postnatal stages. Consequently, mIPSC frequency and amplitude remain unaltered but eIPSCs were reduced compared to the control neurons. This finding was consistent with behavioral experiments, where aggressive epileptic behaviors were induced by PTZ rather than spontaneous epilepsy in endophilin A1 knockout mice.

      (11) Distribution of gephyrin, VGAT, and GABAARg2 differs substantially between the different layers of hippocampal area CA1, and the same goes for the other regions of the hippocampus. However, in Figure 2, it is not clear to me from the sample images which layers of each subregion the authors quantified, or indeed whether they paid attention to which layers they included in their analysis. This can lead to a substantial skewing of the data if different layers were preferentially included in the two genotypes. Please clarify which layers were analysed, and how comparability between WTs and KOs was ensured. This is particularly important given the authors' claim that Endophilin A1 acts equally at all subtypes of GABAergic synapses (lines 373- 376).

      Thanks for the cautiousness! We distinguished each hippocampal subregion based on the anatomical structure in brain slices. Quantification of fluorescent mean intensity of each synaptic protein in all layers of each subregion, as shown in new Figure 2 and Figure S2A-F, revealed that GABAergic synaptic proteins were impaired in both P21 and P60 KO mice.

      We further analyzed the fluorescent signal of core postsynaptic component, gephyrin, in individual layers of each subregion in the hippocampus of mature WT and KO mice, as presented in new Figures S2G-H. Our findings demonstrated a decrease in gephyrin levels across all layers of each subregion in KO mice. Additionally, we examined gephyrin clustering across the soma, axon initial segment (AIS), and dendrites in cultured mature endophilin A1 knockout hippocampal neurons, as shown in new Figure S5E-H. The results showed that gephyrin was affected in all subcellular regions following endophilin A1 knockout.

      Collectively, these data suggest that endophilin A1 functions across all subtypes of GABAergic postsynapses.

      (12) In Figure 3E-F, the authors state that there was no change in the total level of synaptic neurons in EEN1 KO neurons (line 188). However, there is no quantification of the total level of synaptic neurons shown, and based on the immunoblot in Figure 3E, it looks like there is a substantial reduction in NR1, NL2, and g2. The authors should present a quantification of the total levels of these proteins and adjust their statement accordingly if necessary.

      Thanks a lot for your comments. We quantified the total protein levels in Figure 3E and added the result to new Figure 3F, showing that total protein levels were not obviously affected in cultured KO neurons. When normalized to total protein levels, the surface levels of GABA<sub>A</sub> receptors were significantly compromised compared to surface GluN1 and NL2. Furthermore, the total protein levels were not affected in brains of KO mice, as shown in Figures 3K (input) and 3L (S1). Collectively, there was no change in the total level of synaptic proteins in KO neurons.

      (13) In Figure 3G-I, the authors claim, based on super-resolution images as presented here, that Endophilin A1 colocalizes with gephyrin and g2. However, no quantification of this colocalization is presented. The authors should add this quantification to support their claim and indicate how many GABAergic synapses contain Endophilin A1.

      Thank you for the thoughtful comments. The resolution of the images is significantly improved by super-resolution microscopy. As a result, the overlap between the two proteins will become smaller or even disappear. Since no two proteins can occupy the same physical space, they would show lower colocalization and instead exhibit proximal localization. As expected, in Figures 3G and 3H, we observed only small overlap or proximal localization of endophilin A1 with gephyrin or GABA<sub>A</sub>R γ2. To further confirm the localization of endophilin A1 in inhibitory synapses, we co-stained endophilin A1 with both pre- and post-synaptic proteins, gephyrin and Bassoon. Then we quantified the colocalization of endophilin A1 with gephyrin or with Bassoon using the method for super-resolution images described in the reference (Andrew D. McCall. Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy. McCall BMC Bioinformatics (2024) 25:55). The percentage of gephyrin or Bassoon puncta that were in close proximity with endophilin A1 was also calculated, as shown in new video 5 and new Figure S4B-G. These data have been added in the revised manuscript as follows, “We further detected the localization of endophilin A1 to inhibitory synapses by co-immunostaining with both pre- and post-synaptic markers (Figure. S4B and Video 5). Quantitative analysis of super-resolution localization maps revealed that ~ 47 % puncta of gephyrin or Bassoon were proximal to endophilin A1 (Figure. S4G, n \= 14), with a mean distance between endophilin A1- and gephyrin-positive pixels of ∼ 120 nm, or between endophilin A1- and Bassoon-positive pixels of ∼ 130 nm (Figure. S4C-F).”

      (14) In the quantification shown in Figure 3K-L, there are no error bars in the WT data sets. This presumably means that all values were normalized to WT. However, since this artificially eliminates the variance in the WT group, a t-test is no longer valid, since this assumes a normal distribution and normal variance, which are no longer given. The authors should either change the way they normalize their data to maintain the variance in the WT group or perform a different statistical test that can account for the artificial lack of variance in one of the groups.

      Thank you for the suggestions! We modified our analysis approach. Specifically, we used mean value of WTs to normalize data to preserve the variance in the WT group and performed unpaired t-tests to assess statistical significance in Figure 3K-L. Additionally, we replaced the bar graphs with modified graphs showing individual data points. Please see Response to Recommendation (12).

      (15) What is the difference between the coIP experiment in Figure 4E and 3J, right panel? In both cases, an Endophilin A1 IP is performed, and gephyrin, GABAARg2, and GABAARa1 are assessed. However, Figure 3J's right panel indicates that Endophilin A1 does interact with the GABAAR subunits, whereas Figure 4E shows that it does not. How do the authors explain this discrepancy? Were these experiments performed more than once?

      Sorry for the confusion. Figure 3J and Figure 4E show data from immunoisolation assay and conventional co-immunoprecipitation (co-IP), respectively. Immunoisolation allows for the rapid and efficient separation of subcellular membrane compartments using antibodies conjugated to magnetic beads. In Figure 3J, we used antibodies against GABA<sub>A</sub>R α1 subunit or endophilin A1 to isolate the inhibitory postsynaptic membranes or endophilin A1-associated membranous compartments. In contrast, co-immunoprecipitation detects direct protein-protein interactions in detergent-solubilized lysates. For Figure 4E, we applied antibodies against endophilin A1 to precipitate its interaction partners. The results in Figure 3J and Figure 4E demonstrate that endophilin A1 is localized in the inhibitory postsynaptic compartment and directly interacts with gephyrin, but not with GABA<sub>A</sub>Rs. Detailed information regarding the methods used for co-IP and immunoisolation can be found in “GST-pull down, co-immunoprecipitation (IP), and immunoisolation” in the “Material and Methods” section of original manuscript.

      These experiments were repeated multiple times to ensure reliability. In fact, consistent data showing endophilin A1 localization in the inhibitory postsynaptic compartment were observed in Figure 3K, showing the quantified data as well.

      (16) For the colocalization analysis in Figure 5A-C, what percentage of gephyrin puncta contain g2 in the WT and Endophilin A1 KO? Currently, only a correlation coefficient is provided, but not the degree of overlap. Please add this information to the figure.

      Thanks for the comments on the colocalization analysis. We analyzed the percentage of gephyrin puncta overlapping with GABA<sub>A</sub>R γ2 and added the graphs in new Figure 5C.

      (17) Figure 6 investigates how actin depolarization affects GABAergic synapse function, but does not assess how Endophilin A1 contributes to this process. The authors then provide an extremely short statement in the discussion, stating that their data are contradictory to a previous study (lines 412 - 417). This section of the discussion should be expanded to address the specific role of Endophilin A1 in the consequences of actin depolymerization.

      Thanks a lot for the advice. In the original manuscript, we discussed the specific role of endophilin A1 at inhibitory postsynapses as follows in Discussion:

      “As membrane-binding and actin polymerization-promoting activities of endophilin A1 are both required for its function in enhancing iPSD formation and g2–containing GABA<sub>A</sub>R clustering to iPSD, we propose that membrane-bound endophilin A1 promotes postsynaptic assembly by coordinating the plasma membrane tethering of the postsynaptic protein complex and its stabilization with the actin cytomatrix”

      Following your advice, we added a statement in the revised manuscript addressing the role of endophilin A1 in actin polymerization at inhibitory postsynapses, shown as follows, “In the present study, the impaired clustering of gephyrin and GABA<sub>A</sub> γ2 by F-actin depolymerization underscores the essential role of F-actin in the assembly and stabilization of the inhibitory postsynaptic machinery. Membrane-bound endophilin A1 promotes F-actin polymerization beneath the plasma membrane through its interaction with p140Cap, an F-actin regulatory protein, thereby facilitating and/or stabilizing the clustering of gephyrin and γ2-containing GABA<sub>A</sub> ​receptors at postsynapses.”

      (18) Which statistical analysis was conducted in Figure 7F? Given the nature of the data, a repeated measures ANOVA would be necessary to accurately assess the statistical accuracy.

      Sorry for the confusion. We conducted one-way ANOVA followed by Tukey post hoc test at each time point in original Figure 7F. We have employed the method of repeated measures ANOVA followed by Tukey post hoc test as suggested in new Figure 7F. Meanwhile, we reanalyzed data in new Figure 1C with the same method. We also modified the description in “Statistical analysis” and Figure legends for new Figure1C and 7F in revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      Data presentation:

      (1) Figures 2A, B, D, E, G, H. Figures S2A, B, D:

      Add P21 or P60 labels to these figures so that the difference between similarly stained samples (e.g. Figures 2A, B) is obvious to the reader.

      Thanks! We added “P21” or “P60” labels in new Figure 2 and Figure S2 as suggested.

      (2) Figures 4C, D:

      The authors must make their coIP data annotation consistent. In Figure 4C, they use actual microgram amounts when, e.g., describing how much input was present, yet in Figure 4D they use + and -. The authors should pick one.

      Thanks for the comments. We labeled the consistent data annotation in new Figure 4C and 4D, we also changed the label in 4F for the consistent data annotation.

      (3) Figure 5A

      GFP is gray in this figure, but in all other figures, it is blue. Consider changing for presentation reasons.

      Thanks a lot for pointing out the problem. We replaced gray with blue color to indicate GFP in new Figure 5A.

      (4) Figures 6A, C, E, G

      Label graphs as either short-term or long-term drug treatment.

      Thanks for the suggestion. We labeled the graphs as 60 min for short-term or 120 min for long-term drug treatment in new Figure 6A, C, E, G for convenient reading.

      Annotation, grammar, spelling, typing errors:

      (1) Figure 4G:

      Merge and GFP labels are seemingly swapped.

      Thanks a lot for sharp eye. We corrected the labels in new Figure 4G.

      (2) Fig 4I:

      The authors use "Gephryin" instead of GPN. They should be consistent and choose one.

      Sorry for the mistake. We changed the label consistent with other figures in new Figure 4I and rearranged the images in figures for good looking.

      (3) "One-hour or two-hour treatment of mature neurons with nocodazole..."

      Thanks for your advice. We modified the sentence to “Treatment of mature neurons with nocodazole, a microtubule depolymerizing reagent, for one hour (short-term) or two hours (long-term), caused…”.

      (4) The authors should indicate that one-hour is their short-term treatment and that two-hour is their long-term treatment so that when these terms are used later to describe LatA experiments, it is clearer to the reader.

      Thanks for your comments. We modified the statement as seen in Response to Recommendation (3), it is clearer to the reader.

      (5) EEA1. The authors should use a more conventional term EndoA1 so that the manuscript can be searched easily.

      Thanks a lot for the suggestion. We replaced all of the term “EEN1” with “EndoA1” in the revised manuscript.

      Reviewer #3 (Recommendations for the authors):

      Major Points

      (1) The number of observations for the electrophysiology experiments in Figure 1 (dots are neurons) is very low and it is not clear whether the data shown is derived from different mice. The same criticism applies to the data shown in Figures 7G-K.

      We apologize for the low neuron number in electrophysiology experiments. In the patch-clamp experiments, the number of neurons recorded was higher than what is shown in the figures. However, neurons with a membrane resistance (Rm) below 500 MΩ, indicating unstable seals or poor conditions, were excluded from the analysis. Additionally, we added the number of mice from which the data derived in each group in the figure legends for Figure 1, 7 and S1, this point was also raised by Reviewer #1 (Please see Response to Recommendation (1)).

      (2) Images in Figure 2 are shown at low magnification, statements on changes in intensity of inhibitory synaptic markers in the hippocampal region are impossible to interpret. Analysis of inhibitory synapses in vivo would require sparse neuronal labeling and 3D reconstruction, for instance using gephyrin-FingRs (Gross et al., Neuron 2013).

      Thanks for your insightful suggestion. We obtained pCAG_PSD95.FingR-eGFP-CCR5TC and pCAG_GPN.FingR-eGFP-CCR5TC constructs from Addgene (plasmid # 46295 & #46296). We attempted in utero electroporation (IUE) to introduce the DNAs into cortical neurons or hippocampal neurons at E14.5, unfortunately with no success. Following the repetitive operation for numerous times, we could eventually obtain newborn pups of ICR mice after IUE. However, we failed to obtain any newborn pups of C57BL/6J mice due to abortion following the procedure. Furthermore, pregnant C57BL/6J mice (WTs or KOs) did not survive or remained in a poor state of health after surgery. Therefore, we were unable to analyze synapses through sparse labeling and 3D reconstruction by IUE. Alternatively, we obtained commercial AAVs carrying rAAV-EF1a-PSD95.FingR-eGFP-CCR5TC and rAAV-EF1a-mRuby2-Gephyrin.FingR-IL2RGTC, then injected into the CA1 region of EndoA1<sup>fl/fl</sup> mice at P0. Mice were fixed and detected the fluorescent signals in CA1 regions at P21. Consistent with immunostaining with antibodies, decreased mRuby2-Gephyrin.FingR or PSD95.FingR-eGFP was observed in dendrites of KO neurons at P21, as shown in new Figure S3. In combination with electrophysiological recording, PSD fractionation and immunoisolation from brains, these data support our conclusion regarding the effects of endophilin A1 knockout on the inhibitory synapses.

      Additionally, we transfected DIV12 cultured hippocampal neurons with pCAG_PSD95.FingR-eGFP-CCR5TC or pCAG_GPN.FingR-eGFP-CCR5TC and observed fluorescent signals on DIV16. Both the signal intensity and number of GPN.FingR-eGFP clusters were also significantly attenuated, with no obvious changes in PSD95.FingR-eGFP clusters in dendrites of mature neurons, as shown in new Figure S5A-D. We are very pleased that the result further strengthened our original conclusion. We have added the new pieces of data in our revised manuscript.

      (3) Figure 3: surface labeling of GluA1 or the GABAAR gamma 2 subunit is difficult to interpret: the patterns are noisy and the numerous puncta appear largely non-synaptic although this is difficult to judge in the absence of additional synaptic markers. It appears statistics are done on dendritic segments rather than the number of neurons. The legend does not mention how many independent cultures this data is derived from. In their previous study (Yang et al., Front Mol Neurosci 2018), the authors noted a decrease in surface GluA1 levels in the absence of endophilin A1. How do they explain the absence of an effect on surface GluA1 levels in the current study?

      Sorry for the concern and thanks for your comments. First, we assessed changes in the surface levels of excitatory and inhibitory receptors by co-immunostaining in cultured WT and KO hippocampal neurons. Given the very low transfection efficiency of neurons in high density culture, numerous puncta of receptors from adjacent non-transfected neurons were also detected. This approach may contribute to the noisy pattern observed in Figure 3A. Besides, the projections of z-stack for higher magnified dendrites may likely introduced higher background signals. We have now replaced the original images with the newest repeat in new Figure 3A. Moreover, we confirmed a decrease in the surface expression of GABA<sub>A</sub>R γ2 by the biotinylation assay, as shown in Figure 3E. Indeed, we agree that some puncta for surface labeling of receptors seemed to be non-synaptic localization. In order to reflect the decrease in synaptic proteins at synapses, we isolated PSD fraction by biochemical assay and found that gephyrin and GABA<sub>A</sub>R γ2, two major inhibitory postsynaptic components, were reduced in the PSD fraction from KO brains, as shown in Figure 3L. Their colocalization was also attenuated in the absence of endophilin A1, as shown in Figure 5A-C. Combined with electrophysiological recording, these data from multiple assays indicate GluA1 at synapses was not obviously affected but GABA<sub>A</sub>R γ2 at synapses was impaired in endophilin A1 KO neurons in the present study.

      We have corrected the way that the number of samples is defined for statistical analysis as suggested. This point was also raised by Reviewer #1 (Recommendation (2)). We averaged the values from all dendritic segments of a single neuron, such that one neuron equaled one data point. We had replaced the original Figure 3B and 3D (please see Response to Recommendation (2) by Reviewer #1). Additionally, we added the number of independent cultures these data were derived from to figure legends in revised manuscript.

      Previously, we observed a small decrease in surface GluA1 levels in spines under basal conditions and a more pronounced suppression of surface GluA1 accumulation in spines upon chemical LTP in endophilin A1 KO neurons from EndoA1<sup>-/-</sup> mice that knockout endophilin A1 since embryonic development stages (Figure 5C,H. Yang et al., Front Mol Neurosci, 2018). In Figure 3A and B in current study, we analyzed surface receptor levels in GFP-positive dendrites, rather than spines, under basal conditions when endophilin A1 was depleted at the later developmental stage. We found a decrease in surface GABA<sub>A</sub>R γ2 levels but no significant effects on surface GluA1 levels in dendrites. These findings indicate that endophilin A1 primarily affects excitatory synaptic proteins in spines during synaptic plasticity and inhibitory synaptic proteins in dendrites under basal conditions in mature neurons.

      (4) Super-resolution images in Figure 3G, H, I: endophilin A1 puncta look different in panel 3I compared to 3G and 3H, which are very noisy. It is difficult to interpret how specific these EEN1 puncta are. Previous images showing EEN1 distribution in dendrites look different (Yang et al., Front Mol Neurosci 2018); is the same KO-verified antibody being used here? Colocalization of EEN1 with gephyrin or the GABAAR gamma 2 subunit is difficult to interpret; gephyrin mostly does not seem to colocalize with EEN1 in the example shown.

      Sorry for your concerns. As stated previously in Major Points (3), transfection efficiency was very low in cultured neurons and our cultured neurons were at relative high density. As a result, numerous puncta of proteins located in the adjacent non-transfected neurons were also detected, which may contribute to noisy signals observed in Figure 3G-I.

      In our previous paper, we confirmed the specificity of the antibody against endophilin A1 (5A,B. Yang et al., Front Mol Neurosci, 2018). We used the same antibody (rabbit anti-endophilin A1, Synaptic Systems GmbH, Germany) in the current study. While the previous images were obtained using confocal microscopy, the current images in Figures 3G, H, and I were acquired using super-resolution microscopy (SIM). The different patterns observed in the dendrites may be attributed to the difference in image resolution, antibodies dilution and reaction time.

      Reviewer #1 also points out the quantification of colocalization of gephyrin and GABA<sub>A</sub>R γ2 with endophilin A1. Please see Response to Recommendation (13) by Reviewer #1.

      (5) The interaction of gephyrin and endophilin A1 is based on coIP experiments in cells and brain tissue. To convincingly demonstrate that these proteins interact, biophysical experiments with purified proteins are necessary.

      Thanks a lot for your great suggestions on the interaction of endophilin A1 with gephyrin. To convincingly demonstrate their interaction, we performed pull-down assay with purified recombinant proteins and the result shows that both G and E domains of gephyrin were involved in the interaction with endophilin A1. The data has been added to the revised manuscript as new Figure 5I. We also modified the statement about the data and figure legends in the revised manuscript.

      (6) Figure 4G: the gephyrin images are not convincing; the inhibitory postsynaptic element typically looks somewhat elongated; these puncta are very noisy and do not appear to represent iPSDs. The same criticism applies to the images shown in Figures 5 and 7.

      Thanks for the comment. The gephyrin puncta in our images exhibited heterogeneous shapes and sizes, with some appearing somewhat elongated. To address this, we compared the puncta pattern of gephyrin with that shown in the reference. As illustrated in the figure from the reference, gephyrin puncta also displayed distinct shapes and sizes, Figure 3A-F, Neuron 78, 971–985, June 19, 2013). Please note that the images were z-stack projections at higher magnification, as described in the "Materials and Methods" section. This approach may likely introduce higher background signals and may contribute to the much more heterogeneous appearance of the puncta in Figures 4, 5, and 7. As mentioned previously, the numerous gephyrin puncta located in the adjacent non-transfected neurons may also contribute to some of the noisy signals observed. We have replaced the original images with new images in new Figure 4G, 5 and 7.

      Moreover, in order to confirm the effects of endophilin A1 KO on the gephyrin clustering, we also detected the endogenous clusters of gephyrin or PSD95 visualized by GPN.FingR-eGFP or PSD95.FingR-eGFP in cultured mature neurons. The results were consistent with immunostaining with antibodies against gephyrin. Please see Response to Recommendation (2)

      (7) Figure 7E, F: the rescue (Cre + WT) appears to perform better than the control (mCherry + GFP) in the PTZ condition; how do the authors explain this? Mixes of viral vectors were injected, would this approach achieve full rescue?

      Thanks for the thoughtful comment. Mixed viruses were injected bilaterally into the hippocampal CA1 regions. The results showed a full rescue effect by WT endophilin A1 in knockout mice during the early days, with even a little bit better rescue effect than the control group in the later days under the PTZ condition, as shown in Figures 7E and 7F. In the current study, overexpression of endophilin A1 increased the clustering of gephyrin and GABA<sub>A</sub>R γ2 in cultured neurons, as shown in Figures 4I-J and 5D-E. Presumably, the slightly better rescue effects observed in the behavioral tests was likely attributed to the enhanced clustering and/or stabilization of gephyrin/GABA<sub>A</sub>R γ2 by WT endophilin A1 expression in KO neurons in vivo. Moreover, the electrophysiological recording also showed full rescue effects on eIPSC by WT endophilin A1 in KO neurons (Figure 7G-K).

      Minor Points

      (1) The authors mention that they previously found a decrease in eEPSC amplitude in EEN1 KO mice (Yang et al., Front Mol Neurosci 2018). The data in Fig. 1E suggests a decrease in eEPSC amplitude but is not significant here, likely due to the small number of observations. If both eEPSC and iEPSC amplitude are reduced in the absence of EEN1. Would the E/I ratio still be significantly changed?

      We apologize for the confusion. In our previous study, AMPAR-mediated excitatory postsynaptic currents (eEPSCs) were found to be slightly but significantly reduced compared to the control group, while NMDAR-mediated excitatory postsynaptic currents showed no significant difference (Figure 4N,O. Yang et al., Front Mol Neurosci, 2018). In the current study, we adopted a different recording protocol, simultaneously measuring eEPSCs and eIPSCs from the same neuron to calculate the E/I ratio. Unlike previous studies, we did not use inhibitors to suppress GABA receptor activity. As a result, the recorded signals did not distinguish AMPAR-mediated or NMDAR-mediated excitatory postsynaptic currents to reflect total eEPSCs, which may explain the non-significant reduction observed compared to control neurons in this study.

      It is possible that the eEPSC amplitude would show a significant reduction if a larger number of neurons were recorded. Nevertheless, the larger suppression of eIPSCs in the absence of endophilin A1 indicates that the E/I ratio is significantly altered.

      (2) Page 7: the authors mention they aim to exclude effects on presynaptic terminals of deleting endophilin A1 in cultured neurons, is this because of a sparse transfection approach?

      Please clarify.

      Sorry for the confusion. In cultured neurons, we always observed sparse transfection due to the very low transfection efficiency (~ 0.5%). Therefore, we could examine the effects of endophilin A1 knockout specifically in the specific CamKIIa promoter-driven Cre-expressing postsynaptic neurons, while endophilin A1 remained intact in the non-transfected presynaptic neurons.

      (3) The representative blot of the surface biotinylation experiment (Figure 3E) suggests that loss of endophilin A1 also affects GluN1 and Nlgn2 levels, and error bars in panel 3F (lacking individual data points) suggest these experiments were highly variable.

      Sorry for the confusion. Reviewer #1 also raised the question and we quantified the total level of GluN1 and NL2 in Figure 3E. And we replaced the original graphs with scatterplots and means ± S.E.M. Please see the Response to Recommendation (3) & (12) by Reviewer #1.

      (4) Have other studies analyzing inhibitory synapse composition identified endophilin A1 as a component? The rationale for this study seems to be primarily based on the presence of epileptic seizures and E/I imbalance.

      Thank you for your questions. To date, no other studies investigated endophilin A1 as an inhibitory postsynaptic component. We observed the proximal localization of endophilin A1 with inhibitory postsynaptic proteins using super-resolution microscopy (SIM) and quantification results showed ~ 47% puncta of gephyrin correlated with endophilin A1 (Figure 3G-I and S4B-G). We further immunoisolated the inhibitory postsynaptic fraction using GABA<sub>A</sub> receptors and found that endophilin A1 was present in the isolated fraction, and vice versa (Figure 3J). Additionally, we demonstrated that endophilin A1 directly interacted with gephyrin through co-IP and pull-down assays (Figure 5J-I). Together with data from immunolabeling, biochemical assays, electrophysiological recordings, and behavioral tests, these results identified endophilin A1 as an inhibitory postsynaptic component.

      (5) Figure 3J: what are S100 and P100 labels? Is Nlgn2 part of the EEN1 complex? If it is, why are Nlgn2 surface levels not affected by EEN1 loss (Figure 3E, F, K)? Why does EEN1 not interact with Nlgn2 in HEK cells (Figure 4D)?

      Sorry for the confusion. The detailed information regarding S100 and P100 can be found in the “GST-pull down, co-immunoprecipitation (IP), and immunoisolation” in the “Materials and Methods” section. S100 contains soluble proteins, while P100 refers to the membrane fraction after high speed (100,000xg) centrifugation.

      Figures 3J-K and 4C-F showed the data from immunoisolation and conventional co-immunoprecipitation assays, respectively. Immunoisolation, which uses antibodies coupled to magnetic beads, allows for the rapid and efficient separation of subcellular membrane compartments. In Figure 3J-K, we used antibodies against GABA<sub>A</sub>R α1 to isolate membrane protein complexes from the inhibitory postsynaptic fraction. In contrast, co-immunoprecipitation typically detects direct interactions between proteins solubilized by detergent treatment. For Figure 4C-F, FLAG beads were used in HEK293 lysates, or antibodies against endophilin A1 were employed in brain lysates to precipitate direct interaction partners. Combined with the results from Figure 3J-L, the data in 4C-F indicated that endophilin A1 was localized in the inhibitory postsynaptic compartment and directly bound to gephyrin but not to either GABA<sub>A</sub> receptors or Nlgn2 (NL2). This binding promoted the clustering of gephyrin and GABA<sub>A</sub>R γ2 at synapses, facilitating GABA<sub>A</sub>R assembly.

      Nlgn2 (NL2) is a key inhibitory postsynaptic component but does not directly bind to endophilin A1. Consequently, endophilin A1 failed to co-immunoprecipitate with NL2 in the presence of detergent in HEK293 cell lysates (Figure 4D). Furthermore, the surface levels of NL2 or its distribution in PSD fraction were unaffected by the loss of endophilin A1 (Figure 3E, F, K, L). This suggests that mechanisms independent of endophilin A1 orchestrate the surface expression and synaptic distribution of NL2.

      (6) How do the authors interpret the finding that endophilin A1, but not A2 or A3, binds gephyrin? What could explain these differences?

      Thanks for the thoughtful comment. Endophilin As contain BAR and SH3 domains. While the amino acid sequences in the BAR and SH3 domains are highly conserved, the intrinsically disordered loop region between BAR and SH3 domains is highly variable. A study by the Verstreken lab revealed that a human mutation in the unstructured loop region of endophilin A1 increases the risk of Parkinson's disease. They also demonstrated that the disordered loop region controls protein flexibility, which fine-tunes protein-protein and protein-membrane interactions critical for endophilin A1 function (Bademosi et al., Neuron 111, 1402–1422, May 3, 2023). Our previous study showed that endophilin A1 and A3, but not A2, bind to p140Cap through their SH3 domains, despite the high sequence homology in the SH3 domains among these proteins (Figure2A,B. Yang et al., Cell Research, 2015). These findings indicate that each endophilin A likely interacts with specific partners due to distinct key amino acids.

      Additionally, endophilin A1 is expressed at much higher levels than A2 and A3 in neurons, with distinct distribution of them across different brain regions. Our lab demonstrated that the function of A1 at postsynapses (both excitatory and inhibitory synapses) cannot be compensated by A2 or A3. Therefore, it is reasonable that endophilin A1, rather than A2 or A3, binds to gephyrin, even though the underlying mechanisms remain unclear.

      (7) Figure 4G: panels are mislabeled (GFP vs merge).

      Thanks for careful reading and sorry for the mistake. We corrected the label in new Figure 4G. Please see Response to Annotation, grammar, spelling, typing errors:(1) by Reviewer #2.

    1. Reviewer #3 (Public review):

      Summary:

      In summary, the scientists used Visium spatial transcriptomics technology to create a thorough spatial transcriptomic atlas of the adult male mouse adrenal gland and the adipose tissues that surround it. Their primary goals were to map the cell communication network, determine the differentiation direction of various cell types, and find marker genes for various adrenal zones.

      Strengths:

      (1) Undoubtedly, one of the biggest strengths of the manuscript is a spatial transcriptomic o mouse adrenal gland tissue, which, to my knowledge, has not been done before.

      (2) Comprehensive Zonal Characterization: Seven distinct clusters were identified, corresponding to known anatomical and functional regions (ZG, ZF, ZX, medulla, connective tissue, brown and white adipose tissue), each with robust marker gene sets.

      (3) The authors manage to integrate advanced bioinformatical tools such as CellChatDB, Monocle3, and CARD to study the relationship between cell types and differentiation of the tissue.

      (4) The authors manage to identify novel marker genes for some adrenal zones.

      Weaknesses:

      (1) The study focused only on one adult male CD1 IGS mouse, which is a limiting factor for other strains, ages, or females, especially given the sexual dimorphism of the ZX. Although the authors claim that four slices of the adrenal gland have been processed on Visium and sequenced, for "clarity," they show only one, which might bias the results.

      (2) Lack of detailed QC analysis of the Visium slide.

      (3) The study misses the functional validation of the novel marker genes - this needs to be addressed.

      (4) What worries me a lot is the fact that, actually, there might be more than one cell present within a Visium spot, so the only way to define zones is by anatomical observation rather than cellular composition.

      (5) In cell chat analysis, the authors show the strength of the interactions, but miss out on the number of interactions.

      Conclusions:

      The authors' stated goals were mostly accomplished:

      By mapping the mouse adrenal gland's molecular landscape, they were able to clearly establish unique molecular signatures for every anatomical zone.

      Pseudotime study of the cell progression from the capsule through ZG, ZF, and ZX demonstrates that the data strongly support the centripetal differentiation concept. Conclusions on the functional importance of newly discovered marker genes are conjectural and need additional experimental support.

      Nevertheless, several findings are still tentative and will need more experimental support, especially when it comes to the significance of ZX persistence and the functional involvement of recently discovered marker genes.

  4. Jun 2025
    1. 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

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Comments for the authors of Review Commons Manuscript RC-2024-02804:

      The author of the Review Commons manuscript "Antigen flexibility supports the avidity of hemagglutinin-specific antibodies at low antigen densities", present their recent work evaluating hemagglutinin interactions with cellular receptors and antibodies. This manuscript focuses specifically on the avidity of the hemagglutinin using a fluorescence-based assay to measure dissociation kinetics and steady-state binding of antibodies to virions. Their findings confirm that bivalent interactions can offset weak monovalent affinity and that HA ectodomain flexibility is an additional determinant of antibody avidity. These findings are key for our understanding of neutralizing antibodies. Below are some comments that I would like the authors to address as they revise the manuscript.

      Comments:

      1. Can the authors provide justification for the two influenza viruses that they used.

      We selected the lab-adapted IAV strains A/WSN/1933 (H1N1) and A/Hong Kong/1968 (H3N2) for this work because they are well-studied, including in the context of the antibodies used here, S139/1 and C05. While both antibodies bind to more contemporary H3N2 strains, they no not bind to HA from pandemic H1N1. Another feature of these strains is that their HAs have high enough affinity to both antibodies to enable strong signal in our imaging assays. This context for our strain selection has been added in lines 85-88.

      1. The use of filamentous particles is a strength, but authors should detail the role of filamentous vs. spherical in nature and lab settings. This will help researchers that plan to repeat these assays.

      We have revised the text (lines 336-339) to include more context on the biology of filamentous and spherical influenza viruses. In our experiments, HK68 naturally produces filaments in cell culture whereas WSN33 does not. To produce filaments artificially, we replace the M1 sequence from WSN33 with that of M1 from A/Udorn/1972, an H3N2 strain that is closely related to HK68.

      1. Did the authors add the Udorn M1 to the HK68 as well?

      Since HK68 naturally forms filaments, we did not introduce Udorn M1 into this strain. We note that the amino acid sequences of Udorn M1 and HK68 M1 differ only at position 167 (Alanine in Udorn, Threonine in HK68), and that this residue has previously been found to not correlate with virus morphology (10.1016/j.virol.2003.12.009).

      Reviewer #1 (Significance (Required)):

      This manuscript focuses specifically on the avidity of the hemagglutinin using a fluorescence-based assay to measure dissociation kinetics and steady-state binding of antibodies to virions. Thie findings confirm that bivalent interactions can offset weak monovalent affinity and that HA ectodomain flexibility is an additional determinant of antibody avidity. These findings are key for our understanding of neutralizing antibodies.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary

      In this study, Benegal et al. investigate the binding kinetics of HA-head-specific antibodies (S139/1 and C05) to intact influenza virus particles using a fluorescence microscopy-based technique to measure the dissociation rate (koff) of the antibodies. By applying their proposed equilibrium model for bivalent antibody binding to HA, the authors calculated the crosslinking rate (kx), which represents the rate at which a single-bound antibody crosslinks to an additional HA molecule. Their experiments revealed that antigen crosslinking significantly slows koff, reducing it by up to two orders of magnitude. The authors further utilized streptavidin-coated beads conjugated with biotinylated HA or biotinylated BSA at varying concentrations to control HA surface density. Their results demonstrated that the two tested HA-head-specific antibodies retained the ability to crosslink HAs even at ~10-fold lower HA surface densities. In a complementary experiment, they employed an HA-anchor-specific antibody to restrict HA flexibility, which led to reduced binding of S139/1 and C05 IgGs but not their Fab fragments. This finding suggests that HA flexibility, rather than density, is the primary determinant of antibody crosslinking and avidity. Overall, the authors present an innovative approach to elucidating the dissociation and crosslinking kinetics of antibodies targeting intact virions or nanoparticles. The study is well-designed, with alternative interpretations of the results carefully considered and addressed throughout. I have only a few minor comments and suggestions for clarification.

      Minor comments:

      1. In Figure 1, does the grey color of each IgG in panel C indicate the Fc domain? If so, please add the description of the colors to the figure legend. In fact, it may be better to explain all the colors used here (for HA1, HA2, Fab heavy chain, light chain, etc.).

      We have included this information in panel C and the caption for Figure 1.

      1. Under the section," Bivalent binding of S139/1 and C05 persists after ~10-fold reductions in HA surface densities", the beginning of the second paragraph writes, "For both S139/1 and C05 Fab, binding increases linearly with HA density, as expected for a monovalent interaction dictated by absolute HA availability rather than density (Fig. 3D). Interestingly, the same relationship is observed for S139/1 IgG."

      Visually, I think the same relationship also seems to hold for C05 IgG. Would it be better to perform some linear regression and report the R2 value for the fitting so that this assessment can be quantitative?

      We agree with the reviewer's point. In Figure 3 of the revised manuscript, we include the results from a linear regression analysis to make this assessment more quantitative.

      1. At the end of the same page, in the same paragraph, the authors mentioned, "In contrast to the IgG, Fab binding measured at twice the molar concentration of the IgG is nearly undetectable under these conditions, confirming the IgG binding is not occurring through monovalent interactions (Fig. S2E)." What are the conditions you are referring to? In Fig. S2E, there is only the Ab intensity for the Ab binding at 100% HA (and not the other percentages). For the Ab intensity of S139/1 Fab, what is the concentration of the Fab used in Figure 3D? Why could the intensity in this experiment for S139/1 Fab reach ~100,000, whereas that of the 8 nM in Fig. S2E can only reach ~20,000?

      To clarify this point, we have updated Figure 3 to include the antibody concentration used for each experiment. The experiments in Fig 3 are conducted approximately around the respective KD of each IgG or Fab to ensure both consistency and strong signal-to-noise. For S139/1, we use 4nM of IgG, and 25nM of Fab. In Fig S2E, we use a concentration of Fab fragments double to that of the IgG, to reach an equivalent concentration of binding sites and confirm that the IgG binding we see is indeed due to bivalent binding. In this case, we use 4nM of IgG and 8nM of Fab.

      1. Under the section, "Tilting of HA about its membrane anchor contributes to C05 and S139/1 avidity", in the second paragraph, the authors wrote, "If this is correct, we reasoned that avidity could be reduced by constraining tilting of the HA ectodomain. To test this hypothesis, we used FISW84, an antibody that binds to the HA anchor epitope and biases the ectodomain into a tilted conformation (Fig. 4B)."

      Can you use some computational models (maybe the same one you used for Figure 4A) to show that when an HA trimer is bounded by FISW84 Fabs, the tilting of HA is constrained? I think this will help substantiate the assertion above.

      This is an important point. The model that we employ in Figure 4A is suited to predicting the angles sampled by HAs when they are bound by an IgG antibody, but it does not take into consideration clashes with the viral membrane. It is these clashes that we predict based on published structures (reference 35 in the revised manuscript) will constrain HA tilting when FISW84 binds to the HA anchor. We have revised the text (Lines 247-249) to clarify these points.

      1. It would be good if you could mention the strain of HA used in the experiments in Figure 4 in the actual Figure as well (as supposed to just in the figure legend).

      We have added this information to Figure 4 in the revised manuscript.

      1. I do not see a method section for the structure-based model you used in Figure 4. In the text, you cited your previous study (ref 28) for the model, but it would be good to write about this briefly (and how you specifically apply the model in this study) in this current manuscript.

      We have updated the methods to include a subsection ("Geometric Model for Preferred Crosslinking Geometry") on how the structure-based model was set up, along with a corresponding visual in Fig S3 of the angles of freedom given.

      1. In Figure S1 panel D, what is the unit of the antibody concentration? Could you please add it to the graph legend?

      We have updated the figure (S1E in the revised manuscript) to include this information.

      Reviewer #2 (Significance (Required)):

      Previously, this group utilized the same fluorescence-based method to investigate the potency of anti-HA IgG1 antibodies in preventing viral entry versus egress, as well as the tendency of antibodies targeting different HA epitopes to crosslink two HA trimers in cis or in trans (He et al., J Virol, 2024). In this study, they extend their work by evaluating, in-depth, how the density and flexibility of hemagglutinin (HA) on the viral surface influence the binding avidity of anti-HA antibodies. Using two human IgG1 antibodies targeting the HA head, the authors demonstrate that these antibodies can crosslink two HA trimers in cis, even when the trimers are further apart than adjacent HAs. Notably, the study reveals that HA flexibility, rather than density, is the key determinant modulating antibody crosslinking. Even at a 10-fold reduced HA density compared to the original, the antibodies retained their ability to crosslink trimers.

      This study provides critical insights into the relationship between HA density, flexibility, and antibody function, adding to the broader understanding of antibody crosslinking-a topic frequently discussed in the field of influenza research. These findings could have significant implications for vaccine design, particularly for strategies involving the display of the HA ectodomain on nanoparticles, potentially guiding the development of more effective influenza vaccines. Furthermore, the broader relevance of these findings may extend to other viruses with similar structural and immunological properties.

      My expertise lies in the structural determination of antibody-antigen complexes in influenza and other pathogens. While I may not have sufficient expertise to evaluate specific technical details of the fluorescence-based methods employed, the authors have convincingly demonstrated the robustness of their experimental design and interpretation, supported by appropriate controls.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      SUMMARY In "Antigen flexibility supports the avidity of hemagglutinin-specific antibodies at low antigen densities", Benegal et al. develop a microscopy-based assay to measure dissociation of HA head-binding antibodies from intact virions. This assay allows the authors to explore the contribution of IgG bivalent avidity to antibody interaction with native virions, which is not accessible using other methods such as BLI. Using this assay, the authors further explore the effect of HA density on IgG avidity with engineered low-HA virions and then with artificial HA-coated microspheres. In addition to measuring antibody dissociation, the authors perform structural analyses to predict the conformational preferences of many HA IgGs from published structures. The authors conclude that low HA densities (down to ~10%) still support high avidity binding for the 2 IgGs tested, and thus there would be little evolutionary pressure for IAV to reduce the HA density as a strategy to evade immune recognition.

      MAJOR COMMENTS

      The data presented are generally convincing for the two antibodies tested, with some caveats listed below. I believe the microscopy technique is valuable and provides a significant contribution to the field, and I believe that the finding that avidity persists at low densities for IAV is compelling and worth communicating to other virologists. Overall, with the incorporation of the suggested major revisions, this manuscript represents a significant advancement in the field.

      A major limitation of the current study is the small number of antibodies tested. Two antibodies are quite few, particularly since this work attempts to generalize these observations with structural predictions of dozens or hundreds of HA antibodies. While I believe that the resilience of IgG binding to lower epitope densities is likely common to many HA antibodies (or antibodies in general), this work alone does not support this. To this end, the authors should acknowledge their limited sample size in the text or discussion and that the generalization to other antibodies is speculative. Alternatively, the authors could demonstrate with additional antibodies (such as F045-092 which is pointed out in Fig S3A and perhaps group 'i' antibodies according to Fig S3A).

      This is an important point, and we more explicitly acknowledge this limitation in lines 277-278.

      It seems to me lateral diffusion of HA in the viral membrane is an important discussion point that was missed in this manuscript. The authors should comment on what is known about the lateral mobility of HA on virions, and how this could impact the ability of an IgG to crosslink. The authors should comment about whether long range diffusion and/or short range "shuffling" of glycoproteins could contribute to crosslinking preferences of antibodies in addition to the tilt, which is the only movement discussed. As appropriate, the authors should then comment on how this may affect their interpretation of experiments using beads. In experiments on beads, there is certainly no lateral mobility of the HA trimers; what are the consequences of this on the analysis?

      We agree that this is an important consideration, and we have revised the manuscript (lines 296-298) to address these points. Briefly, we have previously performed fluorescence recovery after photobleaching of covalently labeled HA and NA on the surface of filamentous influenza particles (10.7554/eLife.43764; see Figure 1B of this reference for a representative example). This data indicates that long range diffusion does not seem to be occurring on the virion surface. Short range diffusion, or shuffling, has not been observed, but cannot be ruled out, and may increase conformations favorable to bivalent binding.

      Should the authors qualify the limitations in the scope of their experimental results and the system of choice (beads vs. virions) as described in my previous comments, I suggest three experiments that I believe are essential to support the authors' claims. Alternative to qualifying the limitations, two optional experiments are also listed that could support the authors' claims as they are - those require a more extensive experimental undertaking and are thus labeled [OPTIONAL].

      1) The photobleaching experiment shown in Figure S1A. I am concerned that measuring photobleaching in steady state conditions does not properly control for the experimental conditions. In steady state, bleached antibody could unbind and be replaced by fluorescent antibody that has diffused into the field of view. This should be more thoroughly controlled by irreversibly capturing antibody (such as with biotin) and imaging after excess antibody is washed away, or by some other method such as capturing and imaging virus that has been directly labeled with AF555. This should be possible using reagents and techniques already demonstrated by the authors.

      We have updated the supplemental information with a more rigorous control for photobleaching; the revised figures are shown in Fig S1A. In this experiment, fluorescent S139/1 IgG was bound to HK68 virions. The antibody was washed away, and the loss of fluorescence signal was imaged separately under two conditions: 1) Dissociation only; an image was collected at 0s and one at 60s. 2) Dissociation and photobleaching; an image was collected at a rate of 1 frame per second for 60 seconds. The difference between the endpoint intensities from both conditions is not statistically significant. This supports our conclusion that, in the absence of antibodies in solution that can exchange with those bound to virions, photobleaching does not make a detectable contribution to the loss of signal we observe in our antibody dissociation experiments.

      2) In imaging, the authors analyzed only filamentous virions because they exhibit the best signal to noise ratio, which is a reasonable technical simplification. However, this relies on the assumption that glycoprotein presentation is relatively constant between virions of different sizes. It would be helpful to perform some analysis of small virions in any movie where there is sufficient signal. This would support the assumption that rates for small virions are comparable to those of filaments in the same experiment. This should be possible by performing additional analysis on existing data, without requiring additional experiments.

      Thank you for calling our attention to a point that needs clarification. The analysis that was restricted to filaments was for the SEP-HA binding experiments (shown in Fig 3A&B). This was done in order to select only those particles that were not diffraction-limited, so that we could control for any systematic differences in size between the two populations by measuring HA signal per unit particle length. For the dissociation experiments (Fig 2), data was taken from all virions in the fields of view. For this analysis, the normalized dissociation curves were averaged in two ways to account for the potential discrepancy that the reviewer points out. In the first method, the average was taken with each virion equally weighted, while in the second method, the entire field of view was masked and normalized together. Both curves look very similar, suggesting that any potential differences between smaller virions and filaments are not enough to make a quantifiable difference in dissociation rate. A representative dissociation curve with both analyses shown side-by-side has been added in Figure S1B.

      3) In figure 3, C05 fab binding is used to assay HA content of the SEP HA virions. An additional method of confirming HA content that is more independent from the imaging assay would be beneficial, such as a Western blot to quantify HA relative to NP, NA, or M1 etc.

      We have used western blotting to quantify the amount of HA contained relative to M1 in each population. This new data is discussed in lines 163-168 of the revised manuscript and shown in Figure S2C. As noted in the revised text, western blot analysis suggests that the density of native HA is decreased to ~31% its normal level in SEP-HA virions, lower than the ~75% value determined via fluorescence microscopy. One possible reason for this disparity is the presence of virus-like particles in the SEP-HA sample that completely lack wildtype HA. These would be excluded from our imaging analysis but captured by the western blot.

      4) [OPTIONAL] In figure 4, it is depicted that FISW84 biases HA in a tilted conformation, and the authors reasonably propose the reduced flexibility discourages crosslinking by IgGs. From the modeling summarized in Figure S3A, are there any antibodies predicted to prefer crosslinking HA at the same angle FISW84 tilts the ectodomain? Would FISW84 enhance crosslinking by such an antibody?

      This is an interesting suggestion, and we have revised the manuscript (lines 247-249) to clarify our thinking on this point. Based on the structure of the FISW84 Fab (PDB ID 6HJQ), we conclude that binding of a single Fab fragment does not necessarily actively tilt the HA ectodomain in a specific direction. Rather, it restricts tilting in the direction that would cause a steric clash between the Fab and the membrane. As a result, HA can still sample a range of angles, but this range is no longer symmetrical about the ectodomain axis. By reducing the likelihood that two HA ectodomains would tilt towards each other at a favorable angle, we would expect all antibodies to be disadvantaged to some degree. A possible exception could be if three FISW84 Fab fragments manage to bind to a single HA trimer. In this case, the HA ectodomain would be forced to remain perpendicular to the membrane to accommodate them all. This would favor antibodies that prefer binding to HAs where the ectodomains are parallel to each other. In our analysis in Figure S3A, this includes primarily antibodies that bind to the HA central stalk, such as 31.b.09. However, we note that these antibodies may encounter barriers to bivalent binding that we do not consider here, including proximity to the FISW84 epitope and the high density of HA in the membrane.

      5) [OPTIONAL] In figure S3A, the authors display theoretical tilt and spacing preferences for many HA antibodies based on published structures. Interestingly, their group iii antibody is predicted to prefer greater spacing and tilt, and likewise the authors observe increased binding at lower densities (in figure 3E). It would be beneficial to the work to test group i antibodies (base binding) in the dissociation experiments. The behavior of a base binding antibody, particularly at low densities could reinforce the modeling performed for this work.

      This is an excellent suggestion which we are not currently able to pursue for technical reasons. In particular, it would be difficult to distinguish between increased binding of these antibodies at low antigen densities that is due to bivalent attachment (and thus reduced dissociation) versus increased accessibility of the epitope, which may be occluded at higher HA densities.

      The experiments are well explained and supported by methods that would enable reproducibility.

      The authors state "The statistical tests and the number of replicates used in specific cases are described in the figure legends" yet in many cases this information is absent. For the k values in fig 2D, some indication of error or confidence interval would be helpful.

      We have ensured that this information is included in each of the captions. Regarding the k values, formal error propagation is challenging due to the way the k values were derived. Specifically, these values were calculated by fitting the average of the three initial dissociation traces, rather than fitting each replicate individually and then averaging the rate constants. As a result, the usual methods for estimating confidence intervals or standard error of the mean are not directly applicable.

      MINOR COMMENTS

      o Some of the small details in fig 1A and fig S1 are lost due to small figure size - such as the sialic acid residues and lipid bilayer.

      We have resized the figure components.

      o Although described in the text, it could be helpful to incorporate into figure 2 why the BLI data is shown for S129 fab. Perhaps indicate in 2C that that curve is "too fast to accurately measure" and perhaps near the table in 2D indicate the blue data is from Lee et al. It may be fine to simply remove the BLI results from the figure and refer to them only in the discussion of the experiments. Even with the measured data, the difference between fab and IgG is striking enough to support the paper, and the BLI data may be more confusing in the figure than it adds.

      We have updated the caption for Figure 2D to clarify that binding between the S139/1 Fab and A/WSN/1933 HA is approaching the limit of detection in our assay, and that the additional rates are from Lee et al. We have also updated the table to make the presentation of the kinetic parameters more clear.

      o In figure 3A, better describe the fluorescent components in the fluorescent images in the legend.

      We have updated the caption for Figure 3A to describe the fluorescent components shown in the image. Specifically, the panel labeled 'HA' shows signal from a fluorescent FI6v3 scFv, while the panel labeled 'decoy' shows signal from the SEP-HA construct.

      o From personal experience, the flexibility of HA ectodomain can be significantly affected by how much of the membrane proximal linker region is retained or removed. Could the authors comment on how they chose the cutoff for their HA ectodomain used in the bead experiments and their rationale?

      This is an important point, and while the precise impact of the linker on HA flexibility remains uncertain, we agree that it may increase the freedom of motion of the ectodomain relative to the HA membrane anchor. We mention this caveat in the revised text (lines 188-191) and we have added an AlphaFold2 prediction of how our recombinant HA might look to Figure S2D.

      o In Figure S1B, if I understand correctly: black dashed line "IgG equivalent dissociation rate" is the experimental data, magenta "Crosslinking model fit" is the theoretically total antibody bound as described by the mathematical model. Then the gray lines "Double- /singly- bound antibodies plot the theoretical amount of antibody bound once and bound twice. If this is correct, I believe it would be clearer if the singly- and doubly- bound were plotted in separate colors, and that this is explained more clearly in the legend.

      We have revised the figure to show doubly- and singly-bound curves using different line styles.

      o Related to an earlier comment, if lateral diffusion may play a role, how might this differ between different types of antibodies?

      As mentioned in our previous response, we do not anticipate that lateral diffusion makes a significant contribution to antibody binding to the surface of virions, although it may be important on the cell surface.

      o Could the authors comment in the discussion on how their results on virions may translate to the surface of the infected cell, which is also decorated in viral glycoproteins? Early time points of infection could be an in vivo example of low-density HA. What extent may antibody binding and crosslinking affect viral proteins on the cell surface or the immune response?

      This is a very interesting point. Antibody binding to the infected cell surface has been shown to alter viral release and morphology, presumably at lower HA densities than those observed the viral surface. We have added a brief discussion of this point (lines 291-295) to the revised manuscript.

      o The github link in the methods is incorrect or not yet available.

      Thank you for noting this. We have updated the link.

      o Reference 1 has an incorrect or expired link.

      These references have been updated.

      Reviewer #3 (Significance (Required)):

      • This work represents a conceptual advance in our understanding of antibody action on viral pathogens. The authors adapt existing microscopy methodologies to measure antibody avidity in a new way that is better representative of in vivo conditions.

      • To my knowledge, this is the first instance of direct measurement of antibody off-rates from intact virus particles, instead of immobilized protein as in BLI, SPR, or interferometry.

      • This work should be of interest to virologist and biophysicists interested in the cooperative binding of antibodies and the relation of virus structural organization to antibody recognition. Immunologist may also be influenced by this work. This work may be followed up by other researchers similarly measuring the association and dissociation rates of antibodies with single virions, or otherwise comparing fab to IgG binding to gain insight into when crosslinking is or is not occurring.

      • Reviewer expertise: Single-virion imaging, protein complexes, biochemistry, influenza A.

      • I do not have sufficient expertise to evaluate the mathematical models and differential equations for modeling the k-on and k-off rates.

