The Deputy of France announced on his Twitter account that "Bayrou the victory over Nazism does not deserve to be a holiday." This shows he's in agreement with removing this public holiday. Members of the Paris counsel are in disagreement with this decision that the prime Minister is proposing and consider it absurd to remove these public holidays.

"...carried against the Prime Minister "who finds nothing more innovative and absurd than to propose the abolition of May 8" while "France, peace are threatened and where there is an urgent need to strengthen the intergenerational and citizen link".

""liquidator", according to him "of the legacy of the National Council of the Resistance" I looked up the definition of "liquidator" and it is "a person appointed to wind up the affairs of a company or firm."

An RN boss expressed the sentiment of this decision being a direct attack on French history, culture, and France as a whole. I agree.

"deplored a "provocation" and "a direct attack on our history, on our roots, and on the France of work"."

The Prime Minister, François Bayrou, announced that two public holidays will be removed. This decision has attracted much criticism, as people are concerned that these meaningful holidays that are core memories for people will be lost.

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.

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.

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.

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."

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.

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.

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.

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.

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.

Na realidade, toda a programação de atividades com os alunos, sejam em ambientes virtuais ou presenciais, devem ou deveriam partir desta estrutura. Na minha perspetiva a grande diferença reside fundamentalmente no facto de, no contexto de ensino e aprendizagem digital a estrutura ser impreterível devido aos diferentes tempos de organização da aprendizagem (trabalho autónomo, comunicação síncrona e assíncrona) e pelo facto de assentar numa pedagogia socio-construtivista.

(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?

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.

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?....

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.

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.

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.

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.

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.

Um dos caminhos que costumo escolher é trazer as experiências pessoais à tona, na medida em que sintam-se a vontade para compartilhá-las em sala de aula. É uma das formas de engajamento mais eficazes, no meu ponto de vista.

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.

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?

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.

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.

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.

Author Response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review): 

Summary: 

Genome-wide association studies have been an important approach to identifying the genetic basis of human traits and diseases. Despite their successes, for many traits, a substantial amount of variation cannot be explained by genetic factors, indicating that environmental variation and individual 'noise' (stochastic differences as well as unaccounted for environmental variation) also play important roles. The authors' goal was to address whether gene expression variation in genetically identical individuals, driven by historical environmental differences and 'noise', could be used to predict reproductive trait differences. 

Strengths: 

To address this question, the authors took advantage of genetically identical C. elegans individuals to transcriptionally profile 180 adult hermaphrodite individuals that were also measured for two reproductive traits. A major strength of the paper is its experimental design. While experimenters aim to control the environment that each worm experiences, it is known that there are small differences that each worm experiences even when they are grown together on the same agar plate - e.g. the age of their mother, their temperature, the amount of food they eat, and the oxygen and carbon dioxide levels depending on where they roam on the plate. Instead of neglecting this unknown variation, the authors design the experiment up front to create two differences in the historical environment experienced by each worm: 1) the age of its mother and 2) 8 8-hour temperature difference, either 20 or 25 {degree sign}C. This helped the authors interpret the gene expression differences and trait expression differences that they observed. 

Using two statistical models, the authors measured the association of gene expression for 8824 genes with the two reproductive traits, considering both the level of expression and the historical environment experienced by each worm. Their data supports several conclusions. They convincingly show that gene expression differences are useful for predicting reproductive trait differences, predicting ~25-50% of the trait differences depending on the trait. Using RNAi, they also show that the genes they identify play a causal role in trait differences. Finally, they demonstrate an association with trait variation and the H3K27 trimethylation mark, suggesting that chromatin structure can be an important causal determinant of gene expression and trait variation. 

Overall, this work supports the use of gene expression data as an important intermediate for understanding complex traits. This approach is also useful as a starting point for other labs in studying their trait of interest. 

We thank the reviewer for their thorough articulation of the strengths of our study.  

Weaknesses: 

There are no major weaknesses that I have noted. Some important limitations of the work (that I believe the authors would agree with) are worth highlighting, however: 

(1) A large remaining question in the field of complex traits remains in splitting the role of non-genetic factors between environmental variation and stochastic noise. It is still an open question which role each of these factors plays in controlling the gene expression differences they measured between the individual worms. 

Yes, we agree that this is a major question in the field. In our study, we parse out differences driven between known historical environmental factors and unknown factors, but the ‘unknown factors’ could encompass both unknown environmental factors and stochastic noise.

(2) The ability of the authors to use gene expression to predict trait variation was strikingly different between the two traits they measured. For the early brood trait, 448 genes were statistically linked to the trait difference, while for egg-laying onset, only 11 genes were found. Similarly, the total R2 in the test set was ~50% vs. 25%. It is unclear why the differences occur, but this somewhat limits the generalizability of this approach to other traits. 

We agree that the difference in predictability between the two traits is interesting. A previous study from the Phillips lab measured developmental rate and fertility across Caenorhabditis species and parsed sources of variation (1). Results indicated that 83.3% of variation in developmental rate was explained by genetic variation, while only 4.8% was explained by individual variation. In contrast, for fertility, 63.3% of variation was driven by genetic variation and 23.3% was explained by individual variation. Our results, of course, focus only on predicting the individual differences, but not genetic differences, for these two traits using gene expression data. Considering both sets of results, one hypothesis is that we have more power to explain nongenetic phenotypic differences with molecular data if the trait is less heritable, which is something that could be formally interrogated with more traits across more strains.

(3) For technical reasons, this approach was limited to whole worm transcription. The role of tissue and celltype expression differences is important to the field, so this limitation is important. 

We agree with this assessment, and it is something we hope to address with future work.

Reviewer #2 (Public review): 

Summary: 

This paper measures associations between RNA transcript levels and important reproductive traits in the model organism C. elegans. The authors go beyond determining which gene expression differences underlie reproductive traits, but also (1) build a model that predicts these traits based on gene expression and (2) perform experiments to confirm that some transcript levels indeed affect reproductive traits. The clever study design allows the authors to determine which transcript levels impact reproductive traits, and also which transcriptional differences are driven by stochastic vs environmental differences. In sum, this is a rather comprehensive study that highlights the power of gene expression as a driver of phenotype, and also teases apart the various factors that affect the expression levels of important genes. 

Strengths: 

Overall, this study has many strengths, is very clearly communicated, and has no substantial weaknesses that I can point to. One question that emerges for me is about the extent to which these findings apply broadly. In other words, I wonder whether gene expression levels are predictive of other phenotypes in other organisms. I

think this question has largely been explored in microbes, where some studies (PMID: 17959824) but not others (PMID: 38895328) find that differences in gene expression are predictive of phenotypes like growth rate. Microbes are not the primary focus here, and instead, the discussion is mainly focused on using gene expression to predict health and disease phenotypes in humans. This feels a little complicated since humans have so many different tissues. Perhaps an area where this approach might be useful is in examining infectious single-cell populations (bacteria, tumors, fungi). But I suppose this idea might still work in humans, assuming the authors are thinking about targeting specific tissues for RNAseq. 

In sum, this is a great paper that really got me thinking about the predictive power of gene expression and where/when it could inform about (health-related) phenotypes. 

We thank the reviewer for recognizing the strengths of our study. We are also interested in determining the extent to which predictive gene expression differences operate in specific tissues.

Reviewer #3 (Public review): 

Summary: 

Webster et al. sought to understand if phenotypic variation in the absence of genetic variation can be predicted by variation in gene expression. To this end they quantified two reproductive traits, the onset of egg laying and early brood size in cohorts of genetically identical nematodes exposed to alternative ancestral (two maternal ages) and same generation life histories (either constant 20C temperature or 8-hour temperature shift to 25C upon hatching) in a two-factor design; then they profiled genome-wide gene expression in each individual. 

Using multiple statistical and machine learning approaches, they showed that, at least for early brood size, phenotypic variation can be quite well predicted by molecular variation, beyond what can be predicted by life history alone. 

Moreover, they provide some evidence that expression variation in some genes might be causally linked to phenotypic variation. 

Strengths: 

(1) Cleverly designed and carefully performed experiments that provide high-quality datasets useful for the community. 

(2) Good evidence that phenotypic variation can be predicted by molecular variation. 

We thank the reviewer for recognizing the strengths of our study.

Weaknesses:  

What drives the molecular variation that impacts phenotypic variation remains unknown. While the authors show that variation in expression of some genes might indeed be causal, it is still not clear how much of the molecular variation is a cause rather than a consequence of phenotypic variation. 

We agree that the drivers of molecular variation remain unknown. While we addressed one potential candidate (histone modifications), there is much to be done in this area of research. We agree that, while some gene expression differences cause phenotypic changes, other gene expression differences could in principle be downstream of phenotypic differences.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

I have a number of suggestions that I believe will improve the Methods section. 

(1) Strain N2-PD1073 will probably be confusing to some readers. I recommend spelling out that this is the Phillips lab version of N2.

Thank you for this suggestion; we have added additional explanation of this strain in the Methods.