    1. En cuanto a las hipótesis de nivel país, la administración digital (H4) no tiene sustento empírico, pues no es significativo, por lo que se puede deducir que el hecho de que un país tenga alta o baja administración digital, no influirá en la alfabetización digital de sus estudiantes.

      redacción

    2. no se puede descartar ninguna hipótesis

      este lenguaje es un poco enrevesado para referirse a las hipótesis, no queda claro si se encuentra evidencia a favor o no. Se puede decir que se rechaza la hipótesis nula, o que el efecto es estadísticamente significativo.

    3. La variable puede tomar 5 valores plausibles a partir del puntaje logrado por los estudiantes: Bajo del nivel 1 (menos de 407 puntos), Nivel 1 (desde 407 hasta 491 puntos), Nivel 2 (desde 492 hasta 576 puntos), Nivel 3 (desde 557 hasta 661 puntos) y el Nivel 4 (sobre 661 puntos).

      en esta descripción es confuso si la variable es ordinal o continua

    1. In the Seventies, the evolutionary-­biological approach to the study of human behavior grew even more popular. Its leading exponents were Hamilton’s Oxford colleague Richard Dawkins—­who has called Hamilton “the greatest Darwinian of my lifetime”—­and the Harvard entomologist E. O. Wilson, who recalled his imagination being “captured” by Lorenz at a pivotal point in his graduate studies.

      Dawkins and Wilson both influenced by W. D. Hamilton

    2. Baker, Erik. “Trump’s Darwinian America.” Harper’s Magazine, July 2025. https://harpers.org/archive/2025/07/trumps-darwinian-america-erik-baker/.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      Walton et al. set out to isolate new phages targeting the opportunistic pathogen Pseudomonas aeruginosa. Using a double ∆fliF ∆pilA mutant strain, they were able to isolate 4 new phages, CLEW-1. -3, -6, and -10, which were unable to infect the parental PAO1F Wt strain. Further experiments showed that the 4 phages were only able to infect a ∆fliF strain, indicating a role of the MS-protein in the flagellum complex. Through further mutational analysis of the flagellum apparatus, the authors were able to identify the involvement of c-di-GMP in phage infection. Depletion of c-di-GMP levels by an inducible phosphodiesterase renders the bacteria resistant to phage infection, while elevation of c-di-GMP through the Wsp system made the cells sensitive to infection by CLEW-1. Using TnSeq, the authors were able to not only reaffirm the involvement of c-di-GMP in phage infection but also able to identify the exopolysaccharide PSL as a downstream target for CLEW-1. C-di-GMP is a known regulator of PSL biosynthesis. The authors show that CLEW-1 binds directly to PSL on the cell surface and that deletion of the pslC gene resulted in complete phage resistance. The authors also provide evidence that the phage-PSL interaction happens during the biofilm mode of growth and that the addition of the CLEW-1 phage specifically resulted in a significant loss of biofilm biomass. Lastly, the authors set out to test if CLEW-1 could be used to resolve a biofilm infection using a mouse keratitis model. Unfortunately, while the authors noted a reduction in bacterial load assessed by GFP fluorescence, the keratitis did not resolve under the tested parameters. 

      Strengths: 

      The experiments carried out in this manuscript are thoughtful and rational and sufficient explanation is provided for why the authors chose each specific set of experiments. The data presented strongly supports their conclusions and they give present compelling explanations for any deviation. The authors have not only developed a new technique for screening for phages targeting P. aeruginosa, but also highlight the importance of looking for phages during the biofilm mode of growth, as opposed to the more standard techniques involving planktonic cultures. 

      Weaknesses: 

      While the paper is strong, I do feel that further discussions could have gone into the decision to focus on CLEW-1 for the majority of the paper. The paper also doesn't provide any detailed information on the genetic composition of the phages. It is unclear if the phages isolated are temperate or virulent. Many temperate phages enter the lytic cycle in response to QS signalling, and while the data as it is doesn't suggest that is the case, perhaps the paper would be strengthened by further elimination of this possibility. At the very least it might be worth mentioning in the discussion section. 

      Thank you for your review. The genomes of all Clew phages and Ocp-2 have been uploaded [Genbank accession# PQ790658.1, PQ790659.1, PQ790660.1, PQ790661.1, and PQ790662.1]. It turns out that the Clew phage are highly related, which is highlighted by the genomic comparison in the supplementary figure S1. It therefore made sense to focus our in-depth analysis on one of the phage. We have included a supplementary figure (S1A), demonstrating that the other Clew phage also require an intact psl locus for infection, to make that logic clearer. The phage are virulent (there is apparently a bit of a debate about this with regard to Bruynogheviruses, but we have not been able to isolate lysogens). This is now mentioned in the discussion.  

      Reviewer #2 (Public review): 

      This manuscript by Walton et al. suggests that they have identified a new bacteriophage that uses the exopolysaccharide Psl from Pseudomonas aeruginosa (PA) as a receptor. As Psl is an important component in biofilms, the authors suggest that this phage (and others similarly isolated) may be able to specifically target biofilm-growing bacteria. While an interesting suggestion, the manner in which this paper is written makes it difficult to draw this conclusion. Also, some of the results do not directly follow from the data as presented and some relevant controls seem to be missing. 

      Thank you for your review. We would argue that the combination of demonstrating Psl-dependent binding of Clew-1 to P. aeruginosa, as well as demonstration of direct binding of Clew-1 to affinity-purified Psl, indicates that the phage binds directly to Psl and uses it as a receptor. In looking at the recommendations, it appears that the remark about controls refers to not using the ∆pslC mutant alone (as opposed to the ∆fliF2 ∆pslC double mutant) as a control for some of the binding experiments. However, since the ∆fliF2 mutant is more permissive for phage infection, analyzing the effect of deleting pslC in the context of the ∆fliF2 mutant background is the more stringent test. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors): 

      First off, I would like to congratulate the authors on this study and manuscript. It is very well executed and the writing and flow of the paper are excellent. The findings are intriguing and I believe the paper will be very well received by both the phage, Pseudomonas, and biofilm communities. 

      Thank you for your kind review of our work!

      I have very little to critique about the paper but I have listed a few suggestions that I believe could strengthen the paper if corrected: 

      Comments and suggestions: 

      (1) The paper initially describes 4 isolated phages but no rationale is given for why they chose to continue with CLEW-1, as opposed to CLEW-3, -6, and -10. The paper would benefit from going into more detail with phage genomics and perhaps characterize the phage receptor binding to PSL. 

      Clew-1, -3, -6, and -10 are actually quite similar to one another. The genomes are now uploaded to Genbank [accession# PQ790658.1, PQ790659.1, PQ790660.1, and PQ790661.1]. They all require an intact Psl locus for infection, we have updated Fig. S1 to show this for the remaining Clew phage. In the end, it made sense to focus on one of these related phage and characterize it in depth.

      (2) PA14 was used in some experiments but not listed in the strain table. 

      Thank you, this has been added in the resubmission.

      (3) Would have been good to see more strains/isolates used.

      We are currently characterizing the host range of Clew-1. It appears to be pretty limited, but this will likely be included in another paper that will focus on host range, not only of Clew-1, but other biofilm-tropic phage that we have isolated since then.

      (4) Could purified PSL be added to make non-PSL strain (like PA14) susceptible? 

      We have tried adding purified Psl to a psl mutant strain, but this does not result phage sensitivity. Further characterization of the Psl receptor, is something we are currently working on, but will likely be a much bigger story than can be easily accommodated in a revised manuscript.

      (5) No data on resistance development. 

      We have not done this as yet.

      (6) Alternative biofilm models. Both in vitro and in vivo. 

      We agree that exploring the interaction of Clew-1 with biofilms in greater detail is a logical next step. The revised manuscript does have data on the viability of P. aeruginosa biofilm bacteria after Clew-1 infection using either a bead biofilm model or LIVE/DEAD staining of static biofilms. However, expanding on this further (setting up flow-cell biofilms, developing reporters to monitor phage infection, etc.) is beyond the scope of this initial report and characterization of Clew-1.

      (7) There is a mistake in at least one reference. An unknown author is listed in reference 48. DA Garsin is not part of the paper. Might be worth looking into further mistakes in the reference list as I suspect this might be an issue related to the citation software.

      Thank you. Yes, odd how that extra author got snuck in. This has been corrected.

      (8) I don't seem to be able to locate a Genbank file or accession number. If it wasn't performed how was evolutionary relatedness data generated?

      The genomes of all Clew phages and Ocp-2 have been uploaded [Genbank accession# PQ790658.1, PQ790659.1, PQ790660.1, PQ790661.1, and PQ790662.1]

      (9) No genomic information about the isolated phages. Are they temperate or virulent? This would be important information as only strictly lytic phages are currently deemed appropriate for phage therapy. 

      These phage are virulent. We have only been able to isolate resistant bacteria from plaques, but they do not harbor the phage (as detected by PCR). This matches what other researchers have found for Bruynogheviruses.

      Reviewer #2 (Recommendations for the authors): 

      Others have used different PA mutants lacking known phage receptors to pan for new phages. However, it is not totally clear how the screen here was selected for the Psl-specific phage. The authors used flagella and pili mutants and found Clew-1, -3, -6, and -10. These were all Bruynogheviruses. They also isolated a phage that uses the O antigen as a receptor. The family of this latter phage and how it is known to use this as a receptor is not described. 

      Phage Ocp-2 is a Pbunavirus. We added new supplementary figure S3, addressing the O-antigen receptor.

      The authors focused on Clew-1, but the receptor for these other Clew phages is not presented. For Clew-1 the phage could plaque on the fliF deletion mutant but not the wild-type strain. The reason for this never appears to be addressed. The authors leap to consider the involvement of c-di-GMP, but how this relates to fliF appears to be lacking. 

      We have included a supplementary figure demonstrating that all the Clew phage require Psl for infection (Fig. S1A). As noted above, we have uploaded the genomic data that underpins the comparison in our supplementary figure. The phage are all closely related. It therefore made sense to focus on one of the phage for the analysis.  

      It is particularly unclear why this phage doesn't plaque on PAO1 as this strain does make Psl. Related to this, it actually looks like something is happening to PAO1 in Figure S4 (although what units are on the x-axis is not entirely clear).

      We hypothesize that the fraction of susceptible cells in the population dictates whether the phage can make overt plaques. The supplementary figure S4 indicates that a subpopulation of the wild-type culture is susceptible and this is borne out by the fraction of wild type cells that the phage can bind to (~50%). The fliF mutation increases this frequency of susceptible cells to 80-90% (Fig. 3).

      The Tnseq screen to identify receptors is clever and identifies additional phosphodiesterase genes, the deletion of which makes PAO1 susceptible. And the screen to find resistant fliF mutants identified genes involved in Psl. However, the link between the phosphodiesterase mutants and the amount of Psl produced never appears to be established. And the statement that Psl is required for infection (line 130) is never actually tested.

      The link between c-di-GMP and Psl production is well-established in the literature. I think the requirement for Psl in infection is demonstrated multiple ways, including lack of plaque formation on psl mutant strains and lack of phage binding to strains that do not produce Psl, direct binding of the phage to affinity purified Psl.

      Figure 2C describes using a ∆fliF2 strain but how this is different (or if it is different) from ∆fliF described in the text is never explained.

      The difference in the deletions is explained in table S1, in the description for the deletion constructs used in their construction, pEXG2-∆fliF and pEXG2-∆fliF2 (∆fliF2 is smaller than ∆fliF and can be complemented completely with our complementing plasmid, pP37-fliF, which is the reason why we used the ∆fliF2 mutation going forward, rather than the ∆fliF mutation on which the phage was originally isolated).

      Similarly, there is a sentence (line 138) that "Attachment of Clew-1 is Psl-dependent" but this would appear to have no context.

      The relevant figure, Fig. 3, is cited in the next sentence and is the subject of the remaining paragraphs in this section of the manuscript.

      For Figure 3B, why wasn't the single ∆pslC mutant visualized in this analysis? Similar questions relate to the data in Figure 4.

      Analyzing the effect of the pslC deletion in the context of the ∆fliF2 mutant background, which is more permissive for phage infection, is the more stringent test.  

      The efficacy of Clew-1 in the mouse keratitis model is intriguing but it is unclear why the CFU/eye are so variable. The description of how the experiment was actually carried out is not clear. Was only one eye scratched or both? Were controls included with a scratch and no bacteria ({plus minus} phage)?

      One eye was infected. We did not conduct a no-bacteria control (just scratching the cornea is not sufficient to cause disease). The revised manuscript has an updated animal experiment in which we carried the infection forward to 72h with two phage treatments. Following this regiment, there is a significant decrease in CFU, as well as corneal opacity (disease). Variability of the data is a fairly common feature in animal experiments. There are a number of factors, such as does the mouse blink and remove some of the inoculum shortly after deposition of the bacteria or the phage after each treatment that could explain this variability.

    1. Author response:

      The following is the authors’ response to the original reviews

      Life Assessment

      The authors use a synthetic approach to introduce synaptic ribbon proteins into HEK cells and analyze the ability of the resulting assemblies to cluster calcium channels at the active zone. The use of this ground-up approach is valuable as it establishes a system to study molecular interactions at the active zone. The work relies on a solid combination of super-resolution microscopy and electrophysiology, but would benefit from: (i) additional ultrastructural analysis to establish ribbon formation (in the absence of which the claim of these being synthetic ribbons might not be supported; (ii) data quantification (to confirm colocalization of different proteins); (iii) stronger validation of impact on Ca2+ function; (iv) in depth discussion of problems derived from the use of an over-expression approach.

      We thank the editors and the reviewers for the constructive comments and appreciation of our work. Please find a detailed point-to-point response below. In response to the critique received, we have now (i) included an ultrastructural analysis of the SyRibbons using correlative light microscopy and cryo-electron tomography, (ii) performed quantifications to confirm the colocalisation of the various proteins, (iii) discussed and carefully rephrased our interpretation of the role of the ribbon in modulating Ca<sup>2+</sup> channel function and (iv) discussed concerns regarding the use of an overexpression system. 

      Public Reviews:

      Reviewer #1 (Public Review):

      We would like to thank the reviewer for the comments and advice to further improve our manuscript. We have completely overhauled the manuscript taking the suggestions of the reviewer into account.

      (1) Are these truly "synthetic ribbons". The ribbon synapse is traditionally defined by its morphology at the EM level. To what extent these structures recapitulate ribbons is not shown. It has been previously shown that Ribeye forms aggregates on its own. Do these structures look any more ribbonlike than ribeye aggregates in the absence of its binding partners?

      We thank reviewer 1 for their constructive feedback and critique of the work. 

      We agree that traditionally, ribbon synapses have always been defined by the distinct morphology observed at the EM level. However, since the discovery of the core-components of ribbons (RIBEYE and Piccolino) confocal and super-resolution imaging of immunofluorescently labelled ribbons have gained importance for analysing ribbon synapses. A correspondence of RIBEYE immunofluorescent structures at the active zone to electron microscopy observations of ribbons has been established in numerous studies (Wong et al, 2014; Michanski et al, 2019, 2023; Maxeiner et al, 2016; Jean et al, 2018) even though direct correlative approaches have yet to be performed to our knowledge. We have now analysed SyRibbons using cryo-correlative electron-light microscopy. We observe that GFPpositive RIBEYE spots corresponded well with electron-dense structures, as is characteristic for synaptic ribbons (Robertis & Franchi, 1956; Smith & Sjöstrand, 1961; Matthews & Fuchs, 2010). We could also observe SyRibbons within 100 nm of the plasma membrane (see Fig. 3). We have now added this qualitative ultrastructural analysis of SyRibbons in the main manuscript (lines 272 - 294, Fig. 3 and Supplementary Fig. 3).

      (2) No new biology is discovered here. The clustering of channels is accomplished by taking advantage of previously described interactions between RBP2, Ca channels and bassoon. The localization of Ribeye to bassoon takes advantage of a previously described interaction between the two. Even the membrane localization of the complexes required the introduction of a membraneanchoring motif.

      We respectfully disagree with the overall assessment. Our study emphasizes the synthetic establishment of protein assemblies that mimic key aspects of ribbon-type active zone, defining minimum molecular requirements. Numerous previous studies have described the role of the synaptic ribbon in organising the spatial arrangement of Ca<sup>2+</sup> channels, regulating their abundance and possibly also modulating their physiological properties (Maxeiner et al, 2016; Frank et al, 2010; Jean et al, 2018; Wong et al, 2014; Grabner & Moser, 2021; Lv et al, 2016). We would like to highlight that there remain major gaps between existing in vitro and in vivo data; for instance, no evidence for direct or indirect interactions between Ca<sup>2+</sup> channels and RIBEYE have been demonstrated so far. While we do indeed take advantage of previously known interactions between RIBEYE and Bassoon (tom Dieck et al, 2005); between Bassoon, RBP2 and P/Q-type Ca<sup>2+</sup> channels (Davydova et al, 2014); and between RBP2 and Ltype Ca<sup>2+</sup> channels (Hibino et al, 2002), our study tries to bridge these gaps by establishing the indirect link between the synaptic ribbon (RIBEYE) and L-type CaV1.3 Ca<sup>2+</sup> channels using a bottom-up approach, which has previously just been speculative. Our data shows how even in a synapse-naive heterologous expression system, ribbon synapse components assemble Ca<sup>2+</sup> channel clusters and even show a partial localisation of Ca<sup>2+</sup> signal. Moreover, we argue that the established reconstitution approach provides other interesting insights such as laying ground-up evidence supporting the anchoring of the synaptic ribbon by Bassoon. Finally, we expect that the established system will serve future studies aimed at deciphering the role of putative CaV1.3 or CaV1.4 interacting proteins in regulating Ca<sup>2+</sup> channels of ribbon synapses by providing a more realistic Ca<sup>2+</sup> channel assembly that has been available in heterologous expression systems used so far. In response to the reviewers comment we have augmented the discussion accordingly.  

      (3) The only thing ribbon-specific about these "syn-ribbons" is the expression of ribeye and ribeye does not seem to participate in the localization of other proteins in these complexes. Bsn, Cav1.3 and RBP2 can be found in other neurons.

      The synaptic ribbon made of RIBEYE is the key molecular difference in the molecular AZ ultrastructure of ribbon synapses in the eye and the ear. We hypothesize the ribbon to act as a superscaffold that enables AZ with large Ca<sup>2+</sup> channel assemblies and readily releasable pools. In further support of this hypothesis, the present study on synthetic ribbons shows that CaV1.3 Ca<sup>2+</sup> channel clusters are larger in the presence of SyRibbons compared to SyRibbon-less CaV1.3 Ca<sup>2+</sup> channel clusters in tetratransfected HEK cells (Ca<sup>2+</sup> channels, RBP, membrane-anchored Bassoon, and RIBEYE, Fig. 6). In response to the reviewers comment we now added an analysis of triple-transfected HEK cells (Ca<sup>2+</sup> channels, RBP, membrane-anchored Bassoon), in which CaV1.3 Ca<sup>2+</sup> channel clusters again are significantly smaller than at the SyRibbons and indistinguishable from SyRibbon-less CaV1.3 Ca<sup>2+</sup> channel clusters (Fig. 6E, F).

      (4) As the authors point out, RBP2 is not necessary for some Ca channel clustering in hair cells, yet seems to be essential for clustering to bassoon here.

      Here we would like to clarify that RBP2 is indeed important in inner hair cells for promoting a larger complement of CaV1.3 and RBP2 KO mice show smaller CaV1.3 channel clusters and reduced whole cell and single-AZ Ca<sup>2+</sup> influx amplitudes (Krinner et al, 2017). However, a key point of difference we emphasize on is that even though CaV1.3 clusters appeared smaller, they did not appear broken or fragmented as they do upon genetic perturbation of Bassoon (Frank et al, 2010), RIBEYE (Jean et al, 2018) or Piccolino (Michanski et al, 2023). This highlights how there may be a hierarchy in the spatial assembly of CaV1.3 channels at the inner hair cell ribbon synapse (also described in the discussion section “insights into presynaptic Ca<sup>2+</sup> channel clustering and function”) with proteins like RBP2 regulating abundance of CaV1.3 channels at the synapse and organising them into smaller clusters – what we have termed as “nanoclustering”; while Bassoon and RIBEYE may serve as super-scaffolds further organizing these CaV1.3 nanoclusters into “microclusters”. Observations of fragmented Ca<sup>2+</sup> channel clusters and broader spread of Ca<sup>2+</sup> signal seen upon Ca<sup>2+</sup> imaging in RIBEYE and Bassoon mutants (Jean et al, 2018; Frank et al, 2010; Neef et al, 2018), and the absence of such a phenotype in RBP2 mutants (Krinner et al, 2017) may be explained by such a differential role of these proteins in organising Ca<sup>2+</sup> channel spatial assembly. The data of the present study on reconstituted ribbon containing AZs are in line with these observations in inner hair cells: RBP2 appears important to tether Ca<sup>2+</sup> channels to Bassoon and these AZ-like assemblies are organised to their full extent by the presence of RIBEYE. As mentioned in the response to point 3 of the reviewer, we have now further strengthened this point by adding the analysis of SyRibbon-less CaV1.3 Ca<sup>2+</sup> channel clusters in tripletransfected HEK cells (Ca<sup>2+</sup> channels, RBP, membrane-anchored Bassoon, Fig. 6E, F). Moreover, we have revised the discussion accordingly. 

      (5) The difference in Ca imaging between SyRibbons and other locations is extremely subtle.

      We agree with the reviewer on the modest increase in Ca<sup>2+</sup> signal amplitude seen in the presence of  SyRibbons and provide the following reasoning for this observation: 

      (i) It is plausible that due to the overexpression approach, Ca<sup>2+</sup> channels (along with RBP2 and PalmBassoon) still show considerably high expression throughout the membrane even in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B, where Ca<sup>2+</sup> channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons (for an opposing scenario, please see the cell in Fig. 6B upper panel with very localised CaV1.3 distribution underneath SyRibbons). This would of course diminish the difference in the Ca<sup>2+</sup> signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca<sup>2+</sup> channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

      (ii) Nevertheless, we do not expect a remarkably big difference in Ca<sup>2+</sup> influx due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca<sup>2+</sup> current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). However, it was the spatial spread of the Ca2+ signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca2+ hotspots seen in the wild-type controls. 

      So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca<sup>2+</sup> signal by super scaffolding nanoclusters into microclusters (see also our response to points 3 and 4 of the reviewer): this is evident from seeing some spatial confinement of Ca<sup>2+</sup> signals near SyRibbons on top of the diffuse Ca<sup>2+</sup> signal across the rest of the membrane as a result of overexpression in HEK cells. 

      We have now carefully rephrased our interpretation throughout the manuscript and added further explanation in the discussion section.   

      (6) The effect of the expression of palm-Bsn, RBP2 and the combination of the two on Ca-current is ambiguous. It appears that while the combination is larger than the control, it probably isn't significantly different from either of the other two alone (Fig 5). Moreover, expression of Ribeye + the other two showed no effect on Ca current (Figure 7). Also, why is the IV curve right shifted in Figure 7 vs Figure 5?

      We agree with the reviewer that co-expression of palm-Bassoon and RBP2 seems to augment Ca<sup>2+</sup> currents, while the additional expression of RIBEYE results in no change when compared to wild-type controls. We currently do not have an explanation for this observation and would refrain from making any claims without concrete evidence. As the reviewer also correctly pointed out, while the expression of the combination of palm-Bassoon and RBP2 raises Ca<sup>2+</sup> currents, current amplitudes are not significantly different when compared to the individual expression of the two proteins (P > 0.05, Kruskal-Wallis test). In light of this, we have now carefully rephrased our MS. Moreover, we would like to thank reviewer 1 for pointing out the right shift in the IV curve which was due to an error in the values plotted on the x-axis. This has been corrected in the updated version of the manuscript. 

      (7) While some of the IHC is quantified, some of it is simply shown as single images. EV2, EV3 and Figure 4a in particular (4b looks convincing enough on its own, but could also benefit from a larger sample size and quantification)

      We have now added quantifications for the colocalisations of the various transfection combinations depicted in the above-mentioned figures collectively in Supplementary Figure 7 and added the corresponding results and methods accordingly. 

      Reviewer #2 (Public Review):

      We would like to thank the reviewer for the comments and advice to further improve our manuscript.

      (1) Relies on over-expression, which almost certainly diminishes the experimentally-measured parameters (e.g. pre-synapse clustering, localization of Ca2+ entry).

      We acknowledge this limitation highlighted by the reviewer arising from the use of an overexpression system and have carefully rephrased our interpretation and discussed possible caveats in the discussion section. 

      (2) Are HEK cells the best model? HEK cells secrete substances and have a studied-endocytitic pathway, but they do not create neurosecretory vesicles. Why didn't the authors try to reconstitute a ribbon synapse in a cell that makes neurosecretory vesicles like a PC12 cell?

      This is a valid point for discussion that we also had here extensively. We indeed did consider pheochromocytoma cells (PC12 cells) for reconstitution of ribbon-type AZs and also performed initial experiments with these in the initial stages of the project. PC12 cells offer the advantage of providing synaptic-like microvesicles and also endogenously express several components of the presynaptic machinery such as Bassoon, RIM2, ELKS etc (Inoue et al, 2006) such that overexpression of exogenous AZ proteins would have to be limited to RIBEYE only. 

      However, a major drawback of PC12 cells as a model is the complex molecular background of these cells. We have also briefly described this in the discussion section (line 615 – 619). Naïve, undifferentiated PC12 cells show highly heterogeneous expression of various CaV channel types (Janigro et al, 1989); however, CaV1.3, the predominant type in ribbon synapses of the ear, does not seem to express in these cells (Liu et al, 1996). Furthermore, our attempts at performing immunostainings against CaV1.3 and at overexpressing CaV1.3 in PC12 cells did not prove successful and we decided on refraining from pursuing this further (data not shown). 

      On the contrary, HEK293 cells being “synapse-naïve” provide the advantage of serving as a “blank canvas” for performing such reconstitutions, e.g. they lack voltage-gated Ca<sup>2+</sup> channels and multidomain proteins of the active zone. Moreover, an important practical aspect for our choice was the availability of the HEK293 cell line with stable (and inducible) expression of the CaV1.3 Ca<sup>2+</sup> channel complex. Finally, as described in lines 613 – 614 of the discussion section, even though HEK293 cells lack SVs and the molecular machinery required for their release, our work paves way for future studies which could employ delivery of SV machinery via co-expression (Park et al, 2021) which could then be analyzed by the correlative light and electron microscopy workflow we worked out and added during revision. 

      (3) Related to 1 and 2: the Ca channel localization observed is significant but not so striking given the presence of Cav protein and measurements of Ca2+ influx distributed across the membrane. Presumably, this is the result of overexpression and an absence of pathways for pre-synaptic targeting of Ca channels. But, still, it was surprising that Ca channel localization was so diffuse. I suppose that the authors tried to reduce the effect of over-expression by using an inducible Cav1.3? Even so, the accessory subunits were constitutively over-expressed.

      We agree with the reviewer on the modest increase in Ca<sup>2+</sup> signal amplitude seen in the presence of SyRibbons. Yes, we employed inducible expression of the CaV1.3a subunit and tried to reduce the effect of overexpression by testing different induction times. However, we did not observe any major differences in expression and observed large variability in CaV1.3 expression across cells irrespective of induction duration. At all time points, there were cells with diffuse CaV1.3 localisation also in regions without SyRibbons which likely reduced the contrast of the Ca<sup>2+</sup> signal we observe. We provide the following reasoning for this observation: 

      (i) It is plausible that due to the overexpression approach, Ca<sup>2+</sup> channels (along with RBP2 and PalmBassoon) still show considerable expression along the membrane also in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B where Ca<sup>2+</sup> channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons. This would of course diminish the difference in the Ca<sup>2+</sup> signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca<sup>2+</sup> channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

      (ii) Nevertheless, we do not expect a striking difference in Ca<sup>2+</sup> influx amplitude due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca<sup>2+</sup> current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). Instead, it was the spatial spread of the Ca<sup>2+</sup> signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca<sup>2+</sup> hotspots seen in the wildtype controls. 

      So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca<sup>2+</sup> signal by super scaffolding nanoclusters into microclusters: this is evident from seeing some spatial confinement of Ca<sup>2+</sup> signals near SyRibbons on top of the diffuse Ca<sup>2+</sup> signal across the rest of the membrane as a result of overexpression in HEK cells. 

      We have now carefully rephrased our interpretation throughout the manuscript and added further explanation in the discussion section.   

      Reviewer #3 (Public Review):

      We would like to thank the reviewer for the comments and advice to further improve our manuscript.

      (1) The results obtained in a heterologous system (HEK293 cells) need to be interpreted with caution. They will importantly speed the generation of models and hypothesis that will, however, require in vivo validation.

      We acknowledge this limitation highlighted by Reviewer 3 arising from the use of an overexpression system and have carefully rephrased our interpretation and discussed possible caveats in the discussion section. We employed inducible expression of the CaV1.3a subunit and tried to reduce the effect of overexpression by testing different induction times. However, we did not observe any major differences in expression and observed large variability in CaV1.3 expression across cells irrespective of induction duration. At all time points, there were cells with diffuse CaV1.3 localisation, even in regions without SyRibbons and this could reduce the contrast of the Ca<sup>2+</sup> signal we observe. We provide the following reasoning for this observation: 

      (i) It is plausible that due to the overexpression approach, Ca<sup>2+</sup> channels (along with RBP2 and PalmBassoon) still show considerable expression along the membrane also in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B where Ca<sup>2+</sup> channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons. This would of course diminish the difference in the Ca<sup>2+</sup> signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca<sup>2+</sup> channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

      (ii) Nevertheless, we do not expect a striking difference in Ca<sup>2+</sup> influx amplitude due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca<sup>2+</sup> current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). Instead, it was the spatial spread of the Ca<sup>2+</sup> signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca<sup>2+</sup> hotspots seen in the wildtype controls. 

      So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca<sup>2+</sup> signal by super scaffolding nanoclusters into microclusters: this is evident from seeing some spatial confinement of Ca<sup>2+</sup> signals near SyRibbons on top of the diffuse Ca<sup>2+</sup> signal across the rest of the membrane as a result of overexpression in HEK cells. 

      (2) The authors analyzed the distribution of RIBEYE clusters in different membrane compartments and correctly conclude that RIBEYE clusters are not trapped in any of those compartments, but it is soluble instead. The authors, however, did not carry out a similar analysis for Palm-Bassoon. It is therefore unknown if Palm-Bassoon binds to other membrane compartments besides the plasma membrane. That could occur because in non-neuronal cells GAP43 has been described to be in internal membrane compartments. This should be investigated to document the existence of ectopic internal Synribbons beyond the plasma membrane because it might have implications for interpreting functional data in case Ca2+-channels become part of those internal Synribbons.

      In response to this valid concern, we have now included the suggested experiment in Supplementary Figure 1. We investigated the subcellular localisation of Palm-Bassoon and did not find Palm-Bassoon puncta to colocalise with ER, Golgi, or lysosomal markers, suggesting against a possible binding with membrane compartments inside the cell. We have added the following sentence in the results section, line 145 : “Palm-Bassoon does not appear to localize in the ER, Golgi apparatus or lysosomes (Supplementary Fig 1 D, E and F).”

      (3) The co-expression of RBP2 and Palm-Bassoon induces a rather minor but significant increase in Ca2+-currents (Figure 5). Such an increase does not occur upon expression of (1) Palm-Bassoon alone, (2) RBP2 alone or (3) RIBEYE alone (Figure 5). Intriguingly, the concomitant expression of PalmBassoon, RBP2 and RIBEYE does not translate into an increase of Ca2+-currents either (Figure 7).

      We agree with the reviewer that co-expression of palm-Bassoon and RBP2 seems to augment Ca<sup>2+</sup> currents, while the additional expression of RIBEYE results in no change when compared to wild-type controls. We currently do not have an explanation for this observation and would refrain from making any claims without concrete evidence. We also highlight that, while the expression of the combination of palm-Bassoon and RBP2 raises Ca<sup>2+</sup> currents, current amplitudes are not significantly different when compared to the individual expression of the two proteins (P > 0.05, Kruskal-Wallis test). In light of this, we have now carefully rephrased our MS. 

      (4) The authors claim that Ca2+-imaging reveals increased CA2+-signal intensity at synthetic ribbontype AZs. That claim is a subject of concern because the increase is rather small and it does not correlate with an increase in Ca2+-currents.

      Thanks for the comment: please see our response to your first comment and the lines 585 – 610 in the discussion section.

      Recommendations for the authors:  

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors should have a better discussion of problems derived from over-expression.

      Done. Please see above. 

      (2) Ideally, the authors would repeat the study using a secretory cell line, but this is of course not possible. The idea could be brought forth, though.

      As described above in our response to the public review of reviewer 2, we have discussed this idea in the discussion section (refer to lines 615 – 619), emphasizing on both the advantages and the limitations of using a secretory cell line (e.g. PC12 cells) instead of HEK293 cells as a model for performing such reconstitutions. 

      Reviewer #3 (Recommendations For The Authors):

      (1) There are several figures in which colocalization between different proteins is studied only displaying images but without any quantitative data. This should be corrected by providing such a quantitative analysis.

      We have now added quantifications for the colocalisations of the various transfection combinations depicted in the above-mentioned figures collectively in Supplementary Figure 7 and added the corresponding results and methods accordingly. 

      (2) The little increase in Ca2+-currents and Ca2+-influx associated to the clustering of Ca2+-channels to Synribbons is a concern. The authors should discuss if such a minor increase (found only when Palm-Bassoon and RBP2 ae co-expressed) would have or not physiological consequences in an actual synapse. They might discuss the comparison of those results and compare with results obtained in genetically modified mice in which Ca2+-currents are affected upon the removal of AZs proteins. On the other hand, they should explain why Ca2+-currents do not increase when the Synribbons are formed by RIBEYE, Palm-Bassoon and RBP2.

      Done. Please see above. 

      (3) The description of the patch-clamp experiments should be enriched by including representative currents. Did the authors measure tail currents?

      We would like to thank the reviewer for the valuable suggestion and have now added representative currents to the figures (see Supplementary Figure 5B). We agree with the reviewer on the importance of further characterizing the Ca<sup>2+</sup> currents in the presence and absence of SyRibbons by analysis of tail currents for counting the number of Ca<sup>2+</sup> channels by non-stationary fluctuation analysis but consider this to be out of scope of the current study and an objective for future studies. 

      (4) The current displayed in Figure 7 E should be explained better.

      Previous studies have shown that Ca<sup>2+</sup>-binding proteins (CaBPs) compete with Calmodulin to reduce Ca<sup>2+</sup>-dependent inactivation (CDI) and promote sustained Ca<sup>2+</sup> influx in Inner Hair Cells (Cui et al, 2007; Picher et al, 2017). In the absence of CaBPs, CaV1.3-mediated Ca<sup>2+</sup> currents show more rapid CDI as in the case here upon heterologous expression in HEK cells ((Koschak et al, 2001), see also Picher et al 2017 where co-expression of CaBP2 with CaV1.3 inhibits CDI in HEK293 cells). The inactivation kinetics of CaV1.3 are also regulated by the subunit composition (Cui et al, 2007) along with the modulation via interaction partners and given the reconstitution here we do not find the currents very surprising. 

      (5) Is the difference in Ca2+-influx still significantly higher upon the removal of the maximum value measured in positive Syribbons spots (Figure 7, panel K)?

      Yes, on removing the maximum value, the P value increases from 0.01 to 0.03 but remains statistically significant. 

      (6) In summary, although the approach pioneered by the authors is exciting and provides relevant results, there is a major concern regarding the interpretation of the modulation of Ca2+ channels.

      We have now carefully rephrased our interpretation on the modulation of Ca<sup>2+</sup> channels.  

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      Reply to the reviewers

      1. General Statements [optional]

      The authors wish to thank the reviewers for fair and constructive comments and Review Commons for facilitating the process.

      2. Point-by-point description of the revisions

      Point-by-point replies to reviewers' comments on the original submitted manuscript are below. Authors' responses are in plain font.

      Reviewers' comments:

      Reviewer #1

      (Evidence, reproducibility and clarity (Required)):

      Summary: The authors identify cancer-associated ERBB4 mutations that are selected for functional characterization. Utilizing the BaF3 and MCF10A models, the authors investigate the potential oncogenic role for 11 recurrent ERBB4 mutations. Three mutants (S303F, E452K and L798R) were strongly transforming with the ability to transform both cell models, S303F being unique in its ability to transform both models in the absence of NRG-1. The authors perform modeling to decipher potential mechanisms of action of the ERBB4 S303F, E452K and L798R mutations. The authors assess the ability of HER3 mutations to dimerize with other HER family members and demonstrate that ERBB4 S303F can mediate its activating functions by stabilizing homo- and heterodimers with other ERBB receptors and that the heterodimerization is likely cell/tissue context dependent. The authors demonstrate that transforming ERBB4 mutants are sensitive to pan-ERBB inhibitors and drive resistance to EGFR-targeted therapy in EGFR-mutant NSCLC cells.

      Major comments:

      Patient data analysis is performed in more than 15 months ago in January 2024. This analysis should be updated.

      We thank the reviewer for pointing out the aspect of constantly expanding mutation data in clinical cancer sample databases. We reanalyzed the patient data in cBioPortal (data download 02 May 2025). In this new analysis, the distribution of mutations in ERBB4 did not change (Reviewer only Fig. 1A), and the 18 selected mutations were still the most recurrently mutated ERBB4 mutations (Reviewer only Fig. 1B). Reanalysis of updated patient data did not change the initial rationale of the study, or the conclusions in the submitted manuscript.

      Reviewer only Figure 1. Comparison of patient data derived from cBioPortal on January 2024 (01/2024) or May 2025 (05/2025). A) Figure 1B of the original submitted manuscript. B) Supplementary Figure S1C of the original submitted manuscript.

      The rationale for selecting the mutations to be studied is not entirely clear. There are no references to support studying mutations in Fig 1B red boxes.

      We apologize for not being sufficiently clear on our rationale for selecting the mutations for analysis. The spectrum of mutations across the ERBB4 gene do not demonstrate clear hotspots as seen in for example EGFR, KRAS, or BRAF. However, we observed that there are regions (not necessarily individual amino acid changes) in ERBB4 that seem to accumulate more mutations than other regions. Looking more closely, we observed that these "hot regions" tend to be located in areas where activating mutations have been described for other oncogenic ERBB family members and/or target structurally important regions for receptor activation such as dimerization interfaces. We hypothesized that these characteristics would suggest functional relevance for the mutations in these "hot regions". In the revised manuscript (on page 11), we have revised the text describing the selection of mutations for further analysis, and added references to justify our selection:

      "While the missense mutations were distributed across the 1,308 amino acid sequence of ERBB4, lacking obvious hotspot mutations such as observed for example in EGFR or KRAS, clusters of recurrent mutations could be identified (Fig. 1B). These clusters tended to be located in specific regions that are targeted by activating mutations in other oncogenic ERBB family members (Greulich et al., 2005, 2012; Lee et al., 2006; Bose et al., 2013; Jaiswal et al., 2013)and/or are important for receptor activation (Ferguson et al., 2003; Bouyain et al., 2005; Liu et al., 2012), suggesting functional relevance (red boxes in Fig. 1B). Some recurrent mutations were located in the unstructured C-terminal tail of ERBB4 (Fig. 1B). We selected in total 18 ERBB4 missense mutations (indicated in Fig. 1B) that were recurrent and/or located in the abovementioned regions of interest for functional characterization (indicated in Fig. 1B and Supplementary Fig. S1C) - hypothesizing that these mutations would be actionable. Of the different mutants at the same position of ERBB4 amino acid sequence, the most recurrent amino acid change was selected for characterization."

      Cell proliferation should be shown for BaF3 cells for continuity in Figure 2 instead of doubling time.

      We agree that it may cause confusion that the results for the Ba/F3 and MCF10a experiments in Fig. 2C and D (Fig. 2D and E in the revised manuscript) are reported using a different metric. The reason for this is that these assays measure different outputs: in the Ba/F3 assay, the emergence of proliferating cells under IL3 deprivation is measured, with repeated cell viability measurements over time. In the MCF10a experiment, the ability of ERBB4 mutations to sustain the proliferation of MCF10a cells in the absence of EGF is measured, using a fixed time point (8 days). Thus, doubling time, as an indicator for the time required for the emergence of proliferating cells, is more suitable metric to quantify the relative transforming capability of the different ERBB4 mutations in the Ba/F3 cells. In the case of MCF10a cells, the relevant metric is the cell viability (as a surrogate marker for the number of cells) at the endpoint measurement.

      The relative expression of HER3 constructs must be shown for BaF3 and MCF10A cells in Figure 2.

      We assume the reviewer is asking to demonstrate the expression levels of different ERBB4 mutants in the Ba/F3 and MCF10a cells used in experiments in Fig. 2C and D (Fig. 2D and E in the revised manuscript). We would like to thank the reviewer for this very relevant point. Western blots demonstrating the expression levels of different ERBB4 mutants in the Ba/F3 and MCF10a cells have now been added as a data new panel in the Figure 2 (Fig. 2B in the revised manuscript). No ERBB3 expression constructs were introduced into the cells.

      Blots in Figure 4 must be quantified.

      The blots in Figure 4 have now been quantified, and the relative signal intensities are shown below each blot. We thank the reviewer for suggesting this relevant analysis. The analysis revealed two issues that we have now revised:

      1) in Fig. 4D, the dimerization of EGFR with ERBB4 S303F is not convincingly increased when compared to EGFR dimerization with wild-type ERBB4. Therefore, we have omitted that conclusion from the results section:

      "Taking into account these expression level differences, ERBB4 S303F did indeed co-immunoprecipitate more efficiently than wild-type ERBB4 with ERBB2 and EGFR both in the presence or absence of NRG-1 (Fig. 4D), demonstrating that the S303F mutation promotes the formation of ERBB heterodimers."

      Omitting this data does not change our final conclusion, that the ERBB4 S303F mutation leads to enhanced ERBB4 heterodimerization.

      2) In Fig. 4C, the previously published ERBB4 D595V mutant, used as a control in the experiment, does not clearly demonstrate enhanced ERBB4 homodimerization after quantifying the blots. Therefore, we have cropped the lanes representing the ERBB4 D595V mutant from the blot, and omitted the part of the results text that discusses this ERBB4 mutant:

      "ERBB4 homodimers were assessed by crosslinking cell surface proteins with a cell membrane impermeable BS3, enabling detection of ERBB4 dimers as high molecular weight species of ERBB4 in western blot. Another activating extracellular ERBB4 mutation, D595V, was used as a positive control, as we have previously demonstrated D595V to stabilize ERBB4 dimers using the same assay (Kurppa et al., 2016). As predicted by the structural analyses, S303F resulted in more abundant active, phosphorylated ERBB4 dimers than wild-type ERBB4 in the presence of NRG-1, while the activating intracellular domain mutation L798R, that served as a negative control for dimer stabilization, did not (Fig. 4C)."

      Omitting these data does not change our final conclusion, that the ERBB4 S303F mutation leads to enhanced ERBB4 homodimerization.

      There are major concerns with Supplemental files. It is imperative that the effectiveness of HER3 shRNA be shown in S Fig3. These data are not interpretable without this.

      We apologize for confusion related to the supplemental files. The effectiveness of the ERBB3 (HER3) shRNA is shown in the Supplementary Figure S3B of the original submitted manuscript.

      Lanes in S Fig 4 are not marked again making data not interpretable.

      Some of the lanes in the Supplementary Figure 4B were not marked because the experiment contained other ERBB4 constructs in addition to the ones that are marked and discussed in the manuscript text. The reason for leaving the unmarked lanes in the final figure was to emphasize that the bands indicated come from the same membrane, blot and exposure. We understand how this may cause confusion, and thus have now cropped the blots to include only the lanes discussed in the manuscript text.

      It's unclear why Table 1 is included as this is already published data. This previously published data should be summarized in the text.

      We are happy to elaborate the novelty of the data in Table 1 of the original submitted manuscript. The data is from the SUMMIT trial (NCT01953926) (Hyman et al., 2018), the results of which have been published. However, the three patients in the top part of the table were enrolled to the SUMMIT trial based on the ERBB4mutation in their tumor, and the data for these patients have not previously been published. We received these data directly from Puma Biotechnology. In addition, while the ERBB4 mutation status for the patients in the lower part of the table has been published in the supplementary files of the Hyman and others publication, we feel that the patients' ERBB4 mutations merit discussion, and including these patient data in the table would complement the data on the three patients in the top part of the table. Due to these reasons, we feel that the table contains unpublished and relevant data for the study, and would like to keep the table in the manuscript by moving it into the Supplementary Data (Supplementary Table S2).

      To clarify the sources of the patient data, we have modified the methods section related to the table as follows:

      "Neratinib efficacy data, cancer types and co-alterations of patients harboring an ERBB4 alteration, enrolled in PUMA-NER-5201, the SUMMIT trial (NCT01953926), and treated with neratinib as a single agent (240 mg/day) were obtained from Puma Biotechnology (for patients enrolled based on an ERBB4 mutation - previously unpublished data) and cBioPortal (for patients with ERBB4 as a co-altered gene, enrolled based on an ERBB2 or ERBB3 mutation)."

      This text is now moved to "Supplementary Methods" under a new section "Neratinib efficacy in patients" on page 9 of the revised Supplementary Data -file

      There is a disconnect why the last two figures focus on a single model of NSCLC whereas the three most transforming mutations are found most commonly in breast, melanoma and GI tract cancers.

      The reviewer is correct in that the most transforming ERBB4 mutations are indeed found most commonly in beast and esophagogastric cancers and in melanoma. However, in the context of targeted therapy resistance,mutations that confer resistance are often acquired during therapy, and may not represent the typical cancer type-specific mutational patterns. The strongest evidence for a potential role of mutant ERBB4 in therapy resistance comes from the context of EGFR-targeted therapies and lung cancer. As mentioned in the results and discussion sections of the submitted manuscript, ERBB4 mutations identified in patients who developed resistance to EGFR-targeted therapy (Cremolini et al., 2019; Jänne et al., 2022), include the same mutation or mutation in the same residue as analyzed in the current study: the strongly transforming S303F or L798I. In addition, a recent study showed that EGFR-mutant lung cancer patients with co-occurring ERBB4 mutations have shorter relapse-free survival on osimertinib treatment (Vokes et al., 2022). Therefore, we focused on EGFR-mutant lung cancer as the model system to assess, as proof-of-concept, whether activating, transforming ERBB4 mutations are able to confer resistance to EGFR-targeted therapy. To make the transition to cancer therapy resistance and the rationale for choosing the model context more clear, we have added text to the start of the "Activating ERBB4 mutations drive resistance to EGFR-targeted therapy in EGFR-mutant NSCLC cells" -chapter of the revised manuscript:

      "There is emerging evidence associating ERBB4 with cancer therapy resistance across various cancer types and treatment regimens (Merimsky et al., 2001, 2002; Mendoza-Naranjo et al., 2013; Nafi et al., 2014; Saglam et al., 2017; Wege et al., 2018; Wang et al., 2019; Zhang et al., 2023; Debets et al., 2023; Albert et al., 2024; Arribas et al., 2024), including ERBB4 mutations that have been found in patient tumors after acquisition of therapy resistance (Cremolini et al., 2019; Jänne et al., 2022; Vokes et al., 2022; Yaeger et al., 2023; Yuan et al., 2023). Intriguingly, the ERBB4 mutations identified in patients who developed resistance to EGFR-targeted therapy (Cremolini et al., 2019; Jänne et al., 2022), include the same mutation or mutation in the same residue as analyzed in the current study: the strongly transforming S303F or L798I. In addition, co-occurring ERBB4 mutations in EGFR-mutant lung cancer patients have been shown to associate with shorter progression-free survival on EGFR inhibitor therapy (Vokes et al., 2022). These observations point to the possibility that mutant ERBB4 could promote resistance to targeted therapies."

      What are the differences in the recurrent ERBB4 mutant tumors versus ERBB4 wild-type tumors described in Figure 7?

      The reviewer points out a very relevant question. We suspect that in the tumors expressing mutant ERBB4, the activating ERBB4 mutants are able to compensate for the loss of EGFR signaling, particularly since the on-treatment cancer cells demonstrate elevated levels of ERBB4 ligands (Fig. 7C, D). This is analogous to accumulating evidence suggesting that ERBB4 independently and together with ERBB3 (and/or with increased availability of their ligands) compensate for survival and growth signaling upon ERBB2- or EGFR-targeted therapy (Carrión-Salip et al., 2012; Wilson et al., 2012; Nafi et al., 2014; Canfield et al., 2015; Yonesaka et al., 2015; Donoghue et al., 2018; Shi et al., 2018; Debets et al., 2023; Udagawa et al., 2023). Unfortunately, we are unable to approach this hypothesis using samples from the in vivo experiment in Fig.7. The treatment of the mice was stopped after 189 days of treatment in order to assess how many tumors grew back (i.e. how many mice were cured by the treatment). For this reason, we do not have the appropriate controls to analyze ERBB4 mutant-associated changes in on-treatment tumors.

      Figure 7C, D should be moved to supplemental as this is from previously published data and not strictly relevant to data shown in Fig 7.

      The data shown in Fig. 7C and D are a re-analysis of published single-cell RNA-seq data. While the single cell RNA-sequencing data set is previously published, the analysis of ERBB4 ligand expression performed, and shown in Fig. 7C and D has not been published before. We feel that these data provide evidence of a previously unrecognized upregulation of ERBB4 ligand expression in on-treatment EGFR-mutant NSCLC cells in vivo. Furthermore, as discussed in the results section of the original submitted manuscript (page 26; page 28 of the revised manuscript), the upregulation of ERBB4 ligands in the on-treatment tumors provides a plausible mechanism supporting mutant ERBB4 activation upon EGFR inhibitor treatment, as the transforming ERBB4 mutants seem to retain at least partly the dependency of ligand stimulation. Thus, we feel that these data are unpublished and relevant for the manuscript, and we would like to keep these data panels in the main Figure 7.

      Limitations should include consideration of endogenous levels of ERBB4 in the model systems used and disparate expression levels of wt ERBB4 versus ERBB4 mutation.

      We thank the reviewer for pointing out that we have not thoroughly disclosed the endogenous levels of ERBB4 expression in the used model systems. None of the used model systems (MCF10a, Ba/F3, COS-7, PC-9) express detectable levels of ERBB4 protein. This was mentioned in the original submitted manuscript for COS-7 (page 19; page 20 of the revised manuscript), Ba/F3 cells (page 18; page 19 of the revised manuscript), and PC-9 cells (page 24; page 24 of the revised manuscript), but not for MCF10a cells. We have now made this point more clear, and added a sentence "Neither of these models express detectable levels of ERBB4" in the results section under the chapter "Majority of the recurrent ERBB4 mutations are transforming in Ba/F3 or MCF10a cells" (page 12-13 of the revised manuscript), as well as to the discussion section (page 30 of the revised manuscript).

      Regarding the expression levels of different ERBB4 mutants versus ERBB4 wild-type, we have now added the new Figure 2B, showing the expression of all ERBB4 mutants and ERBB4 wild-type in Ba/F3 and MCF10a cells. We have also included the following text describing the expression levels of ERBB4 mutants in the results section under "Majority of the recurrent ERBB4 mutations are transforming in Ba/F3 or MCF10a cells" (page 13 of the revised manuscript):

      "The different ERBB4 mutants demonstrated similar expression levels compared to wild-type ERBB4 in both model systems with the exception of R106C and G907E mutants that were expressed predominantly as immature receptor forms in both models, suggesting defective receptor maturation. Also, the R1304W mutant demonstrated lower expression levels in the Ba/F3 cells, and could not be expressed at all in the MCF10a cells (Fig. 2B)."

      Minor comments:

      Fig1B lists ERBB3 V104V mutation?

      Thank you for noticing this mistake. This has now been corrected in the revised Figure 1B.

      List frequency of ERBB4 mutations in the introduction

      We thank the reviewer for the suggestion and have revised the introduction to include an example of the high frequency of ERBB4 missense mutations in cancer as follows:

      "Yet, despite the high frequency of ERBB4 missense mutations in various cancer types (up to 30% in non-melanoma skin cancer, Supplementary Fig. S1A, B) and characterization of several potentially oncogenic ERBB4 mutations (Prickett et al. 2009; Nakamura et al. 2016; Chakroborty et al. 2022; Kurppa et al. 2016; Tvorogov et al. 2009), the rationale for clinically targeting ERBB4 in cancer has not been fully developed."

      Clarification throughout if cells are serum-starved (how long) if stimulated with NRG-1

      We thank the reviewer for the thoughtful suggestion and have revised the main text and figure legends accordingly; in the revised manuscript on pages 6, 8, 9, 13, 17, 20, 25 and 26 "(10% serum)", on page 25 "following short-term stimulation with NRG-1 after overnight serum starvation (Fig. 6A).", as well as figure legends of Fig. 2, 4, 5, 6, S2, and S3.