(2) I found the details of the experimental design confusing, and I believe a supplemental figure will help. I have listed the following points that could be clarified: 

a. What were the biological replicates? How many worms per replicate?

Biological replicates were defined as experiments set up on different days (in this case, all biological replicates were at least a week apart), and the biological replicate of each worm can be found in Supplementary File 1 on the Phenotypic Data tab.

b. I believe that embryos and L4s were picked to create different aged P0s, and eggs and L4s were picked to separate plates? Is this correct?

Yes, this is correct.

c. What was the spread in the embryo age?

We assume this is asking about the age of the F1 embryos, and these were laid over the course of a 2-hour window.  

d. While the age of the parents is different, there are also features about their growth plates that will be impacted by the experimental design. For example, their pheromone exposure is different due to the role that age plays in the combination of ascarosides that are released. It is worth noting as my reading of the paper makes it seem that parental age is the only thing that matters.

The parents (P0) of different ages likely have differential ascaroside exposure because they are in the vicinity of other similarly aged worms, but the F1 progeny were exposed to their parents for only the 2-hour egg-laying window, in an attempt to minimize this type of effect as much as possible.  

e. Were incubators used for each temperature?

Yes.

f. In line 443, why approximately for the 18 hours? How much spread?

The approximation was based on the time interval between the 2-hour egg-laying window on Day 4 and the temperature shift on Day 5 the following morning. The timing was within 30 minutes of 18 hours either direction.

g.  In line 444, "continually left" is confusing. Does this mean left in the original incubator?

Yes, this means left in the incubator while the worms shifted to 25°C were moved. To avoid confusion, we re-worded this to state they “remained at 20°C while the other half were shifted to 25°C”.

h. In line 445, "all worms remained at 20 {degree sign}C" was confusing to me as to what it indicated. I assume, unless otherwise noted, the animals would not be moved to a new temperature.

This was an attempt to avoid confusion and emphasize that all worms were experiencing the same conditions for this part of the experiment.  

i. What size plates were the worms singled onto?

They were singled onto 6-cm plates.

j. If a figure were to be made, having two timelines (with respect to the P0 and F1) might be useful.

We believe the methods should be sufficient for someone who hopes to repeat the experiment, and we believe the schematic in Figure 1A labeling P0 and F1 generations is sufficient to illustrate the key features of the experimental design.

k. Not all eggs that are laid end up hatching. Are these censored from the number of progeny calculations?

Yes, only progeny that hatched and developed were counted for early brood.

(3) For the lysis, was the second transfer to dH20 also a wash step?

Yes.

(4) What was used for the Elution buffer?

We used elution buffer consisting of 10 mM Tris, 0.1 mM EDTA. We have added this to the “Cell lysate generation” section of the methods

(5) The company that produced the KAPA mRNA-seq prep kit should be listed.

We added that the kit was from Roche Sequencing Solutions.

(6) For the GO analysis - one potential issue is that the set of 8824 genes might also be restricted to specific GO categories. Was this controlled for?

We originally did not explicitly control for this and used the default enrichGO settings with OrgDB = org.Ce.eg.db as the background set for C. elegans. We have now repeated the analysis with the “universe” set to the 8824-gene background set. This did not qualitatively change the significant GO terms, though some have slightly higher or lower p-values. For comparison purposes, we have added the background-corrected sets to the GO_Terms tab of Supplementary File 1 with each of the three main gene groups appended with “BackgroundOf8824”.

Reviewer #2 (Recommendations for the authors): 

(1) The abstract, introduction, and experimental design are well thought through and very clear.

Thank you.

(2) Figure 1B could use a clearer or more intuitive label on the horizontal axis. The two examples help. Maybe the genes (points) on the left side should be blue to match Figure 1C, where the genes with a negative correlation are in the blue cluster.

Thank you for these suggestions. We re-labeled the x-axis as “Slope of early brood vs. gene expression (normalized by CPM)”, which we hope gives readers a better intuition of what the coefficient from the model is measuring. We also re-colored the points previously colored red in Figure 1B to be color-coded depending on the direction of association to match Figure 1C, so these points are now color-coded as pink and purple.  

(3) If red/blue are pos/neg correlated genes in 1C, perhaps different colors should be used to label ELO and brood in Figures 2 and 3. Green/purple?

We appreciate this point, but since we ended up using the cluster colors of pink and purple in Figure 1, we opted to leave Figures 2 and 3 alone with the early brood and ELO colorcoding of red and blue.