      Reviewer #1 (Significance (Required)):

      General assessment: This work fills a gap in cancer research understanding if ERBB4 mutations could be targeted. Concerns and comments need to be addressed before definitive conclusions can be made.

      The authors wish to thank the reviewer for the positive assessment.


      Reviewer #2

      (Evidence, reproducibility and clarity (Required)):

      Ojala et al. report a very extensive exploration of the functional relevance of somatic mutations occurring in the ERBB4 gene. The Authors demonstrate that 11 out of 18 mutations they studied have oncogenic potential, with some of them actionable using clinically available ERBB inhibitors, while giving resistance to EGFR inhibitors.

      A very minor comment. At the beginning of page 21, I'd not define PD as the best respone. The Authors can write that all four patients progressed under treatment.

      We would like to thank the reviewer for the comment. We agree with the reviewer, and have now revised the sentence in question as follows:

      "Two of the three patients that were qualified for the SUMMIT trial due to a mutation in ERBB4, with no other qualifying mutations in ERBB family genes, had an ERBB4 mutation characterized in this study to be transforming (R544W and V840I) (Supplementary Table S2). Yet, neither of these patients, nor the patient with an ERBB4 VUS N465K, responded to neratinib and progressed under treatment (Supplementary Table S2)."

      Reviewer #2 (Significance (Required)):

      The work by Ojala et al. is the most detailed study of mutations occurring in ERBB4. Since these are relatively rare, they have not been properly studied up to now. The study is very well done.

      The authors wish to thank the reviewer for the very positive statement.


      Reviewer #3

      (Evidence, reproducibility and clarity (Required)):

      Summary - This work has mined cBioPortal to identify candidate cancer driver mutations in the gene encoding the ERBB4 receptor tyrosine kinase (Figure 1). These ERBB4 mutations occurred in clusters that are paralogous to activating mutations in other ERBB receptor genes or in clusters predicted to serve as dimerization interfaces of ERBB4. Eighteen such ERBB4 mutations were selected for characterization.

      • These mutants were tested in BaF3 and MCF-10A cells in the context of the ERBB4 JM-a CYT-2 isoform (Figure 2). Several of these ERBB4 mutants exhibited greater agonist-dependent coupling to cell proliferation than wild-type ERBB4. Moreover, some of the mutants exhibited greater agonist-independent coupling to cell proliferation than wild-type ERBB4. Five ERBB4 mutants (S303F, E452K, L798R, R992C, S1289A) exhibited greater activity in the BaF3 cells, whereas nine ERBB4 mutants (S303F, R393W, E452K, R544W, R711C, S774G, L798R, V840I, G870R) exhibited greater activity in the MCF10A cells. Thus, eleven of the ERBB4 mutants (S303F, R393W, E452K, R544W, R711C, S774G, L798R, V840I, G870R, R992C, S1289A) exhibited a gain-of-function phenotype. It should be noted that several of the ERBB4 gain-of-function mutants (R393W, R544W, R711C, V840I, G870R, R992C, S1289A) exhibited cell type specificity.

      • PyMol was used to "model" the effect of the most potent (S303F, E452K, and L798R) gain-of-function mutations on the structure of ERBB4 (Figure 3). These three mutations are predicted to cause increased ERBB4 dimerization.

      • When expressed in MCF-10A cells, the most potent (S303F, E452K, and L798R) gain-of-function ERBB4 mutants exhibited elevated ligand-dependent and ligand-independent tyrosine phosphorylation. This was accompanied by elevated EGFR, ERBB2, and ERBB4 tyrosine phosphorylation and elevated signaling by canonical effector proteins (Figure 4).

      • The homo- and heterodimerization of the most potent ERBB4 mutant (S303F) was studied following transient transfection of COS-7 cells (Figure 4). As predicted, the S303F mutant exhibited greater ERBB4 homodimerization and greater heterodimerization with EGFR and ERBB2, but not with ERBB3.

      • The data from the clinical trial NCT01953926 was mined to evaluate whether the presence of an ERBB4 activating mutation found in this work is associated with sensitivity to the pan-ERBB inhibitor neratinib (Table 1). Surprisingly, a compelling association was NOT found. In contrast, the proliferation of BaF3 cells that express gain-of-function ERBB4 mutants is sensitive to the irreversible pan-ERBB inhibitors neratinib, afatinib, and dacomitinib (Figure 5).

      • Mining the cBioPortal, AACR GENIE, and COSMIC datasets indicates that the three most potent ERBB4 gain-of-function mutants (S303F, E452K, and L798R) exhibit tissue specificity (Supplementary Figure S5). Moreover, the S303F mutation is coincident with a mutation in another ERBB receptor to a much lesser degree than other gain-of-function ERBB4 mutants, particularly E452K. This too is suggestive of differences in the mechanism of action among the gain-of-function ERBB4 mutants (Supplementary Figure S5).

      • To test the effect of ERBB4 gain-of-function mutants on resistance to EGFR inhibitors, PC-9 NSCLC cells (which contain an endogenous gain-of-function EGFR mutant but do not endogenously express ERBB4) were transduced with ERBB4 gain-of-function mutants. In these cells the S303F and L715K mutants exhibited elevated ERBB4 signaling, but the L798R and K935I mutants did not. Nonetheless, the S303F, E715K, and K935I mutants promoted osimertinib resistance upon long-term treatment in vitro, whereas the L798R mutant did not (Figure 6). Moreover, the E715K and S303F mutants caused osimertinib resistance in vivo.

      • Overall, this is an impressive body of work. The experiments have been carefully performed and the data are clearly presented. However, the breadth of this work makes it a bit unfocused and difficult to digest.

      The authors wish to thank the reviewer for the positive statement.

      Major Issues Affecting the Conclusions

      The COS-7 data in Figure 4 are probably generated using supraphysiological levels of ERBB4 expression, raising concerns about the ability to draw general conclusions from these data. This issue should be addressed.

      We appreciate the reviewer's insight on the details concerning experimentation in COS-7 cells. We acknowledge the drawbacks in experiments performed using transient overexpression of proteins in COS-7 cells using vectors with strong viral promoters. To mitigate these drawbacks, we routinely perform transient overexpression in COS-7 cells using the retroviral pBABE-vectors, which have a weak promoter and produce relatively moderate protein expression level. We have included here a reviewer-only figure (Reviewer-only Figure 2) that demonstrates the ERBB4 expression level derived from the pBABE-vector, compared to endogenous expression level of ERBB4 in T47D and MCF7 cells, as well as to ERBB4 expression derived from pcDNA3.1 vector that harbors a strong viral CMV promoter. With this, we hope to convince the reviewer that the ERBB4 expression levels in our COS-7 cell experiments are not supraphysiological.

      Reviewer-only Figure 2. The expression level of ERBB4 in T47D and MCF7 cells, as well as in COS-7 cells transiently transfected with equal amounts of pBABE-puro-gateway-ERBB4JM-aCYT-2 plasmid, or pcDNA3.1.-ERBB4JM-aCYT-2 plasmid.

      The inhibitor data shown in Figure 5 may be over-interpreted. The affinity of neratinib, afatinib, and dacomitinib for EGFR is reportedly higher than the affinity of these drugs for ERBB4. Thus, the failure of ERBB4 gain-of-function mutants to cause resistance to these inhibitors may be because the inhibitors bind to endogenous EGFR and therefore fail to bind to ERBB4.

      We thank the reviewer for the insightful comments. The experiments in Figure 5 were performed in Ba/F3 cells, which do not express endogenous EGFR, or other kinase competent ERBB receptors (Riese et al., 1995). Therefore, it is unlikely that the observed cellular responses to neratinib, afatinib, or dacomitinib are affected by the drugs' preferable binding to EGFR.

      Moreover, the conclusion that the gain-of-function ERBB4 mutants are targetable with these inhibitors appears to be an overreach.

      We have revised our conclusion into that ERBB4 mutants are "sensitive to" these inhibitors, as supported by our data in Figure 5. This revision has been made in the abstract (page 2), introduction section (page 4), results section (page 23), and in the discussion (page 31) of the revised manuscript.

      The inhibitor data shown in Figure 6 demonstrates that activating ERBB4 mutations are sufficient to drive inhibitor resistance. However, these data do not demonstrate that the mutations are necessary to drive inhibitor resistance. Thus, these data are of less value than represented in this work. Knockout or silencing (CRISPR or siRNA) experiments would be more definitive.

      We agree with the reviewer that performing knock-out or silencing experiments to demonstrate the necessity of mutant ERBB4 for inhibitor resistance would strengthen the conclusions. However, the PC-9 cells (or any other EGFR-mutant NSCLC cell lines) do not express endogenous ERBB4, and do not have endogenous ERBB4 mutations. Therefore, knock-out or silencing experiments are unfortunately not possible in this setting.

      Minor Issues That Can Confidently Be Addressed

      In Figure 2, the MCF10A data are more compelling than the BaF3 data. Thus, an argument can be made that the BaF3 data belong in a supplemental figure. However, the combination of data from both cell lines illustrate the fact that ERBB4 mutants appear to exhibit cell type specificity. If this point is emphasized in the text, then Figure 2 should remain as currently presented.

      We agree with the reviewer that our data suggest that the ERBB4 mutants demonstrate a level of context-specificity. This was mentioned in the results section of the original submitted manuscript (page 20; page 21 of the revised manuscript) as well as discussed in the discussion section (page 29; page 29 of the revised manuscript). To emphasize this further, we have revised our conclusions at the end of the "Majority of the recurrent ERBB4 mutations are transforming in Ba/F3 or MCF10a cells" -section as follows:

      "Taken together, these analyses indicate a potential oncogenic role for 11 recurrent ERBB4 mutations. Eight of the mutations were transforming in only one of the models used, suggesting context-specificity. Three mutants (S303F, E452K and L798R) were strongly transforming with the ability to transform both cell models, S303F being unique in its ability to transform both models in the absence of NRG-1."

      The modeling data shown in Figure 3 are a bit under-interpreted. It would appear that the S303F, E452K, and L798R mutants would cause increased ERBB4 signaling by (1) shifting the equilibrium of ERBB4 monomers between the tethered (inactive) state and the extended (active) state or by (2) directly fostering receptor dimerization. The modeling data should be interpreted in the context of these two paradigms.

      We thank the reviewer again for an insightful observation. We have now revised the text describing the modeling data based on the reviewer's suggestions (please see the revised manuscript, under "Structural analysis of the transforming ERBB4 mutations").

      The mechanistic data shown in Figure 4 are also a bit under-interpreted. The data from Figure 2 suggest that ERBB4 gain-of-function mutants are more likely to promote ERBB4 heterodimerization than ERBB4 homodimerization. Do the data from Figure 4 support this hypothesis?

      The authors agree with the reviewer in that the activating ERBB4 mutations lead to increased activation of other ERBB family members (Fig. 4A), supporting a hypothesis that activating ERBB4 mutations lead to increased heterodimerization. We have discussed this throughout the original submitted manuscript, for example making these conclusions:

      Results section, page 16 (page 18 of the revised manuscript): "In summary, these data indicate that S303F, E452K and L798R are activating, gain-of-function ERBB4 mutations that may co-operate with other ERBB receptors in malignant transformation.", page 19 (page 20 of the revised manuscript): "Together, these data suggest that while ERBB4 can be transforming in the absence of other ERBB receptors, mutant ERBB4 co-operates with ERBB3 to promote ligand-independent cell transformation.".

      Discussion section, page 30 (page 31 of the revised manuscript: "Together, these findings imply that ERBB4 heterodimers with other ERBB receptors can contribute to cell transformation and growth, supporting the rationale for pan-ERBB inhibition approach in targeting mutant ERBB4 in cancer."

      Reviewer #3 (Significance (Required)):

      General Assessment: Strengths and Limitations

      • This work makes a significant contribution to the hypothesis that ERBB4 gain-of-function mutants drive multiple human malignancies. However, this work dances around two issues. (1) Is heterodimerization of EGFR or ERBB2 with ERBB4 required for the transforming activity of these ERBB4 mutants? (2) Are these ERBB4 mutants found in the context of the JM-a/CYT-2 isoform or some other isoform? Are these ERBB4 mutants active in the context of isoforms other than JM-a/CYT-2?

      We thank the reviewer for the very positive assessment and insight on specific ERBB4 biology that could affect the functional effect of mutations in ERBB4. We would like to comment on these insights:

      1) Since the strongly transforming ERBB mutations all promoted the activation of EGFR, ERBB2, and ERBB3 (Fig. 4A), it is possible that heterodimerization plays a role in the transforming activity of these ERBB4 mutants. However, our data suggests that EGFR and ERBB2 are not necessary for transformation, since the Ba/F3 cells, where transformation by ERBB4 mutants was observed (Fig. 2D), do not express EGFR or ERBB2. We did see a consistent upregulation of endogenous ERBB3 upon IL3 deprivation in the ERBB4 S303F -expressing Ba/F3 cells (Fig. 4B), which contributed to the ERBB4 S303F -driven, IL3-independent transformation (Supplementary Fig. S3C-D).

      2) None of the analyzed ERBB4 mutations are located in the JM- or CYT-regions of ERBB4, and thus could hypothetically be expressed in the context of any of the four ERBB4 isoforms. However, cancer tissues almost exclusively express the JM-a isoforms of ERBB4, with roughly similar ratios of CYT-1 and CYT-2 isoforms. We chose to use the JM-a CYT-2 isoform in this study, based on our previous work that has implicated the JM-a CYT-2 isoform as being more oncogenic than JM-a CYT-1 isoform, as elaborated in the original submitted manuscript: "The ERBB4 JM-a CYT-2 isoform was used in the studies based on previous findings suggesting that JM-a CYT-2 is the more oncogenic ERBB4 isoform of the cancer-associated isoforms (Veikkolainen et al., 2011) in hematopoietic cell contexts (relevant for the Ba/F3 cell model) (Määttä et al., 2006; Chakroborty et al., 2022)". We do agree with the reviewer that future studies should determine the relative contribution of JM-a CYT-1 and JM-a CYT-2 isoforms in the ability of mutant ERBB4 to drive cancer growth.

      Advance: How Does This Work Advance the Field

      • This work will undoubtedly reinvigorate the ERBB4 field.

      Audience:

      • Those with an interest in the role that ERBB receptors play in human tumors.

      My Expertise:

      • 30+ years of experience studying ERBB receptors.

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      Reply to the reviewers

      Revision Plan

      June 28, 2025

      Manuscript number: RC-2025-02982

      Corresponding author(s): Babita Madan, Nathan Harmston, David Virshup

      General Statements In Wnt signaling, the relative contributions of ‘canonical (β-catenin dependent) and non- canonical (β-catenin independent) signaling remains unclear. Here, we exploited a unique and highly robust in vivo system to study this. Our study is therefore the first comprehensive analysis of the β-catenin independent arm of the Wnt signaling pathway in a cancer model and illustrates how a combination of cis-regulatory elements can determine Wnt-dependent gene regulation.

      We are very pleased with the reviews; it appears we communicated our goal and our findings clearly, and in general the reviewers felt the study provided important information, was well planned and the results were “crystal clear”.

      While more experiments could strengthen and extend the results, we feel our results are already very robust due to the use of multiple replicates in the in vivo system.

      The Virshup lab in Singapore closed July 1, 2025 and so additional wet lab studies are not feasible.

      1. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      Below we address the points raised by the reviewers:

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The article has the merit of addressing a yet-unsolved question in the field (if beta-catenin can also repress genes) that only a limited number of studies has tried to tackle, and provides useful datasets for the community. The system employed is elegant, and the PORCN-inhibition bypassed by a ____constitutively active beta-catenin is clean and ingenious. The manuscript is clearly written.

      We thank the reviewers for their kind comments on the importance of the data. Our orthotopic model provides the opportunity to exploit robust Wnt regulated gene expression in a more responsive microenvironment than can be achieved in cell culture and simple flank xenograft models.

      Here we propose a series of thoughts and comments that, if addressed, would in our opinion improve the study and its description.

      1) We wonder why a xenograft model is necessary to induce a robust WNT response in these cells.

      The authors describe this set-up as a strength, as it is supposed to provide physiological relevance, yet it is not clear to us why this is the case.

      We welcome the opportunity to expand on our choice of an orthotopic xenograft model. It has been long established that cancer cells behave differently in different in vivo locations (Killion et al., 1998). Building on this, we confirmed this in our system that identical pancreatic cancer cells treated with the same PORCN inhibitor had very different responses in vitro, in the flank and in their orthotopic environment (Madan et al., 2018). To quote from our prior paper, “Looking only at genes decreasing more than 1.5-fold at 56 hours, we would have missed 817/1867 (44%) genes using a subcutaneous or 939/1867 (50%) using an in vitro model. Thus, the overall response to Wnt inhibition was reduced in the subcutaneous model and further blunted in vitro. An orthotopic model more accurately represents real biology.

      The reason for this is presumably the very different orthotopic microenvironment, including tissue appropriate stroma-tumor, vascular-tumor, lymphatic-tumor, and humoral interactions.

      Moreover, as the authors homogenize the tumour to perform bulk RNA-seq, we wonder whether they are not only sequencing mRNA from the cancer cells but also from infiltrating immune cells and/or from the surrounding connective tissue.

      In experiments generating RNA-seq data from xenograft models, the resulting sequences can originate from either human (graft) or mouse (host). In order to account for this, following standard practice, we filtered reads prior to alignment using Xenome (Conway et al., 2012). We have added additional text to the methods to highlight this step in our pipeline.

      2) If, as the established view implies, Wnt/beta-catenin only leads to gene activation, pathway

      inhibition would free up the transcriptional machinery - there is evidence that some of its constituents are rate-limiting. The free machinery could now activate some other genes: the net effect observed would be their increased transcription upon Wnt inhibition, irrespective of beta-catenin's presence. Could this be considered as an alternative explanation for the genes that go up in both control and bcat4A lines upon ETC-159 administration? This, we think, is in part corroborated by the absence of enrichment of biological pathways in this group of genes. The genes that are beta-catenin-dependent and downregulated (D&R) are obviously not affected by this alternative explanation.

      This is an interesting suggestion, and we will incorporate this thought into our discussion of potential mechanisms.

      3) The authors mention that HPAF-II are Wnt addicted. Do they die upon ETC-159 administration, and is this effect rescued by exogenous WNT addition?

      We and several others have previously reported that Wnt-addicted cells differentiate and/or senesce upon Wnt withdrawal in vivo but not in vitro. This is related to the broader changes in gene expression in the orthotopic tumors. The effect of PORCN inhibition has been demonstrated by us and others and is rescued by Wnt addition, downstream activation of Wnt signaling by e.g. APC mutation, and, as we show here, stabilized β-catenin.

      4) Line 120: the authors write about Figure 1C: "This demonstrates that the growth of β-cat4A cells in vitro largely requires Wnts to activate β-catenin signaling." The opposite is true: control cells require WNT and form less colony with ETC159, while β-cat4A are independent from Wnt secretion.

      We appreciate the reviewer pointing out our mis-statement. This error has now been corrected in the revised manuscript.

      5) Lines 226-229: "The β-catenin independent repressed genes were notably enriched for motifs bound by homeobox factors including GSC2, POU6F2, and MSGN1. This finding aligns with the known role of non-canonical Wnt signaling in embryonic development" This statement assumes that target genes, or at least the beta-catenin independent ones, are conserved across tissues, including developing organs. This contrasts with the view that target genes in addition to the usual suspects (e.g., AXIN2, SP5 etc.) are modulated tissue-specifically - a view that the authors (and in fact, these reviewers) appear to support in their introduction.

      We agree with the reviewer that a majority of Wnt-regulated genes are tissue specific. Indeed, the β-catenin independent Wnt-repressed genes may also be tissue specific. In other tissues, we speculate that other β-catenin independent Wnt-repressed genes may also have homeobox factor binding sites as well and so the general concept remains valid. We do not have sufficient data in other tissues to resolve this issue.

      7) The luciferase and mutagenesis work presented in Figure 5 are crystal-clear. One important aspect that remains to be clarified is whether beta-catenin and/or TCF7L2 directly bind to the NRE sites. Or do the authors hypothesize that another factor binds here? We suggest the authors to show TCF7L2 binding tracks at the NRE/WRE motifs in the main figures.

      A major question of the reviewers was, can we provide additional evidence that the NRE is bound by LEF/TCF family members. Our initial analysis of more datasets indicates TCF7L2 peaks are enriched on NREs in Wnt-β-catenin responsive cell lines like HCT116 and PANC1. These analyses appear to further support the model that the NRE binds TCF7L2, but we fully agree these analyses can neither prove nor disprove the model.

      In our revision, we will analyze additional cut and run datasets as suggested and look at the HEPG2 datasets suggested by reviewer 1. We are concerned about tissue specificity as some of the genes are not expressed in e.g. HEPG2 or HEK293 cells where datasets are available. However, our data continues to support a functional role for the NRE in the modulation of β-catenin regulated genes. The best analysis would be more ChIP-Seq or Cut and Run assays on tissues, not cells, but these studies are beyond what we can do.

      What about other TCF/LEFs and beta-catenin? Are there relevant datasets that could be explored to test whether all these bind here during Wnt activation?

      As above, We will analyze additional ChIP and Cut & Run datasets to address this question looking at β-catenin and other LEF/TCF family members. We also reflect on the fact that ChIP-Seq does not necessarily imply that the targeted factor (e.g.,TCF7L2) is bound in the target site in all the cells.

      The repression might be mediated by beta-catenin partnering with other factors that bind the NRE even by competing with TCF7L2.

      We appreciate the insightful comments and now incorporate this into our discussion.

      8) In general, while we greatly appreciate the github page to replicate the analysis, we feel that the methods' description is lacking, both concerning analytical details (e.g., the cutoff used for MACS2 peak calling) or basic experimental planning (e.g, how the luciferase assays were performed).

      We thank reviewers for the suggestions and will add further details regarding the analysis

      and experimental planning in the method sections.

      9) The paper might benefit from the addition of quality metrics on the RNA-seq. Interesting for example would be to see a PCA analysis - as a more unbiased approach - rather than the kmeans clustering.

      We have this data and will add it to the revised manuscript.

      10) It seems that in Figure 3A the clusters are mislabelled as compared to Figure 3B and Figure 1. Here the repressor clusters are labelled DR5, DR6 and DN7 whereas in the rest of the paper they are labelled DR1, DR2 and DN1.

      Thank you for pointing out this issue. This has now been corrected in Figure 3.

      11) The siCTNNB1 in Figure 5E is described to be a significant effect in the text whereas in Figure 5E this has a p value of 0.075.

      Thank you for pointing out the p value did not cross the 0.05 threshold. We have modified the text to remove the word ‘significant’.

      12) Line 396: 'Here we confirm and extend the identification of a TCF-dependent negative regulatory element (NRE), where beta-catenin interacts with TCF to repress gene expression'. We suggest caution in stating that beta-catenin and TCF directly repress gene expression by binding to NRE. In the current state the authors do not show that TCF & beta-catenin bind to these elements. See our previous point 7.

      We appreciate the suggestion of the reviewers. We will be more cautious in our interpretation.

      Further suggestions - or food for thoughts:

      13) A frequently asked question in the field concerns the off-target effects of CHIR treatment as opposed to exposure to WNT ligands. CHIR treatment - in parallel to bcat4A overexpression - would allow the authors to delineate WNT independent effects of CHIR treatment and settle this debate.

      We thank the reviewers for suggesting this interesting experiment to sort out the non- Wnt effects of GSK3 inhibition. Such a study would require a new set of animal experiments and a different analysis; we think this is beyond the scope of this manuscript.

      14) We think that Figure 4C could be strengthened by adding more public TCF-related datasets (e.g., from ENCODE) to confirm the observation across datasets from different laboratories. In particular, the HEPG2 could possibly be improved as there is an excellent TCF7L2 dataset available by ENCODE.

      Many more datasets are easily searchable through: https://www.factorbook.org/.

      As above, we will analyze the HEPG2 dataset. We plan on updating Fig 4 with data from analysis from different datasets such as (Blauwkamp et al., 2008; Zambanini et al., 2022).

      15) The authors show that there is no specific spacing between NREs and WREs. This implies that it is not likely that TCF7L2 recognizes both at the same time through the C-clamp. Do the authors think that there might be a pattern discernible when comparing the location of WRE and NRE in relation to the TCF7L2 ChIP-seq peak summit? This would allow inferring whether TCF7L2 more likely directly binds the WRE (presumably) and if the NRE is bound by a cofactor.

      This is an interesting suggestion and we will conduct this analysis as suggested on available datasets (as the result may be different in different tissue types with varying degrees of Wnt/β-catenin signaling).

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Overall, the study provides a solid framework for understanding noncanonical transcriptional ____outputs of Wnt signaling in a cancer context. The majority of the conclusions are well supported by the data. However, there are a few substantive points that require clarification before the manuscript is ready for publication.

      Major Comments

      The authors' central claim-that their findings represent a comprehensive analysis of the β-catenin- independent arm of Wnt signaling and uncover a "cis-regulatory grammar" governing Wnt-dependent gene activation versus repression-is overstated based on the presented data.

      We appreciate the reviewers concern and will temper our language.

      Specifically:

      • Figure 3B identifies TF-binding motifs enriched among different Wnt-responsive gene clusters, but the authors only functionally investigate the role of NRE in β-catenin-dependent repression, particularly in the context of TCF motif interaction.

      • To support a broader claim regarding cis-regulatory grammar, additional analyses are required:

      o What is the distribution of NREs across all clusters? Are they exclusive to β-catenin-dependent repressed clusters, or more broadly present?

      The distribution of the NREs is a statistically significant enrichment; they are observed in the repressed clusters more frequently than expected by chance alone, but they are present elsewhere as well. We have tempered our language around the cis-regulatory grammar.

      o Do NREs interact with other enriched motifs beyond TCF? Is this interaction specific to repression or also involved in activation?

      This is an interesting question beyond the scope of this analysis. Our dataset uses multiple interventions; The NREs may interact with other motifs but we would need more transcriptional analysis data with biological intervention to assess this.

      o A more comprehensive analysis of cis-element combinations is needed to draw conclusions about their collective influence on gene regulation across clusters.

      We agree; This would be a great question if we had TCF binding data in our orthotopic xenograft model. It’s a dataset we do not have, nor do we have the resources to pursue this.

      Other important clarifications:

      • The use of the term "wild-type" to describe HPAF-II cells is potentially misleading. These cells are not genetically wild-type and harbor multiple oncogenic alterations.

      Thank you for pointing this out. We will use the word “parental” in the text

      • The manuscript does not clearly present the kinetics of Wnt target downregulation upon ETC-159 treatment of HPAF-II cells. Understanding whether repression mirrors activation dynamics (e.g., delay or persistence of Wnt effects) is essential to interpreting the system's temporal behavior.

      We previously addressed the temporal dynamics of activation and repression in our more comprehensive time course papers (Harmston et al., 2020; Madan et al., 2018); there are differences in the dynamics that are difficult to tease out in this new dataset as the density of time points is less. Having said that, we will compare the time course and annotate the sets of genes identified in this current study with the data from our original study to provide more information on the temporal dynamics of this system.

      Minor Comment

      • The statement in Figure 1C (lines 119-120) that "growth of β-cat4A cells in vitro largely requires Wnts to activate β-catenin signaling" is inconsistent with the data. As the β-cat4A allele encodes a constitutively active form of β-catenin, Wnts should not be required. Please revise this conclusion for clarity.

      We thank the reviewers for pointing out this mis-statement. We have corrected this.

      Reviewer #2 (Significance (Required)):

      This study offers a systematic classification of Wnt-responsive gene expression dynamics, differentiating between β-catenin-dependent and -independent mechanisms. The insights into temporal expression patterns and the potential role of the NRE element in transcriptional repression add depth to our understanding of Wnt signaling. These findings have relevance for developmental biology, stem cell biology, and cancer research-particularly in understanding how Wnt-mediated repression may influence tumor progression and therapeutic response.

      Nice review; thank you.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      … The work advances understanding of Wnt mediated repression via cis regulatory grammar.

      Major Concerns

      1) Statistical thresholds and clustering - The criteria for classifying β catenin-dependent versus - independent genes rely on FDR cutoffs above or below 0.1. If the more stringent cutoff of 0.05 was used, how many genes would still be considered Wnt regulated?

      We can readily address this in a revised manuscript.

      2) Validation of selected β catenin-dependent and -independent Wnt target genes - While the authors identify β catenin-dependent and -independent Wnt target genes (4 selected genes from different clusters in Fig.2), RT-qPCR based validation of Axin2 has been performed in Fig. S3. Authors should also validate other 3 genes as well.

      We had considered performing qPCR to re-validate some of our gene-expression changes but qPCR analyses is intrinsically more error prone than RNAseq, and we believe the literature shows that qPCR from the same samples will not add any extra utility. Previous studies that have examined this question have reported excellent correlation between the RNAseq and pPCR (Asmann et al., 2009; Griffith et al., 2010; Wu et al., 2014).

      3) NRE mechanistic insight - The most important contribution of this manuscript is the extension of the importance of the NRE motif in Wnt regulated enhancers. But the mutagenesis data provided is insufficient to conclusively nail down that the NREs are responsible for the repression. The effects in the synthetic reporters in Fig. 4D are small - it's not clear that there is much activity in the MimRep to be repressed by the NREs. The data in Fig. 5 is a better context to test the importance of the NREs, but the authors use deletion analysis which is too imprecise and settle for single nucleotide mutants in individual NREs in the ABHD11-AS1 reporter. In the Axin2 report, they mutate sequences outside of the NRE. It's too inconsistent. They should mutate 3 or 4 positions within the NRE in BOTH motifs in the context of the ABHD11-AS1 reporter. Same for the Axin2 reporter.

      We feel our analysis, coupled with the Kim paper (Kim et al., 2017), support the role of the NRE. We agree that more data is always desirable, but in our current circumstances are we cannot add additional wetlab experiments.

      Regarding Figure 4D, this is a synthetic system lacking the endogenous elements in the promoter. We agree with the reviewer that the effect is small but we would also like to point out that adding the well-established 2WRE in front of the MinRep increased the transcription activity to 1.5 fold, which is of similar magnitude change of the 2NRE deceasing the transcriptional activity 1/1.5 = 0.6.

      In Kim et al, it was shown that mutating the 11st nucleotide of the NRE motif showed the strongest effect, so we followed their lead in only mutated the 11st nucleotide in ABHD11- AS1 NRE.

      As for the putative NRE sequence present in AXIN2 promoter, its downstream sequence is polyT (__GTGTTTTTTTT__TTTTTTTTTT), if we only mutate 11st nucleotide to G/C, we could create similar sequence to NRE, so we mutated sequences outside of the NRE to fully disrupt it.

      4) Even if the mutagenesis is done more completely, the results simply replicate that of the Goentoro group. In Kim et al 2017, they provide suggestive (not convincing) evidence that TCFs directly bind to the NRE. The authors of this manuscript should explore that in more detail, e.g., can purified TCF bind to the NRE sequence? Can the authors design experiments to directly test whether beta-catenin is acting through the NRE - their data currently only demonstrates that the NRE provide a negative input to the reporters - that's an important mechanistic difference.

      We point out that our minimal reporter studies with the NRE showed a repressive effect in HCT116 (colorectal cancer cells with stabilized β-catenin) but not HT1080 (sarcoma cells with low Wnt) supporting the importance of β-catenin acting through the NRE (Figs. 4D, 4E).

      We fully agree with the reviewers that additional study of TCF interaction with the NRE would be of value. While EMSA and culture-based ChIP assays would be of some value, the best study should be done in vivo where the system is most robust. We are not in a position to do these studies, but we will add in a discussion of this as a limitation of the current study.

      5) In vertebrates, some TCFs are more repressive than others and TLEs have been implicated in repressive. Exploring these factors in the context of the NRE would increase the value of this story.

      This is an interesting idea but beyond the scope of the current manuscript. It is likely this would be dependent on tissue specific expression, local expression levels, and local binding of co-factors. As we look at other TCF members in other datasets we may be able to address this. Further wetlab experiments are beyond the scope of this work.

      **Referees cross-commenting**

      I respectfully disagree that the luciferase assays are sufficient. Using deletion analysis to understand the function of specific binding sites is insufficient and the more specific mutations of NREs are incomplete. Regarding this paper extending our knowledge of direct transcriptional repression by Wnt/bcat signaling, I don't agree that it adds much - there are numerous datasets where Wnt signaling activates and represses genes - the trick is determining whether any of the repressed genes are the result and direct regulation by TCF/bcat. They don't explore that. The main finding is an extension of the work by Lea Goentoro on the importance of the NRE motif, but they don't address whether TCF directly associates with this sequence. Goentoro argued in the 2017 paper that it does, but that data is unconvincing to me. Can purified TCF bind the NRE? Without that information (done carefully) this manuscript is very limited.

      We respectfully disagree with the reviewer regarding the contribution of this manuscript. There are certainly many datasets looking at Wnt-regulated genes in tissue culture, but these cell-based studies are underpowered to really understand Wnt biology. There are only two papers, ours and Cantú’s, that address Wnt repressed genes in any depth. No prior papers have differentiated β-catenin dependent from β-catenin independent genes before, and certainly not in an orthotopic animal model.

      A major impact of our study is the finding that only 10% of Wnt regulated genes are independent of β-catenin, at least in pancreatic cancer. We feel this is a major contribution. We further add to this analysis by re-enforcing/extend the prior evidence on the NRE in humans (and correct the motif sequence!) for Wnt-repressed genes. Our data supports the fine-tuning of the Wnt/β-catenin regulated genes by a cis-regulatory grammar.

      Reviewer #3 (Significance (Required)):

      Overall, this study advances our understanding of the dual roles of Wnt signaling in gene activation and repression, highlighting the role of the NRE motif. But this is an extension of the original NRE paper (Kim et al 2017) with no mechanistic advance beyond that original work. The transcriptomics in the first part of the manuscript have some value, but similar data sets already exist.

      We respectfully but strongly disagree with the reviewer. First, our work examines the NRE in a large-scale in vivo transcriptome dataset, significantly extending the candidate gene approach of Kim et al. Secondly, we disagree with the comment that “similar data sets already exist.” Indeed, reviewer 1 (C. Cantú) specifically pointed out we had addressed an “yet-unsolved question in the field” on whether and how β-catenin repressed genes.

      __3. __Description of the revisions that have already been incorporated in the transferred manuscript

      To date we have only corrected several typographical errors.

      1. Description of analyses that authors prefer not to carry out

      We fully agree with the reviewers that additional study of TCF interaction with the NRE would be of value. While EMSA and cell culture-based ChIP assays would be of some modest value, they have already been done in vitro by Kim et al. (Kim et al., 2017) and the best next study should be done in vivo in Wnt-responsive cancers or tissues where the biology is most robust (Madan et al., 2018) . We are not in a position to do these studies, but we will add this into the discussion as a limitation of the current study. We also acknowledge that the NRE may interact with other currently unidentified factors.

      Reviewer 1 asked about considering experiments to determine non-Wnt effects of GSK3 inhibitors like CHIR. Such a study, while interesting, would require a new set of animal experiments and a different analysis; we think this is beyond the scope of this manuscript.

      Finally, we note that the Virshup lab at Duke-NUS Medical School in Singapore, where these in vivo studies were performed, has closed as of July 1, 2025 and the various lab members have moved on to new adventures. Because of this, we are unable to undertake new wet-lab studies.

      Thank you for your consideration,

      For the authors,

      David Virshup

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      PAPS is required for all sulfotransferase reactions in which a sulfate group is covalently attached to amino acid residues of proteins or to side chains of proteoglycans. This sulfation is crucial for properly organizing the apical extracellular matrix (aECM) and expanding the lumen in the Drosophila salivary gland. Loss of Papss potentially leads to decreased sulfation, disorganizing the aECM, and defects in lumen formation. In addition, Papss loss destabilizes the Golgi structures.

      In Papss mutants, several changes occur in the salivary gland lumen of Drosophila. The tube lumen is very thin and shows irregular apical protrusions. There is a disorganization of the apical membrane and a compaction of the apical extracellular matrix (aECM). The Golgi structures and intracellular transport are disturbed. In addition, the ZP domain proteins Piopio (Pio) and Dumpy (Dpy) lose their normal distribution in the lumen, which leads to condensation and dissociation of the Dpy-positive aECM structure from the apical membrane. This results in a thin and irregularly dilated lumen.

      1. The authors describe various changes in the lumen in mutants, from thin lumen to irregular expansion. I would like to know the correct lumen diameter, and length, besides the total area, by which one can recognize thin and irregular.

      We have included quantification of the length and diameter of the salivary gland lumen in the stage 16 salivary glands of control, Papss mutant, and salivary gland-specific rescue embryos (Figure 1J, K). As described, Papss mutant embryos have two distinct phenotypes, one group with a thin lumen along the entire lumen and the other group with irregular lumen shapes. Therefore, we separated the two groups for quantification of lumen diameter. Additionally, we have analyzed the degree of variability for the lumen diameter to better capture the range of phenotypes observed (Figure 1K'). These quantifications enable a more precise assessment of lumen morphology, allowing readers to distinguish between thin and irregular lumen phenotypes.

      The rescue is about 30%, which is not as good as expected. Maybe the wrong isoform was taken. Is it possible to find out which isoform is expressed in the salivary glands, e.g., by RNA in situ Hyb? This could then be used to analyze a more focused rescue beyond the paper.

      Thank you for this point, but we do not agree that the rescue is about 30%. In Papss mutants, about 50% of the embryos show the thin lumen phenotype whereas the other 50% show irregular lumen shapes. In the rescue embryos with a WT Papss, few embryos showed thin lumen phenotypes. About 40% of the rescue embryos showed "normal, fully expanded" lumen shapes, and the remaining 60% showed either irregular (thin+expanded) or slightly overexpanded lumen. It is not uncommon that rescue with the Gal4/UAS system results in a partial rescue because it is often not easy to achieve the balance of the proper amount of the protein with the overexpression system.

      To address the possibility that the wrong isoform was used, we performed in situ hybridization to examine the expression of different Papss spice forms in the salivary gland. We used probes that detect subsets of splice forms: A/B/C/F/G, D/H, and E/F/H, and found that all probes showed expression in the salivary gland, with varying intensities. The original probe, which detects all splice forms, showed the strongest signals in the salivary gland compared to the new probes which detect only a subset. However, the difference in the signal intensity may be due to the longer length of the original probe (>800 bp) compared to other probes that were made with much smaller regions (~200 bp). Digoxigenin in the DIG labeling kit for mRNA detection labels the uridine nucleotide in the transcript, and the probes with weaker signals contain fewer uridines (all: 147; ABCFG, 29; D, 36; EFH, 66). We also used the Papss-PD isoform, for a salivary gland-specific rescue experiment and obtained similar results to those with Papss-PE (Figure 1I-L, Figure 4D and E).

      Furthermore, we performed additional experiments to validate our findings. We performed a rescue experiment with a mutant form of Papss that has mutations in the critical rescues of the catalytic domains of the enzyme, which failed to rescue any phenotypes, including the thin lumen phenotype (Figure 1H, J-L), the number and intensity of WGA puncta (Figure 3I, I'), and cell death (Figure 4D, E). These results provide strong evidence that the defects observed in Papss mutants are due to the lack of sulfation.

      Crb is a transmembrane protein on the apicolateral side of the membrane. Accordingly, the apicolateral distribution can be seen in the control and the mutant. I believe there are no apparent differences here, not even in the amount of expression. However, the view of the cells (frame) shows possible differences. To be sure, a more in-depth analysis of the images is required. Confocal Z-stack images, with 3D visualization and orthogonal projections to analyze the membranes showing Crb staining together with a suitable membrane marker (e.g. SAS or Uif). This is the only way to show whether Crb is incorrectly distributed. Statistics of several papas mutants would also be desirable and not just a single representative image. When do the observed changes in Crb distribution occur in the development of the tubes, only during stage 16? Is papss only involved in the maintenance of the apical membrane? This is particularly important when considering the SJ and AJ, because the latter show no change in the mutants.

      We appreciate your suggestion to more thoroughly analyze Crb distribution. We adapted a method from a previous study (Olivares-Castiñeira and Llimargas, 2017) to quantify Crb signals in the subapical region and apical free region of salivary gland cells. Using E-Cad signals as a reference, we marked the apical cell boundaries of individual cells and calculated the intensity of Crb signals in the subapical region (along the cell membrane) and in the apical free region. We focused on the expanded region of the SG lumen in Papss mutants for quantification, as the thin lumen region was challenging to analyze. This quantification is included in Figure 2D. Statistical analysis shows that Crb signals were more dispersed in SG cells in Papss mutants compared to WT.

      A change in the ECM is only inferred based on the WGA localization. This is too few to make a clear statement. WGA is only an indirect marker of the cell surface and glycosylated proteins, but it does not indicate whether the ECM is altered in its composition and expression. Other important factors are missing here. In addition, only a single observation is shown, and statistics are missing.

      We understand your concern that WGA localization alone may not be sufficient to conclude changes in the ECM. However, we observed that luminal WGA signals colocalize with Dpy-YFP in the WT SG (Figure 5-figure supplement 2C), suggesting that WGA detects the aECM structure containing Dpy. The similar behavior of WGA and Dpy-YFP signals in multiple genotypes further supports this idea. In Papss mutants with a thin lumen phenotype, both WGA and Dpy-YFP signals are condensed (Figure 5E-H), and in pio mutants, both are absent from the lumen (Figure 6B, D). We analyzed WGA signals in over 25 samples of WT and Papss mutants, observing consistent phenotypes. We have included the number of samples in the text. While we acknowledge that WGA is an indirect marker, our data suggest that it is a reliable indicator of the aECM structure containing Dpy.

      Reduced WGA staining is seen in papss mutants, but this could be due to other circumstances. To be sure, a statistic with the number of dots must be shown, as well as an intensity blot on several independent samples. The images are from single confocal sections. It could be that the dots appear in a different Z-plane. Therefore, a 3D visualization of the voxels must be shown to identify and, at best, quantify the dots in the organ.

      We have quantified cytoplasmic punctate WGA signals. Using spinning disk microscopy with super-resolution technology (Olympus SpinSR10 Sora), we obtained high-resolution images of cytoplasmic punctate signals of WGA in WT, Papss mutant, and rescue SGs with the WT and mutant forms of Papss-PD. We then generated 3D reconstructed images of these signals using Imaris software (Figure 3E-H) and quantified the number and intensity of puncta. Statistical analysis of these data confirms the reduction of the number and intensity of WGA puncta in Papss mutants (Figure 3I, I'). The number of WGA puncta was restored by expressing WT Papss but not the mutant form. By using 3D visualization and quantification, we have ensured that our results are not limited to a single confocal section and account for potential variations in Z-plane localization of the dots.

      A colocalization analysis (statistics) should be shown for the overlap of WGA with ManII-GFP.

      Since WGA labels multiple structures, including the nuclear envelope and ECM structures, we focused on assessing the colocalization of the cytoplasmic WGA punctate signals and ManII-GFP signals. Standard colocalization analysis methods, such as Pearson's correlation coefficient or Mander's overlap coefficient, would be confounded by WGA signals in other tissues. Therefore, we used a fluorescent intensity line profile to examine the spatial relationship between WGA and ManII-GFP signals in WT and Papss mutants (Figure 3L, L').

      I do not understand how the authors describe "statistics of secretory vesicles" as an axis in Figure 3p. The TEM images do not show labeled secretory vesicles but empty structures that could be vesicles.

      Previous studies have analyzed "filled" electron-dense secretory vesicles in TEM images of SG cells (Myat and Andrew, 2002, Cell; Fox et al., 2010, J Cell Biol; Chung and Andrew, 2014, Development). Consistent with these studies, our WT TEM images show these vesicles. In contrast, Papss mutants show a mix of filled and empty structures. For quantification, we specifically counted the filled electron-dense vesicles (now Figure 3W). A clear description of our analysis is provided in the figure legend.

      1. The quality of the presented TEM images is too low to judge any difference between control and mutants. Therefore, the supplement must present them in better detail (higher pixel number?).

      We disagree that the quality of the presented TEM images is too low. Our TEM images have sufficient resolution to reveal details of many subcellular structures, such as mitochondrial cisternae. The pdf file of the original submission may not have been high resolution. To address this concern, we have provided several original high-quality TEM images of both WT and Papss mutants at various magnifications in Figure 2-figure supplement 2. Additionally, we have included low-magnification TEM images of WT and Papss mutants in Figure 2H and I to provide a clearer view of the overall SG lumen morphology.

      Line 266: the conclusion that apical trafficking is "significantly impaired" does not hold. This implies that Papss is essential for apical trafficking, but the analyzed ECM proteins (Pio, Dumpy) are found apically enriched in the mutants, and Dumpy is even secreted. Moreover, they analyze only one marker, Sec15, and don't provide data about the quantification of the secretion of proteins.

      We agree and have revised our statement to "defective sulfation affects Golgi structures and multiple routes of intracellular trafficking".

      DCP-1 was used to detect apoptosis in the glands to analyze acellular regions. However, the authors compare ST16 control with ST15 mutant salivary glands, which is problematic. Further, it is not commented on how many embryos were analyzed and how often they detect the dying cells in control and mutant embryos. This part must be improved.

      Thank you for the comment. We agree and have included quantification. We used stage 16 samples from WT and Papss mutants to quantify acellular regions. Since DCP-1 signals are only present at a specific stage of apoptosis, some acellular regions do not show DCP-1 signals. Therefore, we counted acellular regions regardless of DCP-1 signals. We also quantified this in rescue embryos with WT and mutant forms of Papss, which show complete rescue with WT and no rescue with the mutant form, respectively. The graph with a statistical analysis is included (Figure 4D, E).

      WGA and Dumpy show similar condensed patterns within the tube lumen. The authors show that dumpy is enriched from stage 14 onwards. How is it with WGA? Does it show the same pattern from stage 14 to 16? Papss mutants can suffer from a developmental delay in organizing the ECM or lack of internalization of luminal proteins during/after tube expansion, which is the case in the trachea.

      Dpy-YFP and WGA show overlapping signals in the SG lumen throughout morphogenesis. Dpy-YFP is SG enriched in the lumen from stage 11, not stage 14 (Figure 5-figure supplement 2). WGA is also detected in the lumen throughout SG morphogenesis, similar to Dpy. In the original supplemental figure, only a stage 16 SG image was shown for co-localization of Dpy-YFP and WGA signals in the SG lumen. We have now included images from stage 14 and 15 in Figure 5-figure supplement 2C.

      Given that luminal Pio signals are lost at stage 16 only and that Dpy signals appear as condensed structures in the lumen of Papss mutants, it suggests that the internalization of luminal proteins is not impaired in Papss mutants. Rather, these proteins are secreted but fail to organize properly.

      Line 366. Luminal morphology is characterized by bulging and constrictions. In the trachea, bulges indicate the deformation of the apical membrane and the detachment from the aECM. I can see constrictions and the collapsed tube lumen in Fig. 6C, but I don't find the bulges of the apical membrane in pio and Np mutants. Maybe showing it more clearly and with better quality will be helpful.

      Since the bulging phenotype appears to vary from sample to sample, we have revised the description of the phenotype to "constrictions" to more accurately reflect the consistent observations. We quantified the number of constrictions along the entire lumen in pio and Np mutants and included the graph in Figure 6F.

      The authors state that Papss controls luminal secretion of Pio and Dumpy, as they observe reduced luminal staining of both in papss mutants. However, the mCh-Pio and Dumpy-YFP are secreted towards the lumen. Does papss overexpression change Pio and Dumpy secretion towards the lumen, and could this be another explanation for the multiple phenotypes?

      Thank you for the comment. To clarify, we did not observe reduced luminal staining of Pio and Dpy in Papss mutants, nor did we state that Papss controls luminal secretion of Pio and Dpy. In Papss mutants, Pio luminal signals are absent specifically at stage 16 (Figure 5H), whereas strong luminal Pio signals are present until stage 15 (Figure 5G). For Dpy-YFP, the signals are not reduced but condensed in Papss mutants from stages 14-16 (Figure 5D, H).

      It remains unclear whether the apparent loss of Pio signals is due to a loss of Pio protein in the lumen or due to epitope masking resulting from protein aggregation or condensation. As noted in our response to Comment 11 internalization of luminal proteins seems unaffected in Papss mutants; proteins like Pio and Dpy are secreted into the lumen but fail to properly organize. Therefore, we have not tested whether Papss overexpression alters the secretion of Pio or Dpy.

      In our original submission, we incorrectly stated that uniform luminal mCh-Pio signals were unchanged in Papss mutants. Upon closer examination, we found these signals are absent in the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly.

      Regulation of luminal ZP protein level is essential to modulate the tube expansion; therefore, Np releases Pio and Dumpy in a controlled manner during st15/16. Thus, the analysis of Pio and Dumpy in NP overexpression embryos will be critical to this manuscript to understand more about the control of luminal ZP matrix proteins.

      Thanks for the insightful suggestion. We overexpressed both the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. It is important to note that these overexpression experiments were done in the presence of the endogenous WT Np.

      Overexpression of Np.WT led to increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. In contrast, overexpression of Np.S990A resulted in a near complete loss of luminal mCh-Pio signals. Pio antibody signals remained strong at the apical membrane but was weaker in the luminal filamentous structures compared to WT.

      Due to the GFP tag present in the UAS-Np.S990A line, we could not reliably analyze Dpy-YFP signals because of overlapping fluorescent signals in the same channel. However, the filamentous Pio signals in the lumen co-localized with GFP signals, suggesting that these structures might also include Dpy-YFP, although this cannot be confirmed definitively.

      These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I.

      Minor: Fig. 5 C': mChe-Pio and Dumpy-YFP are mixed up at the top of the images.

      Thanks for catching this error. It has been corrected.

      Sup. Fig7. A shows Pio in purple but B in green. Please indicate it correctly.

      It has been corrected.

      Reviewer #1 (Significance (Required)):

      In 2023, the functions of Pio, Dumpy, and Np in the tracheal tubes of Drosophila were published. The study here shows similar results, with the difference that the salivary glands do not possess chitin, but the two ZP proteins Pio and Dumpy take over its function. It is, therefore, a significant and exciting extension of the known function of the three proteins to another tube system. In addition, the authors identify papss as a new protein and show its essential function in forming the luminal matrix in the salivary glands. Considering the high degree of conservation of these proteins in other species, the results presented are crucial for future analyses and will have further implications for tubular development, including humans.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: There is growing appreciation for the important of luminal (apical) ECM in tube development, but such matrices are much less well understood than basal ECMs. Here the authors provide insights into the aECM that shapes the Drosophila salivary gland (SG) tube and the importance of PAPSS-dependent sulfation in its organization and function.

      The first part of the paper focuses on careful phenotypic characterization of papss mutants, using multiple markers and TEM. This revealed reduced markers of sulfation (Alcian Blue staining) and defects in both apical and basal ECM organization, Golgi (but not ER) morphology, number and localization of other endosomal compartments, plus increased cell death. The authors focus on the fact that papss mutants have an irregular SG lumen diameter, with both narrowed regions and bulged regions. They address the pleiotropy, showing that preventing the cell death and resultant gaps in the tube did not rescue the SG luminal shape defects and discussing similarities and differences between the papss mutant phenotype and those caused by more general trafficking defects. The analysis uses a papss nonsense mutant from an EMS screen - I appreciate the rigorous approach the authors took to analyze transheterozygotes (as well as homozygotes) plus rescued animals in order to rule out effects of linked mutations.

      The 2nd part of the paper focuses on the SG aECM, showing that Dpy and Pio ZP protein fusions localize abnormally in papss mutants and that these ZP mutants (and Np protease mutants) have similar SG lumen shaping defects to the papss mutants. A key conclusion is that SG lumen defects correlate with loss of a Pio+Dpy-dependent filamentous structure in the lumen. These data suggest that ZP protein misregulation could explain this part of the papss phenotype.

      Overall, the text is very well written and clear. Figures are clearly labeled. The methods involve rigorous genetic approaches, microscopy, and quantifications/statistics and are documented appropriately. The findings are convincing, with just a few things about the fusions needing clarification.

      minor comments 1. Although the Dpy and Qsm fusions are published reagents, it would still be helpful to mention whether the tags are C-terminal as suggested by the nomenclature, and whether Westerns have been performed, since (as discussed for Pio) cleavage could also affect the appearance of these fusions.

      Thanks for the comment. Dpy-YFP is a knock-in line in which YFP is inserted into the middle of the dpy locus (Lye et al., 2014; the insertion site is available on Flybase). mCh-Qsm is also a knock-in line, with mCh inserted near the N-terminus of the qsm gene using phi-mediated recombination using the qsmMI07716 line (Chu and Hayashi, 2021; insertion site available on Flybase). Based on this, we have updated the nomenclature from Qsm-mCh to mCh-Qsm throughout the manuscript to accurately reflect the tag position. To our knowledge, no western blot has been performed on Dpy-YFP or mCh-Qsm lines. We have mentioned this explicitly in the Discussion.

      The Dpy-YFP reagent is a non-functional fusion and therefore may not be a wholly reliable reporter of Dpy localization. There is no antibody confirmation. As other reagents are not available to my knowledge, this issue can be addressed with text acknowledgement of possible caveats.

      Thanks for raising this important point. We have added a caveat in the Discussion noting this limitation and the need for additional tools, such as an antibody or a functional fusion protein, to confirm the localization of Dpy.

      TEM was done by standard chemical fixation, which is fine for viewing intracellular organelles, but high pressure freezing probably would do a better job of preserving aECM structure, which looks fairly bad in Fig. 2G WT, without evidence of the filamentous structures seen by light microscopy. Nevertheless, the images are sufficient for showing the extreme disorganization of aECM in papss mutants.

      We agree that HPF is a better method and intent to use the HPF system in future studies. We acknowledge that chemical fixation contributes to the appearance of a gap between the apical membrane and the aECM, which we did not observe in the HPF/FS method (Chung and Andrew, 2014). Despite this, the TEM images still clearly reveal that Papss mutants show a much thinner and more electron-dense aECM compared to WT (Figure 2H, I), consistent to the condensed WGA, Dpy, and Pio signals in our confocal analyses. As the reviewer mentioned, we believe that the current TEM data are sufficient to support the conclusion of severe aECM disorganization and Golgi defects in Papss mutants.

      The authors may consider citing some of the work that has been done on sulfation in nematodes, e.g. as reviewed here: https://pubmed.ncbi.nlm.nih.gov/35223994/ Sulfation has been tied to multiple aspects of nematode aECM organization, though not specifically to ZP proteins.

      Thank you for the suggestion. Pioneering studies in C. elegans have highlighted the key role of sulfation in diverse developmental processes, including neuronal organization, reproductive tissue development, and phenotypic plasticity. We have now cited several works.

      Reviewer #2 (Significance (Required)):

      This study will be of interest to researchers studying developmental morphogenesis in general and specifically tube biology or the aECM. It should be particularly of interest to those studying sulfation or ZP proteins (which are broadly present in aECMs across organisms, including humans).

      This study adds to the literature demonstrating the importance of luminal matrix in shaping tubular organs and greatly advances understanding of the luminal matrix in the Drosophila salivary gland, an important model of tubular organ development and one that has key matrix differences (such as no chitin) compared to other highly studied Drosophila tubes like the trachea.

      The detailed description of the defects resulting from papss loss suggests that there are multiple different sulfated targets, with a subset specifically relevant to aECM biology. A limitation is that specific sulfated substrates are not identified here (e.g. are these the ZP proteins themselves or other matrix glycoproteins or lipids?); therefore it's not clear how direct or indirect the effects of papss are on ZP proteins. However, this is clearly a direction for future work and does not detract from the excellent beginning made here.

      My expertise: I am a developmental geneticist with interests in apical ECM

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this work Woodward et al focus on the apical extracellular matrix (aECM) in the tubular salivary gland (SG) of Drosophila. They provide new insights into the composition of this aECM, formed by ZP proteins, in particular Pio and Dumpy. They also describe the functional requirements of PAPSS, a critical enzyme involved in sulfation, in regulating the expansion of the lumen of the SG. A detailed cellular analysis of Papss mutants indicate defects in the apical membrane, the aECM and in Golgi organization. They also find that Papss control the proper organization of the Pio-Dpy matrix in the lumen. The work is well presented and the results are consistent.

      Main comments

      • This work provides a detailed description of the defects produced by the absence of Papss. In addition, it provides many interesting observations at the cellular and tissular level. However, this work lacks a clear connection between these observations and the role of sulfation. Thus, the mechanisms underlying the phenotypes observed are elusive. Efforts directed to strengthen this connection (ideally experimentally) would greatly increase the interest and relevance of this work.

      Thank you for this thoughtful comment. To directly test whether the phenotypes observed in Papss mutants are due to the loss of sulfation activity, we generated transgenic lines expressing catalytically inactive forms of Papss, UAS-PapssK193A, F593P, in which key residues in the APS kinase and ATP sulfurylase domains are mutated. Unlike WT UAS-Papss (both the Papss-PD or Papss-PE isoforms), the catalytically inactive UAS-Papssmut failed to rescue any of the phenotypes, including the thin lumen phenotype (Figure 1I-L), altered WGA signals (Figure I, I') and the cell death phenotype (Figure 4D, E). These findings strongly support the conclusion that the enzymatic sulfation activity of Papss is essential for the developmental processes described in this study.

      • A main issue that arises from this work is the role of Papss at the cellular level. The results presented convincingly indicate defects in Golgi organization in Papss mutants. Therefore, the defects observed could stem from general defects in the secretion pathway rather than from specific defects on sulfation. This could even underly general/catastrophic cellular defects and lead to cell death (as observed). This observation has different implications. Is this effect observed in SGs also observed in other cells in the embryo? If Papss has a general role in Golgi organization this would be expected, as Papss encodes the only PAPs synthatase in Drosophila. Can the authors test any other mutant that specifically affect Golgi organization and investigate whether this produces a similar phenotype to that of Papss?

      Thank you for the comment. To address whether the defects observed in Papss mutants stem from general disruption of the secretory pathway due to Golgi disorganization, we examined mutants of two key Golgi components: Grasp65 and GM130.

      In Grasp65 mutants, we observed significant defects in SG lumen morpholgy, including highly irregular SG lumen shape and multiple constrictions (100%; n=10/10). However, the lumen was not uniformly thin as in Papss mutants. In contrast, GM130 mutants-although this line was very sick and difficult to grow-showed relatively normal salivary glands morphology in the few embryos that survived to stage 16 (n=5/5). It is possible that only embryos with mild phenotypes progressed to this stages, limiting interpretation. These data have now been included in Figure 3-figure supplement 2. Overall, while Golgi disruption can affect SG morphology, the specific phenotypes seen in Papss mutants are not fully recapitulated by Grasp65 or GM130 loss.

      • A model that conveys the different observations and that proposes a function for Papss in sulfation and Golgi organization (independent or interdependent?) would help to better present the proposed conclusions. In particular, the paper would be more informative if it proposed a mechanism or hypothesis of how sulfation affects SG lumen expansion. Is sulfation regulating a factor that in turn regulates Pio-Dpy matrix? Is it regulating Pio-Dpy directly? Is it regulating a product recognized by WGA? For instance, investigating Alcian blue or sulfotyrosine staining in pio, dpy mutants could help to understand whether Pio, Dpy are targets of sulfation.

      Thank you for the comment. We're also very interested in learning whether the regulation of the Pio-Dpy matrix is a direct or indirect consequence of the loss of sulfation on these proteins. One possible scenario is that sulfation directly regulates the Pio-Dpy matrix by regulating protein stability through the formation of disulfide bonds between the conserved Cys residues responsible for ZP module polymerization. Additionally, the Dpy protein contains hundreds of EGF modules that are highly susceptible to O-glycosylation. Sulfation of the glycan groups attached to Dpy may be critical for its ability to form a filamentous structure. Without sulfation, the glycan groups on Dpy may not interact properly with the surrounding materials in the lumen, resulting in an aggregated and condensed structure. These possibilities are discussed in the Discussion.

      We have not analyzed sulfation levels in pio or dpy mutants because sulfation levels in mutants of single ZP domain proteins may not provide much information. A substantial number of proteoglycans, glycoproteins, and proteins (with up to 1% of all tyrosine residues in an organism's proteins estimated to be sulfated) are modified by sulfation, so changes in sulfation levels in a single mutant may be subtle. Especially, the existing dpy mutant line is an insertion mutant of a transposable element; therefore, the sulfation sites would still remain in this mutant.

      • Interpretation of Papss effects on Pio and Dpy would be desired. The results presented indicate loss of Pio antibody staining but normal presence of cherry-Pio. This is difficult to interpret. How are these results of Pio antibody and cherry-Pio correlating with the results in the trachea described recently (Drees et al. 2023)?

      In our original submission, we stated that the uniform luminal mCh-Pio signals were not changed in Papss mutants, but after re-analysis, we found that these signals were actually absent from the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly.

      After cleavages by Np and furin, the Pio protein should have three fragments. The N-terminal region contains the N-terminal half of the ZP domain, and mCh-Pio signals show this fragment. The very C-terminal region should localize to the membrane as it contains the transmembrane domain. We think the middle piece, the C-terminal ZP domain, is recognized by the Pio antibody. The mCh-Pio and Pio antibody signals in the WT trachea (Drees et al., 2023) are similar to those in the SG. mCh-Pio signals are detected in the tracheal lumen as uniform signals, at the apical membrane, and in cytoplasmic puncta. Pio antibody signals are exclusively in the tracheal lumen and show more heterogenous filamentous signals.

      In Papss mutants, the middle fragment (the C-terminal ZP domain) seems to be most affected because the Pio antibody signals are absent from the lumen. The loss of Pio antibody signals could be due to protein degradation or epitope masking caused by aECM condensation and protein misfolding. This fragment seems to be key for interacting with Dpy, since Pio antibody signals always colocalize with Dpy-YFP. The N-terminal mCh-Pio fragment does not appear to play a significant role in forming a complex with Dpy in WT (but still aggregated together in Papss mutants), and this can be tested in future studies.

      In response to Reviewer 1's comment, we performed an additional experiment to test the role of Np in cleaving Pio to help organize the SG aECM. In this experiment, we overexpressed the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. Np.WT overexpression resulted in increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. However, overexpression of Np.S990A resulted in the absence of luminal mCh-Pio signals. Pio antibody signals were strong at the apical membrane but rather weak in the luminal filamentous structures. Since the UAS-Np.S990A line has the GFP tag, we could not reliably analyze Dpy-YFP signals due to overlapping Np.S990A.GFP signals in the same channel. However, the luminal filamentous Pio signals co-localized with GFP signals, and we assume that these overlapping signals could be Dpy-YFP signals.

      These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I.

      A proposed model of the Pio-Dpy aECM in WT, Papss, pio, and Np mutants has now been included in Figure 7.

      • What does the WGA staining in the lumen reveal? This staining seems to be affected differently in pio and dpy mutants: in pio mutants it disappears from the lumen (as dpy-YFP does), but in dpy mutants it seems to be maintained. How do the authors interpret these findings? How does the WGA matrix relate to sulfated products (using Alcian blue or sulfotyrosine)?

      WGA binds to sialic acid and N-acetylglucosamine (GlcNAc) residues on glycoproteins and glycolipids. GlcNAc is a key component of the glycosaminoglycan (GAG) chains that are covalently attached to the core protein of a proteoglycan, which is abundant in the ECM. We think WGA detects GlcNAc residues in the components of the aECM, including Dpy as a core component, based on the following data. 1) WGA and Dpy colocalize in the lumen, both in WT (as thin filamentous structures) and Papss mutant background (as condensed rod-like structures), and 2) are absent in pio mutants. WGA signals are still present in a highly condensed form in dpy mutants. That's probably because the dpy mutant allele (dpyov1) has an insertion of a transposable element (blood element) into intron 11 and this insertion may have caused the Dpy protein to misfold and condense. We added the information about the dpy allele to the Results section and discussed it in the Discussion.

      Minor points:

      • The morphological phenotypic analysis of Papss mutants (homozygous and transheterozygous) is a bit confusing. The general defects are higher in Papss homozygous than in transheterozygotes over a deficiency. Maybe quantifying the defects in the heterozygote embryos in the Papss mutant collection could help to figure out whether these defects relate to Papss mutation.

      We analyzed the morphology of heterozygous Papss mutant embryos. They were all normal. The data and quantifications have now been added to Figure 1-figure supplement 3.

      • The conclusion that the apical membrane is affected in Papss mutants is not strongly supported by the results presented with the pattern of Crb (Fig 2). Further evidences should be provided. Maybe the TEM analysis could help to support this conclusion

      We quantified Crb levels in the sub-apical and medial regions of the cell and included this new quantification in Figure 2D. TEM images showed variation in the irregularity of the apical membrane, even in WT, and we could not draw a solid conclusion from these images.

      • It is difficult to understand why in Papss mutants the levels of WGA increase. Can the authors elaborate on this?

      We think that when Dpy (and many other aECM components) are condensed and aggregated into the thin, rod-like structure in Papss mutants, the sugar residues attached to them must also be concentrated and shown as increased WGA signals.

      • The explanation about why Pio antibody and mcherry-Pio show different patterns is not clear. If the antibody recognizes the C-t region, shouldn't it be clearly found at the membrane rather than the lumen?

      The Pio protein is also cleaved by furin protease (Figure 5B). We think the Pio fragment recognized by the antibody should be a "C-terminal ZP domain", which is a middle piece after furin + Np cleavages.

      • The qsm information does not seem to provide any relevant information to the aECM, or sulfation.

      Since Qsm has been shown to bind to Dpy and remodel Dpy filaments in the muscle tendon (Chu and Hayashi, 2021), we believe that the different behavior of Qsm in the SG is still informative. As mentioned briefly in the Discussion, the cleaved Qsm fragment may localize differently, like Pio, and future work will need to test this. We have shortened the description of the Qsm localization in the manuscript and moved the details to the figure legend of Figure 5-figure supplement 3.

      Reviewer #3 (Significance (Required)):

      Previous reports already indicated a role for Papss in sulfation in SG (Zhu et al 2005). Now this work provides a more detailed description of the defects produced by the absence of Papss. In addition, it provides relevant data related to the nature and requirements of the aECM in the SG. Understanding the composition and requirements of aECM during organ formation is an important question. Therefore, this work may be relevant in the fields of cell biology and morphogenesis.

    1. GSI: A GSI, or Global Secondary Index, is a DynamoDB feature that lets you query the table using non-primary key attributes, and it can span all partitions globally. DynamoDB is super-fast because it uses a partition key (and optional sort key) for primary lookups. But sometimes, you want to query by another field — you define a GSI — and DynamoDB will maintain a separate index for that. gives flexibility in querying through miltiple keys w/o deduping data in another table

      howto use: https://hyp.is/9kEfBlN7EfCXVivplSZlYA/www.hellointerview.com/learn/system-design/problem-breakdowns/fb-news-feed

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    1. Author response:

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

      Joint Public Review:

      Summary:

      The authors sought to elucidate the mechanism by which infections increase sleep in Drosophila. Their work is important because it further supports the idea that the blood-brain barrier is involved in brain-body communication, and because it advances the field of sleep research. Using knock-down and knock-out of cytokines and cytokine receptors specifically in the endocrine cells of the gut (cytokines) as well as in the glia forming the blood-brain barrier (BBB) (cytokines receptors), the authors show that cytokines, upd2 and upd3, secreted by entero-endocrine cells in response to infections increase sleep through the Dome receptor in the BBB. They also show that gut-derived Allatostatin (Alst) A promotes wakefulness by inhibiting Alst A signaling that is mediated by Alst receptors expressed in BBB glia. Their results suggest there may be additional mechanisms that promote elevated sleep during gut inflammation.

      The authors suggest that upd3 is more critical than upd2, which is not sufficiently addressed or explained. In addition, the study uses the gut's response to reactive oxygen molecules as a proxy for infection, which is not sufficiently justified. Finally, further verification of some fundamental tools used in this paper would further solidify these findings making them more convincing.

      Strengths:

      (1) The work addresses an important topic and proposes an intriguing mechanism that involves several interconnected tissues. The authors place their research in the appropriate context and reference related work, such as literature about sickness-induced sleep, ROS, the effect of nutritional deprivation on sleep, sleep deprivation and sleep rebound, upregulated receptor expression as a compensatory mechanism in response to low levels of a ligand, and information about Alst A.

      (2) The work is, in general, supported by well-performed experiments that use a variety of different tools, including multiple RNAi lines, CRISPR, and mutants, to dissect both signal-sending and receiving sides of the signaling pathway.

      (3) The authors provide compelling evidence that shows that endocrine cells from the gut are the source of the upd cytokines that increase daytime sleep, that the glial cells of the BBB are the targets of these upds, and that upd action causes the downregulation of Alst receptors in the BBB via the Jak/Stat pathways.

      We are pleased that the reviewers recognized the strength and significance of our findings describing a gut-to-brain cytokine signaling mechanism involving the blood-brain barrier (BBB) and its role in regulating sleep, and we thank them for their comments.

      Weaknesses:

      (1) There is a limited characterization of cell types in the midgut which are classically associated with upd cytokine production.

      We thank the reviewer for raising this point. Although several midgut cell types (including the absorptive enterocytes) may indeed produce Unpaired (Upd) cytokines, our study specifically focused on enteroendocrine cells (EECs), which are well-characterized as secretory endocrine cells capable of exerting systemic effects. As detailed in our response to Results point #2 (please see below), we show that EEC-specific manipulation of Upd signaling is both necessary and sufficient to regulate sleep in response to intestinal oxidative stress. These findings support the role of EECs as a primary source of gut-derived cytokine signaling to the brain. To acknowledge the possible involvement of other source, we have also added a statement to the Discussion in the revised manuscript noting that other, non-endocrine gut cell types may contribute to systemic Unpaired signaling that modulates sleep.

      (2) Some of the main tools used in this manuscript to manipulate the gut while not influencing the brain (e.g., Voilà and Voilà + R57C10-GAL80), are not directly shown to not affect gene expression in the brain. This is critical for a manuscript delving into intra-organ communication, as even limited expression in the brain may lead to wrong conclusions.

      We agree with the reviewer that this is an important point. To address it, we performed additional validation experiments to assess whether the voilà-GAL4 driver in combination with R57C10-GAL80 (EEC>) influences upd2 or upd3 expression in the brain. Our results show that manipulation using EEC> alters upd2 and upd3 expression in the gut (Fig. 1a,b), with new data showing that this does not affect their expression levels in neuronal tissues (Fig. S1a), supporting the specificity of our approach. These new data are now included in the revised manuscript and described in the Results section. This additional validation strengthens our conclusion that the observed sleep phenotypes result from gut-specific cytokine signaling, rather than from effects on Unpaired cytokines produced in the brain.

      (1) >(3) The model of gut inflammation used by the authors is based on the increase in reactive oxygen species (ROS) obtained by feeding flies food containing 1% H2O2. The use of this model is supported by the authors rather weakly in two papers (refs. 26 and 27 ): The paper by Jiang et al. (ref. 26) shows that the infection by Pseudomonas entomophila induces cytokine responses upd2 and 3, which are also induced by the Jnk pathway. In addition, no mention of ROS could be found in Buchon et al. (ref 27); this is a review that refers to results showing that ROS are produced by the NADPH oxidase DUOX as part of the immune response to pathogens in the gut. Thus, there is no strong support for the use of this model.

      We thank the reviewer for raising this point. We agree that the references originally cited did not sufficiently justify the use of H<sub>2</sub>O<sub>2</sub> feeding as a model of gut inflammation. To address this, we have revised the Results section to clarify that we use H<sub>2</sub>O<sub>2</sub> feeding as a controlled method to elevate intestinal ROS levels, rather than as a general model of inflammation. This approach allows us to investigate the specific effects of ROS-induced cytokine signaling in the gut. We have also added additional citations to support the physiological relevance of this model. For instance, Tamamouna et al. (2021) demonstrated that H<sub>2</sub>O<sub>2</sub> feeding induces intestinal stem-cell proliferation – a response also observed during bacterial infection – and Jiang et al. (2009) showed that enteric infections increase upd2 and upd3 expression, which we similarly observe following H<sub>2</sub>O<sub>2</sub> feeding (Fig. 3a). These findings support the use of H<sub>2</sub>O<sub>2</sub> as a tool to mimic specific ROS-linked responses in the gut. We believe this targeted and tractable model is a strength of our study, enabling us to dissect how intestinal ROS modulates systemic physiology through cytokine signaling

      Additionally, we have included a statement in the Discussion acknowledging that ROS generated during infection may activate signaling mechanisms distinct from those triggered by chemically induced oxidative stress, and that exploring these differences in future studies may yield important insights into gut–brain communication. These revisions provide a stronger justification for our model while more accurately conveying both its relevance and its limitations.

      (2) >(4) Likewise, there is no support for the use of ROS in the food instead a direct infection by pathogenic bacteria. Furthermore, it is known that ROS damages the gut epithelium, which in turn induces the expression of the cytokines studied. Thus the effects observed may not reflect the response to infection. In addition, Majcin Dorcikova et al. (2023). Circadian clock disruption promotes the degeneration of dopaminergic neurons in male Drosophila. Nat Commun. 2023 14(1):5908. doi: 10.1038/s41467-02341540-y report that the feeding of adult flies with H2O2 results in neurodegeneration if associated with circadian clock defects. Thus, it would be important to discuss or present controls that show that the feeding of H2O2 does not cause neuronal damage.

      We thank the reviewer for this thoughtful follow-up point. We would like to clarify that we do not claim that the effects observed in our study directly reflect the full response to enteric infection. As outlined in our revised response to comment 3, we have updated the manuscript to more precisely describe the H<sub>2</sub>O<sub>2</sub>-feeding paradigm as a model that induces local intestinal ROS responses comparable to, but not equivalent to, those observed during pathogenic challenges. This revised framing highlights both the potential similarities and differences between chemically induced oxidative stress and infection-induced responses. Indeed, in the revised Discussion, we now explicitly acknowledge that ROS generated during infection may engage distinct signaling mechanisms compared to exogenous H<sub>2</sub>O<sub>2</sub> and emphasize the value of future studies in delineating these pathways. We are currently pursuing this direction in an independent ongoing study investigating the effects of enteric infections. However, for the present work, we chose to focus on the effects of ROS-induced responses in isolation, as this provides a clean and well-controlled context to dissect the specific contribution of oxidative stress to cytokine signaling and sleep regulation.

      To further address the reviewer’s concern, we have also included new data (a TUNEL stain for apoptotic DNA fragmentation) in the revised manuscript showing that H<sub>2</sub>O<sub>2</sub> feeding does not damage neuronal tissues under our experimental conditions (Fig. S3f,g). This addresses the point raised regarding the potential neurotoxicity of H<sub>2</sub>O<sub>2</sub>, as described by Majcin Dorcikova et al. (2023), and supports the specificity of the sleep phenotypes observed in our study. We believe these revisions and clarifications strengthen the manuscript and make our interpretation more precise.

      (3) >(5) The novelty of the work is difficult to evaluate because of the numerous publications on sleep in Drosophila. Thus, it would be very helpful to read from the authors how this work is different and novel from other closely related works such as: Li et al. (2023) Gut AstA mediates sleep deprivation-induced energy wasting in Drosophila. Cell Discov. 23;9(1):49. doi: 10.1038/s41421-023-00541-3.

      Our work highlights a distinct role for gut-derived AstA in sleep regulation compared to findings by Lin et al. (Cell Discovery, 2023)[1], who showed that gut AstA mediates energy wasting during sleep deprivation. Their study focused on the metabolic consequences of sleep loss, proposing that sleep deprivation increases ROS in the gut, which then promotes the release of the glucagon-like hormone adipokinetic hormone (AKH) through gut AstA signaling, thereby triggering energy expenditure.

      In contrast, our study addresses the inverse question – how ROS in the gut influences sleep. In our model, intestinal ROS promotes sleep, raising the intriguing possibility – cleverly pointed out by the reviewers – that ROS generated during sleep deprivation might promote sleep by inducing Unpaired cytokine signaling in the gut. According to our findings, this suppresses wake-promoting AstA signaling in the BBB, providing a mechanism to promote sleep as a restorative response to gut-derived oxidative stress and potentially limiting further ROS accumulation. Importantly, our findings support a wakepromoting role for EEC-derived AstA, demonstrated by several lines of evidence. First, EEC-specific knockdown of AstA increases sleep. Second, activation of AstA<sup>+</sup> EECs using the heat-sensitive cation channel Transient Receptor Potential A1 (TrpA1) reduces sleep, and this effect is abolished by simultaneous knockdown of AstA, indicating that the sleep-suppressing effect is mediated by AstA and not by other peptides or secreted factors released by these cells. Third, downregulation of AstA receptor expression in BBB glial cells increases sleep, further supporting the existence of a functional gut AstA– glia arousal pathway. We have now included new data in the revised manuscript showing that AstA release from EECs is downregulated during intestinal oxidative stress (Fig. 7k,l,m). This suggests that this wake-promoting signal is suppressed both at its source (the gut endocrine cells), by unknown means, and at its target, the BBB, via Unpaired cytokine signaling that downregulates AstA receptor expression. This coordinated downregulation may serve to efficiently silence this arousal-promoting pathway and facilitate sleep during intestinal stress. These new data, along with an expanded discussion, provide further mechanistic insight into gut-derived AstA signaling and strengthen our proposed model.

      This contrasts with the interpretation by Lin et al., who observed increased AstA peptide levels in EECs after antioxidant treatment and interpreted this as peptide retention. However, peptide accumulation may result from either increased production or decreased release, and peptide levels alone are insufficient to distinguish between these possibilities. To resolve this, we examined AstA transcript levels, which can serve as a proxy for production. Following oxidative stress (24 h of 1% H<sub>2</sub>O<sub>2</sub> feeding and the following day), when animals show increased sleep (Fig. 7e), we observed a decrease in AstA transcript levels followed by an increase in peptide levels (Fig. 7k,l,m), suggesting that oxidative stress leads to reduced gut AstA production and release. Furthermore, we recently found that a class of EECs that produce the hormone Tachykinin (Tk) and are distinct from the AstA<sup>+</sup> EECs express the ROSsensitive cation channel TrpA1 (Ahrentløv et al., 2025, Nature Metabolism2). In these Tk<sup>+</sup> EECs, TrpA1 mediates ROS-induced Tk hormone release. In contrast, single-cell RNA-seq data[3] do not support TrpA1 expression in AstA<sup>+</sup> EECs, consistent with our findings that ROS does not promote AstA release – an effect that would be expected if TrpA1 were functionally expressed in AstA<sup>+</sup> EECs. This contradicts the findings of Lin et al., who reported TrpA1 expression in AstA<sup>+</sup> EECs. We have now included relevant single-cell data in the revised manuscript (Fig. S6f) showing that TrpA1 is specifically expressed in Tk<sup>+</sup> EECs, but not in AstA<sup>+</sup> EECs, and we have expanded the discussion to address discrepancies in TrpA1 expression and AstA regulation.

      Taken together, our results reveal a dual-site regulatory mechanism in which Unpaired cytokines released from the gut act at the BBB to downregulate AstA receptor expression, while AstA release from EECs is simultaneously suppressed. We thank the reviewers for raising this important point. We have also included a discussion the other point raised by the reviewers – the possibility that ROS generated during sleep deprivation may engage the same signaling pathways described here, providing a mechanistic link between sleep deprivation, intestinal stress, and sleep regulation.

      Recommendations for the authors:

      A- Material and Methods:

      (1) Feeding Assay: The cited publication (doi.org:10.1371/journal.pone.0006063) states: "For the amount of label in the fly to reflect feeding, measurements must therefore be confined to the time period before label egestion commences, about 40 minutes in Drosophila, a time period during which disturbance of the flies affects their feeding behavior. There is thus a requirement for a method of measuring feeding in undisturbed conditions." Was blue fecal matter already present on the tube when flies were homogenized at 1 hour? If so, the assay may reflect gut capacity rather than food passage (as a proxy for food intake). In addition, was the variability of food intake among flies in the same tube tested (to make sure that 1-2 flies are a good proxy for the whole population)?

      We agree that this is an important point for feeding experiments. We are aware of the methodological considerations highlighted in the cited study and have extensive experience using a range of feeding assays in Drosophila, including both short- and long-term consumption assays (e.g., dye-based and CAFE assays), as well as automated platforms such as FLIC and FlyPAD (Nature Communications, 2022; Nature Metabolism, 2022; and Nature Metabolism, 2025)[2,4,5].

      For the dye-based assay, we carefully selected a 1-hour feeding window based on prior optimization. Since animals were not starved prior to the assay, shorter time points (e.g., 30 minutes) typically result in insufficient ingestion for reliable quantification. A 1-hour period provides a robust readout while remaining within the timeframe before significant label excretion occurs under our experimental conditions. To support the robustness of our findings, we complemented the dye-based assay with data from FLIC, which enables automated, high-resolution monitoring of feeding behavior in undisturbed animals over extended periods. The FLIC results were consistent with the dye-based data, strengthening our confidence in the conclusions. To minimize variability and ensure consistency across experiments, all feeding assays were performed at the same circadian time – Zeitgeber Time 0 (ZT0), corresponding to 10:00 AM when lights are turned on in our incubators. This time point coincides with the animals' natural morning feeding peak, allowing for reproducible comparisons across conditions. Regarding variability among flies within tubes, each biological replicate in the dye assay consisted of 1–2 flies, and results were averaged across multiple replicates. We observed good consistency across samples, suggesting that these small groups reliably reflect group-level feeding behavior under our conditions.

      (2) Biological replicates: whereas the number of samples is clearly reported in each figure, the number of biological replicates is not indicated. Please include this information either in Material and methods or in the relevant figure legends. Please also include a description of what was considered a biological replicate.

      We have now clarified in the Materials and Methods section under Statistics that all replicates represent independent biological samples, as suggested by the reviewers.

      (3) Control Lines: please indicate which control lines were used instead of citing another publication. If preferred, this information could be supplied as a supplementary table.

      We now provide a clear description of the control lines used in the Materials and Methods section. Specifically, all GAL4 and GAL80 lines used in this study were backcrossed for several generations into a shared w<sup>1118</sup> background and then crossed to the same w<sup>1118</sup> strain used as the genetic background for the UAS-RNAi, <i.CRISPR, or overexpression lines. This approach ensures, to a strong approximation, that the only difference between control and experimental animals is the presence or absence of the UAS transgene.

      (4) Statistical analyses: for some results (e.g., those shown in Figure 3d), it could be useful to test the interaction between genotype and treatment.

      We thank the reviewer for this helpful suggestion. In response, we have now performed two-way ANOVA analyses to assess genotype × treatment (diet) interaction effects for the relevant data, including those shown in Figure 3d as well as additional panels where animals were exposed to oxidative stress and sleep phenotypes were measured. We have added the corresponding interaction p-values in the updated figure legends for Figures 3d, 3k, 5a–c, 5f, 5h, 5i, 6c, 6e, and 7e. All of these tests revealed significant interaction effects, supporting the conclusion that the observed differences in sleep phenotypes are specifically dependent on the interaction between genetic manipulation (e.g., cytokine or receptor knockdown) and oxidative stress. These additions reinforce the interpretation that Unpaired cytokine signaling, glial JAK-STAT pathway activity, and AstA receptor regulation functionally interact with intestinal ROS exposure to modulate sleep. We thank the reviewer for suggesting this improvement.

      (5) Reporting of p values. Some are reported as specific values whereas others are reported as less than a specific value. Please make this reporting consistent across different figures.

      All p-values reported in the manuscript are exact, except in cases where values fall below p < 0.0001. In those instances, we use the inequality because the Prism software package (GraphPad, version 10), which was used for all statistical analyses, does not report more precise values. We believe this reporting approach reflects standard practice in the field.

      (6) Please include the color code used in each figure, either in the figure itself or in the legend.

      We have now clarified the color coding in all relevant figures. In particular, we acknowledge that the meaning of the half-colored circles used to indicate H<sub>2</sub>O<sub>2</sub> treatment was not previously explained. These have now been clearly labeled in each figure to indicate treatment conditions.

      (7) The scheme describing the experimental conditions and the associated chart is confusing. Please improve.

      We have improved the schematic by replacing “ROS” with “H<sub>2</sub>O<sub>2</sub>” to more clearly indicate the experimental condition used. Additionally, we have added the corresponding circle annotations so that they now also appear consistently above the relevant charts. This revised layout enhances clarity and helps readers more easily interpret the experimental conditions. We believe these changes address the reviewer’s concern and make the figure significantly more intuitive.

      8) Please indicate which line was used for upd-Gal4 and the evidence that it faithfully reflects upd3 expression.

      We have now clarified in the Materials and Methods section that the upd3-GAL4 line used in our study is Bloomington stock #98420, which drives GAL4 expression under the control of approximately 2 kb of sequence upstream of the upd3 start codon. This line has previously been used as a transcriptional reporter for upd3 activity. The only use of this line was to illustrate reporter expression in the EECs. To support this aspect of Upd3 expression, we now include new data in the revised manuscript using fluorescent in situ hybridization (FISH) against upd3, which confirms the presence of upd3 transcripts in prospero-positive EECs of the adult midgut (Fig. S1b). Additionally, we show that upd3 transcript levels are significantly reduced in dissected midguts following EEC-specific knockdown using multiple independent RNAi lines driven by voilà-GAL4, both alone and in combination with R57C10-GAL80, consistent with endogenous expression in these cells (Fig. 1a,b).

      To further address the reviewer’s concern and provide additional support for the endogenous expression of upd3 in EECs, we performed targeted knockdown experiments focusing on molecularly defined EEC subpopulations. The adult Drosophila midgut contains two major EEC subtypes characterized by their expression of Allatostatin C (AstC) or Tachykinin (Tk), which together encompass the vast majority of EECs. To selectively manipulate these populations, we used AstC-GAL4 and Tk-GAL4 drivers – both knock-in lines in which GAL4 is inserted at the respective endogenous hormone loci. This design enables precise GAL4 expression in AstC- or Tk-expressing EECs based on their native transcriptional profile. To eliminate confounding neuronal expression, we combined these drivers with R57C10GAL80, restricting GAL4 activity to the gut and generating AstC<sup>Gut</sup>> and Tk<sup>Gut</sup>> drivers. Using these tools, we knocked down upd2 and upd3 selectively in the AstC- or Tk-positive EECs. Knockdown of either cytokine in AstC-positive EECs significantly increased sleep under homeostatic conditions, recapitulating the phenotype observed with knockdown in all EECs (Fig. 1m-o). In contrast, knockdown of upd2 or upd3 in Tk-positive EECs had no effect on sleep (Fig. 1p-r). Furthermore, we show in the revised manuscript that selective knockdown of upd2 or upd3 in AstC-positive EECs abolishes the H<sub>2</sub>O<sub>2</sub>-induced increase in sleep (Fig. 3f–h). These findings demonstrate that Unpaired cytokine signaling from AstC-positive EECs is essential for mediating the sleep response to intestinal oxidative stress, highlighting this specific EEC subtype as a key source of cytokine-driven regulation in this context. These new results indicate that AstC-positive EECs are a primary source of the Unpaired cytokines that regulate sleep, while Tk-positive EECs do not appear to contribute to this function. Importantly, upd3 transcript levels were significantly reduced in dissected midguts following AstC<sup>Gut</sup> driven knockdown (Fig. S1r), further confirming that upd3 is endogenously expressed in AstC-positive EECs. Thus we have bolstered our confidence that upd3 is indeed expressed in EECs, as illustrated by the reporter line, through several means.

      (9) Please indicate which GFP line was used with upd-Gal4 (CD8, NLS, un-tagged, etc). The Material and Methods section states that it was "UAS-mCD8::GFP (#5137);", however, the stain does not seem to match a cell membrane pattern but rather a nuclear or cytoplasmic pattern. This information would help the interpretation of Figure 1C.

      We confirm that the GFP reporter line used with upd3-GAL4 was obtained from Bloomington stock #98420. As noted by the Bloomington Drosophila Stock Center, “the identity of the UAS-GFP transgene is a guess,” and the subcellular localization of the GFP fusion is therefore uncertain. We agree with the reviewer that the signal observed in Figure 1c does not display clear membrane localization and instead appears diffuse, consistent with cytoplasmic or partially nuclear localization. In any case, what we find most salient is the reporter’s labeling of Prospero-positive EECs in the adult midgut, consistent with upd3 expression in these cells. This conclusion is further supported by multiple lines of evidence presented in the revised manuscript, as mentioned above in response to question #8: (1) fluorescent in situ hybridization (FISH) for upd3 confirms expression in EECs (Fig. S1b), (2) EEC-specific RNAi knockdown of upd3 reduces transcript levels in dissected midguts, and (3) publicly available single-cell RNA sequencing datasets[3] also indicate that upd3 is expressed at low levels in a subset of adult midgut EECs under normal conditions. We have also clarified in the revised Materials and Methods section that GFP localization is undefined in the upd3-GAL4 line, to guide interpretation of the reporter signal.

      B- Results

      (1) Figure 1: According to previous work (10.1016/j.celrep.2015.06.009, http://flygutseq.buchonlab.com/data?gene=upd3%0D%0A), in basal conditions upd3 is expressed as following: ISC (35 RPKM), EB (98 RPKM), EC (57 RPKM), and EEC (8 RPKM). Accordingly, even complete KO in EECs should eliminate only a small fraction of upd3 from whole guts, even less considering the greater abundance of other cell types such as ECs compared to EECs. It would be useful to understand where this discrepancy comes from, in case it is affecting the conclusion of the manuscript. While this point per se does not affect the main conclusions of the manuscript, it makes the interpretation of the results more difficult.

      We acknowledge the previously reported low expression of upd3 in EECs. However, the FlyGut-seq site appears to be no longer available, so we could not directly compare other related genes. Nonetheless, our data – based on in situ hybridization, reporter expression, and multiple RNAi knockdowns – consistently support upd3 expression in EECs. These complementary approaches strengthen the conclusion that EECs are an important source of systemic upd3 under the conditions tested.

      (2) Figure 1: The upd2-3 mutants show sleep defects very similar to those of EEC>RNAi and >Cas9. It would thus be helpful to try to KO upd3 with other midgut drivers (An EC driver like Myo1A or 5966GS and a progenitor driver like Esg or 5961GS) to validate these results. Such experiments might identify precisely which cells are involved in the gut-brain signaling reported here.

      We appreciate the reviewer’s suggestion and agree that exploring other potential sources of Upd3 in the gut is an interesting direction. In this study, we have focused on EECs, which are the primary hormone-secreting cells in the intestine and thus the most likely candidates for mediating systemic effects such as gut-to-brain signaling. While it is possible that other gut cell types – such as enterocytes (e.g., Myo1A<sup>+</sup>) or intestinal progenitors (e.g., Esg<sup>+</sup>) – also contribute to Upd3 production, these cells are not typically endocrine in nature. Demonstrating their involvement in gutto-brain communication would therefore require additional, extensive validation beyond the scope of the current study. Importantly, our data show that manipulating Upd3 specifically in EECs is both necessary and sufficient to modulate sleep in response to intestinal ROS, strongly supporting the conclusion that EEC-derived cytokine signaling underlies the observed phenotype. In contrast, manipulating cytokines in other gut cells could produce indirect effects – such as altered proliferation, epithelial integrity, or immune responses – that complicate the interpretation of behavioral outcomes like sleep. For these reasons, we chose to focus on EECs as the source of endocrine signals mediating gut-to-brain communication. However, to address this point raised by the reviewer, we have now included a statement in the Discussion acknowledging that other non-endocrine gut cell types may also contribute to the systemic Unpaired signaling that modulates sleep in response to intestinal oxidative stress.

      (3) Figure 3: "This effect mirrored the upregulation observed with EEC-specific overexpression of upd3, indicating that it reflects physiologically relevant production of upd3 by the gut in response to oxidative stress." Please add (Figure 3a) at the end of this sentence.

      We have now added “(Figure 3a)” at the end of the sentence to clearly reference the relevant data.

      (4) For Figure 3b, do you have data showing that the increased amount of sleep was due to the addition of H2O2 per se, rather than the procedure of adding it?

      We have added new data to address this point. To ensure that the observed sleep increase was specifically due to the presence of H<sub>2</sub>O<sub>2</sub> and not an effect of the food replacement procedure, we performed a control experiment in which animals were fed standard food prepared using the same protocol and replaced daily, but without H<sub>2</sub>O<sub>2</sub>. These animals did not exhibit increased sleep, confirming that the sleep effect is attributable to intestinal ROS rather than the supplementation procedure itself (Fig. S3a). Thanks for the suggestion.

      (5) In the text it is stated that "Since 1% H2O2 feeding induced robust responses both in upd3 expression and in sleep behavior, we asked whether gut-derived Unpaired signaling might be essential for the observed ROS-induced sleep modulation. Indeed, EEC-specific RNAi targeting upd2 or upd3 abolished the sleep response to 1% H2O2 feeding." While it is indeed true that there is no additional increase in sleep time due to EEC>upd3 RNAi, it is also true that EEC>upd3 RNAi flies, without any treatment, have already increased their sleep in the first place. It is then possible that rather than unpaired signaling being essential, an upper threshold for maximum sleep allowed by manipulation of these processes was reached. It would be useful to discuss this point.

      Several findings argue against a ceiling effect and instead support a requirement for Unpaired signaling in mediating ROS-induced sleep. Animals with EEC-specific upd2 or upd3 knockdown or null mutation not only fail to increase sleep following H<sub>2</sub>O<sub>2</sub> treatment but actually exhibit reduced sleep during oxidative stress (Fig. 3e, k, l; Fig. 5e, f), suggesting that Unpaired signaling is required to sustain sleep under these conditions. Similarly, animals with glial dome knockdown also show reduced sleep under oxidative stress, closely mirroring the phenotype of EEC-specific upd3 RNAi animals (Fig. 5a–c, g–i). These results support the conclusion that gut-to-glia Unpaired cytokine signaling is necessary for maintaining elevated sleep during oxidative stress. In the absence of this signaling, animals exhibit increased wakefulness. We identify AstA as one such wake-promoting signal that is suppressed during intestinal stress. We present new data showing that this pathway is downregulated not only via Unpaired-JAK/STAT signaling in glial cells but also through reduced AstA release from the gut in the revised manuscript. This model, in which Unpaired cytokines promote sleep during intestinal stress by suppressing arousal pathways, is discussed throughout the manuscript to address the reviewer’s point.

      (6) In Figure 3k, the dots highlighting the experiment show an empty profile, a full one, and a half one. Please define what the half dots represent.

      We have now clarified the color coding in all relevant figures. Specifically, we acknowledge that the meaning of the half-colored circles indicating H<sub>2</sub>O<sub>2</sub> treatment was not previously defined – it indicates washout or recovery time. In the revised version, these symbols are now clearly labeled in each figure to indicate the treatment condition, ensuring consistent and intuitive interpretation across all panels.

      (7) The authors used appropriate GAL4 and RNAi lines to the knockdown dome, a upd2/3 JAK-STATlinked receptor, specifically in neurons and glia, respectively, in order to identify the CNS targets of upd2/3 cytokines produced by enteroendocrine cells (EECs). Pan-neuronal dome knockdown did not alter daytime sleep in adult females, yet pan-glial dome knockdown phenocopied effects of upd2/3 knockdown in EECs. They also observed that EEC-specific knockdown of upd2 and upd3 led to a decrease in JAK-STAT reporter activity in repo-positive glial cells. This supports the authors' conclusion that glial cells, not neurons, are the targets by which unpaired cytokines regulate sleep via JAK-STAT signaling. However, they do not show nighttime sleep data of pan-neuronal and pan-glial dome knockdowns. It would strengthen their conclusion if the nighttime sleep of pan-glial dome knockdown phenocopied the upd2/3 knockdowns as well, provided the pan-neuronal dome knockdown did not alter nighttime sleep.

      We have now added nighttime sleep data for both pan-glial and pan-neuronal domeless knockdowns in the revised manuscript (Fig. 2a). Glial knockdown increased nighttime sleep, similar to EEC-specific upd2/3 knockdown, while neuronal knockdown had no effect. These results further support the glial cells’ being the relevant target of gut-derived Unpaired signaling.

      (8) The authors only used one method to induce oxidative stress (hydrogen peroxide feeding). It would strengthen their argument to test multiple methods of inducing oxidative stress, such as lipopolysaccharide (LPS) feeding. In addition, it would be useful to use a direct bacterial infection to confirm that in flies, the infection promotes sleep. Additionally, flies deficient in Dome in the BBB and infected should not be affected in their sleep by the infection. These experiments would provide direct support for the mechanism proposed. Finally, the authors should add a primary reference for using ROS as a model of bacterial infection and justify their choice better.

      We agree that directly comparing different models of intestinal stress, such as bacterial infection or LPS feeding, would provide valuable insight into how gut-derived signals influence sleep in response to infection. As noted in our detailed responses above, we now include an expanded rationale for our use of H<sub>2</sub>O<sub>2</sub> feeding as a controlled and well-established method for inducing intestinal ROS – one of the key physiological responses to enteric infection and inflammation. In the revised Discussion, we explicitly acknowledge that pathogenic infections – which trigger both intestinal ROS and additional immune pathways – may engage distinct or complementary mechanisms compared to chemically induced oxidative stress. We emphasize the importance of future studies aimed at dissecting these differences. In fact, we are actively pursuing this direction in ongoing work examining sleep responses to enteric infection. For the purposes of the present study, however, we chose to focus on a tractable and specific model of ROS-induced stress to define the contribution of Unpaired cytokine signaling to gut-brain communication and sleep regulation. This approach allowed us to isolate the effect of oxidative stress from other confounding immune stimuli and identify a glia-mediated signaling mechanism linking gut epithelial stress to changes in sleep behavior.

      (9) To confirm that animals lacking EEC Unpaired signaling are not more susceptible to ROS-induced damage, the authors assessed the survival of upd2 and upd3 knockdowns on 1% H2O2 and concluded they display no additional sensitivity to oxidative stress compared to controls. It may be useful to include other tests of sensitivity to oxidative stress, in addition to survival.

      We appreciate the reviewer’s suggestion. In our view, survival is a highly informative and stringent readout, as it reflects the overall physiological capacity of the animal to withstand oxidative stress. Importantly, our data show that animals lacking EEC-derived Unpaired signaling do not exhibit reduced survival following H<sub>2</sub>O<sub>2</sub> exposure, indicating that their oxidative stress resistance is not compromised. Furthermore, we previously confirmed that feeding behavior is unaffected in these animals, suggesting that their ability to ingest food (and thus the stressor) is not impaired. As a molecular complement to these assays in response to this point and others, we have also performed an assessment of neuronal apoptosis (a TUNEL assay, Fig. S3f,g). This assay did not identify an increase in cell death in the brains of animals fed peroxide-containing medium. Thus, gross neurological health, behavior, and overall survival appear to be resilient to the environmental treatment regime we apply here, suggesting that the outcomes we observe arise from signaling per se.

      (10) The authors confirmed that animals lacking EEC-derived upd3 displayed sleep suppression similar to controls in response to starvation. These results led the authors to conclude that there is a specific requirement for EEC-derived Unpaired signaling in responding to intestinal oxidative stress. However, they previously showed that EEC-specific knockdown of upd3 and upd2 led to increased daytime sleep under normal feeding conditions. Their interpretations of their data are inconsistent.

      We appreciate the reviewer’s comment. While animals lacking EEC-derived Unpaired signaling show increased baseline sleep under normal feeding conditions, they still exhibit a robust reduction in sleep when subjected to starvation – comparable to that of control animals (Fig. S3h–j). This demonstrates that they retain the capacity to appropriately modulate sleep in response to metabolic stress. Thus, the sleep-promoting phenotype under normal conditions does not reflect a generalized inability to adjust sleep behavior. Rather, it highlights a specific role for Unpaired signaling in mediating sleep responses to intestinal oxidative stress, not in broadly regulating all sleep-modulating stimuli.

      (11) The authors report a significant increase in JAK-STAT activity in surface glial cells at ZT0 in animals fed 1% H2O2-containing food for 20 hours. This response was abolished in animals with EECspecific knockdown of upd2 or upd3. The authors confirmed there were no unintended neuronal effects on upd2 or upd3 expression in the heads. They also observed an upregulation of dome transcript levels in the heads of animals with EEC-specific knockdown of upd3 fed 1% H2O2-containing food for 15 hours, which they interpret to be a compensatory mechanism in response to low levels of the ligand. This assay is inconsistent with previous experiments in which animals were fed hydrogen peroxide for 20 hours.

      We thank the reviewer for identifying this discrepancy. The inconsistency arose from a labeling error in the manuscript. Both the JAK-STAT reporter assays in glial cells and the dome expression measurements were performed following 15 hours of H<sub>2</sub>O<sub>2</sub> feeding, not 20 hours as previously stated. We have now corrected this in the revised manuscript.

      (12) The authors show that animals with glia-specific dome knockdown did not have decreased survival on H2O2-containing food, and displayed normal rebound sleep in the morning following sleep deprivation. These results potentially undermine the significance of the paper. If the normal sleep response to oxidative stress is an important protective mechanism, why would oxidative stress not decrease survival in dome knockdown flies (that don't have the normal sleep response to oxidative stress)? This suggests that the proposed mechanism is not important for survival. The authors conclude that Dome-mediated JAK-STAT signaling in the glial cells specifically regulates ROS-induced sleep responses, which their results support.

      We agree that our survival data show that glial dome knockdown does not reduce survival under continuous oxidative stress. However, we believe this does not undermine the importance of the sleep response as an adaptive mechanism. In our survival assay, animals were continuously exposed to 1% H<sub>2</sub>O<sub>2</sub> without the opportunity to recover. In contrast, under natural conditions, oxidative stress is likely to be intermittent, and the ability to mount a sleep response may be particularly important for promoting recovery and maintaining homeostasis during or after transient stress episodes. Thus, while the JAK-STAT-mediated sleep response may not directly enhance survival under constant oxidative challenge, it likely plays a critical role in adaptive recovery under natural conditions.

      (13) Altogether, the authors conclude that enteric oxidative stress induces the release of Unpaired cytokines which activate the JAK-STAT pathway in subperineurial glia of the BBB, which leads to the glial downregulation of receptors for AstA, which is a wake-promoting factor also released by EECs. This mechanism is supported by their results, however, this research raises some intriguing questions, such as the role of upd2 versus upd3, the role of AstA-R1 versus AstA-R2, the importance of this mechanism in terms of survival, the sex-specific nature of this mechanism, and the role that nutritional availability plays in the dual functionality of Unpaired cytokine signaling in regards to sleep.

      We thank the reviewer for highlighting these important questions. Our data suggest that Upd2 and Upd3, while often considered partially redundant, both contribute to sleep regulation, with stronger effects observed for Upd3. This is consistent with prior studies indicating overlapping but non-identical roles for these cytokines. Similarly, although AstA-R1 and AstA-R2 can both be activated by AstA, knockdown of AstA-R2 consistently produces more robust sleep phenotypes, suggesting a predominant role in mediating this effect. The possibility of sex-specific regulation is indeed compelling. While our study focused on females, many gut hormones show sex-dependent activity, and we recognize this as an important avenue for future research. Finally, we have included new data in the revised manuscript showing that gut-derived AstA is downregulated under oxidative stress, further supporting our model in which Unpaired signaling suppresses arousal pathways during intestinal stress

      (14)Data Availability: It is indicated that: "Reasonable data requests will be fulfilled by the lead author". However, eLife's guidelines for data sharing require that all data associated with an article to be made freely and widely available.

      We thank the reviewer for pointing this out. We have revised the Data Availability section of the manuscript to clarify that all data will be made freely available from the lead contact without restriction, in accordance with eLife’s open data policy.

      References

      (1) Li, Y., Zhou, X., Cheng, C., Ding, G., Zhao, P., Tan, K., Chen, L., Perrimon, N., Veenstra, J.A., Zhang, L., and Song, W. (2023). Gut AstA mediates sleep deprivaPon-induced energy wasPng in Drosophila. Cell Discov 9, 49. 10.1038/s41421-023-00541-3. (2) Ahrentlov, N., Kubrak, O., Lassen, M., Malita, A., Koyama, T., Frederiksen, A.S., Sigvardsen, C.M., John, A., Madsen, P., Halberg, K.A., et al. (2025). Protein-responsive gut hormone Tachykinin directs food choice and impacts lifespan. Nature Metabolism. 10.1038/s42255-025-01267-0.

      (3) Li, H., Janssens, J., De Waegeneer, M., Kolluru, S.S., Davie, K., Gardeux, V., Saelens, W., David, F.P.A., Brbic, M., Spanier, K., et al. (2022). Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly. Science 375, eabk2432. 10.1126/science.abk2432.

      (4) Kubrak, O., Koyama, T., Ahrentlov, N., Jensen, L., Malita, A., Naseem, M.T., Lassen, M., Nagy, S., Texada, M.J., Halberg, K.V., and Rewitz, K. (2022). The gut hormone AllatostaPn C/SomatostaPn regulates food intake and metabolic homeostasis under nutrient stress. Nature communicaPons 13, 692. 10.1038/s41467-022-28268-x.

      (5) Malita, A., Kubrak, O., Koyama, T., Ahrentlov, N., Texada, M.J., Nagy, S., Halberg, K.V., and Rewitz, K. (2022). A gut-derived hormone suppresses sugar appePte and regulates food choice in Drosophila. Nature Metabolism 4, 1532-1550. 10.1038/s42255-022-00672-z.