(4) I am unfamiliar with this type of beta values, but I thought the explanation and figure were very clear. It could be helpful to bold beta1 and beta2 in the top panels of Figure 2, so the readers are not searching around for those among all the other betas. It could also be helpful to add an English phrase to the vertical axes inFigures 2C and 2D, in addition to the beta1 and beta2. Something like "overall effect (beta1)" and"environment-controlled effect (beta2)". Or maybe "effect of environment + stochastic expression differences

(beta1)" and "effect of stochastic expression differences alone (beta2)". I guess those are probably too big to fit on the figure, but it might be nice to have a label somewhere on this figure connecting them to the key thing you are trying to measure - the effect of gene expression and environment.

Thank you for these suggestions. We increased the font sizes and bolded β1 and β2 in Figure 2A-B. In Figure 2C-D, we added a parenthetical under β1 to say “(env + noise)” and β2 to say “(noise)”. We agree that this should give the reader more intuition about what the β values are measuring.  

Reviewer #3 (Recommendations for the authors): 

The authors collected individuals 24 hours after the onset of egg laying for transcriptomic profiling. This is a well-designed experiment to control for the physiological age of the germline. However, this does not properly control for somatic physiological age. Somatic age can be partially uncoupled from germline age across individuals, and indeed, this can be due to differences in maternal age (Perez et al, 2017). This is because maternal age is associated with increased pheromone exposure (unless you properly controlled for it by moving worms to fresh plates), which causes a germline-specific developmental delay in the progeny, resulting in a delayed onset of egg production compared to somatic development (Perez et al. 2021). You control for germline age, therefore, it is likely that the progeny of day 1 mothers are actually somatically older than the progeny of day 3 mothers. This would predict that many genes identified in these analyses might just be somatic genes that increase or decrease their expression during the young adult stage. 

For example, the abundance of collagen genes among the genes negatively associated (including col-20, which is the gene most significantly associated with early brood) is a big red flag, as collagen genes are known to be changing dynamically with age. If variation in somatic vs germline age is indeed what is driving the expression variation of these genes, then the expectation is that their expression should decrease with age. Vice versa, genes positively associated with early brood that are simply explained by age should be increasing.  So I would suggest that the authors first check this using time series transcriptomic data covering the young adult stage they profiled. If this is indeed the case, I would then suggest using RAPToR ( https://github.com/LBMC/RAPToR ), a method that, using reference time series data, can estimate physiological age (including tissue-specific one) from gene expression. Using this method they can estimate the somatic physiological age of their samples, quantify the extent of variation in somatic age across individuals, quantify how much of the observed differences in expressions are explained just by differences in somatic age and correct for them during their transcriptomic analysis using the estimated soma age as a covariate (https://github.com/LBMC/RAPToR/blob/master/vignettes/RAPToR-DEcorrection-pdf.pdf). 

This should help enrich a molecular variation that is not simply driven by hidden differences between somatic and germline age. 

To first address some of the experimental details mentioned for our paper, parents were indeed moved to fresh plates where they were allowed to lay embryos for two hours and then removed. Thus, we believe this minimizes the effects of ascarosides as much as possible within our design. As shown in the paper, we also identified genes that were not driven by parental age and for all genes quantified to what extent each gene’s association was driven by parental age. Thus, it is unlikely that differences in somatic and germline age is the sole explanatory factor, even if it plays some role. We also note that we accounted for egg-laying onset timing in our experimental design, and early brood was calculated as the number of progeny laid in the first 24 hours of egg-laying, where egg-laying onset was scored for each individual worm to the hour. The plot of each worm’s ELO and early brood traits is in Figure S1. Nonetheless, we read the RAPToR paper with interest, as we highlighted in the paper that germline genes tend to be positively associated with early brood while somatic genes tend to be negatively associated. While the RAPToR paper discusses using tissue-specific gene sets to stage genetically diverse C. elegans RILs, the RAPToR reference itself was not built using gene expression data acquired from different C. elegans tissues and is based on whole worms, typically collected in bulk. I.e., age estimates in RILs differ depending on whether germline or somatic gene sets are used to estimate age when the the aging clock is based on N2 samples. Thus, it is unclear whether such an approach would work similarly to estimate age in single worm N2 samples. In addition, from what we can tell, the RAPToR R package appears to implement the overall age estimate, rather than using the tissue-specific gene sets used for RILs in the paper. Because RAPToR would be estimating the overall age of our samples using a reference that is based on fewer samples than we collected here, and because we already know the overall age of our samples measured using standard approaches, we believe that estimating the age with the package would not give very much additional insight.  

Bonferroni correction: 

First, I think there is some confusion in how the author report their p-values: I don't think the authors are using a cut-off of Bonferroni corrected p-value of 5.7 x 10-6 (it wouldn't make sense). It's more likely that they are using a Bonferroni corrected p of 0.05 or 0.1, which corresponds to a nominal p value of 5.7 x 10-6, am I right?