    1. Author response:

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

      Reviewer #1 (Public Review)>

      Summary:

      This research group has consistently performed cutting-edge research aiming to understand the role of hormones in the control of social behaviors, specifically by utilizing the genetically tractable teleost fish, medaka, and the current work is no exception. The overall claim they make, that estrogens modulate social behaviors in males and females is supported, with important caveats. For one, there is no evidence these estrogens are generated by "neurons" as would be assumed by their main claim that it is NEUROestrogens that drive this effect. While indeed the aromatase they have investigated is expressed solely in the brain, in most teleosts, brain aromatase is only present in glial cells (astrocytes, radial glia). The authors should change this description so as not to mislead the reader. Below I detail more specific strengths and weaknesses of this manuscript.

      We thank the reviewer for this very positive evaluation of our work and greatly appreciate their helpful comments and suggestions for improving the manuscript. We agree with the comment that the term “neuroestrogens” is misleading. Therefore, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript, including the title.

      In the following sections, “neuroestrogens” has been revised to align with the surrounding context.

      Line 21: “in the brain, also known as neuroestrogens,” → “in the brain.”

      Line 28: “neuroestrogens” → “these estrogens.”

      Line 30: “mechanism of action of neuroestrogens” → “mode of action of brain-derived estrogens.”

      Line 43: “brain-derived estrogens, also called neuroestrogens,” → “estrogens.”

      Line 74: “neuroestrogen synthesis is selectively impaired while gonadal estrogen synthesis remains intact” → “estrogen synthesis in the brain is selectively impaired while that in the gonads remains intact.”

      Line 77: “neuroestrogens” → “these estrogens.”

      Line 335: “levels of neuroestrogens” → “brain estrogen levels.”

      Line 338: “neuroestrogens” → “these estrogens.”

      Line 351: “neuroestrogens” → “these estrogens.”

      Line 357: “neuroestrogen action” → “the action of brain-derived estrogens.”

      Line 359: “neuroestrogens” → “estrogen synthesis in the brain.”

      Line 390: “active synthesis of neuroestrogens” → “active estrogen synthesis in the brain.”

      Line 431: “neuroestrogens” → “estrogens in the brain.”

      Line 431: “neuroestrogen action” → “the action of brain-derived estrogens.”

      Line 433: “neuroestrogen action” → “their action.”

      Strengths:

      Excellent use of the medaka model to disentangle the control of social behavior by sex steroid hormones.

      The findings are strong for the most part because deficits in the mutants are restored by the molecule (estrogens) that was no longer present due to the mutation.

      Presentation of the approach and findings are clear, allowing the reader to make their own inferences and compare them with the authors'.

      Includes multiple follow-up experiments, which lead to tests of internal replication and an impactful mechanistic proposal.

      Findings are provocative not just for teleost researchers, but for other species since, as the authors point out, the data suggest mechanisms of estrogenic control of social behaviors may be evolutionarily ancient.

      We again thank the reviewer for their positive evaluation of our work.

      Weaknesses:

      (1) As stated in the summary, the authors attribute the estrogen source to neurons and there isn't evidence this is the case. The impact of the findings doesn't rest on this either.

      As noted in Response to reviewer #1’s summary comment, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript.

      Line 63: We have also added the text “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (18– 20).” Following this addition, “This observation suggests” in the subsequent sentence has been replaced with “These observations suggest.”

      The following references (#18–20), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      P. M. Forlano, D. L. Deitcher, D. A. Myers, A. H. Bass, Anatomical distribution and cellular basis for high levels of aromatase activity in the brain of teleost fish: aromatase enzyme and mRNA expression identify glia as source. J. Neurosci. 21, 8943–8955 (2001).

      N. Diotel, Y. Le Page, K. Mouriec, S. K. Tong, E. Pellegrini, C. Vaillant, I. Anglade, F. Brion, F. Pakdel, B. C. Chung, O. Kah, Aromatase in the brain of teleost fish: expression, regulation and putative functions. Front. Neuroendocrinol. 31, 172–192 (2010).

      A. Takeuchi, K. Okubo, Post-proliferative immature radial glial cells female-specifically express aromatase in the medaka optic tectum. PLoS One 8, e73663 (2013).

      (2) The d4 versus d8 esr2a mutants showed different results for aggression. The meaning and implications of this finding are not discussed, leaving the reader wondering.

      Line 282: As the reviewer correctly noted, circles were significantly reduced in mutant males of the Δ8 line, whereas no significant reduction was observed in those of the Δ4 line. However, a tendency toward reduction was evident in the Δ4 line (P = 0.1512), and both lines showed significant differences in fin displays. Based on these findings, we believe our conclusion that esr2a<sup>−/−</sup> males exhibit reduced aggression remains valid. To clarify this point and address potential reader concerns, we have revised the text as follows: “esr2a<sup>−/−</sup> males from both the Δ8 and Δ4 lines exhibited significantly fewer fin displays than their wildtype siblings (P = 0.0461 and 0.0293, respectively). Circles followed a similar pattern, with a significant reduction in the Δ8 line (P = 0.0446) and a comparable but non-significant decrease in the Δ4 line (P = 0.1512) (Fig. 5L; Fig. S8E), showing less aggression.”

      (3) Lack of attribution of previously published work from other research groups that would provide the proper context of the present study.

      In response to this and other comments from this reviewer, we have revised the Introduction and Discussion sections as follows.

      Line 56: “solely responsible” in the Introduction has been modified to “largely responsible”.

      Line 57: “This is consistent with the recent finding in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (10)” has been revised to “This is consistent with recent observations in a few teleost species that genetic ablation of AR severely impairs male-typical behaviors (13–16) and with findings in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in maletypical courtship (12)” to include previous studies on the behavior of AR mutant fish (Yong et al., 2017; Alward et al., 2020; Ogino et al., 2023; Nishiike and Okubo, 2024) in the Introduction.

      Line 65: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)” has been added to the Introduction. This addition provides an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015).

      Line 367: “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31– 33)” has been edited to read “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      After the revisions described above, the following references (#13, 14, and 22) have been added to the reference list, with other references renumbered accordingly:

      L. Yong, Z. Thet, Y. Zhu, Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. J. Exp. Biol. 220, 3017–3021 (2017).

      B. A. Alward, V. A. Laud, C. J. Skalnik, R. A. York, S. A. Juntti, R. D. Fernald, Modular genetic control of social status in a cichlid fish. Proc. Natl. Acad. Sci. U.S.A. 117, 28167–28174 (2020).

      L. A. O’Connell, H. A. Hofmann, Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology 153, 1341–1351 (2012).

      (4) There are a surprising number of citations not included; some of the ones not included argue against the authors' claims that their findings were "contrary to expectation".

      Line 68: As detailed in Response to reviewer #1’s comment 3 on weaknesses, we have cited previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015) in the Introduction.

      The following revisions have also been made to avoid phrases such as “contrary to expectation” and “unexpected.”

      Line 76: “Contrary to our expectations” → “Remarkably.”

      Line 109: “Contrary to this expectation, however” → “Nevertheless.”

      Line 135: “Again, contrary to our expectation, cyp19a1b<sup>−/−</sup> males” → “cyp19a1b<sup>−/−</sup> males.”

      Line 333: “unexpected” → “noteworthy.”

      Line 337: “unexpected” → “notable.”

      (5) The experimental design for studying aggression in males has flaws. A standard test like a resident intruder test should be used.

      We agree that the resident-intruder test is the most commonly used method for assessing aggression. However, medaka form shoals and lack strong territoriality, and even slight dominance differences between the resident and the intruder can increase variability in the results, compromising data consistency. Therefore, in this study, we adopted an alternative approach: placing four unfamiliar males together in a tank and quantifying aggressive interactions in total. This method allows for the assessment of aggression regardless of territorial tendencies, making it more appropriate for our investigation.

      (6) While they investigate males and females, there are fewer experiments and explanations for the female results, making it feel like a small addition or an aside.

      We agree that the data and discussion for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021, Curr Biol, 1699–1710). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when combined with our prior findings, the female data in this study offer valuable insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      (7) The statistics comparing "experimental to experimental" and "control to experimental" aren't appropriate.

      The reviewer raises concerns about the statistical analysis used for Figures 4C and 4E, suggesting that Bonferroni’s test should be used instead of Dunnett’s test. However, Dunnett’s test is commonly used to compare treatment groups to a reference group that receives no treatment, as in our study. Since we do not compare the treated groups with each other, we believe Dunnett’s test is the most appropriate choice.

      Line 619: The reviewer’s concern may have arisen from the phrase “comparisons between control and experimental groups” in the Materials and Methods. We have revised it to “comparisons between untreated and E2-treated groups in Fig. 4, C and D” for clarity.

      Reviewer #2 (Public Review):

      Summary:

      The novelty of this study stems from the observations that neuro-estrogens appear to interact with brain androgen receptors to support male-typical behaviors. The study provides a step forward in clarifying the somewhat contradictory findings that, in teleosts and unlike other vertebrates, androgens regulate male-typical behaviors without requiring aromatization, but at the same time estrogens appear to also be involved in regulating male-typical behaviors. They manipulate the expression of one aromatase isoform, cyp19a1b, that is purported to be brain-specific in teleosts. Their findings are important in that brain estrogen content is sensitive to the brain-specific cyp19a1b deficiency, leading to alterations in both sexual behavior and aggressive behavior. Interestingly, these males have relatively intact fertility rates, despite the effects on the brain.

      We thank this reviewer for their positive evaluation of our work and constructive comments, which we found very helpful in improving the manuscript.

      That said, the framing of the study, the relevant context, and several aspects of the methods and results raise concerns. Two interpretations need to be addressed/tempered:

      (1) that the rescue of cyp19a1b deficiency by tank-applied estradiol is not necessarily a brain/neuroestrogen mode of action, and

      Line 155: cyp19a1b-deficient males exhibited a severe reduction in brain E2 levels, yet their peripheral E2 levels remained comparable to those in wild-type males. Given this hormonal milieu and the lack of behavioral change in wild-type males following E2 treatment, the observed recovery of mating behavior in cyp19a1b-deficient males following E2 treatment can be best explained by the restoration of brain E2 levels. However, as the reviewer pointed out, we cannot rule out the possibility that bath-immersed E2 influenced behavior through an indirect peripheral mechanism. To address this concern, we have modified the text as follows: “These results suggest that reduced E2 in the brain is the primary cause of the mating defects, highlighting a pivotal role of brain-derived estrogens in male mating behavior. However, caution is warranted, as an indirect peripheral effect of bath-immersed E2 on behavior cannot be ruled out, although this is unlikely given the comparable peripheral E2 levels in cyp19a1b-deficient and wild-type males. In contrast to mating.”

      (2) the large increases in peripheral and brain androgen levels in the cyp19a1b deficient animals imply some indirect/compensatory effects of lifelong cyp19a1b deficiency.

      As stated in line 151, androgen/AR signaling has a strong facilitative effect on male-typical behaviors in teleosts. If increased androgen levels in the periphery and brain affected behavior, the expected effect would be facilitative. However, cyp19a1b-deficient males exhibited impaired male-typical behaviors, suggesting that elevated androgen levels were unlikely to be responsible. Although chronic androgen elevation could cause androgen receptor desensitization, which could lead to behavioral suppression, our long-term androgen treatments have consistently promoted, rather than inhibited, male-typical behaviors (e.g., Nishiike et al., Proc Natl Acad Sci USA 121:e2316459121). Hence, this possibility is also highly unlikely.

      Reviewer #3 (Public Review):

      Summary:

      Taking advantage of the existence in fish of two genes coding for estrogen synthase, the enzyme aromatase, one mostly expressed in the brain (Cyp19a1b) and the other mostly found in the gonads (Cyp19a1a), this study investigates the role of neuro-estrogens in the control of sexual and aggressive behavior in teleost fish. The constitutive deletion of Cyp19a1b reduced brain estrogen content by 87% in males and about 50% in females. It led to reduced sexual and aggressive behavior in males and reduced sexual behavior in females. These effects are reversed by adult treatment with estradiol thus indicating that they are activational in nature. The deletion of Cyp19a1b is associated with a reduced expression of the genes coding for the two androgen receptors, ara, and arb, in brain regions involved in the regulation of social behavior. The analysis of the gene expression and behavior of mutants of estrogen receptors indicates that these effects are likely mediated by the activation of the esr1 and esr2a isoforms. These results provide valuable insight into the role of neuro-estrogens in social behavior in the most abundant vertebrate taxa. While estrogens are involved in the organization of the brain and behavior of some birds and rodents, neuro-estrogens appear to play an activational role in fish through a facilitatory action of androgen signaling.

      We thank this reviewer for their positive evaluation of our work and comments that have improved the manuscript.

      Strengths:

      Evaluation of the role of brain "specific" Cyp19a1 in male teleost fish, which as a taxa are more abundant and yet proportionally less studied than the most common birds and rodents. Therefore, evaluating the generalizability of results from higher vertebrates is important. This approach also offers great potential to study the role of brain estrogen production in females, an understudied question in all taxa.

      Results obtained from multiple mutant lines converge to show that estrogen signaling drives aspects of male sexual behavior.

      The comparative discussion of the age-dependent abundance of brain aromatase in fish vs mammals and its role in organization vs activation is important beyond the study of the targeted species.

      We again thank the reviewer for their positive evaluation of our work.

      Weaknesses:

      (1) The new transgenic lines are under-characterized. There is no evaluation of the mRNA and protein products of Cyp19a1b and ESR2a.

      We did not directly assess the function of cyp19a1b and esr2a in our mutant fish. However, the observed reduction in brain E2 levels, with no change in peripheral E2 levels, in cyp19a1b-deficient fish strongly supports the loss of cyp19a1b function. This is stated in the Results section (line 97) as follows: “These results show that cyp19a1b-deficient fish have reduced estrogen levels coupled with increased androgen levels in the brain, confirming the loss of cyp19a1b function.”

      Line 473: A previous study reported that female medaka lacking esr2a fail to release eggs due to oviduct atresia (Kayo et al., 2019, Sci Rep 9:8868). Similarly, in this study, some esr2a-deficient females exhibited spawning behavior but were unable to release eggs, although the sample size was limited (Δ8 line: 2/3; Δ4 line: 1/1). In contrast, this was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function. To incorporate this information into the manuscript, the following text has been added to the Materials and Methods: “A previous study reported that esr2a-deficient female medaka cannot release eggs due to oviduct atresia (59). Likewise, some esr2a-deficient females generated in this study, despite the limited sample size, exhibited spawning behavior but were unable to release eggs (Δ8 line: 2/3; Δ4 line: 1/1), while such failure was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function.”

      The following reference (#59), cited in the newly added text above, have been included in the reference list:

      D. Kayo, B. Zempo, S. Tomihara, Y. Oka, S. Kanda, Gene knockout analysis reveals essentiality of estrogen receptor β1 (Esr2a) for female reproduction in medaka. Sci. Rep. 9, 8868 (2019).

      (2) The stereotypic sequence of sexual behavior is poorly described, in particular, the part played by the two sexual partners, such that the conclusions are not easily understandable, notably with regards to the distinction between motivation and performance.

      Line 103: To provide a more detailed description of medaka mating behavior, we have revised the text from “The mating behavior of medaka follows a stereotypical pattern, wherein a series of followings, courtship displays, and wrappings by the male leads to spawning” to “The mating behavior of medaka follows a stereotypical sequence. It begins with the male approaching and closely following the female (following). The male then performs a courtship display, rapidly swimming in a circular pattern in front of the female. If the female is receptive, the male grasps her with his fins (wrapping), culminating in the simultaneous release of eggs and sperm (spawning).”

      (3) The behavior of females is only assessed from the perspective of the male, which raises questions about the interpretation of the reduced behavior of the males.

      In medaka, female mating behavior is largely passive, except for rejecting courtship attempts and releasing eggs. Therefore, its analysis relies on measuring the latency to receive following, courtship displays, or wrappings from the male and the frequency of courtship rejection or wrapping refusal. We understand the reviewer’s perspective that cyp19a1b-deficient females might not be less receptive but instead less attractive to males, potentially leading to reduced male mating efforts. However, since these females are approached and followed by males at levels comparable to wild-type females, this possibility appears unlikely. Moreover, cyp19a1b-deficient females tend to avoid males and exhibit a slightly female-oriented sexual preference. While these traits are closely associated with reduced sexual receptivity, they do not readily align with reduced sexual attractiveness. Therefore, it is more plausible to conclude that these females have decreased receptivity rather than being less attractive to males.

      (4) At no point do the authors seem to consider that a reduced behavior of one sex could result from a reduced sensory perception from this sex or a reduced attractivity or sensory communication from the other sex.

      Line 112: As noted above, the impaired mating behavior of cyp19a1b-deficient females is unlikely to be due to reduced attractiveness to males. Similarly, mating behavior tests using esr2b-deficient females as stimulus females suggest that the impaired mating behavior of cyp19a1b-deficient males cannot be attributed to reduced attractiveness to females. However, the possibility that their impaired mating behavior could be attributed to altered cognition or sexual preference cannot be ruled out. To reflect this in the manuscript, we have revised the text “, suggesting that they are less motivated to mate” to “. These results suggest that they are less motivated to mate, though an alternative interpretation that their cognition or sexual preference may be altered cannot be dismissed.”

      (5) Aspects of the methods are not detailed enough to allow proper evaluation of their quality or replication of the data.

      In response to this and other specific comments from this reviewer, we have revised the Materials and Methods section to include more detailed descriptions of the methods.

      Line 469: The following text has been added to describe the method for domain identification in medaka Esr2a: “The DNA- and ligand-binding domains of medaka Esr2a were identified by sequence alignment with yellow perch (Perca flavescens) Esr2a, for which these domain locations have been reported (58).”

      The following reference (#58), cited in the newly added text above, have been included in the reference list:

      S. G. Lynn, W. J. Birge, B. S. Shepherd, Molecular characterization and sex-specific tissue expression of estrogen receptor α (esr1), estrogen receptor βa (esr2a) and ovarian aromatase (cyp19a1a) in yellow perch (Perca flavescens). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 149, 126–147 (2008).

      Line 540: The text “, and the total area of signal in each brain nucleus was calculated using Olyvia software (Olympus)” has been revised to include additional details on the single ISH method as follows: “. The total area of signal across all relevant sections, including both hemispheres, was calculated for each brain nucleus using Olyvia software (Olympus). Images were converted to a 256-level intensity scale, and pixels with intensities from 161 to 256 were considered signals. All sections used for comparison were processed in the same batch, without corrections between samples.”

      Line 596: The following text has been added to include additional details on the double ISH method: “Cells were identified as coexpressing the two genes when Alexa Fluor 555 and fluorescein signals were clearly observed in the cytoplasm surrounding DAPI-stained nuclei, with intensities markedly stronger than the background noise.”

      (6) It seems very dangerous to use the response to a mutant abnormal behavior (ESR2-KO females) as a test, given that it is not clear what is the cause of the disrupted behavior.

      esr2b-deficient females have fully developed ovaries, a normal sex steroid milieu, and sexual attractiveness to males comparable to wild-type females, yet they are completely unreceptive to male courtship (Nishiike et al., 2021, Curr Biol, 1699–1710). Although, as the reviewer noted, the detailed mechanisms underlying this phenotype remain unclear, it is evident that the loss of estrogen/Esr2b signaling in the brain severely impairs sexual receptivity. Therefore, using esr2b-deficient females as stimulus females in the mating behavior test eliminates the influence of female sexual receptivity and male attractiveness to females, enabling the exclusive assessment of male mating motivation. This rationale is already presented in the Results section (lines 116–120), and we believe this experimental design offers a robust framework for assessing male mating motivation.

      Additionally, the mating behavior test with esr2b-deficient females complemented the test with wildtype females, and its results were not the sole basis for our discussion of the male mating behavior phenotype. The results of both tests were largely concordant, and we believe that the conclusions drawn from them are highly reliable.

      Meanwhile, in the test with esr2b-deficient females, cyp19a1b-deficient males were courted more frequently by these females than wild-type males. As the reviewer noted, this may suggest an anomaly in the test. Accordingly, we have confined our discussion to the possibility that “Perhaps cyp19a1b<sup>−/−</sup> males are misidentified as females by esr2b-deficient females because they are reluctant to court or they exhibit some female-like behavior” (line 131).

      (7) Most experiments are weakly powered (low sample size) and analyzed by multiple T-tests while 2 way ANOVA could have been used in several instances. No mention of T or F values, or degrees of freedom.

      Histological analysis was conducted with a relatively small sample size, as our previous experience suggested that interindividual variability in the results would not be substantial. As significant differences were detected in many analyses, further increasing the sample size is unnecessary.

      Although two-way ANOVA could be used instead of multiple T-tests for analyzing the data in Figures 4D, 4F, 6D, S4A, and S4B, we applied the Bonferroni–Dunn correction to control for multiple pairwise comparisons in multiple T-tests. As this comparison method is equivalent to the post hoc test following two-way ANOVA, the statistical results are identical regardless of whether T-tests or two-way ANOVA are used.

      For the data in Figures 4D, 4F, S4A, and S4B, the primary focus is on whether relative luciferase activity differs between E2-treated and untreated conditions for each mutant construct. Therefore, two-way ANOVA is not particularly relevant, as assessing the main effect of construct type or its interaction with E2 treatment does not provide meaningful insights. Similarly, in Figure 6D, the focus is solely on whether wild-type and mutant females differ in time spent at each distance. Given this, two-way ANOVA is unnecessary, as analyzing the main effect of distance is not meaningful.

      Accordingly, two-way ANOVA was not employed in this study, and therefore, its corresponding F values were not included. As the figure legends specify the sample sizes for all analyses, specifying degrees of freedom separately was deemed unnecessary.

      (8) The variability of the mRNA content for the same target gene between experiments (genotype comparison vs E2 treatment comparison) raises questions about the reproducibility of the data (apparent disappearance of genotype effect).

      As the reviewer pointed out, the overall area of ara expression is larger in Figure 2J than in Figure 2F. However, the relative area ratios of ara expression among brain nuclei are consistent between the two figures, indicating the reproducibility of the results. Thus, this difference is unlikely to affect the conclusions of this study.

      Additionally, the differences in ara expression in pPPp and arb expression in aPPp between wild-type and cyp19a1b-deficient males appear less pronounced in Figures 2J and 2K than in Figures 2F and 2H. This is likely attributable to the smaller sample size used in the experiments for Figures 2J and 2K, resulting in less distinct differences. However, as the same genotype-dependent trends are observed in both sets of figures, the conclusion that ara and arb expression is reduced in cyp19a1b-deficient male brains remains valid.

      (9) The discussion confuses the effects of estrogens on sexual differentiation (developmental programming = permanent) and activation (= reversible activation of brain circuits in adulthood) of the brain and behavior. Whether sex differences in the circuits underlying social behaviors exist is not clear.

      We recognize that the effects of adult steroids are sometimes not considered to be sexual differentiation, as they do not differentiate the neural substrate, but rather transiently activate the already masculinized or feminized substrate. Arnold (2017, J Neurosci Res 95:291–300) contends that all factors that cause sex differences, including the transient effects of adult steroids, should be incorporated into a theory of sexual differentiation, and indeed, these effects may be the most potent proximate factors that make males and females different. We concur with this perspective and have adopted it as a foundation for our manuscript.

      In teleosts, early developmental exposure to steroids has minimal impact, and sexual differentiation relies primarily on steroid action in adulthood (Okubo et al., 2022, Spectrum of Sex, pp. 111–133). This is evidenced by the effective reversal of sex-typical behaviors through experimental hormonal manipulation in adult teleosts and the absence of transient early-life steroid surges observed in mammals and birds. Accordingly, our discussion on brain sexual differentiation, including the statement in line 347, “This variation among species may represent the activation of neuroestrogen synthesis at life stages critical for sexual differentiation of behavior that are unique to each species”, remains well-supported. Additionally, given these considerations, while sex differences in neural circuit activation are evident in teleosts, substantial structural differences in these circuits are unlikely.

      (10) Overall, the claims regarding the activational role of neuro-estrogens on male sexual behavior are supported by converging evidence from multiple mutant lines. The role of neuroestrogens on gene expression in the brain is mostly solid too. The data for females are comparatively weaker. Conclusions regarding sexual differentiation should be considered carefully.

      We agree that the data for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when integrated with our prior findings, the data on females in this study provide significant insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      The authors set out to answer an intriguing question regarding the hormonal control of innate social behaviors in medaka. Specifically, they wanted to test the effects of cyp19a1b mutation on mating and aggression in males. They also test these effects in females. Their approach takes them down several distinct experimental pathways, including one investigating how cyp19a1a function is related to androgen receptor expression and how estrogens themselves may act on the androgen receptor to modulate its expression, as well as how different esr genes may be involved. The study and its results are valuable and a clear, general conclusion of a pathway from brain aromatase>estrogens>esr genes> androgen receptor can be made. This is important, novel, and impactful. However, there are issues with how the study logic is set up, the approach for assessing certain behaviors, the statistics used, the interpretation of findings, and placing the findings in the proper context based on previous work, which manifests as a general issue where previous work is not properly attributed to.

      Thank you for your thoughtful review. We have carefully addressed each specific comment, as detailed below.

      Major comments:

      (1) The background for the rationale of the current study is misleading and lacks proper context. The authors root the logic of their experiment in determining whether estrogens regulate male-typical behaviors because the current assumption is androgens are "solely responsible" for male-typical behaviors in teleosts. This is not the case. Previous studies have shown aromatase/estrogens are involved in male-typical aggression in teleosts. For example, to name a couple:

      Huffman, L. S., O'Connell, L. A., & Hofmann, H. A. (2013). Aromatase regulates aggression in the African cichlid fish Astatotilapia burtoni. Physiology & behavior, 112, 77-83.

      O'Connell, L. A., & Hofmann, H. A. (2012). Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology, 153(3), 1341-1351.

      And even a recent paper sheds light on a possible AR>aromatase.estradiol hypothesis of male typical behaviors:

      Lopez, M. S., & Alward, B. A. (2024). Androgen receptor deficiency is associated with reduced aromatase expression in the ventromedial hypothalamus of male cichlids. Annals of the New York Academy of Sciences.

      Interestingly, the authors cite Hufmann et al in the discussion, so I don't understand why they make the claims they do about estrogens and male-typical behavior.

      Related to this, is an issue of proper attribution to published work. Indeed, missing are key references from lab groups using AR mutant teleosts. Here are a couple:

      Yong, L., Thet, Z., & Zhu, Y. (2017). Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. Journal of Experimental Biology, 220(17), 3017-3021.

      Alward, B. A., Laud, V. A., Skalnik, C. J., York, R. A., Juntti, S. A., & Fernald, R. D. (2020). Modular genetic control of social status in a cichlid fish. Proceedings of the National Academy of Sciences, 117(45), 28167-28174.

      Ogino, Y., Ansai, S., Watanabe, E., Yasugi, M., Katayama, Y., Sakamoto, H., ... & Iguchi, T. (2023). Evolutionary differentiation of androgen receptor is responsible for sexual characteristic development in a teleost fish. Nature communications, 14(1), 1428.

      As noted in Response to reviewer #1’s comment 3 on weaknesses, we have revised the Introduction and Discussion sections as follows.

      Line 56: “solely responsible” in the Introduction has been modified to “largely responsible”.

      Line 57: The text “This is consistent with the recent finding in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (10)” has been revised to “This is consistent with recent observations in a few teleost species that genetic ablation of AR severely impairs male-typical behaviors (13–16) and with findings in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (12)” to include previous studies on the behavior of AR mutant fish (Yong et al., 2017; Alward et al., 2020; Ogino et al., 2023; Nishiike and Okubo, 2024) in the Introduction.

      Line 65: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)” has been added to the Introduction, providing an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015).

      Line 367: “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31– 33)” has been edited to read “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      After the revisions described above, the following references (#13, 14, and 22) have been added to the reference list:

      L. Yong, Z. Thet, Y. Zhu, Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. J. Exp. Biol. 220, 3017–3021 (2017).

      B. A. Alward, V. A. Laud, C. J. Skalnik, R. A. York, S. A. Juntti, R. D. Fernald, Modular genetic control of social status in a cichlid fish. Proc. Natl. Acad. Sci. U.S.A. 117, 28167–28174 (2020).

      L. A. O’Connell, H. A. Hofmann, Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology 153, 1341–1351 (2012).

      While Lopez and Alward (2024) provide valuable insights into the regulation of cyp19a1b expression by androgens, our study focuses specifically on the functional aspects of cyp19a1b. Expanding the discussion to include expression regulation would divert from the primary focus of our manuscript. For this reason, we have opted not to cite this reference.

      (2) As it is now, the authors are only citing a book chapter/review from their own group. This is a serious issue as it does not provide the proper context for the work. The authors need to fix their issues of attribution to previously published work and the proper interpretation of the work that they are aware of as it pertains to ideas proposed on the roles of androgens and estrogens in the control of male-typical behaviors. This is also important to get the citations right because the common use of "contrary to expectations" when describing their results is actually not correct. Many of the observations are expected to a degree. However, this doesn't take away from a generally stellar experimental design and mostly clear results. The authors do not need to rely on enhancing the impact of their paper by making false claims of unexpected findings. The depth and clarity of your findings are where the impact of your work is.

      As detailed in Response to reviewer #1’s comment 3 on weaknesses, we have cited previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015) in the Introduction.

      Additionally, as noted in Response to reviewer #1’s comment 4 on weaknesses, we have made the following revisions to avoid phrases such as “contrary to expectation” and “unexpected.”

      Line 76: “Contrary to our expectations” → “Remarkably.”

      Line 109: “Contrary to this expectation, however” → “Nevertheless.”

      Line 135: “Again, contrary to our expectation, cyp19a1b<sup>−/−</sup> males” → “cyp19a1b<sup>−/−</sup> males.”

      Line 333: “unexpected” → “noteworthy.”

      Line 337: “unexpected” → “notable.”

      (3) The experimental design for studying aggression in males has flaws. A standard test like a residentintruder test should be used. An assay in which only male mutants are housed together? I do not understand the logic there and the logic for the approach isn't even explained. Too many confounds that are not controlled for. It makes it seem like an aspect of the study that was thrown in as an aside.

      As noted in Response to reviewer #1’s comment 5 on weaknesses, medaka form shoals and lack strong territoriality. As a result, even slight differences in dominance between the resident and intruder can substantially impact the outcomes of the resident-intruder test. Therefore, we adopted an alternative approach in this study.

      (4) Hormonal differences in the mutants seem to vary based on sex, and the meaning of these differences, or how they affect interpreting the findings, wasn't discussed. There was no acknowledegment of the fact that female central E2 was still at 50%, meaning the "rescue" experiments using peripheral injections are not given the proper context. For example, this is different than giving a fish with only 16% of their normal central E2 an E2 injection. Missing as well is a clear hypothesis for why E2 injections did not rescue aggression deficits in cyp19a1b mutant males.

      Line 385: As the reviewer pointed out, the degree of brain estrogen reduction in cyp19a1b-deficient fish differs greatly between males and females. This is likely because females receive a large supply of estrogens from the ovaries. Given that estrogen levels in cyp19a1b-deficient females were 50% of those in wild-type females, it can be inferred that half of their brain estrogens are synthesized locally, while the other half originates from the ovaries. This is an important finding, and we have already noted in the Discussion that “females have higher brain levels of estrogens, half of which are synthesized locally in the brain (i.e., neuroestrogens)” However, as this explanation was not sufficiently clear, we have revised it to “females have higher brain levels of estrogens, with half being synthesized locally and the other half supplied by the ovaries.”

      The reviewer raised a concern that conducting the estrogen rescue experiment in females, where 50% of brain estrogens remain, might be inappropriate. However, as this experiment was conducted exclusively in males, this concern is not applicable.

      Line 377: As noted in the reviewer’s subsequent comment, the failure of aggression recovery in E2treated cyp19a1b-deficient males could be due to insufficient induction of ara/arb expression in aggression-relevant brain regions. To address this concern, we have inserted the following statement into the Discussion after “the development of male behaviors may require moderate neuroestrogen levels that are sufficient to induce the expression of ara and arb, but not esr2b, in the underlying neural circuitry”: “This may account for the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study.”

      (5) In relation to that, the "null" results may have some of the most interesting implications, but they are barely discussed. For example, what does it mean that E2 didn't restore aggression in male cyp19 mutants? Is this a brain region factor? Could this relate to findings from Lopez et al NYAS, where male and female Ara mutants show different effects on brain-region-specific aromatase expression? And maybe this relates to the different impact of estrogens on ar expression. Were the different effects impacted in aggression areas? Maybe this is why E2 injection didn't retore aggression in males. You could make the argument that: (1) E2 doesn't restore ar expression in aggression regions and that's why there was no rescue. Or (2) that the circuits in adulthood that regulate aggression are NOT dependent on aggression but in early development they are. Another null finding not expanded on is why the two esr2a mutant lines showed differences. There is no reason to trust one line over the other, meaning we still don't know whether esr2a is required for latency to follow.

      As stated in our response to the previous comment, we have added the following text to the Discussion (line 377): “This may account for the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study.” Meanwhile, as discussed in lines 341–342, it is highly unlikely that the neural circuits regulating aggression are primarily influenced by early-life estrogen exposure, because androgen administration in adulthood alone is sufficient to induce high levels of aggression in both sexes. This notion is further supported by previous observations that cyp19a1b expression in the brain is minimal during embryonic development (Okubo et al., 2011, J Neuroendocrinol, 23:412–423).

      The findings of Lopez and Alward (2024) pertain to the regulation of cyp19a1b expression by androgen receptors. While this represents an important aspect of neuroendocrine regulation, it does not appear to be directly relevant to our discussion on cyp19a1b-mediated regulation of androgen receptor expression.

      To ensure the reliability of behavioral analyses in mutant fish, we consider a phenotype valid only when it is consistently observed in two independent mutant lines. In the mating behavior test examining esr2adeficient males using esr2b-deficient females as stimulus females, Δ8 line males exhibited a shorter latency to initiate following than wild-type males, whereas Δ4 line males did not. This discrepancy led us to refrain from drawing conclusions about the role of esr2a in mating behavior, even though the mating behavior test using wild-type females as stimulus females yielded consistent results in the Δ8 and Δ4 lines. Therefore, we do not consider the reviewer’s concern to be a significant issue.

      (6) Not sure what's going on with the statistics, but it is not appropriate here to treat a "control" group as special. All groups are "experimental" groups. There is nothing special about the control group in this context. all should be Bonferroni post-hoc tests.

      Line 619: As detailed in Response to reviewer #1’s comment 7 on weaknesses, we consider Dunnett’s test the most appropriate choice for the experiments presented in Figures 4C and 4E. We acknowledge that the reviewer’s concern may stem from the phrase “comparisons between control and experimental groups” in the Materials and Methods section. To clarify this point, we have revised it to “comparisons between untreated and E2-treated groups in Fig. 4, C and D” for clarity.

      Minor comments:

      Line 47: then how can you say the aromatization hypothesis is "correct"? it only applies to a few species so far. Need to change the framing, not state so strongly such a vague thing as a hypothesis being "correct".

      Line 45: To address this concern, we have modified “widely accepted as correct” to “widely acknowledged”, ensuring a more precise characterization.

      Figure 1: looks like a dosage effect in males but not females. this should be discussed at some point, even if just to mention a dosage effect exists and put it in context.

      Line 91: We have revised the sentence “In males, brain E2 in heterozygotes (cyp19a1b+/−) was also reduced to 45% of the level in wild-type siblings (P = 0.0284) (Fig. 1A)” by adding “, indicating a dosage effect of cyp19a1b mutation” to make this point explicit.

      Were male cyp19 KO aggressive towards females?

      We have not observed cyp19a1b-deficient males exhibiting aggressive behavior towards females in our experiments. Therefore, we do not consider them aggressive toward females.

      Please explain how infertility would lead to reduced mating.

      Line 142: As the reviewer has questioned, even if cyp19a1b-deficient males exhibit infertility due to efferent duct obstruction, it is difficult to imagine that this directly leads to reduced mating. However, the inability to release sperm could indirectly affect behavior. To address this, we have added “, possibly due to the perception of impaired sperm release” after “If this is also the case in medaka, the observed behavioral defects might be secondary to infertility.”

      Describe something about the timing of the treatment here. How can peripheral E2 injections restore it when peripheral levels are normal? Did these injections restore central levels? This needs to be shown experimentally.

      Line 517: As described in the Materials and Methods, E2 treatment was conducted by immersing fish in E2-containing water for 4 days. However, we had not explicitly stated that the water was changed daily to maintain the nominal concentration. To clarify this and address reviewer #2’s comment 9, we have revised “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan) or vehicle (ethanol) alone by immersion in water for 4 days” to “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan), which was first dissolved in 100% ethanol (vehicle), or with the vehicle alone by immersion in water for 4 days, with daily water changes to maintain the nominal concentration.”

      Line 522: The treatment effectively restored mating activity and ara/arb expression in the brain, suggesting a sufficient increase in brain E2 levels. However, we did not measure the actual increase, and its extent remains uncertain. To reflect this in the manuscript, we have now added the following sentence: “Although the exact increase in brain E2 levels following E2 treatment was not quantified, the observed positive effects on behavior and gene expression suggest that it was sufficient.”

      I know the nomenclature differs among those who study teleosts, but it's ARa and then gene is ar1 (as an example; arb would be ar2). You're recommended the following citation to remain consistent:

      Munley, K. M., Hoadley, A. P., & Alward, B. A. (2023). A phylogenetics-based nomenclature system for steroid receptors in teleost fishes. General and Comparative Endocrinology, 114436.

      Paralogous genes resulting from the third round of whole-genome duplication in teleosts are typically designated by adding the suffixes “a” and “b” to their gene symbols. This convention also applies to the two androgen receptor genes, commonly referred to as ara and arb. While the alternative names ar1 and ar2 may gain broader acceptance in the future, ara and arb remain more widely used at present. Therefore, we have chosen to retain ara and arb in this manuscript.

      Line 268: how is this "suggesting" less aggression? They literally showed fewer aggressive displays, so it doesn't suggest it - it literally shows it.

      Line 285: Following this thoughtful suggestion, we have changed “suggesting less aggression” to “showing less aggression.”

      Line 317: how can you still call it the primary driver?

      The stimulatory effects of aromatase/estrogens on male-typical behaviors are exerted through the potentiation of androgen/AR signaling. Thus, we still believe that androgens—specifically 11KT in teleosts—serve as the primary drivers of these behaviors.

      Line 318: not all deficits, like aggression, were rescued.

      Line 334: To address this comment, “These behavioral deficits were rescued by estrogen administration, indicating that reduced levels of neuroestrogens are the primary cause of the observed phenotypes: in other words, neuroestrogens are pivotal for male-typical behaviors in teleosts” has been modified and now reads “Deficits in mating were rescued by estrogen administration, indicating that reduced brain estrogen levels are the primary cause of the observed mating impairment; in other words, brain-derived estrogens are pivotal at least for male-typical mating behaviors in teleosts.”

      Line 324: what do you mean by "sufficient"? To show that, you'd have to castrate the male and only give estrogen back. the authors continue to overstate virtually every aspect of their study, seemingly in an unnecessary manner.

      Line 341: Our intention was to convey that brain-derived estrogens early in life are not essential for the expression of male-typical behaviors in teleosts. However, we recognize that the term “sufficient” could be misinterpreted as implying that estrogens alone are adequate, without contributions from other factors such as androgens. To clarify this, we have revised the text from “neuroestrogen activity in adulthood is sufficient for the execution of male-typical behaviors, while that in early in life is not requisite. Thus, while” to “brain-derived estrogens early in life is not essential for the execution of male-typical behaviors. While.”

      Line 329: so? in adult mice, amygdala aromatase neurons still regulate aggression. The amount in adulthood seems less important compared to site-specific functions.

      Line 346: We do not intend to suggest that brain aromatase activity in adulthood plays a negligible role in male behaviors in rodents, as we have already acknowledged its necessity in the Introduction (lines 42–43). To enhance clarity and prevent misinterpretation, we have added “, although it remains important for male behavior in adulthood” to the end of the sentence: “brain aromatase activity in rodents reaches its peak during the perinatal period and thereafter declines with age.”

      Line 351: This contradicts what you all have been saying.

      Line 65: As mentioned in Response to reviewer #1’s comment 3 on weaknesses, the following text has been added to the Introduction: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)”, providing an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015). With this revision, we believe the inconsistency has been addressed.

      Line 367: Additionally, we have revised the sentence from “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31–33)” to “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      Line 360: change to "...possibility that is not mutually exclusive,"

      Line 378: We have revised the phrase as suggested from “Another possibility, not mutually exclusive,” to “Another possibility that is not mutually exclusive.”

      Line 363: but it didn't rescue aggression

      Line 381: In response, we have revised the sentence from “This possibility is supported by the present observation that estrogen treatment facilitated mating behavior in cyp19a1b-deficient males but not in their wild-type siblings” to “This possibility is at least likely for mating behavior, as estrogen treatment facilitated mating behavior in cyp19a1b-deficient males but not in their wild-type siblings.”

      Line 367: on average

      To explain the sex differences in the role of aromatase, what about the downstream molecular or neural targets? In mammals, hodology is related to sex differences. there could be convergent sex differences in regulating the same type of behaviors as well.

      Our findings demonstrate that brain-derived estrogens promote the expression of ara, arb, and their downstream target genes vt and gal in males, while enhancing the expression of npba, a downstream target of Esr2b signaling, in females. The identity of additional target genes and their roles in specific neural circuits remain to be elucidated, and we aim to address these in future research.

      Lines 378-382: this doesn't logically follow. pgf2a could be the target of estrogens which in the intact animal do regulate female sexual receptivity. And how can you say this given that your lab has shown in esr2b mutants females don't mate?

      We agree that PGF2α signaling may be activated by estrogen signaling, as stated in lines 404–407: “the present finding provides a likely explanation for this apparent contradiction, namely, that neuroestrogens, rather than or in addition to ovarian-derived circulating estrogens, may function upstream of PGF2α signaling to mediate female receptivity.” The observation that esr2b-deficient females do not accept male courtship is also stated in lines 401–403: “we recently challenged it by showing that female medaka deficient for esr2b are completely unreceptive to males, and thus estrogens play a critical role in female receptivity.”

      Line 396-397: or the remaining estrogens are enough to activate esr2b-dependent female-typical mating behaviors.

      We agree that cyp19a1b deficiency did not completely preclude female mating behavior, most likely because residual estrogens in the brains of cyp19a1b-deficient females enable weak activation of Esr2b signaling. However, the relevant section in the Discussion is not focused on examining why mating behavior persisted, but rather on considering the implications of this finding for the neural circuits regulating mating behavior. Therefore, incorporating the suggested explanation here would shift the focus and would not be appropriate.

      Line 420-421: this is a lot of variation. Was age controlled for?

      The time required for medaka to reach sexual maturity varies with rearing density and food availability. Due to space constraints, we adjust these parameters as needed, which led to variation in the ages of the experimental fish. However, since all experiments were conducted using sibling fish of the same age that had just reached sexual maturity, we believe this does not affect our conclusions.

      Line 457: have these kits been validated in medaka?

      Although we have not directly validated its applicability in medaka, its extensive use in this species suggests that it us unlikely to pose any issues (e.g., Ussery et al., 2018, Aquat Toxicol, 205:58–65; Lee et al., 2019, Ecotoxicol Environ Saf, 173:174–181; Kayo et al., 2020, Gen Comp Endocrinol, 285:113272; Fischer et al., 2021, Aquat Toxicol, 236:105873; Royan et al., 2023, Endocrinology, 164:bqad030).

      Line 589, re fish that spawned: how many times did this happen? Please note it is based on genotype and experiment. This could be important.

      Line 627: In response to this comment, we have added the following details: “Specifically, 7/18 cyp19a1b<sup>+/+</sup>, 11/18 cyp19a1b<sup>+/−</sup>, and 6/18 cyp19a1b<sup>−/−</sup> males were excluded in Fig. 1D; 6/10 cyp19a1b<sup>+/+</sup>, 3/10 cyp19a1b<sup>+/−</sup>, and 6/10 cyp19a1b<sup>−/−</sup> females were excluded in Fig. 6B; 2/23 esr1+/+ and 5/24 esr1−/− males were excluded in Fig. S7; 2/24 esr2a+/+ and 3/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8A; 0/23 esr2a+/+ and 0/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8B.”

      Reviewer #2 (Recommendations For The Authors):

      Abstract:

      (A1) The framing of neuroestrogens being important for male-typical rodents, and not for other vertebrate lineages, does not account for other groups (birds) in which this is true (the authors can consult their cited work by Balthazart (Reference 6) for extensive accounting of this). This makes the novelty clause in the abstract "indicating that neuro-estrogens are pivotal for male-typical behaviors even in nonrodents" less surprising and should be acknowledged by the authors by amending or omitting this novelty clause. The findings regarding androgen receptor transcription (next sentence) are more important and pertinent.

      Line 27: We recognize that the aromatization hypothesis applies to some birds, including zebra finches, as stated in the Introduction (lines 48–49) and Discussion (lines 432–433). However, this was not reflected in the Abstract. Following the reviewer’s suggestion, we have changed “in non-rodents” to “in teleosts.”

      (A2) The medaka line that has been engineered to have aromatase absent in the brain is presented briefly in the abstract, but can be misinterpreted as naturally occurring. This should be amended, by including something like "engineered" or "directed mutant" before 'male medaka fish'.

      Line 24: We have added “mutagenesis-derived” before “male medaka fish” in response to this comment.

      Introduction:

      (I1) The paragraph on teleost brain aromatase should acknowledge that while the capacity for estrogen synthesis in the brain is 100-1000 fold higher in teleosts as compared to rodents and other vertebrates, the majority of this derives from glial and not neural sources. This can be confusing for readers since the term 'neuroestrogens' often refers to the neuronal origin and signalling. And this observation includes the exclusive radial glial expression of cyp19a1b in medaka (Diotel et al., 2010), and first discovered in midshipman (Forlano et al., 2001), each of which should also be cited here. In addition, the authors expend much text comparing teleosts and rodents, but it is worth expanding these kinds of comparisons, especially by pointing out that parts of the primate brain are found to densely express aromatase (see work by Ei Terasawa and others).

      In response to this comment and a similar comment from reviewer #1, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript.

      Line 63: We have also added the text “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (18– 20).” As a result of this addition, we have changed “This observation suggests” to “These observations suggest” in the subsequent sentence.

      Line 51: Additionally, to include information on aromatase in the primate brain, we have added the following text: “In primates, the hypothalamic aromatization of androgens to estrogens plays a central role in female gametogenesis (10) but is not essential for male behaviors (7, 8).”

      The following references (#10 and 18–20), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      E. Terasawa, Neuroestradiol in regulation of GnRH release. Horm. Behav. 104, 138–145 (2018).

      P. M. Forlano, D. L. Deitcher, D. A. Myers, A. H. Bass, Anatomical distribution and cellular basis for high levels of aromatase activity in the brain of teleost fish: aromatase enzyme and mRNA expression identify glia as source. J. Neurosci. 21, 8943–8955 (2001).

      N. Diotel, Y. Le Page, K. Mouriec, S. K. Tong, E. Pellegrini, C. Vaillant, I. Anglade, F. Brion, F. Pakdel, B. C. Chung, O. Kah, Aromatase in the brain of teleost fish: expression, regulation and putative functions. Front. Neuroendocrinol. 31, 172–192 (2010).

      A. Takeuchi, K. Okubo, Post-proliferative immature radial glial cells female-specifically express aromatase in the medaka optic tectum. PLoS One 8, e73663 (2013).

      (I2) It is difficult to resolve from the introduction and work cited how restricted cyp19a1b is to the medaka brain. Important for the results of this study, it is not clear whether it is more of a bias in the brain vs other tissues, or if the cyp19a1b deficiency is restricted to the brain, and gonadal/peripheral cyp19 expression persists. The authors need to improve their consideration of the alternatives, i.e., that this manipulation is not somehow affecting: 1) peripheral aromatase expression (either cyp19a1a or cyp19a1b) in the gonad or elsewhere, 2) compensatory processes, such as other steroidogenic genes (are androgen synthesizing enzymes increasing?).

      Our previous study demonstrated that cyp19a1b is expressed in the gonads, but at levels tens to hundreds of times lower than those in the brain (Okubo et al., 2011, J Neuroendocrinol 23:412–423). Additionally, a separate study in medaka reported that cyp19a1b expression in the ovary is considerably lower than that of cyp19a1a (Nakamoto et al., 2018, Mol Cell Endocrinol 460:104–122). Given these observations, any potential effect of cyp19a1b knockout on peripheral estrogen synthesis is likely negligible. Indeed, Figures S1C and S1D confirm that cyp19a1b knockout does not alter peripheral E2 levels.

      Line 72: To incorporate this information into the Introduction and address the following comment, we have added the following text: “In medaka, cyp19a1b is also expressed in the gonads, but only at a level tens to hundreds of times lower than in the brain and substantially lower than that of cyp19a1a (26, 27).”

      The following references (#26 and 27), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      K. Okubo, A. Takeuchi, R. Chaube, B. Paul-Prasanth, S. Kanda, Y. Oka, Y. Nagahama, Sex differences in aromatase gene expression in the medaka brain. J. Neuroendocrinol. 23, 412–423 (2011).

      M. Nakamoto, Y. Shibata, K. Ohno, T. Usami, Y. Kamei, Y. Taniguchi, T. Todo, T. Sakamoto, G. Young, P. Swanson, K. Naruse, Y. Nagahama, Ovarian aromatase loss-of-function mutant medaka undergo ovary degeneration and partial female-to-male sex reversal after puberty. Mol. Cell. Endocrinol. 460, 104–122 (2018).

      We have not assessed whether the expression of other steroidogenic enzymes is altered in cyp19a1bdeficient fish, and this may be investigated in future studies.

      (I3) Related, there are documented sex differences in the brain expression of cyp19a1b especially in adulthood (Okubo et al 2011) and this study should be cited here for context.

      Line 72: As stated in our previous response, we have cited Okubo et al. (2011) by adding the following sentence: “In medaka, cyp19a1b is also expressed in the gonads, but only at a level tens to hundreds of times lower than in the brain and substantially lower than that of cyp19a1a (26, 27).”

      Methods

      (M1) The rationale is unclear as presented for using mutagen screening for cype19a1b while using CRISPR for esr2a. Are there methodological/biochemical reasons why the authors chose to not use the same method for both?

      At the time we generated the cyp19a1b knockouts, genome editing was not yet available, and the TILLING-based screening was the only method for obtaining mutants in medaka. In contrast, by the time we generated the esr2a knockouts, CRISPR/Cas9 had become available, enabling a more efficient and convenient generation of knockout lines. This is why the two knockout lines were generated using different methods.

      (M2) Measurement of steroids in biological matrices is not straightforward, and it is good that the authors use multiple extraction steps (organic followed by C18 columns) before loading samples on the ELISA plates, which are notoriously sensitive. Even though these methods have been published before by this group of authors previously, the quality control and ELISA performance values (recovery, parallelism, etc.) should be presented for readers to evaluate.

      Thank you for appreciating our sample purification method. Unfortunately, we have not evaluated the recovery rate or parallelism, but we recognize this a subject for future studies.

      (M3) Mating behavior - E2 treated males were not co-housed with social partners for the full 24 hr before testing, but instead a few hours (?) prior to testing. The rationale for this should be spelled out explicitly.

      Line 494: In response to this comment, we have added “to ensure the efficacy of E2 treatment” to the end of the sentence “The set-up was modified for E2-treated males, which were kept on E2 treatment and not introduced to the test tanks until the day of testing.”

      (M4) The E2 treatment is listed as 1ng/ml vs. vehicle (ethanol). Is the E2 dissolved in 100% ethanol for administration to the tank water? Clarification is needed.

      Line 517: As the reviewer correctly assumed, E2 was first dissolved in 100% ethanol before being added to the tank water. To provide this information and address reviewer #1’s minor comment 5, we have revised “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan) or vehicle (ethanol) alone by immersion in water for 4 days” to “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan), which was first dissolved in 100% ethanol (vehicle), or with the vehicle alone by immersion in water for 4 days, with daily water changes to maintain the nominal concentration.”

      (M5) The authors exclude fish from the analysis of courtship display behavior for those individuals that spawned immediately at the start of the testing (and therefore it was impossible to register courtship display behaviors). How often did fish in the various treatment groups exhibit this "fast spawning" behavior? Was the occurrence rate different by treatment group? It is unlikely that these omissions from the data set drove large-scale patterns, but an indication of how often this occurred would be reassuring.

      Line 627: In response to this comment, we have included the following details: “Specifically, 7/18 cyp19a1b<sup>+/+</sup>, 11/18 cyp19a1b<sup+/−</sup>, and 6/18 cyp19a1b<sup>−/−</sup> males were excluded in Fig. 1D; 6/10 cyp19a1b+/+, 3/10 cyp19a1b+/−, and 6/10 cyp19a1b<sup>−/−</sup> females were excluded in Fig. 6B; 2/23 esr1+/+ and 5/24 esr1−/− males were excluded in Fig. S7; 2/24 esr2a+/+ and 3/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8A; 0/23 esr2a+/+ and 0/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8B.” These data indicate that the proportion of excluded males is nearly constant within each trial and is independent of the genotype of the focal fish.

      Results

      (R1) It is striking to see the genetic-'dose' dependent suppression of brain E2 content by heterozygous and homozygous cyp19a1b deficiency, indicating that, as the authors point out, the majority of E2 in the male medaka brain (and 1/2 in the female brain) have a brain-derived origin. It is important also for the interpretation that there are large compensatory increases in brain levels of androgens, when E2 levels drop in the cyp19a1b mutant homozygotes. This latter point should receive more attention.

      Also, there are large increases in peripheral androgen levels in the homozygote mutants for cyp19a1b in both males and females. This indicates a peripheral effect in addition to the clear brain knockdown of E2 synthesis. These nuances need to be addressed.

      In response to this comment, we have revised the Results section as follows:

      Line 91: “, indicating a dosage effect of cyp19a1b mutation” has been added to the end of the sentence “In males, brain E2 in heterozygotes (cyp19a1b<sup>+/−</sup>) was also reduced to 45% of the level in wild-type siblings (P = 0.0284) (Fig. 1A).”

      Line 94: To draw more attention to the increase in brain androgen levels caused by cyp19a1b deficiency, “Brain levels of testosterone” has been modified to “Strikingly, brain levels of testosterone.”

      Line 100: “Their peripheral 11KT levels also increased 3.7- and 1.8-fold, respectively (P = 0.0789, males; P = 0.0118, females) (Fig. S1, C and D)” has been modified and now reads “In addition, peripheral 11KT levels in cyp19a1b<sup>−/−</sup> males and females increased 3.7- and 1.8-fold, respectively (P = 0.0789, males; P = 0.0118, females) (Fig. S1, C and D), indicating peripheral influence in addition to central effects.”

      (R2) The interpretation on page 4 that cyp19a1b deficient males are 'less motivated' to mate is premature, given the behavioral measures used in this study. There are several competing explanations for these findings (e.g., alterations in motivation, sensory discrimination, preference, etc.) that could be followed up in future work, but the current results are not able to distinguish among these possibilities.

      Line 112: We agree that the possibility of altered cognition or sexual preference cannot be dismissed. To incorporate this perspective, we have revised the text “, suggesting that they are less motivated to mate” to “These results suggest that they are less motivated to mate, though an alternative interpretation that their cognition or sexual preference may be altered cannot be dismissed.”

      (R3) On page 5, the authors present that peripheral E2 manipulation (delivery to the fish tank) restores courtship behavior in males, and then go on to erroneously conclude that this demonstrates "that reduced E2 in the brain was the primary cause of the mating defects, indicating a pivotal role of neuroestrogens in male mating behavior." Because this is a peripheral E2 treatment, there can be manifold effects on gonadal physiology or other endocrine events that can have indirect effects on the brain and behavior. Without manipulation of E2 directly to the brain to 'rescue' the cyp19a1b deficiency, the authors cannot conclude that these effects are directly on the central nervous system. Tellingly, the tank E2 treatment did not rescue aggressive behavior, suggestive of the potential for indirect effects.

      Line 155: As detailed in Response to reviewer #2’s specific comment 1, we have revised the text from “These results demonstrated that reduced E2 in the brain was the primary cause of the mating defects, indicating a pivotal role of neuroestrogens in male mating behavior. In contrast” to “These results suggest that reduced E2 in the brain is the primary cause of the mating defects, highlighting a pivotal role of brain-derived estrogens in male mating behavior. However, caution is warranted, as an indirect peripheral effect of bath-immersed E2 on behavior cannot be ruled out, although this is unlikely given the comparable peripheral E2 levels in cyp19a1b-deficient and wild-type males. In contrast to mating.”

      (R4) The downregulation of androgen-dependent gene expression (vasotocin in pNVT and galanin in pPMp) in the cyp19a1b deficient males (Figure 3) could be due to exceedingly high levels of brain androgens in the cyp19a1b deficient males. The best way to test the idea that estrogens can restore the expression to be more wild-type directly (like what is happening for ara and arb) is to look at these same markers (vasotocin and galanin) in these same brain areas in the brains of E2-treated males. The authors should have these brains from Figure 2. Unless I missed something, those experiments were not performed/reported here. It is clear that the ara and arb receptors have EREs and are 'rescued' by E2 treatment, but in principle, there could be indirect actions for reasons stated above for the behavior due to the peripheral E2 tank application.

      Thank you for your insightful comment. We agree that the current results cannot exclude the possibility that excessive androgen levels caused the downregulation of vt and gal. However, our previous studies showed that excessive 11KT administration to gonadectomized males and females increased the expression of these genes to levels comparable to wild-type males (Yamashita et al., 2020, eLife, 9:e59470; Kawabata-Sakata et al., 2024, Mol Cell Endocrinol 580:112101), making this scenario unlikely. That said, testing whether estrogen treatment restores vt and gal expression in cyp19a1bdeficient males would be informative, and we see this as an important direction for future research.

      Discussion

      (D1) The authors need to clarify whether EREs are found in other vertebrate AR introns, or is this unique to the teleost genome duplication?

      We have identified multiple ERE-like sequences within intron 1 of the mouse AR gene. However, sequence data alone do not provide sufficient evidence of their functionality, rendering this information of limited relevance. Therefore, we have chosen not to include this discussion in the current paper.

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors are strongly encouraged to report information regarding the effect of Cyp19a1b deletion on the brain content of aromatase protein (ideally both isoforms investigated separately) as the two isoforms are mostly but not completely brain vs gonad specific. The analysis of other tissues would also strengthen the characterization of this model.

      We agree that measuring aromatase protein levels in the brain of our fish would be valuable for confirming the loss of cyp19a1b function. However, as no suitable method is currently available, this issue will need to be addressed in future studies. While this constitutes indirect evidence, the observed reduction in brain E2 levels, with no change in peripheral E2 levels, in cyp19a1b-deficient fish strongly suggests the loss of cyp19a1b function, as noted in Response to reviewer #3’s comment 1 on weaknesses.

      (2) As presented, this study reads as niche work. A better description of the behavior and reproductive significance of the different aspects of the behavioral sequence would allow a better understanding of the results and would thus allow the non-specialist to appreciate the significance of the observations.

      Line 103: In response to this comment and Reviewer #3’s comment 2 on weaknesses, we have revised the sentence from “The mating behavior of medaka follows a stereotypical pattern, wherein a series of followings, courtship displays, and wrappings by the male leads to spawning” to “The mating behavior of medaka follows a stereotypical sequence. It begins with the male approaching and closely following the female (following). The male then performs a courtship display, rapidly swimming in a circular pattern in front of the female. If the female is receptive, the male grasps her with his fins (wrapping), culminating in the simultaneous release of eggs and sperm (spawning)” in order to provide a more detailed description of medaka mating behavior.

      (3) The data regarding female behavior are limited and incomplete. It is suggested to keep this for another manuscript unless data on the behavior of the female herself is added. Indeed, analyzing female's behavior from the male's perspective complicates the interpretation of the results while a description of what the females do would provide valuable and interpretable information.

      We thank the reviewer for this thoughtful suggestion and agree that the data and discussion for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when combined with our prior findings, the female data in this study offer valuable insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      (4) In Figure 2, the validity to run multiple T-tests rather than a two-way ANOVA comparing TRT and genotype is questionable. Moreover, why are the absolute values in CTL higher than in the initial experiment comparing genotypes for ara in PPa, pPPp, and NVT as well as for arb in aPPp. More importantly, these graphs do not seem to reproduce the genotype effects for ara in pPPp and NVT and for arb in aPPp.

      The data in Figures 2J and 2K were analyzed with an exclusive focus on the difference between vehicletreated and E2-treated males, without considering genotype differences. Therefore, the use of T-tests for significance testing is appropriate.

      As the reviewer noted, the overall ara expression area is larger in Figure 2J than in Figure 2F. However, as detailed in Response to reviewer #3’s comment 8 on weaknesses, the relative area ratios of ara expression among brain nuclei are consistent between the two figures, indicating the reproducibility of the results. Thus, we consider this difference unlikely to affect the conclusions of this study.

      Additionally, the differences in ara expression in pPPp and arb expression in aPPp between wild-type and cyp19a1b-deficient males appear smaller in Figures 2J and 2K compared to Figures 2F and 2H. This is likely due to the smaller sample size used in the experiments for Figures 2J and 2K, which makes the differences less distinct. However, since the same genotype-dependent trends are observed in both sets of figures, the conclusion that ara and arb expression is reduced in cyp19a1b-deficient male brains remains valid.

      (5) More information is required regarding the analysis of single ISH - How was the positive signal selected from the background in the single ISH analyses? How was this measure standardized across animals? How many sections were imaged per region? Do the values represent unilateral or bilateral analysis?

      Line 540: Following this comment, we have provided additional details on the single ISH method in the manuscript. Specifically, “, and the total area of signal in each brain nucleus was calculated using Olyvia software (Olympus)” has been revised to “The total area of signal across all relevant sections, including both hemispheres, was calculated for each brain nucleus using Olyvia software (Olympus). Images were converted to a 256-level intensity scale, and pixels with intensities from 161 to 256 were considered signals. All sections used for comparison were processed in the same batch, without corrections between samples.”

      (6) More information should be provided in the methods regarding the image analysis of double ISH. In particular, what were the criteria to consider a cell as labeled are not clear. This is not clear either from the representative images.

      Line 596: To provide additional details on the single ISH method in the manuscript, we have added the following sentence: “Cells were identified as coexpressing the two genes when Alexa Fluor 555 and fluorescein signals were clearly observed in the cytoplasm surrounding DAPI-stained nuclei, with intensities markedly stronger than the background noise.”

      (7) There is no description of the in silico analyses run on ESR2a in the methods.

      The method for identifying estrogen-responsive element-like sequences in the esr2a locus is described in line 549: “Each nucleotide sequence of the 5′-flanking region of ara and arb was retrieved from the Ensembl medaka genome assembly and analyzed for potential canonical ERE-like sequences using Jaspar (version 5.0_alpha) and Match (public version 1.0) with default settings.”

      However, the method for domain identification in Esr2a was not described. Therefore, we have added the following text in line 469: “The DNA- and ligand-binding domains of medaka Esr2a were identified by sequence alignment with yellow perch (Perca flavescens) Esr2a, for which these domain locations have been reported (58).”

      The following reference (#58), cited in the newly added text above, have been included in the reference: S. G. Lynn, W. J. Birge, B. S. Shepherd, Molecular characterization and sex-specific tissue expression of estrogen receptor α (esr1), estrogen receptor βa (esr2a) and ovarian aromatase (cyp19a1a) in yellow perch (Perca flavescens). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 149, 126–147 (2008).

      (8) Information about the validation steps of the EIA that were carried out as well as the specificity of the antibody the steroids and the extraction efficacy should be provided.

      We have not directly validated the applicability of the EIA kit, but its extensive use in medaka suggests that it us unlikely to pose any issues (e.g., Ussery et al., 2018, Aquat Toxicol, 205:58–65; Lee et al., 2019, Ecotoxicol Environ Saf, 173:174–181; Kayo et al., 2020, Gen Comp Endocrinol, 285:113272; Fischer et al., 2021, Aquat Toxicol, 236:105873; Royan et al., 2023, Endocrinology, 164:bqad030).

      The specificity (cross-reactivity) of the antibodies is detailed as follows.

      (1) Estradiol ELISA kits: estradiol, 100%; estrone, 1.38%; estriol, 1.0%; 5α-dihydrotestosterone, 0.04%; androstenediol, 0.03%; testosterone, 0.03%; aldosterone, <0.01%; cortisol, <0.01%; progesterone, <0.01%.

      (2) Testosterone ELISA kits: testosterone, 100%; 5α-dihydrotestosterone, 27.4%; androstenedione, 3.7%; 11-ketotestosterone, 2.2%; androstenediol, 0.51%; progesterone, 0.14%; androsterone, 0.05%; estradiol, <0.01%.

      (3) 11-Keto Testosterone ELISA kits: 11-ketotestosterone, 100%; adrenosterone, 2.9%; testosterone, <0.01%.

      As this information is publicly available on the manufacturer’s website, we deemed it unnecessary to include it in the manuscript.

      Unfortunately, we have not evaluated the extraction efficacy of the samples, but we recognize this a subject for future studies.

      (9) I wonder whether the evaluation of the impact of the mutation by comparing the behavior of a group of wild-type males to a group of mutated males is the most appropriate. Justifying this approach against testing the behavior of one mutated male facing one or several wild-type males would be appreciated.

      We agree that the resident-intruder test, in which a single focal resident is confronted with one or more stimulus intruders, is the most commonly used method for assessing aggression. However, medaka form shoals and lack strong territoriality, and even slight dominance differences between the resident and the intruder can increase variability in the results, compromising data consistency. Therefore, in this study, we adopted an alternative approach: placing four unfamiliar males together in a tank and quantifying aggressive interactions in total. This method allows for the assessment of aggression regardless of territorial tendencies, making it more appropriate for our investigation.

      (10) Lines 329-331: this sentence should be rephrased as it contributes to the confusion between sexual differentiation and activation of circuits. The restoration of sexual behavior by adult estrogen treatment pleads in favor of an activational role of neuro-estrogens on behavior rather than an organizational role. Therefore, referring to sexual differentiation is misleading, even more so that the study never compares sexes.

      As detailed in Response to reviewer #3’s comment 9 on weaknesses, we consider that all factors that cause sex differences, including the transient effects of adult steroids, need to be incorporated into a theory of sexual differentiation. In teleosts, since steroids during early development have little effect and sexual differentiation primarily relies on steroid action in adulthood, our discussion on brain sexual differentiation remains valid, including the statement in line 347: “This variation among species may represent the activation of neuroestrogen synthesis at life stages critical for sexual differentiation of behavior that are unique to each species.”

      (11) Lines 384-386: I may have missed something but I do not see data supporting the notion that neuroestrogens may function upstream of PGF2a signaling to mediate female receptivity.

      Line 403: We acknowledge that our explanation was insufficient and apologize for any confusion. To clarify this point, “Given that estrogen/Esr2b signaling feminizes the neural substrates that mediate mating behavior, while PGF2α signaling triggers female sexual receptivity,” has been added before the sentence “The present finding provides a likely explanation for this apparent contradiction, namely, that neuroestrogens, rather than or in addition to ovarian-derived circulating estrogens, may function upstream of PGF2α signaling to mediate female receptivity.”

      Additional alteration

      Reference list (line 682): a preprint article has now been published in a peer-reviewed journal, and the information has been updated accordingly as follows: “bioRxiv doi: 10.1101/2024.01.10.574747 (2024)” to “Proc. Natl. Acad. Sci. U.S.A. 121, e2316459121 (2024).”

    1. Tụi mình là nhóm 6 đứa vừa có chuyến "chạy trốn deadline" ở Nha Trang, và một trong những quyết định đúng đắn nhất của cả đám là đặt tour Bình Hưng 1 ngày của Nha Trang Travel. Đi chơi theo nhóm mà tự túc thì kiểu gì cũng có một đứa phải đứng ra lo hết mọi thứ, nên lần này tụi mình quyết định phó thác cho tour và chỉ việc xách mông đi chơi thôi. Kết quả là một ngày vui quên lối về, nên phải ngoi lên review liền cho nóng!

      Aloha cả nhà!

      Tụi mình là nhóm 6 đứa vừa có chuyến "chạy trốn deadline" ở Nha Trang, và một trong những quyết định đúng đắn nhất của cả đám là đặt tour Bình Hưng 1 ngày của Nha Trang Travel. Đi chơi theo nhóm mà tự túc thì kiểu gì cũng có một đứa phải đứng ra lo hết mọi thứ, nên lần này tụi mình quyết định phó thác cho tour và chỉ việc xách mông đi chơi thôi. Kết quả là một ngày vui quên lối về, nên phải ngoi lên review liền cho nóng!

    1. Art. 448

      OJ 411 – SDI-1. SUCESSÃO TRABALHISTA. AQUISIÇÃO DE EMPRESA PERTENCENTE A GRUPO ECONÔMICO. RESPONSABILIDADE SOLIDÁRIA DO SUCESSOR POR DÉBITOS TRABALHISTAS DE EMPRESA NÃO ADQUIRIDA. INEXISTÊNCIA. - O sucessor não responde solidariamente por débitos trabalhistas de empresa não adquirida, integrante do mesmo grupo econômico da empresa sucedida, quando, à época, a empresa devedora direta era solvente ou idônea economicamente, ressalvada a hipótese de má-fé ou fraude na sucessão.

      Nesses termos, tem-se:

      Empresa sucessora que adquire apenas uma empresa de grupo econômico não responde solidariamente pelos débitos trabalhistas das demais empresas do grupo não adquiridas, desde que, no momento da sucessão, a devedora direta fosse solvente ou idônea, salvo se demonstrada fraude ou má-fé.


      OJ SDI-1 92. DESMEMBRAMENTO DE MUNICÍPIOS. RESPONSABILIDADE TRABALHISTA Em caso de criação de novo município, por desmembramento, cada uma das novas entidades responsabiliza-se pelos direitos trabalhistas do empregado no período em que figurarem como real empregador.

    1. Author response:

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

      We thank all reviewers for the highly detailed review and the time and effort which has been invested in this review. It is clear from the reviews that we’ve had the privilege to have our work extensively and thoroughly checked by knowledgeable experts, for which we are very grateful. We have read their perspectives, questions and suggested improvements with great interest. We have reflected on the public review in detail and have included detailed responses below. First, we would like to respond to four main issues pointed out by the editor and reviewers:

      (1) Lack of yield data in the manuscript: Yield data has been collected in most of the sites and years of our study, and these have already been published and cited in our manuscript. In the appendix of our manuscript, we included a table with yield data for the sites and years in which the beetle diversity was studied. These data show that strip cropping does not cause a systematic yield reduction.

      (2) Sampling design clarification: Our paper combines data from trials conducted at different locations and years. On the one hand this allows an analysis of a comprehensive dataset, but on the other hand in some cases this resulted in variations in how data were collected or processed (e.g. taxonomic level of species identification). We have added more details to the sections on sampling design and data analysis to increase clarity and transparency.

      (3) Additional data analysis: In the revised manuscript we present an analysis on the responses of abundances of the 12 most common ground beetle genera to strip cropping. This gives better insight in the variation of responses among ground beetle taxa.

      (4) Restrict findings to our system: We nuanced our findings further and focused more on the implications of our data on ground beetle communities, rather than on agrobiodiversity in a broader sense.

      Below we also respond to the editor and reviewers in more detail.

      Reviewing Editor Comments:

      (1) You only have analyzed ground beetle diversity, it would be important to add data on crop yields, which certainly must be available (note that in normal intercropping these would likely be enhanced as well).

      Most yield data have been published in three previous papers, which we already cited or cite now (one was not yet published at the time of submission). Our argumentation is based on these studies. We had also already included a table in the appendix that showed the yield data that relates specifically to our locations and years of measurement. The finding that strip cropping does not majorly affect yield is based on these findings. We revised the title of our manuscript to remove the explicit focus on yield.

      (2) Considering the heterogeneous data involving different experiments it is particularly important to describe the sampling design in detail and explain how various hierarchical levels were accounted for in the analysis.

      We agree that some important details to our analysis were not described in sufficient detail. Especially reviewer 2 pointed out several relevant points that we did account for in our analyses, but which were not clear from the text in the methods section. We are convinced that our data analyses are robust and that our conclusions are supported by the data. We revised the methods section to make our approach clearer and more transparent.

      (3) In addition to relative changes in richness and density of ground beetles you should also present the data from which these have been derived. Furthermore, you could also analyze and interpret the response of the different individual taxa to strip cropping.

      With our heterogeneous dataset it was quite complicated to show overall patterns of absolute changes in ground beetle abundance and richness, especially for the field-level analyses. As the sampling design was not always the same and occasionally samples were missing, the number of year series that made up a datapoint were different among locations and years. However, we always made sure that for the comparison of a paired monoculture and strip cropping field, the number of year series was always made equal through rarefaction. That is, the number of ground beetle(s) (species) are always expressed as the number per 2 to 6 samples. Therefore, we prefer to stick to relative changes as we are convinced that this gives a fairer representation of our complex dataset.

      We agree with the second point that both the editor and several reviewers pointed out. The indicator species analyses that we used were biased by rare species, and we now omit this analysis. Instead, we included a GLM analysis on the responses of abundances of the 12 most common ground beetle genera to strip cropping. We chose for genera here (and not species) as we could then include all locations and years within the analyses, and in most cases a genus was dominated by a single species (but notable exceptions were Amara and Harpalus, which were often made up of several species). We illustrate these analyses still in a similar fashion as we did for the indicator species analysis.

      (4) Keep to your findings and don't overstate them but try to better connect them to basic ecological hypotheses potentially explaining them.

      After careful consideration of the important points that reviewers point out, we decided to nuance our reasoning about biodiversity conservation along two key lines: (1) the extent to which ground beetles can be indicators of wider biodiversity changes; and (2) our findings that are not as straightforward positive as our narrative suggests. We still believe that strip cropping contributes positively to carabid communities, and have carefully checked the text to avoid overstatements.

      Reviewer #1 (Public review):

      Summary:

      This study demonstrates that strip cropping enhances the taxonomic diversity of ground beetles across organically-managed crop systems in the Netherlands. In particular, strip cropping supported 15% more ground beetle species and 30% more individuals compared to monocultures.

      Strengths:

      A well-written study with well-analyzed data of a complex design. The data could have been analyzed differently e.g. by not pooling samples, but there are pros and cons for each type of analysis and I am convinced this will not affect the main findings. A strong point is that data were collected for 4 years. This is especially strong as most data on biodiversity in cropping systems are only collected for one or two seasons. Another strong point is that several crops were included.

      We thank reviewer 1 for their kind words and agree with this strength of the paper. The paper combines data from trials conducted at different locations and years. On the one hand this allows an analysis of a comprehensive dataset, but on the other hand in some cases there were slight variations in how data were collected or processed (e.g. taxonomic level of species identification).

      Weaknesses:

      This study focused on the biodiversity of ground beetles and did not examine crop productivity. Therefore, I disagree with the claim that this study demonstrates biodiversity enhancement without compromising yield. The authors should present results on yield or, at the very least, provide a stronger justification for this statement.

      We acknowledge that we indeed did not formally analyze yield in our study, but we have good reason for this. The claim that strip cropping does not compromise yield comes from several extensive studies (Juventia & van Apeldoorn, 2024; Ditzler et al., 2023; Carillo-Reche et al., 2023) that were conducted in nearly all the sites and years that we included in our study. We chose not to include formal analyses of productivity for two key reasons: (1) a yield analysis would duplicate already published analyses, and (2) we prefer to focus more on the ecology of ground beetles and the effect of strip cropping on biodiversity, rather than diverging our focus also towards crop productivity. Nevertheless, we have shown the results on yield in Table S6 and refer extensively to the studies that have previously analyzed this data (line 203-207, 217-221).

      Reviwer #1 (Recommendations for the authors):

      This is a well-written study on the effects of strip cropping on ground-beetle diversity. As stated above the study is well analyzed, presented, and written but you should not pretend that you analyzed yield e.g. lines 25-27 "We show that strip cropping...enhance ground beetle biodiversity without incurring major yield loss.

      We understand the confusion caused by this sentence, and it was never our intention to give the impression that we analyzed yield losses. These findings were based on previous research by ourselves and colleagues, and we have now changed the sentence to reflect this (line 25-27).

      I think you assume that yield does not differ between strip cropping and monoculture. I am not sure this is correct as one crop might attract pests or predators spilling over to the other crop. I am also not sure if the sowing and harvest of the crop will come with the same costs. So if you assume this, you should only do it in the main manuscript and not the abstract, to justify this better.

      With three peer-reviewed papers on the same fields as we studied, we can convincingly state that strip cropping in organic agriculture generally does not result in major yield loss, although exceptions exist, which we refer to in the discussion.

      In the introduction lines 28-43, you refer to insect biomass decline. I wonder if you would like to add the study of Loboda et al. 2017 in Ecography. It seems not fitting as it is from the Artic but also the other studies you cite are not only coming from agricultural landscapes and this study is from the same time as the Hallmann et al. 2017 study and shows a decline in flies of 80%

      We have removed the sentence that this comment refers to, to streamline the introduction more.

      Lines 50-51. You only have one citation for biodiversity strategies in agricultural systems. I suggest citing Mupepele et al. 2021 in TREE. This study refers to management but also the policies and societal pressures behind it.

      We have added this citation and a recent paper by Cozim-Melges et al. (2024) here (line 49-52).

      In the methods, I am missing a section on species identifications. This would help to understand why you used "taxonomic richness".

      Thanks for pointing this out. We have now included a new section on ground beetle identification (line 304-309 in methods).

      Figure 1 is great and I like that you separated the field and crop-level data, although there is no statistical power for the crop-specific data. I personally would move k to the supplements. It is very detailed and small and therefore hard to read

      We chose to keep figure 1k, as in our view it gives a good impression of the scale of the experiment, the number of crops included and the absolute numbers of caught species.

      Reviewer #2 (Public review):

      Summary:

      The authors aimed to investigate the effects of organic strip cropping on carabid richness and density as well as on crop yields. They find on average higher carabid richness and density in strip cropping and organic farming, but not in all cases.

      We did not intend to investigate the effect of strip cropping on crop yields, but rather place our work in the framework of earlier studies that already studied yield. All the monocultures and strip cropping fields were organic farms. Our findings thus compare crop diversity effects within the context of organic farming.

      Strengths:

      Based on highly resolved species-level carabid data, the authors present estimates for many different crop types, some of them rarely studied, at the same time. The authors did a great job investigating different aspects of the assemblages (although some questions remain concerning the analyses) and they present their results in a visually pleasing and intuitive way.

      We appreciate the kind words of reviewer 2 and their acknowledgement of the extensiveness of our dataset. In our opinion, the inclusion of many different crops is indeed a strength, rarely seen in similar studies; and we are happy that the figures are appreciated.

      Weaknesses:

      The authors used data from four different strip cropping experiments and there is no real replication in space as all of these differed in many aspects (different crops, different areas between years, different combinations, design of the strip cropping (orientation and width), sampling effort and sample sizes of beetles (differing more than 35 fold between sites; L 100f); for more differences see L 237ff). The reader gets the impression that the authors stitched data from various places together that were not made to fit together. This may not be a problem per se but it surely limits the strength of the data as results for various crops may only be based on small samples from one or two sites (it is generally unclear how many samples were used for each crop/crop combination).

      The paper indeed combines data from trials conducted at different locations and years. On the one hand this allows an analysis of a comprehensive dataset, but on the other hand in some cases there were slight differences in the experimental design. At the time that we did our research, there were only a handful of farmers that were employing strip cropping within the Netherlands, which greatly reduced the number of fields for our study. Therefore, we worked in the sites that were available and studied as many crops on these sites. Since there was variation in the crops grown in the sites, for some crops we have limited replication. In the revision we have explained this more clearly (line 297-300).

      One of my major concerns is that it is completely unclear where carabids were collected. As some strips were 3m wide, some others were 6m and the monoculture plots large, it can be expected that carabids were collected at different distances from the plot edge. This alone, however, was conclusively shown to affect carabid assemblages dramatically and could easily outweigh the differences shown here if not accounted for in the models (see e.g. Boetzl et al. (2024) or Knapp et al. (2019) among many other studies on within field-distributions of carabids).

      Point well taken. Samples were always taken at least 10 meters into the field, and always in the middle of the strip. This would indeed mean that there is a small difference between the 3- and 6m wide strips regarding distance from another strip, but this was then only a difference of 1.5 to 3 meters from the edge. A difference that, based on our own extensive experience with ground beetle communities, will not have a large impact on the findings of ground beetles. The distance from field/plot edges was similar between monocultures and strip cropped fields. We present a more detailed description of the sampling design in the methods of the revised manuscript (line 294-297).

      The authors hint at a related but somewhat different problem in L 137ff - carabid assemblages sampled in strips were sampled in closer proximity to each other than assemblages in monoculture fields which is very likely a problem. The authors did not check whether their results are spatially autocorrelated and this shortcoming is hard to account for as it would have required a much bigger, spatially replicated design in which distances are maintained from the beginning. This limitation needs to be stated more clearly in the manuscript.

      To be clear, this limitation relates to the comparison that we did for the community compositions of ground beetles in two crops either in strip cropping or monocultures. In this case, it was impossible to avoid potential autocorrelation due to our field design. We also acknowledge this limitation in the results section (line 130-133). However, for our other analyses we corrected for spatial autocorrelation by including variables per location, year and crop. This grouped samples that were spatially autocorrelated. Therefore, we don’t see this as a discrepancy of our other analyses.

      Similarly, we know that carabid richness and density depend strongly on crop type (see e.g. Toivonen et al. (2022)) which could have biased results if the design is not balanced (this information is missing but it seems to be the case, see e.g. Celeriac in Almere in 2022).

      We agree and acknowledge that crop type can influence carabid richness and density, which is why we have included variables to account for differences caused by crops. However, we did not observe consistent differences between crops in how strip cropping affected ground beetle richness and density. Therefore, we don’t think that crop types would have influenced our conclusions on the overall effect of strip cropping.

      A more basic problem is that the reader neither learns where traps were located, how missing traps were treated for analyses how many samples there were per crop or crop combination (in a simple way, not through Table S7 - there has to have been a logic in each of these field trials) or why there are differences in the number of samples from the same location and year (see Table S7). This information needs to be added to the methods section.

      Point well taken. We have clarified this further in the revised manuscript (line 294-301, 318-322). As we combined data from several experimental designs that originally had slightly different research questions, this in part caused differences between numbers of rounds or samples per crop, location or year.

      As carabid assemblages undergo rapid phenological changes across the year, assemblages that are collected at different phenological points within and across years cannot easily be compared. The authors would need to standardize for this and make sure that the assemblages they analyze are comparable prior to analyses. Otherwise, I see the possibility that the reported differences might simply be biased by phenology.

      We agree and we dealt with this issue by using year series instead of using individual samples of different rounds. This approach allowed us to get a good impression of the entire ground beetle community across seasons. For our analyses we had the choice to only include data from sampling rounds that were conducted at the same time, or to include all available data. We chose to analyze all data, and made sure that the number of samples between strip cropping and monoculture fields per location, year and crop was always the same by pooling and rarefaction.

      Surrounding landscape structure is known to affect carabid richness and density and could thus also bias observed differences between treatments at the same locations (lower overall richness => lower differences between treatments). Landscape structure has not been taken into account in any way.

      We did not include landscape structure as there are only 4 sites, which does not allow a meaningful analysis of potential effects landscape structure. Studying how landscape interacts with strip cropping to influence insect biodiversity would require at least, say 15 to 20 sites, which was not feasible for this study. However, such an analysis may be possible in an ongoing project (CropMix) which includes many farms that work with strip cropping.

      In the statistical analyses, it is unclear whether the authors used estimated marginal means (as they should) - this needs to be clarified.

      In the revised manuscript we further clarified this point (line 365-366, 373-374).

      In addition, and as mentioned by Dr. Rasmann in the previous round (comment 1), the manuscript, in its current form, still suffers from simplified generalizations that 'oversell' the impact of the study and should be avoided. The authors restricted their analyses to ground beetles and based their conclusions on a design with many 'heterogeneities' - they should not draw conclusions for farmland biodiversity but stick to their system and report what they found. Although I understand the authors have previously stated that this is 'not practically feasible', the reason for this comment is simply to say that the authors should not oversell their findings.

      In the revised manuscript, we nuanced our findings by explaining that strip cropping is a potentially useful tool to support ground beetle biodiversity in agricultural fields (line 33-35).

      Reviewer #2 (Recommendations for the authors):

      In addition to the points stated under 'Weaknesses' above, I provide smaller comments and recommendations:

      Overall comments:

      (i) The carabid images used in the figures were created by Ortwin Bleich and are copyrighted. I could not find him accredited in the acknowledgements; the figure legends simply state that the images were taken from his webpage. Was his permission obtained? This should be stated.

      We have received written permission from Ortwin Bleich for using his pictures in our figures, and have accredited him for this in the acknowledgements (line 455-456).

      (ii) There is a great confusion in the field concerning terminology. The authors here use intercropping and strip cropping, a specific form of intercropping, interchangeably. I advise the authors to stick to strip cropping as it is more precise and avoids confusion with other forms of intercropping.

      We agree with the definitions given by reviewer 2 and had already used them as such in the text. We defined strip cropping in the first paragraph of the introduction and do not use the term “intercropping” after this definition to avoid confusion.

      Comments to specific lines:

      Line 19: While this is likely true, there is so far not enough compelling evidence for such a strong statement blaming agriculture. Please rephrase.

      Changed the sentence to indicate more clearly that it is one of the major drivers, but that the “blame” is not solely on agriculture (line 18-19).

      Line 22: Is this the case? I am aware of strip cropping being used in other countries, many of them in Europe. Why the focus on 'Dutch'?

      Indeed, strip cropping is now being pioneered by farmers throughout Europe. However in the Netherlands, some farmers have been pioneering strip cropping already since 2014. We have added this information to indicate that our setting is in the Netherlands, and as in our opinion it gives a bit more context to our manuscript.

      Line 24: I would argue that carabids are actually not good indicators for overall biodiversity in crop fields as they respond in a very specific way, contrasting with other taxa. It is commonly observed that carabids prefer more disturbed habitats and richness often increases with management intensity and in more agriculturally dominated landscapes - in stark contrast to other taxa like wild bees or butterflies.

      We have reworded this sentence to reflect that they are not necessarily indicators of wide agricultural biodiversity, but that they do hold keystone positions within food webs in agricultural systems (line 23-25).

      Line 31: This statement here is also too strong - carabids are not overall biodiversity and patterns found for carabids likely differ strongly from patterns that would be observed in other taxa. This study is on carabids and the conclusion should thus also refer to these in order to avoid such over-simplified generalizations.

      We agree and have nuanced this sentence to indicate that our findings are only on ground beetles (line 33-35). However, we would like to point out that the statement that “patterns found for carabids likely differ strongly from patterns that would be observed in other taxa” assumes a disassociation between carabids and other taxa.

      Line 41: I am sure the authors are aware of the various methodological shortcomings of the dataset used in Hallmann et al. (2017) which likely led to an overestimation of the actual decline. Analysing the same data, Müller et al. (2023) found that weather can explain fluctuations in biomass just as well as time. I thus advise not putting too much focus on these results here as they seem questionable.

      We have removed this sentence to streamline the introduction, thus no longer mentioning the percentages given by Hallmann et al. (2017).

      Line 46: Surely likely but to my knowledge this is actually remarkably hard to prove. Instead of using the IPBES report here that simply states this as a fact, it would be better to see some actual evidence referenced.

      We removed IPBES as a source and changed this for Dirzo et al. (2014), a review that shows the consequences of biodiversity decline on a range of different ecosystem services and ecological functions (line 45-47).

      Line 52ff: I am not sure whether this old land-sparing vs. land-sharing debate is necessary here. The authors could simply skip it and directly refer to the need of agricultural areas, the dominating land-use in many regions, to become more biodiversity-friendly. It can be linked directly to Line 61 in my opinion which would result in a more concise and arguably stronger introduction.

      After reconsidering, we agree with reviewer 2 that this section was redundant and we have removed the lines on land-sparing vs land-sharing.

      Line 59: Just a note here: this argument is not meaningful when talking about strip cropping in the Netherlands as there is virtually no land left that could be converted (if anything, agricultural land is lost to construction). The debate on land-use change towards agriculture is nowadays mostly focused on the tropics and the Global South.

      We argue that strip cropping could play an important role as a measure that does not necessarily follow the trade-off between biodiversity and agriculture for a context beyond the Netherlands (line 52-58).

      Line 69: Does this statement really need 8 references?

      Line 71: ... and this one 5 additional ones?

      We have removed excess references in these two lines (line 62-66).

      Line 74: But also likely provides the necessary crop continuity for many crop pests - the authors should keep in mind that when practitioners read agricultural biodiversity, they predominantly think of weeds and insect pests.

      We agree with reviewer 2 that agricultural biodiversity is still a controversial topic. However, as the focus in this manuscript is more on biodiversity conservation, rather than pest management, we prefer to keep this sentence as is. In other published papers and future work we focus more on the role of strip cropping for pest management.

      Line 83: Consider replacing 'moments' maybe - phenological stages or development stages?

      Although we understand the point of reviewer 2, we prefer to keep it at moments, as we did not focus on phenological stages and we only wanted to say that we set pitfall traps at several moments throughout the year. However, by placing the pitfall traps at several moments throughout the year, we did capture several phenological stages.

      Line 86: Not only farming practices - there are also massive fluctuations between years in the same crop with the same management due to effects of the weather in the previous reproductive season. Interpreting carabid assemblage changes is therefore not straightforward.

      We absolutely agree that interpreting carabid assemblage is not straightforward, but as we did not study year or crop legacy effects we chose to keep this sentence to maintain focus on our research goals.

      Line 88: 'ecolocal'?

      Typo, should have been ecological. Changed (line 81).

      Line 90: 'As such, they are often used as indicator group for wider insect diversity in agroecosystems' - this is the third repetition of this statement and the second one in this paragraph - please remove. Having worked on carabids extensively myself, I also think that this is not the true reason - they are simply easy to collect passively.

      We agree with the reviewer and have removed this sentence.

      Line 141: I have doubts about the value of the ISA looking at the results. Anchomenus dorsalis is a species extremely common in cereal monoculture fields in large parts of Europe, especially in warmer and drier conditions (H. griseus was likely only returned as it is generally rare and likely only occurred in few plots that, by chance, were strip-cropped). It can hardly be considered an indicator for diverse cropping systems but it was returned as one here (which I do not doubt). This often happens with ISA in my experience as they are very sensitive to the specific context of the data they are run on. The returned species are, however, often not really useable as indicators in other contexts. I thus believe they actually have very limited value. Apart from this, we see here that both monocultures and strip cropping have their indicators, as would likely all crop types. I wonder what message we would draw from this ...

      On close reconsideration, we agree with the reviewer that the ISAs might have been too sensitive to rare species that by chance occur in one of two crop configurations. To still get an idea on what happens with specific ground beetle groups, we chose to replace the ISAs with analyses on the 12 most common ground beetle genera. For this purpose we have added new sections to the methods (line 368-374) and results (line 135-143), replaced figure 2 and table S5, and updated the discussion (line 182-200).

      Line 165: Carabid activity is high when carabids are more active. Carabids can be more active either when (i) there are simply more carabid individuals or /and (ii) when they are starved and need to search more for prey. More carabid activity does thus not necessarily indicate more individuals, it can indicate that there is less prey. This aspect is missing here and should be discussed. It is also not true that crop diversification always increases prey biomass - especially strip cropping has previously been shown to decrease pest densities (Alarcón-Segura et al., 2022). Of course, this is a chicken-egg problem (less pests => less carabids or more carabids => less pests ?) ... this should at least be discussed.

      We have rewritten this paragraph to further discuss activity density in relation to food availability (line 175-185).

      Line 178: These species are not exclusively granivorous - this speculation may be too strong here.

      Line 185: true for all but C. melanocephalus - this species is usually more associated with hedgerows, forests etc.

      After removing the ISA’s, we also chose to remove this paragraph and replace it with a paragraph that is linked to the analyses on the 12 most common genera (line 182-200).

      Line 202: These statements are too strong for my taste - the authors should add an 'on average' here. The data show that they likely do not always enhance richness by 15 % and as the authors state, some monocultures still had higher richness and densities.

      “on average” added (line 211)

      Line 203: 'can lead' - the authors cannot tell based on their results if this is always true for all taxa.

      Changed to “can lead” (line 213)

      Line 205: What is 'diversification' here?

      This concerns measures like hedgerows or flower strips. We altered the sentence to make this clearer (line 215-216).

      Line 208: Does this statement need 5 references? (as in the introduction, the reader gets the impression the authors aimed to increase the citation count of other articles here).

      We have removed excess references (line 219-221).

      Line 222: How many are 'a few'? Maybe state a proportion.

      We only found two species, we’ve changed the sentence accordingly (line 232-233).

      Line 224: As stated above, I would not overstress the results of the ISAs - the authors stated themselves that the result for A. dorsalis is likely only based on one site ...

      We removed this sentence after removing the ISAs.

      Line 305: I think there is an additional nested random level missing - the transect or individual plot the traps were located in (or was there only one replicate for each crop/strip in each experiment)? Hard to tell as the authors provide no information on the actual sample sizes.

      Indeed, there was one field or plot per cropping system per crop per location per year from which all the samples were taken. Therefore the analysis does not miss a nested random level. We provided information on sample sizes in Table S7.

      Line 314ff: The authors describe that they basically followed a (slightly extended) Chao-Hill approach (species richness, Shannon entropy & inverse Simpson) without the sampling effort / sample completeness standardization implemented in this approach and as a reader I wonder why they did not simply just use the customary Chao-Hill approach.

      We were not aware of the Chao-Hill approach, and we see it as a compliment that we independently came up with an approach similar to a now accepted approach.

      Line 329: Unclear what was nested in what here - location / year / crop or year / location / crop ?

      For the crop-level analyses, the nested structure was location > year > crop. This nested structure was chosen as every location was sampled across different years and (for some locations) the crops differed among years. However, as we pooled the samples from the same field in the field-level analyses, using the same random structure would have resulted in each individual sampling unit being distinguished as a group. Therefore, the random structure here was only location > year. We explain this now more clearly in lines 329 and 355-357.

      Line 334: I can see why the authors used these distributions but it is presented here without any justification. As a side note: Gamma (with log link) would likely be better for the Shannon model as well (I guess it cannot be 0 or negative ...).

      We explain this now better in lines 360-364.

      Line 341: Why Hellinger and not simply proportions?

      We used Hellinger transformation to give more weight to rarer species. Our pitfall traps were often dominated by large numbers of a few very abundant / active species. If we had used proportions, these species would have dominated the community analyses. We clarified this in the text (line 379-381).

      Line 348: An RDA is constrained by the assumptions / model the authors proposed and "forces" the data into a spatial ordination that resembles this model best. As the authors previously used an unconstrained PERMANOVA, it would be better to also use an NMDS that goes along with the PERMANOVA.

      The initial goal of the RDA was not to directly visualize the results of the PERMANOVA, but to show whether an overall crop configuration effect occurred, both for the whole dataset and per location. We have now added NMDS figures to link them to the PERMANOVA and added these to the supplementary figures (fig S6-S8). We also mention this approach in the methods section (line 387-390).

      Line 355f: This is also a clear indication of the strong annual fluctuations in carabid assemblages as mentioned above.

      Indeed.

      Line 361: 'pairwise'.

      Typo, we changed this.

      Line 362: reference missing.

      Reference added (line 405)

      References

      Alarcón-Segura, V., Grass, I., Breustedt, G., Rohlfs, M., Tscharntke, T., 2022. Strip intercropping of wheat and oilseed rape enhances biodiversity and biological pest control in a conventionally managed farm scenario. J. Appl. Ecol. 59, 1513-1523.

      Boetzl, F.A., Sponsler, D., Albrecht, M., Batáry, P., Birkhofer, K., Knapp, M., Krauss, J., Maas, B., Martin, E.A., Sirami, C., Sutter, L., Bertrand, C., Baillod, A.B., Bota, G., Bretagnolle, V., Brotons, L., Frank, T., Fusser, M., Giralt, D., González, E., Hof, A.R., Luka, H., Marrec, R., Nash, M.A., Ng, K., Plantegenest, M., Poulin, B., Siriwardena, G.M., Tscharntke, T., Tschumi, M., Vialatte, A., Van Vooren, L., Zubair-Anjum, M., Entling, M.H., Steffan-Dewenter, I., Schirmel, J., 2024. Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis. Proceedings of the Royal Society B: Biological Sciences 291, 20232383.

      Hallmann, C.A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., Stenmans, W., Müller, A., Sumser, H., Hörren, T., Goulson, D., de Kroon, H., 2017. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS One 12, e0185809.

      Knapp, M., Seidl, M., Knappová, J., Macek, M., Saska, P., 2019. Temporal changes in the spatial distribution of carabid beetles around arable field-woodlot boundaries. Scientific Reports 9, 8967.

      Müller, J., Hothorn, T., Yuan, Y., Seibold, S., Mitesser, O., Rothacher, J., Freund, J., Wild, C., Wolz, M., Menzel, A., 2023. Weather explains the decline and rise of insect biomass over 34 years. Nature.

      Toivonen, M., Huusela, E., Hyvönen, T., Marjamäki, P., Järvinen, A., Kuussaari, M., 2022. Effects of crop type and production method on arable biodiversity in boreal farmland. Agriculture, Ecosystems & Environment 337, 108061.

      Reviewer #3 (Public review):

      Summary:

      In this paper, the authors made a sincere effort to show the effects of strip cropping, a technique of alternating crops in small strips of several meters wide, on ground beetle diversity. They state that strip cropping can be a useful tool for bending the curve of biodiversity loss in agricultural systems as strip cropping shows a relative increase in species diversity (i.e. abundance and species richness) of the ground beetle communities compared to monocultures. Moreover, strip cropping has the added advantage of not having to compromise on agricultural yields.

      Strengths:

      The article is well written; it has an easily readable tone of voice without too much jargon or overly complicated sentence structure. Moreover, as far as reviewing the models in depth without raw data and R scripts allows, the statistical work done by the authors looks good. They have well thought out how to handle heterogenous, yet spatially and temporarily correlated field data. The models applied and the model checks performed are appropriate for the data at hand. Combining RDA and PCA axes together is a nice touch.

      We thank reviewer 3 for their kind words and appreciation for the simple language and analysis that we used.

      Weaknesses:

      The evidence for strip cropping bringing added value for biodiversity is mixed at best. Yes, there is an increase in relative abundance and species richness at the field level, but it is not convincingly shown this difference is robust or can be linked to clear structural and hypothesised advantages of the strip cropping system. The same results could have been used to conclude that there are only very limited signs of real added value of strip cropping compared to monocultures.

      Point well taken. We agree that the effect of strip cropping on carabid beetle communities are subtle and we nuanced the text in the revised version to reflect this. See below for more details on how we revised the manuscript to reflect this point.

      There are a number of reasons for this:

      (1) Significant differences disappear at crop level, as the authors themselves clearly acknowledge, meaning that there are no differences between pairs of similar crops in the strip cropping fields and their respective monoculture. This would mean the strips effectively function as "mini-monocultures".

      This is indeed in line with our conclusions. Based on our data and results, the advantages of strip cropping seem mostly to occur because crops with different communities are now on the same field, rather than that within the strips you get mixtures of communities related to different crops. We discussed this in the first paragraph of the discussion in the original submission (line 161-164).

      The significant relative differences at the field level could be an artifact of aggregation instead of structural differences between strip cropping and monocultures; with enough data points things tend to get significant despite large variance. This should have been elaborated further upon by the authors with additional analyses, designed to find out where differences originate and what it tells about the functioning of the system. Or it should have provided ample reason for cautioning in drawing conclusions about the supposed effectiveness of strip cropping based on these findings.

      We believe that this is a misunderstanding of our approach. In the field-level analyses we pooled samples from the same field (i.e. pseudo-replicates were pooled), resulting in a relatively small sample size of 50 samples. We revised the methods section to better explain this (line 318-322). Therefore, the statement “with enough data points things tend to get significant” is not applicable here.

      (2) The authors report percentages calculated as relative change of species richness and abundance in strip cropping compared to monocultures after rarefaction. This is in itself correct, however, it can be rather tricky to interpret because the perspective on actual species richness and abundance in the fields and treatments is completely lost; the reported percentages are dimensionless. The authors could have provided the average cumulative number of species and abundance after rarefaction. Also, range and/or standard error would have been useful to provide information as to the scale of differences between treatments. This could provide a new perspective on the magnitude of differences between the two treatments which a dimensionless percentage cannot.

      We agree that this would be the preferred approach if we would have had a perfectly balanced dataset. However, this approach is not feasible with our unbalanced design and differences in sampling effort. While we acknowledge the limitation of the interpretation of percentages, it does allow reporting relative changes for each combination of location, year and crop. The number of samples on which the percentages were based were always kept equal (through rarefaction) between the cropping systems (for each combination of location, year and crop), but not among crops, years and location. This approach allowed us to make a better estimation whenever more samples were available, as we did not always have an equal number of samples available between both cropping systems. For example, sometimes we had 2 samples from a strip cropped field and 6 from the monoculture, here we would use rarefaction up to 2 samples (where we would just have a better estimation from the monoculture). In other cases, we had 4 samples in both strip cropped and monoculture fields, and we chose to use rarefaction to 4 samples to get a better estimation altogether. Adding a value for actual richness or abundance to the figures would have distorted these findings, as the variation would be huge (as it would represent the number of ground beetle(s) species per 2 to 6 pitfall samples). Furthermore, the dimension that reviewer 3 describes would thus be “The number of ground beetle species / individuals per 2 to 6 samples”, not a very informative unit either.

      (3) The authors appear to not have modelled the abundance of any of the dominant ground beetle species themselves. Therefore it becomes impossible to assess which important species are responsible (if any) for the differences found in activity density between strip cropping and monocultures and the possible life history traits related reasons for the differences, or lack thereof, that are found. A big advantage of using ground beetles is that many life history traits are well studied and these should be used whenever there is reason, as there clearly is in this case. Moreover, it is unclear which species are responsible for the difference in species richness found at the field level. Are these dominant species or singletons? Do the strip cropping fields contain species that are absent in the monoculture fields and are not the cause of random variation or sampling? Unfortunately, the authors do not report on any of these details of the communities that were found, which makes the results much less robust.

      Thank you for raising this point. We have reconsidered our indicator species analysis and found that it is rather sensitive for rare species and insensitive to changes in common species. Therefore, we have replaced the indicator species analyses with a GLM analysis for the 12 most common genera of ground beetles in the revised manuscript. This will allow us to go more in depth on specific traits of the genera which abundances change depending on the cropping system. In the revised manuscript, we will also discuss these common genera more in depth, rather than focusing on rarer species (line 135-143, 182-200 in discussion). Furthermore, we have added information on rarity and habitat preference to the table that shows species abundances per location (Table S2), and mention these aspects briefly in the results (line 145-153).

      (4) In the discussion they conclude that there is only a limited amount of interstrip movement by ground beetles. Otherwise, the results of the crop-level statistical tests would have shown significant deviation from corresponding monocultures. This is a clear indication that the strips function more like mini-monocultures instead of being more than the sum of its parts.

      This is in line with our point in the first paragraph of the discussion and an important message of our manuscript.

      (5) The RDA results show a modelled variable of differences in community composition between strip cropping and monoculture. Percentages of explained variation of the first RDA axis are extremely low, and even then, the effect of location and/or year appear to peak through (Figure S3), even though these are not part of the modelling. Moreover, there is no indication of clustering of strip cropping on the RDA axis, or in fact on the first principal component axis in the larger RDA models. This means the explanatory power of different treatments is also extremely low. The crop level RDA's show some clustering, but hardly any consistent pattern in either communities of crops or species correlations, indicating that differences between strip cropping and monocultures are very small.

      We agree and we make a similar point in the first paragraph of the discussion (line 160-162).

      Furthermore, there are a number of additional weaknesses in the paper that should be addressed:

      The introduction lacks focus on the issues at hand. Too much space is taken up by facts on insect decline and land sharing vs. land sparing and not enough attention is spent on the scientific discussion underlying the statements made about crop diversification as a restoration strategy. They are simply stated as facts or as hypotheses with many references that are not mentioned or linked to in the text. An explicit link to the results found in the large number of references should be provided.

      We revised the introduction by omitting the land sharing vs. land sparing topic and better linking references to our research findings.

      The mechanistic understanding of strip cropping is what is at stake here. Does strip cropping behave similarly to intercropping, a technique that has been proven to be beneficial to biodiversity because of added effects due to increased resource efficiency and greater plant species richness? This should be the main testing point and agenda of strip cropping. Do the biodiversity benefits that have been shown for intercropping also work in strip cropping fields? The ground beetles are one way to test this. Hypotheses should originate from this and should be stated clearly and mechanistically.

      We agree with the reviewer and clarified this research direction clearer in the introduction of the revised manuscript (line 66-72).

      One could question how useful indicator species analysis (ISA) is for a study in which predominantly highly eurytopic species are found. These are by definition uncritical of their habitat. Is there any mechanistic hypothesis underlying a suspected difference to be found in preferences for either strip cropping or monocultures of the species that were expected to be caught? In other words, did the authors have any a priori reasons to suspect differences, or has this been an exploratory exercise from which unexplained significant results should be used with great caution?

      Point well taken. We agree that the indicator species analysis has limitations and therefore now replaced this with GLM analysis for the 12 most common ground beetle genera.

      However, setting these objections aside there are in fact significant results with strong species associations both with monocultures and strip cropping. Unfortunately, the authors do not dig deeper into the patterns found a posteriori either. Why would some species associate so strongly with strip cropping? Do these species show a pattern of pitfall catches that deviate from other species, in that they are found in a wide range of strips with different crops in one strip cropping field and therefore may benefit from an increased abundance of food or shelter? Also, why would so many species associate with monocultures? Is this in any way logical? Could it be an artifact of the data instead of a meaningful pattern? Unfortunately, the authors do not progress along these lines in the methods and discussion at all.

      We thank reviewer 3 for these valuable perspectives. In the revised manuscript, we further explored the species/genera that respond to cropping systems and discuss these findings in more detail in the revised manuscript (line 182-200 in discussion).

      A second question raised in the introduction is whether the arable fields that form part of this study contain rare species. Unfortunately, the authors do not elaborate further on this. Do they expect rare species to be more prevalent in the strip cropping fields? Why? Has it been shown elsewhere that intercropping provides room for additional rare species?

      The answer is simply no, we did not find more rare species in strip cropping. In the revised manuscript, we added a column for rarity (according to waarneming.nl) in the table showing abundances of species per location (table S2). We only found two rare species, one of which we only found a single individual and one that was more related to the open habitat created by a failed wheat field. We discuss this more in depth in the revised results (line 145-153).

      Considering the implications the results of this research can have on the wider discussion of bending the curve and the effects of agroecological measures, bold claims should be made with extreme restraint and be based on extensive proof and robust findings. I am not convinced by the evidence provided in this article that the claim made by the authors that strip cropping is a useful tool for bending the curve of biodiversity loss is warranted.

      We believe that strip cropping can be a useful tool because farmers readily adopt it and it can result in modest biodiversity gains without yield loss. However, strip cropping is indeed not a silver bullet (which we also don’t claim). We nuanced the implications of our study in the revised manuscript (line 30-35, 232-237).

      Reviewer #3 (Recommendations for the authors):

      General comments:

      (1) I am missing the R script and data files in the manuscript. This is a serious drawback in assessing the quality of the work.

      Datasets and R scripts will be made available upon completion of the manuscript.

      (2) I have doubts about the clarity of the title. It more or less states that strip cropping is designed in order to maintain productivity. However, the main objective of strip cropping is to achieve ecological goals without losing productivity. I suggest a rethink of the title and what it is the authors want to convey.

      As the title lead to false expectations for multiple reviewers regarding analyses on yield, we chose to alter the title and removed any mention of yield in the title.

      (3) Line 22: I would add something along the lines of: "As an alternative to intercropping, strip cropping is pioneerd by Dutch farmers... " This makes the distinction and the connection between the two more clear.

      In our opinion, strip cropping is a form of intercropping. We have changed this sentence to reflect this point better. (line 21-22)

      (4) Line 24: "these" should read "they"

      After changing this sentence, this typo is no longer there (line 24).

      (5) Line 34-48. I think this introduction is too long. The paper is not directly about insect decline, so the authors could consider starting with line 43 and summarising 34-42 in one or two sentences.

      Removed a sentence on insect declines here to make the introduction more streamlined.

      (6) Line 51-59. I am not convinced the land sparing - land sharing idea adds anything to the paper. It is not used in the discussion and solicits much discussion in and of itself unnecessary in this paper. The point the authors want to make is not arable fields compared to natural biodiversity, but with increases in biodiversity in an already heavily degraded ecosystem; intensive agriculture. I think the introduction should focus on that narrative, instead of the land sparing-sharing dichotomy, especially because too little attention is spent on this narrative.

      We removed the section on land-sparing vs land-sharing as it was indeed off-topic.

      (7) Line 85. Dynamics is not correctly used here. It should read Ground beetle communities are sensitive.

      Changed accordingly (line 78-79).

      (8) Line 90-91. Here, it should be added that ground beetles are used as indicators for ground-dwelling insect diversity, not wider insect diversity in agricultural systems. In fact, Gerlach et al., the reference included, clearly warn against using indicator groups in a context that is too wide for a single indicator group to cover and Van Klink (2022) has recently shown in a meta-analysis that the correlation between trends in insect groups is often rather poor.

      We removed the sentence that claimed ground beetles to be indicators of general biodiversity, and have focused the text in general more on ground beetle biodiversity, rather than general biodiversity.

      (9) Line 178: was there a high weed abundance measured in the stripcropping fields? Or has there been reports on higher weed abundance in general? The references provided do not appear to support this claim.

      To our knowledge, there is only one paper on the effect of strip cropping on weeds (Ditzler et al., 2023). This paper shows strip cropping (and more diverse cropping systems) reduce weed cover, but increase weed richness and diversity. We mistakenly mentioned that crop diversification increases weed seed biomass, but have changed this accordingly to weed seed richness. The paper from Carbonne et al. (2022) indeed doesn’t show an effect of crop diversification on weeds. However, it does show a positive relation between weed seed richness and ground beetle activity density. We have moved this citation to the right place in the sentence (line 172-175).

      (10) Line 279-288. The description of sampling with pitfalls is inadequate. Please follow the guidelines for properly incorporating sufficient detail on pitfall sampling protocols as described in Brown & Matthews 2016,

      We were sadly not aware of this paper prior to the experiments, but have at least added information on all characteristics of the pitfall traps as mentioned in the paper (line 290-294).

      (11) Lines 307-310. What reasoning lies behind the choice to focus on the most beetle-rich monocultures? Do the authors have references for this way of comparing treatments? Is there much variation in the monocultures that solicits this approach? It would be preferable if the authors could elaborate on why this method is used, provide references that it is a generally accepted statistical technique and provide additional assesments of the variation in the data so it can be properly related to more familiar exploratory data analysis techniques.

      We ran two analyses for the field-level richness and abundance. First we used all combinations of monocultures and strip cropping. However, as strip cropping is made up of (at least) 2 crops, we had 2 constituent monocultures. As we would count a comparison with the same strip cropped field twice when we included both monocultures, we also chose to run the analyses again with only those monocultures that had the highest richness and abundance. This choice was done to get a conservative estimate of ground beetle richness increases through strip cropping. We explained this methodology further in the statistical analysis section (line 329-335).

      In Figure S6 the order of crop combinations is altered between 2021 on the left and 2022 on the right. This is not helpful to discover any possible patterns.

      We originally chose this order as it represented also the crop rotations, but it is indeed not helpful without that context. Therefore, we chose to change the order to have the same crop combinations within the rows.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public review):

      (1) Why was V1 separated from the rest of the visual cortex, and why the rest of the areas were simply lumped into an EVC ROI? It would be helpful to understand the separation into ROIs.

      We thank the reviewer for raising the concerns regarding the definition of ROI. Our approach to analyze V1 separately was based on two key considerations. First, previous studies consistently identify V1 as the main locus of sensory-like templates during featurespecific preparatory attention (Kok et al., 2014; Aitken et al., 2020). Second, V1 shows the strongest orientation selectivity within the visual hierarchy (Priebe, 2016). In contrast, the extrastriate visual cortex (EVC; comprising V2, V2, V3AB and V4) demonstrates broader selectivity, such as complex features like contour and texture (Grill-Spector & Malach, 2004). Thus, we think it would be particularly informative to analyze V1 data separately as our experiment examines orientation-based attention. We should also note that we conducted MVPA separately for each visual ROIs (V2, V3, V3AB and V4). After observing similar patterns of results across these regions, we averaged the decoding accuracies into a single value and labeled it as EVC. This approach allowed us to simplify data presentation while preserving the overall data pattern in decoding performance. We now added the related explanations on the ROI definition in the revised texts (Page 26; Line 576-581).

      (2) It would have been helpful to have a behavioral measure of the "attended" orientation to show that participants in fact attended to a particular orientation and were faster in the cued condition. The cue here was 100% valid, so no such behavioral measure of attention is available here.

      We thank the reviewer for the comments. We agree that including valid and neutral cue trials would have provided valuable behavioral measures of attention; Yet, our current design was aimed at maximizing the number of trials for decoding analysis due to fMRI time constraints. Thus, we could not fit additional conditions to measure the behavioral effects of attention. However, we note that in our previous studies using a similar feature cueing paradigm, we observed benefits of attentional cueing on behavioral performance when comparing valid and neutral conditions (Liu et al., 2007; Jigo et al., 2018). Furthermore, our neural data indeed demonstrated attention-related modulation (as indicated by MVPA results, Fig. 2 in the main texts) so we are confident that on average participants followed the instruction and deployed their attention accordingly. We now added the related explanations on this point in the revised texts (Page 23; Line 492-498).

      (3) As I was reading the manuscript I kept thinking that the word attention in this manuscript can be easily replaced with visual working memory. Have the authors considered what it is about their task or cognitive demand that makes this investigation about attention or working memory?

      We thank the reviewer for this comment. We added the following extensive discussion on this point in the revised texts (Page 18; Line 363-381).

      “It could be argued that preparatory attention relies on the same mechanisms as working memory maintenance. While these functions are intuitively similar and likely overlap, there is also evidence indicating that they can be dissociated (Battistoni et al., 2017). In particular, we note that in our task, attention is guided by symbolic cues (color-orientation associations), while working memory tasks typically present the actual visual stimulus as the memorandum. A central finding in working memory studies is that neural signals during WM maintenance are sensory in nature, as demonstrated by generalizable neural activity patterns from stimulus encoding to maintenance in visual cortex (Harrison & Tong, 2009; Serences et al., 2009; Rademaker et al., 2019). However, in our task, neural signals during preparation were nonsensory, as demonstrated by a lack of such generalization in the No-Ping session (see also Gong et al., 2022). We believe that the differences in cue format and task demand in these studies may account for such differences. In addition to the difference in the sensory nature of the preparatory versus delay-period activity, our ping-related results also exhibited divergence from working memory studies (Wolff et al., 2017; 2020). While these studies used the visual impulse to differentiate active and latent representations of different items (e.g., attended vs. unattended memory item), our study demonstrated the active and latent representations of a single item in different formats (i.e., non-sensory vs. sensory-like). Moreover, unlike our study, the impulse did not evoke sensory-like neural patterns during memory retention (Wolff et al., 2017). These observations suggest that the cognitive and neural processes underlying preparatory attention and working memory maintenance could very well diverge. Future studies are necessary to delineate the relationship between these functions both at the behavioral and neural level.”

      (4) If I understand correctly, the only ROI that showed a significant difference for the crosstask generalization is V1. Was it predicted that only V1 would have two functional states? It should also be made clear that the only difference where the two states differ is V1.

      We thank the reviewer for this comment. We would like to clarify that our analyses revealed similar patterns of preparatory attentional representations in V1 and EVC. During the Ping session, the cross-task generalization analyses revealed decodable information in both V1 and EVC (ps < 0.001), significantly higher than that in the No-Ping session for V1 (independent t-test: t(38) = 3.145, p = 0.003; Cohen’s d = 0.995) and EVC (independent t-test: t(38) = 2.153, p = 0.038, Cohen’s d = 0.681) (Page 10; Line 194-196). While both areas maintained similar representations, additional measures (Mahalanobis distance, neural-behavior relationship and connectivity changes) showed more robust ping-evoked changes in V1 compared to EVC. This differential pattern likely reflects the primary role of V1 in orientation processing, with EVC showing a similar but weaker response profile. We have revised the text to clarity this point (Page 16; Line 327-329).

      (5) My primary concern about the interpretation of the finding is that the result, differences in cross-task decoding within V1 between the ping and no-ping condition might simply be explained by the fact that the ping condition refocuses attention during the long delay thus "resharpening" the template. In the no-ping condition during the 5.5 to 7.5 seconds long delay, attention for orientation might start getting less "crisp." In the ping condition, however, the ping itself might simply serve to refocus attention. So, the result is not showing the difference between the latent and non-latent stages, rather it is the difference between a decaying template representation and a representation during the refocused attentional state. It is important to address this point. Would a simple tone during the delay do the same? If so, the interpretation of the results will be different.

      We thank the reviewer for this comment. The reviewer proposed an alternative account suggesting that visual pings may function to refocus attention, rather than reactivate latent information during the preparatory period. If this account holds (i.e., attention became weaker in the no-ping condition and it was strengthened by the ping due to re-focusing), we would expect to observe a general enhancement of attentional decoding during the preparatory period. However, our data reveal no significant differences in overall attention decoding between two conditions during this period (ps > 0.519; BF<sub>excl</sub> > 3.247), arguing against such a possibility.

      The reviewer also raised an interesting question about whether an auditory tone during preparation could produce effects similar to those observed with visual pings. Although our study did not directly test this possibility, existing literature provides some relevant evidence. In particular, prior studies have shown that latent visual working memory contents are selectively reactivated by visual impulses, but not by auditory stimuli (Wolff et al., 2020). This finding supports the modality-specificity for visually encoded contents, suggesting that sensory impulses must match the representational domain to effectively access latent visual information, which also argues against the refocusing hypothesis above. However, we do think that this is an important question that merits direct investigation in future studies. We now added the related discussion on this point in the revised texts (Page 10, Line 202-203; Page 19, Line 392395).

      (6) The neural pattern distances measured using Mahalanobis values are really great! Have the authors tried to use all of the data, rather than the high AMI and low AMI to possibly show a linear relationship between response times and AMI?

      We thank the reviewer for this comment. We took the reviewer’s suggestion to explore the relationship between attentional modulation index (AMI) and RTs across participants for each session (see Figure 3). In the No-Ping session, we observed no significant correlation between AMI and RT (r = -0.366, p = 0.113). By contrast, the same analysis in the Ping condition revealed a significantly negative correlation (r = -0.518, p = 0.019). These results indicate that the attentional modulations evoked by visual impulse was associated with faster RTs, supporting the functional relevance of activating sensory-like representations during preparation. We have now included these inter-subject correlations in the main texts (Page 13, Line 258-264; Fig 3D and 3E) along with within-subject correlations in the Supplementary Information (Page 6, Line, 85-98; S3 Fig).

      (7) After reading the whole manuscript I still don't understand what the authors think the ping is actually doing, mechanistically. I would have liked a more thorough discussion, rather than referencing previous papers (all by the co-author).

      We thank the reviewer for this comment regarding the mechanistic basis of visual pings. We agree that this warrants deeper discussion. One possibility, as informed by theoretical studies of working memory, is that the sensory-like template could be maintained via an “activity-silent” mechanism through short-term changes in synaptic weights (Mongillo et al., 2008). In this framework, a visual impulse may function as nonspecific inputs that momentarily convert latent traces into detectable activity patterns (Rademaker & Serences, 2017). Related to our findings, it is unlikely that the orientation-specific templates observed during the Ping session emerged from purely non-sensory representations and were entirely induced by an exogenous ping, which was devoid of any orientation signal. Instead, the more parsimonious explanation is that visual impulse reactivated pre-existing latent sensory signals. To our knowledge, the detailed circuit-level mechanism of such reactivation is still unclear; existing evidence only suggests a relationship between ping-evoked inputs and the neural output (Wolff et al., 2017; Fan et al., 2021; Duncan et al., 2023). We now included the discussion on this point in the main texts (Page 19, Line 383-401).

      Reviewer #2 (Public review):

      (1) The origin of the latent sensory-like representation. By 'pinging' the neural activity with a high-contrast, task-irrelevant visual stimulus during the preparation period, the authors identified the representation of the attentional feature target that contains the same information as perceptual representations. The authors interpreted this finding as a 'sensory-like' template is inherently hosted in a latent form in the visual system, which is revealed by the pinging impulse. However, I am not sure whether such a sensory-like template is essentially created, rather than revealed, by the pinging impulses. First, unlike the classical employment of the pinging technique in working memory studies, the (latent) representation of the memoranda during the maintenance period is undisputed because participants could not have performed well in the subsequent memory test otherwise. However, this appears not to be the case in the present study. As shown in Figure 1C, there was no significant difference in behavioral performance between the ping and the no-ping sessions (see also lines 110-125, pg. 5-6). In other words, it seems to me that the subsequent attentional task performance does not necessarily rely on the generation of such sensory-like representations in the preparatory period and that the emergence of such sensory-like representations does not facilitate subsequent attentional performance either. In such a case, one might wonder whether such sensory-like templates are really created, hosted, and eventually utilized during the attentional process. Second, because the reference orientations (i.e. 45 degrees and 135 degrees) have remained unchanged throughout the experiment, it is highly possible that participants implicitly memorized these two orientations as they completed more and more trials. In such a case, one might wonder whether the 'sensory-like' templates are essentially latent working memory representations activated by the pinging as was reported in Wolff et al. (2017), rather than a functional signature of the attentional process.

      We thank the reviewer for this comment. We agree that the question of whether the sensory-like template is created or merely revealed by visual pinging is crucial for the understanding our findings. First, we acknowledge that our task may not be optimized for detecting changes in accuracy, as the task difficulty was controlled using individually adjusted thresholds (i.e., angular difference). Nevertheless, we observed some evidence supporting the neural-behavioral relationships. In particular, the impulse-driven sensory-like template in V1 contributed to facilitated faster RTs during stimulus selection (Page 12, Fig. 3D and 3E in the main texts; also see our response to R1, Point 6).

      Second, the reviewer raised an important concern about whether the attended feature might be stored in the memory system due to the trial-by-trial repetition of attention conditions (attend 45º or attend 135º). Although this is plausible, we don’t think it is likely. We note that neuroimaging evidence shows that attended working memory contents maintain sensory-like representations in visual cortex (Harrison & Tong, 2009; Serences et al., 2009; Rademaker et al., 2019), with generalizable neural activity patterns from perception to working memory delay-period, whereas unattended items in multi-item working memory tasks are stored in a latent state for prospective use (Wolff et al., 2017). Importantly, our task only required maintaining a single attentional template at a time. Thus, there was no need to store it via latent representations, if participants simply used a working memory mechanism for preparatory attention. Had they done so, we should expect to find evidence for a sensory template, i.e., generalizable neural pattern between perception and preparation in the No-Ping condition, which was not what we found. We have mentioned this point in the main texts (Page 18, Line 367-372).

      (2) The coexistence of the two types of attentional templates. The authors interpreted their findings as the outcome of a dual-format mechanism in which 'a non-sensory template' and a latent 'sensory-like' template coexist (e.g. lines 103-106, pg. 5). While I find this interpretation interesting and conceptually elegant, I am not sure whether it is appropriate to term it 'coexistence'. First, it is theoretically possible that there is only one representation in either session (i.e. a non-sensory template in the no-ping session and a sensory-like template in the ping session) in any of the brain regions considered. Second, it seems that there is no direct evidence concerning the temporal relationship between these two types of templates, provided that they commonly emerge in both sessions. Besides, due to the sluggish nature of fMRI data, it is difficult to tell whether the two types of templates temporally overlap.

      We thank the reviewer for the comment regarding our interpretation of the ‘coexistence’ of non-sensory and sensory-like attentional template. While we acknowledge the limitations of fMRI in resolving temporal relationships between these two types of templates, several aspects of our data support a dual-format interpretation.

      First, our key findings remained consistent for the subset of participants (N=14) who completed both No-Ping and Ping sessions in counterbalanced order. It thus seems improbable that participants systematically switched cognitive strategies (e.g., using non-sensory templates in the No-Ping session versus sensory-like templates in the Ping session) in response to the task-irrelevant, uninformative visual impulse. Second, while we agree with the reviewer that the temporal dynamics between these two templates remain unclear, it is difficult to imagine that orientation-specific templates observed during the Ping session emerged de novo from a purely non-sensory templates and an exogenous ping. In other words, if there is no orientation information at all to begin with, how does it come into being from an orientation-less external ping? It seems to us that the more parsimonious explanation is that there was already some orientation signal in a latent format, and it was activated by the ping, in line with the models of “activity-silent” working memory. To address these concerns, we have added the related discussion of these alternative interpretations in the main texts (Page 19, Line 387-391)

      (3) The representational distance. The authors used Mahalanobis distance to quantify the similarity of neural representation between different conditions. According to the authors' hypothesis, one would expect greater pattern similarity between 'attend leftward' and 'perceived leftward' in the ping session in comparison to the no-ping session. However, this appears not to be the case. As shown in Figures 3B and C, there was no major difference in Mahalanobis distance between the two sessions in either ROI and the authors did not report a significant main effect of the session in any of the ANOVAs. Besides, in all the ANOVAs, the authors reported only the statistic term corresponding to the interaction effect without showing the descriptive statistics related to the interaction effect. It is strongly advised that these descriptive statistics related to the interaction effect should be included to facilitate a more effective and intuitive understanding of their data.

      We thank the reviewer for this comment. We expected greater pattern similarity between 'attend leftward' and 'perceived leftward' in the Ping session in comparison to the Noping session. This prediction was supported by a significant three-way interaction effect between session × attended orientation × perceived orientation (F(1,38) = 5.00, p = 0.031, η<sub>p</sub><sup>2</sup> = 0.116). In particular, there was a significant interaction between attended orientation × perceived orientation (F(1,19) = 9.335, p = 0.007, η<sub>p</sub><sup>2</sup> = 0.329) in the Ping session, but not in the No-Ping session (F(1,19) = 0.017, p = 0.898, η<sub>p</sub><sup>2</sup> = 0.001). These above-mentioned statistical results were reported in the original texts. In addition, this three-way mixed ANOVA (session × attended orientation × perceived orientation) on Mahalanobis distance in V1 revealed no significant main effects (session: F(1,38) = 0.009, p = 0.923, η<sub>p</sub><sup>2</sup> < 0.001; attended orientation: F(1,38) = 0.116, p = 0.735, η<sub>p</sub><sup>2</sup> = 0.003; perceived orientation: (F(1,38) = 1.106, p = 0.300, η<sub>p</sub><sup>2</sup> = 0.028). We agree with the reviewer that a complete reporting of analyses enhances understanding of the data. Therefore, we have now included the main effects in the main texts (Page 11, Line 233).

      We thank the reviewer for the suggestion regarding the inclusion of descriptive statistics for interaction effects. However, since the data were already visualized in Fig. 3B and 3C in the main texts, to maintain conciseness and consistency with the reporting style of other analyses in the texts, we have opted to include these statistics in the Supplementary Information (Page 5, Table 1).

      Reviewer #3 (Public review):

      (1) The title is "Dual-format Attentional Template," yet the supporting evidence for the nonsensory format and its guiding function is quite weak. The author could consider conducting further generalization analysis from stimulus selection to preparation stages to explore whether additional information emerges.

      We thank the reviewer for this comment. Our approach to investigate whether preparatory attention is encoded in sensory or non-sensory format - by training classifier using separate runs of perception task – closely followed methods from previous studies (Stokes et al., 2009; Peelen et al., 2011; Kok et al., 2017). Following the reviewer’s suggestion, we performed generalization analyses by training classifiers on activity during the stimulus selection period and testing them preparatory activity. However, we observed no significant generalization effects in either No-Ping and Ping sessions (ps > 0.780). This null result may stem from a key difference in the neural representations: classifiers trained on neural activity from stimulus selection period necessarily encode both target and distractor information, thus relying on somewhat different information than classifier trained exclusively on isolated target information in the perception task.

      (2) In Figure 2, the author did not find any decodable sensory-like coding in IPS and PFC, even during the impulse-driven session, indicating that these regions do not represent sensory-like information. However, in the final section, the author claimed that the impulse-driven sensorylike template strengthens informational connectivity between sensory and frontoparietal areas. This raises a question: how can we reconcile the lack of decodable coding in these frontoparietal regions with the reported enhancement in network communication? It would be helpful if the author provided a clearer explanation or additional evidence to bridge this gap.

      We thank the reviewer for this comment. We would like to clarity that although we did not observe sensory-like coding during preparation in frontoparietal areas, we did observe attentional signals in these regions, as evidenced by the above-chance within-task attention decoding performance (Fig. 2 in the main texts). This could reflect different neural codes in different areas, and suggests that inter-regional communication does not necessarily require identical representational formats. It seems plausible that the representation of a non-sensory attentional template in frontoparietal areas supports top-down attentional control, consistent with theories suggesting increasing abstraction as the cortical hierarchy ascends (Badre, 2008; Brincat et al., 2018), and their interaction with the sensory representation in the visual areas is enhanced by the visual impulse.

      (3) Given that the impulse-driven sensory-like template facilitated behavior, the author proposed that it might also enhance network communication. Indeed, they observed changes in informational connectivity. However, it remains unclear whether these changes in network communication have a direct and robust relationship with behavioral improvements.

      We thank the reviewer for the suggestion. To examine how network communication relates to behavior, we performed a correlation analysis between information connectivity (IC) and RTs across participants (see Figure S5). We observed a trend of correlations between V1-PFC connectivity and RTs in the Ping session (r = -0.394, p = 0.086), but not in the NoPing session (r = -0.046, <i.p\</i> = 0.846). No significant correlations were found between V1-IPS and RTs (\ps\ > 0.400) or between ICs and accuracy (ps > 0.399). These results suggests that ping-enhanced connectivity might contributed to facilitated responses. Although we may not have sufficient statistical power to warrant a strong conclusion, we think this result is still highly suggestive, so we now added the texts in the Supplementary Information (Page 8, Line 116121; S5 Fig) and mentioned this result in the main texts (Page 14, Line 292-293).

      (4) I'm uncertain about the definition of the sensory-like template in this paper. Is it referring to the Ping impulse-driven condition or the decodable performance in the early visual cortex? If it is the former, even in working memory, whether pinging identifies an activity-silent mechanism is currently debated. If it's the latter, the authors should consider whether a causal relationship - such as "activating the sensory-like template strengthens the informational connectivity between sensory and frontoparietal areas" - is reasonable.

      We apologize for the confusions. The sensory-like template by itself does not directly refer to representations under Ping session or the attentional decoding in early visual cortex. Instead, it pertains to the representational format of attentional signals during preparation. Specifically, its existence is inferred from cross-task generalization, where neural patterns from a perception task (perceive 45º or perceive 135º) generalize to an attention task (attend 45 º or attend 135º). We think this is a reasonable and accepted operational definition of the representational format. Our findings suggest that the sensory-like template likely existed in a latent state and was reactivated by visual pings, aligning more closely with the first account raised by the reviewer.

      We agree with the reviewer that whether ping identifies an activity-silent mechanism is currently debated (Schneegans & Bays, 2017; Barbosa et al., 2021). It is possible that visual impulse amplified a subtle but active representation of the sensory template during attentional preparation and resulted in decodable performance in visual cortex. Distinguishing between these two accounts likely requires neurophysiological measurements, which are beyond the scope of the current study. We have explicitly addressed this limitation in our Discussion (Page 19, Line 395-399).

      Nevertheless, the latent sensory-like template account remains plausible for three reasons. First, our interpretation aligns with theoretical framework proposing that the brain maintains more veridical, detailed target templates than those typically utilized for guiding attention (Wolfe, 2021; Yu et al., 2023). Second, this explanation is consistent with the proposed utility of latent working memory for prospective use, as maintaining a latent sensory-like template during preparation would be useful for subsequent stimulus selection. The latter point was further supported by the reviewer’s suggestion about whether “activating the sensory-like template strengthens the informational connectivity between sensory and frontoparietal areas is reasonable”. Our additional analyses (also refer to our response to Reviewer 3, Point 3) suggested that impulse-enhanced V1-PFC connectivity was associated with a trend of faster behavioral responses (r = -0.394, p = 0.086; see Supplementary Information, Page 8, Line 116-121; S5 Fig). Considering these findings in totality, we think it is reasonable to suggest that visual impulse may strengthen information flow among areas to enhance attentional control.

      Recommendation for the Authors:

      Reviewer #1 (Recommendation for the authors):

      I hate to suggest another fMRI experiment, but in order to make strong claims about two states, I would want to see the methodological and interpretation confounds addressed. Ping condition - would a tone lead to the same result of sharpening the template? If so, then why? Can a ping be manipulated in its effectiveness? That would be an excellent manipulation condition.

      We thank the reviewer for the comments. Please refer to our reply to Reviewer 1, Point 5 for detailed explanation.

      Reviewer #2 (Recommendation for the authors):

      It is strongly advised that these descriptive statistics related to the interaction effect should be included to facilitate a more effective understanding of their data.

      We thank the reviewer for the comments. We now included the relevant descriptive statistics in the Supplementary Information, Table 1.

      Reviewer #3 (Recommendation for the authors):

      In addition to p-values, I see many instances of 'ps'. Does this indicate the plural form of p?

      We used ‘ps’ to denote the minimal p-value across multiple statistical analyses, such as when applying identical tests to different region groups.

      References

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      Badre, D. (2008). Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes. Trends in Cognitive Sciences, 12(5), 193-200.

      Barbosa, J., Lozano-Soldevilla, D., & Compte, A. (2021). Pinging the brain with visual impulses reveals electrically active, not activity-silent, working memories. PLoS Biology, 19(10), e3001436.

      Battistoni, E., Stein, T., & Peelen, M. V. (2017). Preparatory attention in visual cortex. Annals of the New York Academy of Sciences, 1396(1), 92-107.

      Brincat, S. L., Siegel, M., von Nicolai, C., & Miller, E. K. (2018). Gradual progression from sensory to task-related processing in cerebral cortex. Proceedings of the National Academy of Sciences, 115(30), E7202-E7211.

      Duncan, D. H., van Moorselaar, D., & Theeuwes, J. (2023). Pinging the brain to reveal the hidden attentional priority map using encephalography. Nature Communications, 14(1), 4749.

      Grill-Spector, K., & Malach, R. (2004). The human visual cortex. Annual Review of Neuroscience, 27(1), 649-677.

      Gong, M., Chen, Y., & Liu, T. (2022). Preparatory attention to visual features primarily relies on nonsensory representation. Scientific Reports, 12(1), 21726.

      Fan, Y., Han, Q., Guo, S., & Luo, H. (2021). Distinct Neural Representations of Content and Ordinal Structure in Auditory Sequence Memory. Journal of Neuroscience, 41(29), 6290–6303.

      Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458(7238), 632-635.

      Jigo, M., Gong, M., & Liu, T. (2018). Neural determinants of task performance during feature-based attention in human cortex. eNeuro, 5(1).

      Kok, P., Failing, M. F., & de Lange, F. P. (2014). Prior expectations evoke stimulus templates in the primary visual cortex. Journal of Cognitive Neuroscience, 26(7), 1546-1554.

      Kok, P., Mostert, P., & De Lange, F. P. (2017). Prior expectations induce prestimulus sensory templates. Proceedings of the National Academy of Sciences, 114(39), 10473-10478.

      Liu, T., Stevens, S. T., & Carrasco, M. (2007). Comparing the time course and efficacy of spatial and feature-based attention. Vision Research, 47(1), 108-113.

      Mongillo, G., Barak, O., & Tsodyks, M. (2008). Synaptic theory of working memory. Science, 319(5869), 1543-1546.

      Peelen, M. V., & Kastner, S. (2011). A neural basis for real-world visual search in human occipitotemporal cortex. Proceedings of the National Academy of Sciences, 108(29), 12125-12130. Priebe, N. J. (2016). Mechanisms of orientation selectivity in the primary visual cortex. Annual Review of Vision Science, 2(1), 85-107.

      Rademaker, R. L., & Serences, J. T. (2017). Pinging the brain to reveal hidden memories. Nature Neuroscience, 20(6), 767-769.

      Rademaker, R. L., Chunharas, C., & Serences, J. T. (2019). Coexisting representations of sensory and mnemonic information in human visual cortex. Nature Neuroscience, 22(8), 1336-1344.

      Serences, J. T., Ester, E. F., Vogel, E. K., & Awh, E. (2009). Stimulus-specific delay activity in human primary visual cortex. Psychological Science, 20(2), 207-214.

      Schneegans, S., & Bays, P. M. (2017). Restoration of fMRI decodability does not imply latent working memory states. Journal of Cognitive Neuroscience, 29(12), 1977-1994.

      Stokes, M., Thompson, R., Nobre, A. C., & Duncan, J. (2009). Shape-specific preparatory activity mediates attention to targets in human visual cortex. Proceedings of the National Academy of Sciences, 106(46), 19569-19574.

      Wolfe, J. M. (2021). Guided Search 6.0: An updated model of visual search. Psychonomic Bulletin & Review, 28(4), 1060-1092.

      Wolff, M. J., Jochim, J., Akyürek, E. G., & Stokes, M. G. (2017). Dynamic hidden states underlying working-memory-guided behavior. Nature Neuroscience, 20(6), 864 – 871.

      Wolff, M. J., Kandemir, G., Stokes, M. G., & Akyürek, E. G. (2020). Unimodal and bimodal access to sensory working memories by auditory and visual impulses. Journal of Neuroscience, 40(3), 671-681.

      Yu, X., Zhou, Z., Becker, S. I., Boettcher, S. E., & Geng, J. J. (2023). Good-enough attentional guidance. Trends in Cognitive Sciences, 27(4), 391-403.

    1. Reviewer #2 (Public review):

      The study by Setogawa et al. aims to understand the role that different striatal subregions belonging to parallel brain circuits have in associative learning and discrimination learning (S-O-R and S-R tasks). Strengths of the study are the use of multiple methodologies to measure and manipulate brain activity in rats, from microPET imaging to excitotoxic lesions and multielectrode recordings across anterior dorsolateral (aDLS), posterior ventral lateral (pVLS)and dorsomedial (DMS) striatum.

  5. doc-0o-54-prod-00-apps-viewer.googleusercontent.com doc-0o-54-prod-00-apps-viewer.googleusercontent.com
    1. impulsos deamor se traspongan en impulsos de agresión hacia el objeto. A raíz de ello, la pulsiónde destrucción queda liberada y quiere aniquilar al objeto, o al menos hace como situviera ese propósito.

      Si la pulsion es un mecanismo biologico para garantizar la supervivencia, el impulso vital por satisfacer una necesidad, ¿qué es entonces el impulso?

    Annotators

    URL

    doc-0o-54-prod-00-apps-viewer.googleusercontent.com/viewer2/prod-00/pdf/5t1t5i3e17791dee5dgr72fetf35lghc/rlnv3pb7arenl3gap8lt9upr6jtorus1/1751166375000/3/113937841593299532125/APznzabYljbqV8NI-CeEODerOWONdiORFkxOLfzgK532WJAuv7dMi4MFZYn1690Sj69ajgF-GUo8NmxconsjAngiOQI30zKfUWWgZwHPVPmtf8cedoM-zPzWWtq39whWGNfHqXbjQVjMRXDki-8rfOfUeBg948uBbwGIIP5nYEGtgcoEFTmdVDpjEZsGiORZF-w9gUjZcFMqRJrIx_syJ-U1KKWeoa7zepmaYihj6_Dg2ktNw7LTAZmKQrgvMlKI8aT_iV4dj373UOPJrDhm6utrQLtVmYv97nb9tj5koucF_mRK7Zzawo7N4mh5QpnBWlh-8WGBDsxauyb2b14A2n4GgSpdJRqBdiRcF84BrQwEV1Nb9O7oQ1bgFS1iykocgxwnREJkYKQjQZ4onu3elXHeR9_Wlqfyt-sn7hkT8PFIw7gvaQscuCUwT8HEs_nOQ665VYSHitptsjXNuTWkghcevXb45tyUJB9BS-wdJ9U5h7HqsKLtuX8eEGuuLpTAzijuyjt137MHgWerGSpQx2q6jkvO9tQx8_ookCfUe-WKxaXIKFhfv_vcyAAfd6XdB9EjguRzCGDHq53N5riCQA3cYCAV9SB0CltG9koXAnSAyQH0g_UIUDnxK98lC_BEnFBV1gwk2iwoEqsE98KvMY4uos3JvIrvecsDxB6TqTsEp392CNoQIAlfKAIHq5h60fo9bs2cPFfjmIb65xSnMMAfMS31qwXQz4yZ2x0j-CLJfk0lqzDL865ApMnGC4JP65wkDbdDmT1SlfoBvGmQ0_llXcEJmn6Fx2uGTYd0UK9nJXiirNwiWUvgku_exXtBURA71vkIYY-KjUtdQa5P59RrKM_RJCzCDuX334T4tFsSlusdM8OI09DDiKlPlBkOHpgWJXTg2Brdg15GZ29kvL3b_oW8GtViLvi_EHhPcN4kxf2O9AMg5H5_T6MxwB3nGLIXYb5vRPMyGz3GdAo-1axgmVfqYnv-0mWNMTtg3ytMkpW8WrG-8MIVz-1rVPWMJwOxMR8Jaw72ZQZCS5qUFEZ3l_iCrM9yh68E3YVW_Qy3rOj8R1YbGQnwWJDqPQhiQYOA-xwtzAiLDw_UB1ymN1NC3GZb4JlgjGVGX2StWUyzalFtuHsvnJ3NUjSZAA2EeWSeGnXK1rwctPzUsPph6-Bjknbws-ExR1xGdLUnzfW8KYO3CDP8tYY36hVNbdfYDlxo8hvsQVM6dZX3Hjl4Yx2mIyPejiEusSQ_h6wtCgDqMMtGJVYEAZRljHnVAua9nKGOOD5pA4aCYhqnjU1AvlmQuBAlCG2rFZjQ0-hgLbcehAU3Tb97TJ1oY6QZWWGVlJuiILzLj-Usom1y86DReQBjoTVnNhcWyIZTa9VmbAFf1-kMAXCYEfvkqWcduyNhfvsANKpXTbSeyBbf1aZVaan7IV7BBcNEYr3Kk3FrxXNIfzHzAP6qyFxaVDPreWB94Y3uSOUQJAaPyaY0ALbOGACU4KIXnZLVmj8WZZx0KwlrqT3rXWyPuzrp-2XQNogKAiSUR4ewKpUDBV6UwJWkJbIavjySU7Rl6dHM_iB2vOibUIZavgwmD2EP3fgc-cXqvFkkspq2NZvE87tiW-gG6a3CfmIrjIk5PNxZV6lBx1DbCREFPLsGL9dZyHXrG48s-Q5knmhWejDRasB6jqkMK9-x0NPMqCSaPE1PicJp5lnG3QNCv_sS1PKINy5nE8GwFNzo6cU1YLxiH1SJrbItmuUOEmjyCcTuiPrRbwTYoIo75eggUvvMQlJPAVN1lCoP7Ct1zAj5ZPKk7bWuBju18MOLE6qLj57VlgYjIiu3bv-V1nhof0iRd2Q89MM1GJBLYin5ZX6mlgGOPH53pwQgvar9nA4sWLDEEpacimXFZkSGGMH-KBjFqRXWvHWpjEtJnBBSXcxUz-2TCgFprHm7syZgHJ3Vw8B_qFDJqquoL-Ve3_oNjIGkCG2J39JEpEPsVEjJuDAR2bwGgySC2fiwY6HKOMom4sdjyBAOkznRnyaz79huUli8I1U__8xIaYZFXjqgDuAeeFMXiyn-dUj1_CvjCbqif_HE8fpmETX9UswXa_KkqjEazhtNmr3Q4xnOnD5iJaYllyWyAWYNGc5o0UU7GmF0Zy7T0A==
  6. drive.google.com drive.google.com
  7. drive.google.com drive.google.com
    1. gr ́aficas devueltas por el mismo circuito

      O sea, lo que entiendo es que muestran los gráficos de la figura 10, no los discuten, e inmediatamente se ponen a hablar de otros resultados (que no muestran) al doble de frecuencia. A ver, todo gráfico que se presenta debe ser primero referido en el texto y segundo discutido exhaustivamente en el mismo. Acá primero hay que hacer una figura con paneles (a) (b) (c) y (d) y aclarar que es una figura pensada para mostrar distintos valores de C. Segundo describir absolutamente todo lo que ve en cada panel remarcando el efecto del valor de capacitancia en el rizado. Tercero priorizar formalidad en el escrito, el circuito que hicieron "no devuelve figuras", la curvas no se planchan "mas rápidamente" Recién después de eso uno empezaría a contar que pasa cuando se dobla la frecuencia y en el contexto de esa discusión se presenta la tabla 5

    2. Resultados y comparaci ́on de valorespara diodo LED:4

      igual que antes, hay que referir en el texto la figura y las tablas. No es conveniente referir las figuras o las tablas como "la siguiente" "la del costado" etc. Esto es por que si ustedes mandan un trabajo a publicar el editor les puede mover las figuras hacia donde el quiere.

    1. desde el mundo islámico se alzan voces y grupos que pretenden «reconquistar»Al-Andalus (es decir España) y sustraerlo del dominio de los infieles

      e por fim, o discurso islâmico de grupos que pretendem reconquistar Al-Andalus e subtraílo do domínio dos infieis (cristãos).

    2. determinar cuándo y porqué comenzó a usarse el vocablo «reconquista» paradesignar el enfrentamiento protagonizado en la península ibérica por cristianosy musulmanes, puesto que en la Edad Media dicho término nunca se utilizó

      para Ríos Saloma existem 3 problemas particulares no conceito: primeiro, o fato de nunca ter sido usado o termo na idade média.

    1. o be catastrophic to the formation of life-essential car-bon and oxygen. A similar calculation of the tolerance forshifts in the EM fine-structure constant αem suggests thatcarbon-oxygen based life can withstand shifts of ≃ 2.5%in αem. Beyond such relatively small changes in the funda-mental parameters, the anthropic principle appears neces-sary to explain the observed abundances of 12C and 16O.We also note that the fine-tuning in the fundamental pa-rameters is much more severe than the one in the energydifference ε.
    1. La sanzione estingue l'obbligo della vaccinazione ma non permette comunque la frequenza all'asilo nido e alle scuole dell'infanzia a meno di adempimento delle vaccinazioni. L'esonero è previsto "ove sussista un accertato pericolo per la salute dell'individuo"

      No, la sanzione amministrativa per inadempienza non estingue l’obbligo vaccinale.

      In Italia, l’obbligo vaccinale (ad esempio per il morbillo) è normato dalla Legge 119/2017, che prevede:

      Un obbligo vaccinale per l’iscrizione a scuola (per i bambini da 0 a 16 anni).

      Una sanzione amministrativa pecuniaria per i genitori che non vaccinano i figli e non giustificano l’omissione (es. per motivi medici).

      Ma il pagamento della sanzione non equivale ad "assolvere" l’obbligo: lo Stato può continuare a sollecitare la vaccinazione.

      In sintesi:

      ✅ Paghi la sanzione → eviti il procedimento amministrativo o le multe successive? NO

      Pagare la sanzione non significa che sei in regola: l’obbligo rimane e può essere fatto valere in altre forme (es. esclusione scolastica). ** Questa impostazione è confermata da diverse sentenze (anche del TAR) e dalla prassi del Ministero della Salute.**

    1. Author response:

      Public Review

      Joint Public Review:

      This manuscript presents an algorithm for identifying network topologies that exhibit a desired qualitative behaviour, with a particular focus on oscillations. The approach is first demonstrated on 3-node networks, where results can be validated through exhaustive search, and then extended to 5-node networks, where the search space becomes intractable. Network topologies are represented as directed graphs, and their dynamical behaviour is classified using stochastic simulations based on the Gillespie algorithm. To efficiently explore the large design space, the authors employ reinforcement learning via Monte Carlo Tree Search (MCTS), framing circuit design as a sequential decision-making process.

      This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

      Strengths

      The evidence presented is strong and compelling. The authors validate their results for 3-node networks through exhaustive search, and the findings for 5-node networks are consistent with previously reported motifs, lending credibility to the approach. The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective, and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

      Weaknesses

      The principal weakness of the manuscript lies in the interpretation of biological robustness. The authors identify network topologies that sustain oscillatory behaviour despite perturbations to the system or parameters. However, in many cases, this persistence is due to the presence of partially redundant oscillatory motifs within the network. While this observation is interesting and of clear value for circuit design, framing it as evidence of evolutionary robustness may be misleading. The "mutant" systems frequently exhibit altered oscillatory properties, such as changes in frequency or amplitude. From a functional cellular perspective, mere oscillation is insufficient - preservation of specific oscillation characteristics is often essential. This is particularly true in systems like circadian clocks, where misalignment with environmental cycles can have deleterious effects. Robustness, from an evolutionary standpoint, should therefore be framed as the capacity to maintain the functional phenotype, not merely the qualitative behaviour.

      A secondary limitation is that, despite the methodological advances, the scale of the systems explored remains modest. While moving from 3- to 5-node systems is non-trivial, five elements still represent a relatively small network. It is somewhat surprising that the algorithm does not scale further, particularly when considering the performance of MCTS in other domains - for instance, modern chess engines routinely explore far larger decision trees. A discussion on current performance bottlenecks and potential avenues for improving scalability would be valuable.

      Finally, it is worth noting that the emergence of oscillations in a model often depends not only on the topology but also critically on parameter choices and the nature of the nonlinearities. The use of Hill functions and high Hill coefficients is a common strategy to induce oscillatory dynamics. Thus, the reported results should be interpreted within the context of the modelling assumptions and parameter regimes employed in the simulations.

      We thank the reviewers for their careful consideration of our work and for the interesting feedback and scientific discussion. We are working on a revised text based on their recommendations, which will include some of the discussion below.

      This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

      We thank the reviewers for their positive assessment of our work’s impact!

      The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective, and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

      We appreciate these kind comments about our work’s merits. We are excited to share our reinforcement learning (RL) based method with the fields of systems and synthetic biology, and we consider it a valuable tool for the systematic analysis and design of larger-scale regulatory networks!

      The principal weakness of the manuscript lies in the interpretation of biological robustness. The authors identify network topologies that sustain oscillatory behaviour despite perturbations to the system or parameters… [However, these] "mutant" systems frequently exhibit altered oscillatory properties, such as changes in frequency or amplitude. From a functional cellular perspective, mere oscillation is insufficient - preservation of specific oscillation characteristics is often essential. This is particularly true in systems like circadian clocks, where misalignment with environmental cycles can have deleterious effects. Robustness, from an evolutionary standpoint, should therefore be framed as the capacity to maintain the functional phenotype, not merely the qualitative behaviour.

      We thank the reviewers for their attention to this point. In the large-scale circuit search, summarized in Figures 4A and 4B, we ran a search for 5-component oscillators that can spontaneously oscillate even when subjected to the deletion of a random gene. Some of the best performing circuits under these conditions exhibited a design feature we call “motif multiplexing,” in which multiple smaller motifs are interleaved in a way that makes oscillation possible under many different mutational scenarios. Interestingly, despite not selecting for preservation of frequency, the 3Ai+3Rep circuit (a 5-gene circuit highlighted in Figure 5) anecdotally appears to have a natural frequency that is robust to partial gene knockdowns, although not to complete gene deletions. As shown in Figure 5C, this circuit has a natural frequency of 6 cycles/hr (with one particular parameterization), and it can sustain a knockdown of any of its 5 genes to 50% of the wild-type transcription rate without altering the natural frequency by more than 20%.

      However, we agree that there are salient differences between this training scenario and natural evolution. The revised text will clarify that these differences limit what conclusions can be drawn about biological evolution by analogy. As the reviewers point out, we use the presence of spontaneous oscillations (with or without the deletion) as a measure of fitness, regardless of frequency, so as to screen for designs with promising behavior. Also, the deletion mutations introduced during training likely represent larger perturbations to the system than a typical mutation encountered during genome replication (for example, a point mutation in a response element leading to a moderate change in binding affinity). Finally, we do not introduce any entrainment. Real circadian oscillators are aligned to a 24-hour period (“entrained”) by environmental inputs such as light and temperature. For this reason, natural circadian clocks may have natural frequencies that are slightly shorter or longer than 24 hours, although a close proximity to the 24-hour period does seem to be an important selective factor [1].

      ...despite the methodological advances, the scale of the systems explored remains modest. While moving from 3- to 5-node systems is non-trivial, five elements still represent a relatively small network. It is somewhat surprising that the algorithm does not scale further, particularly when considering the performance of MCTS in other domains - for instance, modern chess engines routinely explore far larger decision trees. A discussion on current performance bottlenecks and potential avenues for improving scalability would be valuable.

      We thank the reviewers for their attention to this point. The main limitation we encountered to exploring circuits with more than 5 nodes in this work was the poor computational scaling of the Gillespie stochastic simulation algorithm, rather than a limitation of MCTS itself. While the average runtime of a 3-node circuit simulation was roughly 7 seconds, this number increased to 18-20 seconds with 5-node circuits. For this reason, we limited the search to topologies with ≤15 interaction arrows (15 sec/simulation). In general, the simulation time was proportional to the square of the number of transcription factors (TFs). We will revise the text to include the reason for stopping at 5 nodes, which is significant for understanding CircuiTree’s scaling properties.

      With regards to scaling, an important advantage of CircuiTree is its ability to generate useful candidate designs after exploring only a portion of the search space. Like exhaustive search, given enough time, MCTS will comprehensively explore the search space and find all possible solutions. However, for large search spaces, RL-based agents are generally given a finite number of simulations (or time) to learn as much as possible.

      Across machine learning (ML) applications [2] and particularly with RL models [3], this training time tends to obey a power law with respect to the underlying complexity of the problem. Thus we can use the complexity of the 3-node and 5-node searches to infer the current scaling limits of CircuiTree. The first oscillator topology was discovered after 2,280 simulations for the 3-node search, and in the 5-node search, the first oscillator using 5 nodes appeared at ~8e5 simulations, resulting in a power law of Y ~ 84.4 X<sup>0.333</sup>. Thus, useful candidate designs may be found for 6-node and 7-node searches after 4.5e7 and 5.26e9 simulations, respectively, even though these spaces contain 1.5e17 and 2.5e23 topologies, respectively. Thus, running a 7-node search with the current implementation of CircuiTree would require resources close to the current boundaries of computation, requiring roughly 1.8 million CPU-hours, or 2 weeks on 5,000 CPUs, assuming a 1-second simulation. These points will be incorporated into both the results and discussion sections in our revised text.

      However, we are optimistic about CircuiTree’s potential to scale to much larger circuits with modifications to its algorithm. CircuiTree uses the original (so-called “vanilla”) implementation of MCTS, which has not been used in professional game-playing AIs in over a decade. Contemporary RL-based game-playing engines leverage deep neural networks to dramatically reduce the training time, using value networks to identify game-winning positions and policy networks to find game-winning moves. AlphaZero, developed by Google DeepMind to learn games by self-play and without domain knowledge, outperformed all other chess AIs after 44 million training games, much smaller than the 10^43 possible chess states [4]. Similarly, the game of go has 10<sup>170</sup> possible states, but AlphaZero outperformed other AIs after only 140 million games [4]. Large circuits live in similarly large search spaces; for example, 19-node and 20-node circuits represent spaces of 10<sup>172</sup> and 10<sup>190</sup> possible topologies. The revised text will include this discussion and identify value and policy networks, as well as more scalable simulation paradigms such as ODEs and neural ODEs, as our future directions for improving CircuiTree’s scalability.

      Finally, our revised discussion will note some important differences between game-playing and biological circuit design. Unlike deterministic games like chess, the final value of a circuit topology is determined stochastically, by running a simulation whose fitness depends on the parameter set and initial conditions. Thus, state-for-state, it is possible that training an agent for circuit design may inherently require more simulations to achieve the same level of certainty compared to classical games. Additionally, while we often possess a priori knowledge about a game such as its overall difficulty or certain known strategies, we lack this frame of reference when searching for circuit designs. Thus, it remains challenging to know if and when a large space of designs has been “satisfactorily” or “comprehensively” searched, since the answer depends on data that are unknown, namely the quantity, quality, and location of solutions residing in the search space.

      Not accounting for redundancy due to structural symmetries

      Finally, it is worth noting that the emergence of oscillations in a model often depends not only on the topology but also critically on parameter choices and the nature of the nonlinearities. The use of Hill functions and high Hill coefficients is a common strategy to induce oscillatory dynamics. Thus, the reported results should be interpreted within the context of the modelling assumptions and parameter regimes employed in the simulations.

      In our dynamical modeling of transcription factor (TF) networks, we do not rely on continuum assumptions about promoter occupancy such as Hill functions. Rather, we model each reaction - transcription, translation, TF binding/unbinding, and degradation - explicitly, and individual molecules appear and disappear via stochastic birth and death events. Many natural TFs are homodimers that bind cooperatively to regulate transcription; similarly, we assume that pairs of TFs bind more stably to their response element than individual TFs. Thus, our model has similar cooperativity to a Hill function, and it can be shown that in the continuum limit, the effective Hill coefficient is always ≤2. Our revision will clarify this aspect of the modeling and include a derivation of this property. Currently, the parameter values used in the figures are shown in Table 2. In the revised text, these will be displayed in the body of the text as well for clarity.

      Bibliography (1) Spoelstra, K., Wikelski, M., Daan, S., Loudon, A. S. I., & Hau, M. (2015). Natural selection against a circadian clock gene mutation in mice. PNAS, 113(3), 686–691. https://doi.org/https://doi.org/10.1073/pnas.1516442113<br /> (2) Neumann, O., & Gros, C. (2023). Scaling Laws for a Multi-Agent Reinforcement Learning Model. The Eleventh International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=ZrEbzL9eQ3W (3) Jones, A. L. (2021). Scaling Scaling Laws with Board Games. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2104.03113 (4) Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that Masters Chess, Shogi, and go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors present a novel CRISPR/Cas9-based genetic tool for the dopamine receptor dop1R2. Based on the known function of the receptor in learning and memory, they tested the efficacy of the genetic tool by knocking out the receptor specifically in mushroom body neurons. The data suggest that dop1R2 is necessary for longer-lasting memories through its action on ⍺/ß and ⍺'/ß' neurons but is dispensable for short-term memory and thus in ɣ neurons. The experiments impressively demonstrate the value of such a genetic tool and illustrate the specific function of the receptor in subpopulations of KCs for longer-term memories. The data presented in this manuscript are significant.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript examines the role of the dopamine receptor, Dop1R2, in memory formation. This receptor has complex roles in supporting different stages of memory, and the neural mechanisms for these functions are poorly understood. The authors are able to localize Dop1R2 function to the vertical lobes of the mushroom body, revealing a role in later (presumably middle-term) aversive and appetitive memory. In general, the experimental design is rigorous, and statistics are appropriately applied. While the manuscript provides a useful tool, it would be strengthened further by additional mechanistic studies that build on the rich literature examining the roles of dopamine signaling in memory formation. The claim that Dop1R2 is involved in memory formation is strongly supported by the data presented, and this manuscript adds to a growing literature revealing that dopamine is a critical regulator of olfactory memory. However, the manuscript does not necessarily extend much beyond our understanding of Dop1R2 in memory formation, and future work will be needed to fully characterize this reagent and define the role of Dop1R2 in memory.

      Strengths:

      (1) The FRT lines generated provide a novel tool for temporal and spatially precise manipulation of Dop1R2 function. This tool will be valuable to study the role of Dop1R2 in memory and other behaviors potentially regulated by this gene.

      (2) Given the highly conserved role of Dop1R2 in memory and other processes, these findings have a high potential to translate to vertebrate species.

      Weaknesses:

      (1) The authors state Dop1R2 associates with two different G-proteins. It would be useful to know which one is mediating the loss of aversive and appetitive memory in Dop1R2 knockout flies.

      We thank you for the insightful comment. We agree that it would be very useful to know which G-proteins are transmitting Dop1R2 signaling. To that extent, we examined single-cell transcriptomics data to check the level of co-expression of Dop1R2 with G-proteins that are of interest to us. (Figure 1 S1)

      Lines 312-325

      “Some RNA binding proteins and Immediate early genes help maintain identities of Mushroom body cells and are regulators of local transcription and translation (de Queiroz et al., 2025; Raun et al., 2025). So, the availability of different G-proteins may change in different lobes and during different phases of memory. The G-protein via which GPCRs signal, may depend on the pool of available G-proteins in the cell/sub-cellular region (Hermans, 2003)., Therefore, Dop1R2 may signal via different G-proteins in different compartments of the Mushroom body and also different compartments of the neuron. We looked at Gαo and Gαq as they are known to have roles in learning and forgetting (Ferris et al., 2006; Himmelreich et al., 2017). We found that Dop1R2 co-expresses more frequently with Gαo than with Gαq (Figure 1 S1). While there is evidence for Dop1R2 to act via Gαq (Himmelreich et al., 2017). It is difficult to determine whether this interaction is exclusive, or if Dop1R2 can also be coupled to other G-proteins. It will be interesting to determine the breadth of G-proteins that are involved in Dop1R2 signaling.”

      (2) It would be interesting to examine 24hr aversive memory, in addition to 24hr appetitive memory.

      This is indeed an important point and we agree that it will complete the assessment of temporally distinct memory traces. We therefore performed the Aversive LTM experiments and include them in the results.

      Lines 208-228

      “24h memory is impaired by loss of Dop1R2

      Next, we wanted to see if later memory forms are also affected. One cycle of reward training is sufficient to create LTM (Krashes & Waddell, 2008), while for aversive memory, 5-6 cycles of electroshock-trainings are required to obtain robust long-term memory scores (Tully et al., 1994). So, we looked at both, 24h aversive and appetitive memory. For aversive LTM, the flies were tested on the Y-Maze apparatus as described in (Mohandasan et al., (2022).

      Flipping out Dop1R2 in the whole MB causes a reduced 24h memory performance (Figure 4A, E). No phenotype was observed when Ddop1R2 was flipped out in the γ-lobe (Figure 4B, F). However, similar to 2h memory, loss of Ddop1R2 in the α/β-lobes (Figure 4C, G) or the α’/β’-lobes (Figure 4D, H) causes a reduction in memory performance. Thus, Dop1R2 seems to be involved in aversive and appetitive LTM in the α/β-lobes and the α’/β’-lobes.

      Previous studies have shown mutation in the Dop1R2 receptor leads to improvement in LTM when a single shock training paradigm is used (Berry et al., 2012). As we found that it disrupts LTM, we wanted to verify if the absence of Dop1R2 outside the MB is what leads to an improvement in memory. To that extent, we tested panneuronal flip-out of Dop1R2 flies for 6hr and 24hr memory upon single shock using the elav-Gal4 driver. We found that it did not improve memory at both time points (Figure 4 S1). Confirming that flipping out Dop1R2 panneuronally does not improve LTM (Figure 4 S1C) and highlighting its irrelevance in memory outside the MB.”

      (3) The manuscript would be strengthened by added functional analysis. What are the DANs that signal through Dop1R. How do these knockouts impact MBONs?

      We thank you for this question. We indeed agree that it is a highly relevand and open question, how distinct DANs signal via distinct Dopamine receptors. Our work here uniquely focusses on Dop1R2 within the MB. We aim to investigate other DopRs and the connection between DANs in the future using similar approaches.

      (4) Also in Figure 2, the lobe-specific knockouts might be moved to supplemental since there is no effect. Instead, consider moving the control sensory tests into the main figure.

      We thank you for this suggestion and understand that in Figure 2 no significant difference is seen. However, we have emphasized in the text that the results from the supplementary figures are just to confirm that the modifications made at the Dop1R2 locus did not alter its normal function.

      Lines 156-162

      “We wanted to see if flipping out Dop1R2 in the MB affects memory acquisition and STM by using classical olfactory conditioning. In short, a group of flies is presented with an odor coupled to an electric shock (aversive) or sugar (appetitive) followed by a second odor without stimulus. For assessing their memory, flies can freely choose between the odors either directly after training (STM) or at a later timepoint.

      To ensure that the introduced genetic changes to the Dop1R2 locus do not interfere with behavior we first checked the sensory responses of that line”

      (5) Can the single-cell atlas data be used to narrow down the cell types in the vertical lobes that express Dop1R2? Is it all or just a subset?

      This is indeed an interesting question, and we thank you for mentioning it. To address this as best as we could, we analyzed the single cell transcriptomic data from (Davie et al., 2018) and presented it in Figure 1 S1.

      Reviewer #3 (Public Review):

      Summary:

      Kaldun et al. investigated the role of Dopamine Receptor Dop1R2 in different types and stages of olfactory associative memory in Drosophila melanogaster. Dop1R2 is a type 1 Dopamine receptor that can act both through Gs-cAMP and Gq-ERCa2+ pathways. The authors first developed a very useful tool, where tissue-specific knock-out mutants can be generated, using Crispr/Cas9 technology in combination with the powerful Gal4/UAS gene-expression toolkit, very common in fruit flies.

      They direct the K.O. mutation to intrinsic neurons of the main associative memory centre fly brain-the mushroom body (MB). There are three main types of MB-neurons, or Kenyon cells, according to their axonal projections: a/b; a'/b', and g neurons.

      Kaldun et al. found that flies lacking dop1R2 all over the MB displayed impaired appetitive middle-term (2h) and long-term (24h) memory, whereas appetitive short-term memory remained intact. Knocking-out dop1R2 in the three MB neuron subtypes also impaired middle-term, but not short-term, aversive memory.

      These memory defects were recapitulated when the loss of the dop1R2 gene was restricted to either a/b or a'/b', but not when the loss of the gene was restricted to g neurons, showcasing a compartmentalized role of Dop1R2 in specific neuronal subtypes of the main memory centre of the fly brain for the expression of middle and long-term memories.

      Strengths:

      (1) The conclusions of this paper are very well supported by the data, and the authors systematically addressed the requirement of a very interesting type of dopamine receptor in both appetitive and aversive memories. These findings are important for the fields of learning and memory and dopaminergic neuromodulation among others. The evidence in the literature so far was generated in different labs, each using different tools (mutants, RNAi knockdowns driven in different developmental stages...), different time points (short, middle, and long-term memory), different types of memories (Anesthesia resistant, which is a type of protein synthesis independent consolidated memory; anesthesia sensitive, which is a type of protein synthesis-dependent consolidated memory; aversive memory; appetitive memory...) and different behavioral paradigms. A study like this one allows for direct comparison of the results, and generalized observations.

      (2) Additionally, Kaldun and collaborators addressed the requirement of different types of Kenyon cells, that have been classically involved in different memory stages: g KCs for memory acquisition and a/b or a'/b' for later memory phases. This systematical approach has not been performed before.

      (3) Importantly, the authors of this paper produced a tool to generate tissue-specific knock-out mutants of dop1R2. Although this is not the first time that the requirement of this gene in different memory phases has been studied, the tools used here represent the most sophisticated genetic approach to induce a loss of function phenotypes exclusively in MB neurons.

      Weaknesses:

      (1) Although the paper does have important strengths, the main weakness of this work is that the advancement in the field could be considered incremental: the main findings of the manuscript had been reported before by several groups, using tissue-specific conditional knockdowns through interference RNAi. The requirement of Dop1R2 in MB for middle-term and long-term memories has been shown both for appetitive (Musso et al 2015, Sun et al 2020) and aversive associations (Plaçais et al 2017).

      Thank you for this comment. We believe that the main takeaway from the paper is the elegant tool we developed, to study the role of Dop1R2 in fruit flies by effectively flipping it out spatio-temporally. Additionally, we studied its role in all types of olfactory associative memory to establish it as a robust tool that can be used for further research in place of RNAi knockouts which are shown to be less efficient in insects as mentioned in the texts in line 394-398.

      “The genetic tool we generated here to study the role of the Dop1R2 dopamine receptor in cells of interest, is not only a good substitute for RNAi knockouts, which are known to be less efficient in insects (Joga et al., 2016), but also provides versatile possibilities as it can be used in combination with the powerful genetic tools of Drosophila.”

      (2) The approach used here to genetically modify memory neurons is not temporally restricted. Considering the role of dopamine in the correct development of the nervous system, one must consider the possible effects that this manipulation can have in the establishment of memory circuits. However, previous studies addressing this question restricted the manipulation of Dop1R2 expression to adulthood, leading to the same findings than the ones reported in this paper for both aversive and appetitive memories, which solidifies the findings of this paper.

      We thank you for this comment and we agree that it would be important to show a temporally restricted effect of Dop1R2 knockout. To assess this and rule out potential developmental defects we decided to restrict the knockout to the post-eclosion stage and to include these results.

      Lines 230-250

      “Developmental defects are ruled out in a temporally restricted Dop1R2 conditional knockout.

      To exclude developmental defects in the MB caused by flip-out of Dop1R2, we stained fly brains with a FasII antibody. Compared to genetic controls, flies lacking Dop1R2 in the mushroom body had unaltered lobes (Figure 4 S2C).

      Regardless, we wanted to control for developmental defects leading to memory loss in flip-out flies. So, we generated a Gal80ts-containing line, enabling the temporal control of Dop1R2 knockout in the entire mushroom body (MB). Given that the half-life of the receptor remains unknown, we assessed both aversive short-term memory (STM) and long-term memory (LTM) to determine whether post-eclosion ablation of Dop1R2 in the MB produced differences compared to our previously tested line, in which Dop1R2 was constitutively knocked out from fertilization. To achieve this, flies were maintained at 18°C until eclosion and subsequently shifted to 30°C for five to seven days. On the fifth day, training was conducted, followed by memory testing. Our results indicate that aversive STM was not significantly impaired in Dop1R2-deficient MBs compared to control flies (Figure 4 S3), consistent with our previous findings (Figure 2). However, aversive LTM was significantly impaired relative to control lines (Figure 4 S3), which also aligned with prior observations. These findings strongly indicate that memory loss caused by Dop1R2 flip-out is not due to developmental defects.”

      (3) The authors state that they aim to resolve disparities of findings in the field regarding the specific role of Dop1R2 in memory, offering a potent tool to generate mutants and addressing systematically their effects on different types of memory. Their results support the role of this receptor in the expression of long-term memories, however in the experiments performed here do not address temporal resolution of the genetic manipulations that could bring light into the mechanisms of action of Dop1R2 in memory. Several hypotheses have been proposed, from stabilization of memory, effects on forgetting, or integration of sequences of events (sensory experiences and dopamine release).

      We thank you for this comment. We agree that it would be interesting to dissect the memory stages by knocking out the receptor selectively in some of them (encoding, consolidation, retrieval). However, our tool irreversibly flips out Dop1R2 preventing us from investigating the receptor’s role in retrieval. Our results show that the receptor is dispensable for STM formation (Figure 2, Figure 4 Supplement 3), suggesting that it is not involved in encoding new information. On the other hand, it is instead involved in consolidation and/or retrieval of long-term and middle-term memories (Figure 3, Figure 4, Figure 5B).

      Overall, the authors generated a very useful tool to study dopamine neuromodulation in any given circuit when used in combination with the powerful genetic toolkit available in Drosophila. The reports in this paper confirmed a previously described role of Dop1R2 in the expression of aversive and appetitive LTM and mapped these effects to two specific types of memory neurons in the fly brain, previously implicated in the expression and consolidation of long-term associative memories.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) On the first view, the results shown here are different from studies published earlier, while in the same line with others (e.g. Sun et al, for appetitive 24h memories). For example, Berry et al showed that the loss of dop1R2 impairs immediate memory, while memory scores are enhanced 3h, 6h, and 24h after training. Further, they showed data that shock avoidance, at least for higher shock intensities, is reduced in mutant (damb) flies. All in all, this favors how important it is to improve the genetic tools for tissue-specific manipulation. Despite the authors nicely discussing their data with respect to the previous studies, I wondered whether it would be suitable to use the new tool and knock out dop1R2 panneuronally to see whether the obtained data match the results published by Berry et al.. Further, as stated in line 105ff: "As these studies used different learning assays - aversive and appetitive respectively as well as different methods, it is unclear if Dop1R2 has different functions for the different reinforcement stimulus" I wondered why the authors tested aversive and appetitive learning for STM and 2h memory, but only appetitive memory for 24h.

      Thank you for this comment. To that extent, as mentioned above in response to reviewer #2, we included in the results the aversive LTM experiment (Figure 4). Moreover, we performed experiments along the line of Berry et al. using our tool as shown in Figure 4 S1. Our results support that Dop1R2 is required for LTM, rather than to promote forgetting.

      (2) Line 165ff: I can´t find any of the supplementary data mentioned here. Please add the corresponding figures.

      Thank you for pointing this out. In that line we don’t refer to any supplementary data, but to the Figure 1F, showing the absence of the HA-tag in our MB knock-out line. We have clarified this in the text (lines 151-153)

      (3) I can't imagine that the scale bar in Figure 1D-F is correct. I would also like to suggest to show a more detailed analysis of the expression pattern. For example, both anterior and posterior views would be appropriate, perhaps including the VNC. This would allow the expression pattern obtained with this novel tool to be better compared with previously published results. Also, in relation to my comment above (1), it may help to understand the functional differences with previous studies, especially as the authors themselves state that the receptor is "mainly" expressed in the mushroom body (line 99). It would be interesting to see where else it is expressed (if so). This would also be interesting for the panneuronal knockdown experiment suggested under (1). If the receptor is indeed expressed outside the mushroom body, this may explain the differences to Berry et al.

      Thank you for noting this, there was indeed a mistake in the scale bar which we now fixed. Since with our HA-tag immunostaining we could not detect any noticeable signal outside of the MB, we decided to analyze previously existing single cell transcriptomics data that showed expression of the receptor in 7.99% of cells in the VNC and in 13.8% of cells outside the MB (lines 98-100) confirming its sparse expression in the nervous system. The lack of detection of these cells is likely due to the sparse and low expression of the protein. The HA-tag allows to detect the endogenous level of the locus (it is possible that a Gal4/UAS amplification of the signal might allow to detect these cells).

      Regarding the panneuronal knockout, we decided to try to replicate the experiment shown in Berry et al. in Figure 4 S1 and found that Dop1R2 is required for LTM.

      (4) Related to learning data shown in Figures 2-4, the authors should show statistical differences between all groups obtained in the ANOVA + PostHoc tests. Currently, only an asterisk is placed above the experimental group, which does not adequately reflect the statistical differences between the groups. In addition, I would like to suggest adding statistical tests to the chance level as it may be interesting to know whether, for example, scores of knockout flies in 3C and 3D are different from the chance level.

      Many thanks for this correction, we agree with the fact that the way significance scores were shown was not informative enough. We fixed the point by now showing significance between all the control groups and the experimental ones. We also inserted the chance level results in the figure legends.

      (5) Unfortunately, the manuscript has some typing errors, so I would like to ask the authors to check the manuscript again carefully.

      Some Examples:

      Line 31: the the

      Line 56: G-Protein

      Line 64: c-AMP

      Line 68: Dopamine

      Line 70: G-Protein (It alternates between G-protein and G-Protein)

      Line 76: References are formatted incorrectly

      Line 126: Ha-Tag (It alternates between Ha and HA)

      Line 248: missing space before the bracket...is often found

      Thank you for noticing these errors, we have now corrected the spelling throughout the manuscript.

      (6) In the figures the axes are labelled Preference Index (Pref"I"). In the methods, however, the calculation formula is defined as "PREF".

      We thank you for drawing attention to this. To avoid confusion, we changed the definition in the methods section so that it could be clear and coherent (“Memory tests” paragraph in the methods section).

      “PREF = ((N<sub>arm1</sub> - N<sub>arm2</sub>) 100) / N<sub>total</sub> the two preference indices were calculated from the two reciprocal experiments. The average of these two PREFs gives a learning index (LI). LI = (PREF<sub>1</sub> + PREF<sub>2</sub>) / 2.

      In case of all Long-term Aversive memory experiments, Y-Maze protocol was adapted to test flies 24 hours post training. Testing using the Y-Maze was done following the protocol as described in (Mohandasan et al., 2022) where flies were loaded at the bottom of 20-minutes odorized 3D-printed Y-Mazes from where they would climb up to a choice point and choose between the two odors. The learning index was then calculated after counting the flies in each odorized vial as follows: LI = ((N<sub>CS-</sub> - N<sub>CS+</sub>) 100) / N<sub>total</sub>. Where NCS- and NCS+ are the number of flies that were found trapped in the untrained and trained odor tube respectively.

      Reviewer #2 (Recommendations For The Authors):

      (1) In Figures 2 and 3, the legends running two different subfigures is confusing. Would be helpful to find a different way to present.

      Thank you for your suggestion. We modified how we present legends, placing them vertically so that it is clearer.

      (2) Use additional drivers to verify middle and long-term memory phenotypes.

      We agree that it would be interesting to see the role of Dop1R2 in other neurons. To that extent, we looked at long term aversive memory in flies where the receptor was panneuronaly flipped out, and did not find evidence that suggested involvement of Dop1R2 in memory processes outside the MB. (Figure 4 S1)

      (3) Additional discussion of genetic background for fly lines would be helpful.

      Thank you for your advice. We have mentioned the genetic background of flies in the key resources table of the methods sections. Additionally, we also included further explanation on how the lines were created and their genetic background (see “Fly Husbandry” paragraph in the methods section).

      “UAS-flp;;Dop1R2 cko flies and Gal4;Dop1R2<sup>cko</sup> flies were crossed back with ;;Dop<sup>cko</sup> flies to obtain appropriate genetic controls which were heterozygous for UAS and Gal4 but not Dop1R2<sup>cko</sup>.”

      Reviewer #3 (Recommendations For The Authors):

      Line 109 states that to resolve the problem a tool is developed to knock down Dop1R2 in s spatial and temporal specific manner- while I agree that this is within the potential of the tool, there is no temporal control of the flipase action in this study; at least I cannot find references to the use of target/gene switch to control stages of development or different memory phases. However the version available for download is missing supplementary information, so I did not have access to supplementary figures and tables.

      Thank you for the comment, as mentioned before it would be great to be able to dissect the memory phases. We show in lines 232 – 250 and Figure 4 S3 that the temporally restricted flip-out to the post-eclosion life stage gave us coherent results with the previous findings, ruling out potential developmental defects.

      In relation to my comment on the possible developmental effects of the loss of the gene, Figure 1F could showcase an underdeveloped g lobe when looking at the lobe profiles. I understand this is not within the scope of the figure, but maybe a different z projection can be provided to confirm there are no obvious anatomical alterations due to the loss of the receptor.

      We understand the doubt about the correct development of the MB and we thank you for your insightful comment. To that extent we decided to perform a FasII immunostaining that could show us the MB in the different lines (Figure 4 S2) and it appears that there are no notable differences in the lobes development in our knockout line.

      It seems that the obvious missing piece of the puzzle would be to address the effects of knocking out Dop1R2 in aversive LTM. The idea of systematically addressing different types of memory at different time points and in different KCs is the most attractive aspect of this study beyond the technical sophistication, and it feels that the aim of the study is not delivered without that component.

      We agree and we thank you for the clarification. As mentioned above in response to Reviewer #2, we decided to test aversive LTM as described in lines –208-228, Figure 4, Figure 4 S1.

      Some statements of the discussion seem too vague, and I think could benefit from editing:

      Line 284 "however other receptors could use Gq and mediate forgetting"- does this refer to other dopamine receptors? Other neuromodulators? Examples?

      Thank you for pointing this out. We Agree and therefore decided to omit this line.

      Line 289 "using a space training protocol and a Dop1R2 line" - this refers to RNAi lines, but it should be stated clearly.

      That is correct, we thank you for bringing attention to this and clarified it in the manuscript.

      –Lines 329-330

      “Interestingly, using a spaced training protocol and a Dop1R2 RNAi knockout line another study showed impaired LTM (Placais et al., 2017).”

      The paragraph starting in line 305 could be re-written to improve clarity and flow. Some statements seem disconnected and require specific citations. For example "In aversive memory formation, loss of Dop1R2 could lead to enhanced or impaired memory, depending on the activated signaling pathways and the internal state of the animal...". This is not accurate. Berry et al 2012 report enhanced LTM performance in dop1R2 mutants whereas Plaçais et al 2017 report LTM defects in Dop1R2 knock-downs, but these different findings do not seem to rely on different internal states or signaling pathways. Maybe further elaboration can help the reader understand this speculation.

      We agree and we thank you for this advice. We decided to add additional details and citations to validate our speculation

      Lines 350-353

      “In aversive memory formation, loss of Dop1R2 could lead to enhanced or impaired memory, depending on the activated signaling pathways. The signaling pathway that is activated further depends on the available pool of secondary messengers in the cell (Hermans, 2003) which may be regulated by the internal state of the animal.”

      "...for reward memory formation, loss of Dop1R2 seems to impair memory", this seems redundant at this point, as it has been discussed in detail, however, citations should be provided in any case (Musso 2015, Sun 2020)

      Thank you for noting this. We recognize the redundancy and decided to exclude the line.

      Finally, it would be useful to additionally refer to the anatomical terminology when introducing neuron names; for example MBON MVP2 (MBON-g1pedc>a/b), etc.

      Thank you for this suggestion. We understand the importance of anatomical terminologies for the neurons. Therefore, we included them when we introduce neurons in the paper.

      We thank you for your observations. We recognize their value, so we have made appropriate changes in the discussion to sound less vague and more comprehensive.

    1. As such, you become immersed in the virtual space.

      Should we consider any ethical concerns in trying to recreate or simulate traumatic or sacred experiences for the sake of immersion, specially in archeological studies?

    1. abstract work can impact people beyond the scholarly realm

      Isn't it challenging for researchers/ archeology students to navigate situations where the preferred forms of communication for one group (e.g., open data, interactive maps, blogs) are at odds with the norms of another group (e.g., traditional publications, restricted access, or cultural protocols)?

    1. unessay

      I wonder how can this be practically implemented in archaeology courses that must still meet traditional accreditation or departmental assessment standards? How can this approach work for classes with large groups of students?

    1. ‘the digital humanities’ has been percolating in the academy. It is a successor idea to ‘humanities computing’

      Here the text mentions that digital humanities is a successor to 'humanities computing' but does not clearly define either. I think it would be helpful to distinguish the methodological or philosophical differences between the two.

    2. Watrall

      I know Watrall has been cited multiple times in the text, but I think it would be useful to cite here as well in order for easier accessibility to the reference where he "identifies the emergence of the web as being not so much a boon for computational archaeology"

    3. Ben Marwick

      Perhaps some of Ben Marwick's works could be cited here so people can see his research and methodologies. I don't see a mention of his work even in the References list.

  8. drive.google.com drive.google.com
    1. norte magn ́etico y un m ́ınimo correspondiente al sur magn ́etico

      la componente vertical no se mide respecto del norte o sur magnético, no me queda claro que fue lo que quisieron. Deberían haber aclarado mejor en la descripción de la experiencia como fue que ""giraron" la sonda. En estos casos lo mejor es definir un sistema de coordenadas y referir la descripción a ese sistema, sin ambigüedades

    2. se extendi ́o

      se extendió significa que se hizo otro ajuste? o es el mismo ajuste con otro modelo, distinto a la ecuación 2.2, que contempla dos solenoides superpuestos?, no queda claro.

    1. Phenotypes range from markedly hypoplastic (thin) enamel (either uniformly withspacing between adjacent teeth or irregularly giving rise to pits or grooves) tovarying degrees of hypomineralization (poorly formed enamel) with altered colourand translucency. In many cases both hypoplasia and hypomineralization are seentogether. The colour of the teeth is presumed to reflect the degree of hypomineralization ofthe enamel – the darker the colour the more severe the degree of hypomineralization

      ① Phenotypes range from markedly hypoplastic (thin) enamel (either uniformly with spacing between adjacent teeth or irregularly giving rise to pits or grooves) to varying degrees of hypomineralization (poorly formed enamel) with altered colour and translucency. ① Fenotipler belirgin hipoplastik (ince) mine yapısından (ya komşu dişler arasında düzgün boşluklarla ya da düzensiz, çukurlar veya oluklar oluşturarak) değişen derecelerde hipomineralizasyona (kötü oluşmuş mine) kadar değişir; renk ve saydamlıkta değişiklikler olur.

      ② In many cases both hypoplasia and hypomineralization are seen together. ② Birçok vakada hem hipoplazi hem de hipomineralizasyon birlikte görülür.

      ③ The colour of the teeth is presumed to reflect the degree of hypomineralization of the enamel – the darker the colour the more severe the degree of hypomineralization. ③ Dişlerin rengi, mine hipomineralizasyon derecesini yansıttığı varsayılır – renk ne kadar koyuysa, hipomineralizasyon o kadar şiddetlidir.

    Annotators

    1. rise to either missing teeth, usually the maxillary lateral incisor, and/or supernumeraryteeth adjacent to the cleft. It is extremely rare for the canine tooth to be affected inthe same way

      ① In patients affected by dentoalveolar clefting, because of disruption of the dental lamina at that site, there may be abnormal induction or proliferation. ① Dentoalveoler yarıkla etkilenmiş hastalarda, o bölgede dental lamina’nın bozulması nedeniyle anormal indüksiyon veya proliferasyon olabilir.

      ② This may give rise to either missing teeth, usually the maxillary lateral incisor, and/or supernumerary teeth adjacent to the cleft. ② Bu durum genellikle maksiller lateral kesici dişin eksikliği ve/veya yarık bitişiğinde fazla sayıda diş oluşmasına neden olabilir.

      ③ It is extremely rare for the canine tooth to be affected in the same way. ③ Kanin dişin aynı şekilde etkilenmesi son derece nadirdir.

    2. n patients affected by dentoalveolar clefting, because of disruption of the dentallamina at that site, there may be abnormal induction or proliferation. This may give

      ① In patients affected by dentoalveolar clefting, because of disruption of the dental lamina at that site, there may be abnormal induction or proliferation. ① Dentoalveoler yarıkla etkilenmiş hastalarda, o bölgede dental lamina’nın bozulması nedeniyle anormal indüksiyon veya proliferasyon olabilir.

      ② This may give rise to either missing teeth, usually the maxillary lateral incisor, and/or supernumerary teeth adjacent to the cleft. ② Bu durum genellikle maksiller lateral kesici dişin eksikliği ve/veya yarık bitişiğinde fazla sayıda diş oluşmasına neden olabilir.

      ③ It is extremely rare for the canine tooth to be affected in the same way. ③ Kanin dişin aynı şekilde etkilenmesi son derece nadirdir.

    Annotators

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Overall, the data presented in this manuscript is of good quality. Understanding how cells control RPA loading on ssDNA is crucial to understanding DNA damage responses and genome maintenance mechanisms. The authors used genetic approaches to show that disrupting PCNA binding and SUMOylation of Srs2 can rescue the CPT sensitivity of rfa1 mutants with reduced affinity for ssDNA. In addition, the authors find that SUMOylation of Srs2 depends on binding to PCNA and the presence of Mec1.

      Comments on revisions:

      I am satisfied with the revisions made by the authors, which helped clarify some points that were confusing in the initial submission.

      Thank you.

      Reviewer #2 (Public Review):

      This revised manuscript mostly addresses previous concerns by doubling down on the model without providing additional direct evidence of interactions between Srs2 and PCNA, and that "precise sites of Srs2 actions in the genome remain to be determined." One additional Srs2 allele has been examined, showing some effect in combination with rfa1-zm2. Many of the conclusions are based on reasonable assumptions about the consequences of various mutations, but direct evidence of changes in Srs2 association with PNCA or other interactors is still missing. There is an assumption that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects, which may not be the case. How SLX4 might interact with Srs2 is unclear to me, again assuming that the SLX4 defect is "surgical" - removing only one of its many interactions.

      Previous studies have already provided direct evidence for the interaction between Srs2 and PCNA through the Srs2’s PIM region (Armstrong et al, 2012; Papouli et al, 2005); we have added these citations in the text. Similarly. Srs2 associations with SUMO and Rad51 have also been demonstrated (Colavito et al, 2009; Kolesar et al, 2016; Kolesar et al., 2012), and these studies were cited in the text.

      We did not state that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects. We only assessed whether these previously characterized mutant alleles could mimic srs2∆ in rescuing rfa1-zm2 defects.

      We assessed the genetic interaction between slx4-RIM and srs2-∆PIM mutants, and not the physical interaction between the two proteins. As we described in the text, our rationale for this genetic test is based on that the reports that both slx4 and srs2 mutants impair recovery from the Mec1 induced checkpoint, thus they may affect parallel pathways of checkpoint dampening.

      One point of concern is the use of t-tests without some sort of correction for multiple comparisons - in several figures. I'm quite sceptical about some of the p < 0.05 calls surviving a Bonferroni correction. Also in 4B, which comparison is **? Also, admittedly by eye, the changes in "active" Rad53 seem much greater than 5x. (also in Fig. 3, normalizing to a non-WT sample seems odd).

      Claims made in this work were based only on pairwise comparison not multi-comparison. We have now made this point clearer in the graphs and in Method. As the values were compared between a wild-type strain and a specific mutant strain, or between two mutants, we believe that t-test is suitable for statistical analysis.

      Figure 4B, ** indicates that the WT value is significantly different from that of the slx4-RIM srs2-∆PIM double mutant and from that of srs2-∆PIM single mutant. We have modified the graph to indicate the pair-wide comparison. The 5-fold change of active Rad53 levels was derived by comparing the values between the srs2∆ PIM slx4<sup>RIM</sup>-TAP double mutant and wild-type Slx4-TAP. In Figure 3, normalization to the lowest value affords better visualization. This is rather a stylish issue; we would like to maintain it as the other reviewers had no issues.

      What is the WT doubling time for this strain? From the FACS it seems as if in 2 h the cells have completed more than 1 complete cell cycle. Also in 5D. Seems fast...

      Wild-type W303 strain has less than 90 min doubling time as shown by many labs, and our data are consistent with this. The FACS profiles for wild-type cells shown in Figures 3C, 4C, and 5C are consistent with each other, showing that after G1 cells entered the cell cycle, they were in G2 phase at the 1-hour time points, and then a percentage of the cells exited the first cell cycle by two hours.

      I have one over-arching confusion. Srs2 was shown initially to remove Rad51 from ssDNA and the suppression of some of srs2's defects by deleting rad51 made a nice, compact story, though exactly how srs2's "suppression of rad6" fit in isn't so clear (since Rad6 ties into Rad18 and into PCNA ubiquitylation and into PCNA SUMOylation). Now Srs2 is invoked to remove RPA. It seems to me that any model needs to explain how Srs2 can be doing both. I assume that if RPA and Rad51 are both removed from the same ssDNA, the ssDNA will be "trashed" as suggested by Symington's RPA depletion experiments. So building a model that accounts for selective Srs2 action at only some ssDNA regions might be enhanced by also explaining how Rad51 fits into this scheme.

      While the anti-recombinase function of Srs2 was better studied, its “anti-RPA” role in checkpoint dampening was recently described by us (Dhingra et al, 2021) following the initial report by the Haber group some time ago (Vaze et al, 2002). A better understanding of this new role is required before we can generate a comprehensive picture of how Srs2 integrates the two functions (and possibly other functions). Our current work addresses this issue by providing a more detailed understanding of this new role of Srs2.

      Single molecular data showed that Srs2 strips both RPA and Rad51 from ssDNA, but this effect is highly dynamic (i.e. RPA and Rad51 can rebind ssDNA after being displaced) (De Tullio et al, 2017). As such, generation of “deserted” ssDNA regions lacking RPA and Rad51 in cells can be an unlikely event. Rather, Srs2 can foster RPA and Rad51 dynamics on ssDNA. Additional studies will be needed to generate a model that integrates the anti-recombinase and the anti-RPA roles of Srs2.

      As a previous reviewer has pointed out, CPT creates multiple forms of damage. Foiani showed that 4NQO would activate the Mec1/Rad53 checkpoint in G1- arrested cells, presumably because there would be singlestrand gaps but no DSBs. Whether this would be a way to look specifically at one type of damage is worth considering; but UV might be a simpler way to look. As also noted, the effects on the checkpoint and on viability are quite modest. Because it isn't clear (at least to me) why rfa1 mutants are so sensitive to CPT, it's hard for me to understand how srs2-zm2 has a modest suppressive effect: is it by changing the checkpoint response or facilitating repair or both? Or how srs2-3KR or srs2-dPIM differ from rfa1-zm2 in this respect. The authors seem to lump all these small suppressions under the rubric of "proper levels of RPA-ssDNA" but there are no assays that directly get at this. This is the biggest limitation.

      CPT treatment is an ideal condition to examine how cells dampen the DNA damage checkpoint, because while most genotoxic conditions (e.g. 4NQO, MMS) induce both the DNA replication checkpoint and the DNA damage checkpoint, CPT was shown to only induced the latter (Menin et al, 2018; Minca & Kowalski, 2011; Redon et al, 2003; Tercero et al, 2003). Future studies examining 4NQO and UV conditions can further expand our understanding of checkpoint dampening in different conditions.

      We have previously provided evidence to support the conclusion that srs2 suppression of rfa1-zm is partly mediated by changing checkpoint levels (Dhingra et al., 2021). We cannot exclude the possibility that the suppression may also be related to changes of DNA repair; we have now added this note in the text.

      Regarding direct testing RPA levels on DNA, we have previously shown that srs2∆ increased the levels of chromatin associated Rfa1 and this is suppressed by rfa1-zm2 (Dhingra et al., 2021). We have now included chromatin fractionation data to show that srs2-∆PIM also led to an increase of Rfa1 on chromatin, and this was suppressed by rfa1-zm2 (new Fig. S2).

      Srs2 has also been implicated as a helicase in dissolving "toxic joint molecules" (Elango et al. 2017). Whether this activity is changed by any of the mutants (or by mutations in Rfa1) is unclear. In their paper, Elango writes: "Rare survivors in the absence of Srs2 rely on structure-specific endonucleases, Mus81 and Yen1, that resolve toxic joint-molecules" Given the involvement of SLX4, perhaps the authors should examine the roles of structure-specific nucleases in CPT survival?

      Srs2 has several roles, and its role in RPA antagonism can be genetically separated from its role in Rad51 regulation as we have shown in our previous work (Dhingra et al., 2021) and this notion is further supported by evidence presented in the current work. Srs2’s role in dissolving "toxic joint molecules” was mainly observed during BIR (Elango et al, 2017). Whether it is related to checkpoint dampening will be interesting to address in the future but is beyond of the scope of the current work that seeks to answer the question how Srs2 regulates RPA during checkpoint dampening. Similarly, determining the roles of Mus81 and Yen1 and other structural nucleases in CPT survival is a worthwhile task but it is a research topic well separated from the focus of this work.

      Experiments that might clarify some of these ambiguities are proposed to be done in the future. For now, we have a number of very interesting interactions that may be understood in terms of a model that supposes discriminating among gaps and ssDNA extensions by the presence of PCNA, perhaps modified by SUMO. As noted above, it would be useful to think about the relation to Rad6.

      Several studies have shown that Srs2’s functional interaction with Rad6 is based on Srs2-mediated recombination regulation (reviewed by (Niu & Klein, 2017). Given that recombinational regulation by Srs2 is genetically separable from the Srs2 and RPA antagonism (Dhingra et al., 2021), we do not see a strong rationale to examine Rad6 in this work, which addresses how Srs2 regulates RPA. With this said, this study has provided basis for future studies of possible cross-talks among different Srs2-mediated pathways.

      Reviewer #3 (Public Review):

      The superfamily I 3'-5' DNA helicase Srs2 is well known for its role as an anti-recombinase, stripping Rad51 from ssDNA, as well as an anti-crossover factor, dissociating extended D-loops and favoring non-crossover outcome during recombination. In addition, Srs2 plays a key role in in ribonucleotide excision repair. Besides DNA repair defects, srs2 mutants also show a reduced recovery after DNA damage that is related to its role in downregulating the DNA damage signaling or checkpoint response. Recent work from the Zhao laboratory (PMID: 33602817) identified a role of Srs2 in downregulating the DNA damage signaling response by removing RPA from ssDNA. This manuscript reports further mechanistic insights into the signaling downregulation function of Srs2.

      Using the genetic interaction with mutations in RPA1, mainly rfa1-zm2, the authors test a panel of mutations in Srs2 that affect CDK sites (srs2-7AV), potential Mec1 sites (srs2-2SA), known sumoylation sites (srs2-3KR), Rad51 binding (delta 875-902), PCNA interaction (delta 1159-1163), and SUMO interaction (srs2SIMmut). All mutants were generated by genomic replacement and the expression level of the mutant proteins was found to be unchanged. This alleviates some concern about the use of deletion mutants compared to point mutations. Double mutant analysis identified that PCNA interaction and SUMO sites were required for the Srs2 checkpoint dampening function, at least in the context of the rfa1-zm2 mutant. There was no effect of this mutants in a RFA1 wild type background. This latter result is likely explained by the activity of the parallel pathway of checkpoint dampening mediated by Slx4, and genetic data with an Slx4 point mutation affecting Rtt107 interaction and checkpoint downregulation support this notion. Further analysis of Srs2 sumoylation showed that Srs2 sumoylation depended on PCNA interaction, suggesting sequential events of Srs2 recruitment by PCNA and subsequent sumoylation. Kinetic analysis showed that sumoylation peaks after maximal Mec1 induction by DNA damage (using the Top1 poison camptothecin (CPT)) and depended on Mec1. This data are consistent with a model that Mec1 hyperactivation is ultimately leading to signaling downregulation by Srs2 through Srs2 sumoylation. Mec1-S1964 phosphorylation, a marker for Mec1 hyperactivation and a site found to be needed for checkpoint downregulation after DSB induction, did not appear to be involved in checkpoint downregulation after CPT damage. The data are in support of the model that Mec1 hyperactivation when targeted to RPA-covered ssDNA by its Ddc2 (human ATRIP) targeting factor, favors Srs2 sumoylation after Srs2 recruitment to PCNA to disrupt the RPA-Ddc2-Mec1 signaling complex. Presumably, this allows gap filling and disappearance of long-lived ssDNA as the initiator of checkpoint signaling, although the study does not extend to this step.

      Strengths:

      (1) The manuscript focuses on the novel function of Srs2 to downregulate the DNA damage signaling response and provide new mechanistic insights.

      (2) The conclusions that PCNA interaction and ensuing Srs2-sumoylation are involved in checkpoint downregulation are well supported by the data.

      Weaknesses:

      (1) Additional mutants of interest could have been tested, such as the recently reported Pin mutant, srs2-Y775A (PMID: 38065943), and the Rad51 interaction point mutant, srs2-F891A (PMID: 31142613).

      (2) The use of deletion mutants for PCNA and RAD51 interaction is inferior to using specific point mutants, as done for the SUMO interaction and the sites for post-translational modifications.

      (3) Figure 4D and Figure 5A report data with standard deviations, which is unusual for n=2. Maybe the individual data points could be plotted with a color for each independent experiment to allow the reader to evaluate the reproducibility of the results.

      Comments on revisions:

      In this revision, the authors adequately addressed my concerns. The only issue I see remaining is the site of Srs2 action. The authors argue in favor of gaps and against R-loops and ssDNA resulting from excessive supercoiling. The authors do not discuss ssDNA resulting from processing of onesided DSBs, which are expected to result from replication run-off after CPT damage but are not expected to provide the 3'-junction for preferred PCNA loading. Can the authors exclude PCNA at the 5'-junction at a resected DSB?

      We have now added a sentence stating that we cannot exclude the possibility that PCNA may be positioned at a 5’-junction, as this can be observed in vitro, albert that PCNA loading was seen exclusively at a 3’-junction in the presence of RPA (Ellison & Stillman, 2003; Majka et al, 2006).

      Recommendations For the authors:

      Reviewer #2 (Recommendations For the authors):

      A Bonferroni correction should be made for the multiple comparisons in several figures.

      Specific comments:

      l. 41. This is a too long and confusing sentence.

      Sentence shortened: “These data suggest that Srs2 recruitment to PCNA proximal ssDNA-RPA filaments followed by its sumoylation can promote checkpoint recovery, whereas Srs2 action is minimized at regions with no proximal PCNA to permit RPA-mediated ssDNA protection”.

      l. 60. Identify Ddc2 and Mec1 as ATRIP and ATR.

      Done.

      l. 125 "fails to downregulate RPA levels on chromatin and Mec1-mediated DDC..." fails to downregulate RPA and fails to reduce Mec1-mediated DDC?

      Sentence modified: “fails to downregulate both the RPA levels on chromatin and the Mec1-mediated DDC”

      l. 204 "consistent with the notion that Srs2 has roles beyond RPA regulation"... What other roles? It's stripping of Rad51? Removing toxic joint molecules? Something else?

      Sentence modified: “consistent with the notion that Srs2 has roles beyond RPA regulation, such as in Rad51 regulation and removing DNA joint molecules”.

      l. 249 "Significantly, srs2-ΔPIM and -3KR increased the percentage of rfa1-zm2 cells transitioning into the G1 phase" No. Just back to normal. As stated in l. 258: "258 We found that srs2-ΔPIM and srs2-3KR mutants on their own behaved normally in the two DDC assays described above." All of these effects are quite small.

      Sentence modified: “Compared with rfa1-zm2 cells, srs2-∆PIM rfa1-zm2 and srs2-3KR rfa1-zm2 cells showed increased percentages of cells transitioning into the G1 phase”.

      l. 468 "Our previous work has provided several lines of evidence to support that Rad51 removal by Srs2 is separable from the Srs2-RPA antagonism (Dhingra et al., 2021). What evidence? See my comment above about not having both proteins removed at the same time.

      We have addressed this point in our initial rebuttal and some key points are summarized below. In our previous report (Dhingra et al., 2021), we provided several lines of evidence to support the conclusion that Rad51 is not relevant to the Srs2-RPA antagonism. For example, while rad51∆ rescues the hyper-recombination phenotype of srs2∆ cells, rad51∆ did not affect the hyper-checkpoint phenotype of srs2∆. In contrast, rfa1-zm1/zm2 have the opposite effects, that is, rfa1zm1/zm2 suppressed the hyper-checkpoint, but not the hyper-recombination, phenotype of srs2∆ cells. The differential effects of rad51∆ and rfa1-zm1/zm2 were also seen for the ATPase dead allele of Srs2 (srs2K41A). For example, rfa1-zm2 rescued hyper-checkpoint and CPT sensitivity of srs2-K41A cells, while rad51∆ had neither effect. These and other data described by Dhingra et al (2021) suggest that Srs2’s effects on checkpoint vs. recombination can be separated genetically. Consistent with our conclusion summarized above, deleting the Rad51 binding domain in Srs2 (srs2-∆Rad51BD) has no effect on rfa1-zm2 phenotype in CPT (Fig. 2D). This data provides yet another evidence that Srs2 regulation of Rad51 is separable from the Srs2RPA antagonism.

      l. 525 "possibility, we tested the separation pin of Srs2 (Y775), which was shown to enables its in vitro helicase activity during the revision of our work..." ?? there was helicase activity during the revision of your work? Please fix the sentence.

      Sentence modified: “we tested the separation pin of Srs2 (Y775). This residue was shown to be key for the Srs2’s helicase activity in vitro in a report that was published during the revision of our work (Meir et al, 2023).”

      Fig. 3. "srs2-ΔPIM and -3KR allow better G1 entry of rfa1-zm2 cells." is it better entry or less arrest at G2/M? One implies better turning off of a checkpoint, the other suggests less activation of the checkpoint.

      This is a correct statement. For all strains examined in Figure 3, cells were seen in G2/M phase after 1-hour CPT treatment, suggesting proper arrest.

      References:

      Armstrong AA, Mohideen F, Lima CD (2012) Recognition of SUMO-modified PCNA requires tandem receptor motifs in Srs2. Nature 483: 59-63

      Colavito S, Macris-Kiss M, Seong C, Gleeson O, Greene EC, Klein HL, Krejci L, Sung P (2009) Functional significance of the Rad51-Srs2 complex in Rad51 presynaptic filament disruption. Nucleic Acids Res 37: 6754-6764.

      De Tullio L, Kaniecki K, Kwon Y, Crickard JB, Sung P, Greene EC (2017) Yeast Srs2 helicase promotes redistribution of single-stranded DNA-bound RPA and Rad52 in homologous recombination regulation. Cell Rep 21: 570-577

      Dhingra N, Kuppa S, Wei L, Pokhrel N, Baburyan S, Meng X, Antony E, Zhao X (2021) The Srs2 helicase dampens DNA damage checkpoint by recycling RPA from chromatin. Proc Natl Acad Sci U S A 118: e2020185118

      Elango R, Sheng Z, Jackson J, DeCata J, Ibrahim Y, Pham NT, Liang DH, Sakofsky CJ, Vindigni A, Lobachev KS et al (2017) Break-induced replication promotes formation of lethal joint molecules dissolved by Srs2. Nat Commun 8: 1790

      Ellison V, Stillman B (2003) Biochemical characterization of DNA damage checkpoint complexes: clamp loader and clamp complexes with specificity for 5' recessed DNA. PLoS Biol 1: E33

      Kolesar P, Altmannova V, Silva S, Lisby M, Krejci L (2016) Pro-recombination Role of Srs2 Protein Requires SUMO (Small Ubiquitin-like Modifier) but Is Independent of PCNA (Proliferating Cell Nuclear Antigen) Interaction. J Biol Chem 291: 7594-7607.

      Kolesar P, Sarangi P, Altmannova V, Zhao X, Krejci L (2012) Dual roles of the SUMO-interacting motif in the regulation of Srs2 sumoylation. Nucleic Acids Res 40: 7831-7843.

      Majka J, Binz SK, Wold MS, Burgers PM (2006) Replication protein A directs loading of the DNA damage checkpoint clamp to 5'-DNA junctions. J Biol Chem 281: 27855-27861

      Meir A, Raina VB, Rivera CE, Marie L, Symington LS, Greene EC (2023) The separation pin distinguishes the pro- and anti-recombinogenic functions of Saccharomyces cerevisiae Srs2. Nat Commun 14: 8144

      Menin L, Ursich S, Trovesi C, Zellweger R, Lopes M, Longhese MP, Clerici M (2018) Tel1/ATM prevents degradation of replication forks that reverse after Topoisomerase poisoning. EMBO Rep 19: e45535

      Minca EC, Kowalski D (2011) Replication fork stalling by bulky DNA damage: localization at active origins and checkpoint modulation. Nucleic Acids Res 39: 2610-2623

      Niu H, Klein HL (2017) Multifunctional roles of Saccharomyces cerevisiae Srs2 protein in replication, recombination and repair. FEMS Yeast Res 17: fow111

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    1. Early in the postradiation period, scarring fibrosis and edema begin to appear lymphaticchannels are thought to be relatively radio‑resistant. Radiation‑induced fibrosis impairs thelymphatic and venous channels. Edema is most prominent in the submental region followingirradiation for anterior tongue and floor of the mouth and occasionally severe enoughcompromise tongue mobility and salivary control further impending denture wearing andspeech articulation. The severely of edema varies by time of the day (worse on waking and earlymorning and day to day)

      ❶ Early in the postradiation period, scarring fibrosis and edema begin to appear lymphatic channels are thought to be relatively radio‑resistant. ❶ Radyoterapi sonrası erken dönemde, yara izi oluşturan fibroz ve ödem görünmeye başlar; lenfatik kanalların nispeten radyasyona dayanıklı olduğu düşünülmektedir.

      ❷ Radiation‑induced fibrosis impairs the lymphatic and venous channels. ❷ Radyasyon kaynaklı fibroz, lenfatik ve venöz kanalları olumsuz etkiler.

      ❸ Edema is most prominent in the submental region following irradiation for anterior tongue and floor of the mouth and occasionally severe enough compromise tongue mobility and salivary control further impending denture wearing and speech articulation. ❸ Ödem, özellikle ön dil ve ağız tabanı için uygulanan radyasyon sonrası submental bölgede belirgindir ve bazen dil hareketliliğini ve tükürük kontrolünü o kadar etkiler ki, protez takmayı ve konuşmayı zorlaştırır.

      ❹ The severely of edema varies by time of the day (worse on waking and early morning and day to day). ❹ Ödemin şiddeti günün saatine göre değişir (uyanırken ve sabah erken saatlerde daha kötü olup, gün içinde değişkenlik gösterir).

    2. Limited mouth opening can interfere with proper oral hygiene and dental treatment.Tongue blades can be used to gradually increase the mandibular opening. Dynamic bite openingappliances have also been used. Primary treatment is essentially to exercise the involvedmuscles. For patients who experience reduced mouth opening the intensity and frequency of theexercises should be increased.Trismus and fibrosis will continue following RT, which will increase in severity with time.This condition will only improve with constant exercise regimen. Exercise should be performeddeliberately at regular intervals followed by a period of rest. More frequently and diligently theexercise regimen, the more beneficial the result. Chronic trismus gradually converts into fibrosisof the muscles and at this late stage stretching of muscle is not favored as a solution. Exercisemust begin early in treatment regimen

      ❶ Limited mouth opening can interfere with proper oral hygiene and dental treatment. ❶ Sınırlı ağız açıklığı, uygun ağız hijyeni ve diş tedavisini engelleyebilir.

      ❷ Tongue blades can be used to gradually increase the mandibular opening. ❷ Alt çenenin açılmasını kademeli olarak artırmak için dil spatulaları kullanılabilir.

      ❸ Dynamic bite opening appliances have also been used. ❸ Dinamik ısırık açma aparatı da kullanılmaktadır.

      ❹ Primary treatment is essentially to exercise the involved muscles. ❹ Temel tedavi, ilgili kasların egzersiz yapılmasıdır.

      ❺ For patients who experience reduced mouth opening the intensity and frequency of the exercises should be increased. ❺ Ağız açıklığı azalan hastalarda egzersizlerin yoğunluğu ve sıklığı artırılmalıdır.

      ❻ Trismus and fibrosis will continue following RT, which will increase in severity with time. ❻ Trismus ve fibrozis radyoterapiden sonra devam edecek ve zamanla şiddeti artacaktır.

      ❼ This condition will only improve with constant exercise regimen. ❼ Bu durum ancak sürekli bir egzersiz programı ile iyileşir.

      ❽ Exercise should be performed deliberately at regular intervals followed by a period of rest. ❽ Egzersizler düzenli aralıklarla bilinçli şekilde yapılmalı ve ardından dinlenme dönemi gelmelidir.

      ❾ More frequently and diligently the exercise regimen, the more beneficial the result. ❾ Egzersiz programı ne kadar sık ve titizlikle uygulanırsa sonuç o kadar faydalı olur.

      ❿ Chronic trismus gradually converts into fibrosis of the muscles and at this late stage stretching of muscle is not favored as a solution. ❿ Kronik trismus zamanla kasların fibrozisine dönüşür ve bu ileri aşamada kas germe çözüm olarak tercih edilmez.

      ⓫ Exercise must begin early in treatment regimen. ⓫ Egzersiz, tedavi programının erken döneminde başlamalıdır.

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