Yes, we used a nominal p-value of 5.7 x 10-6 to correspond to a Bonferroni-corrected p-value of 0.05, calculated as 0.05/8824. We have re-worded this wherever Bonferroni correction was mentioned.

Second, Bonferroni is an overly stringent correction method that has now been substituted by the more powerful Benjamini Hochberg method to control the false discovery rate. Using this might help find more genes and better characterize the molecular variation, especially the one associated with ELO?

We agree that Bonferroni is quite stringent and because we were focused on identifying true positives, we may have some false negatives. Because all nominal p-values are included in the supplement, it is straightforward for an interested reader to search the data to determine if a gene is significant at any other threshold.   

Minor comments: 

(1) "In our experiment, isogenic adult worms in a common environment (with distinct historical environments) exhibited a range of both ELO and early brood trait values (Fig S1A)" I think this and the figure is not really needed, Figure S1B is already enough to show the range of the phenotypes and how much variation is driven by the life history traits.

We agree that the information in S1A is also included in S1B, but we think it is a little more straightforward if one is primarily interested in viewing the distribution for a single trait.

(2) Line 105 It should be Figure S2, not S3.

Thank you for catching this mistake.

(3) Gene Ontology on positive and negatively associated genes together: what about splitting the positive and negative?

We have added a split of positive and negative GO terms to the GO_Terms tab of Supplement File 1. Broadly speaking, the most enriched positively associated genes have many of the same GO terms found on the combined list that are germline related (e.g., involved in oogenesis and gamete generation), whereas the most enriched negatively associated genes have GO terms found on the combined list that are related to somatic tissues (e.g., actin cytoskeleton organization, muscle cell development). This is consistent with the pattern we see for somatic and germline genes shown in Figure 4.

(4) A lot of muscle-related GOs, can you elaborate on that?

Yes, there are several muscle-related GOs in addition to germline and epidermis. While we do not know exactly why from a mechanistic perspective these muscle-related terms are enriched, it may be important to note that many of these terms have highly overlapping sets of genes which are listed in Supplementary File 1. For example, “muscle system process” and “muscle contraction” have the exact same set of 15 genes causing the term to be significantly enriched. Thus, we tend to not interpret having many GO terms on a given tissue as indicating that the tissue is more important than others for a given biological process. While it is clear there are genes related to muscle that are associated with early brood, it is not yet clear that the tissue is more important than others.  

(5) "consistent with maternal age affecting mitochondrial gene expression in progeny " - has this been previously reported?

We do not believe this particular observation has been reported. It is important to note that these genes are involved in mitochondrial processes, but are expressed from the nuclear rather than mitochondrial genome. We re-worded the quoted portion of the sentence to say “consistent with parental age affecting mitochondria-related gene expression in progeny”.

(6) PCA: "Therefore, the optimal number of PCs occurs at the inflection points of the graph, which is after only7 PCs for early brood (R2 of 0.55) but 28 PCs for ELO (R2 of 0.56)." 

Not clear how this is determined: just graphically? If yes, there are several inflection points in the plot. How did you choose which one to consider? Also, a smaller component is not necessarily less predictive of phenotypic variation (as you can see from the graph), so instead of subsequently adding components based on the variance, they explain the transcriptomic data, you might add them based on the variance they explain in the phenotypic data? To this end, have you tried partial least square regression instead of PCA? This should give gene expression components that are ranked based on how much phenotypic variance they explain.  

Thank you for this thoughtful comment. We agree that, unlike for Figure 3B, there is some interpretation involved on how many PCs is optimal because additional variance explained with each PC is not strictly decreasing beyond a certain number of PCs. Our assessment was therefore made both graphically and by looking at the additional variance explained with each additional PC. For example, for early brood, there was no PC after PC7 that added more than 0.04 to the R2. We could also have plotted early brood and ELO separately and had a different ordering of PCs on the x-axis. By plotting the data this way, we emphasized that the factors that explain the most variation in the gene expression data typically explain most variation in the phenotypic data.  

(7) The fact that there are 7 PC of molecular variation that explain early brood is interesting. I think the authors can analyze this further. For example, could you perform separate GO enrichment for each component that explains a sizable amount of phenotypic variance? Same for the ELO.  

Because each gene has a PC loading in for each PC, and each PC lacks the explanatory power of combined PCs, we believe doing GO Terms on the list of genes that contribute most to each PC is of minimal utility. The power of the PCA prediction approach is that it uses the entire transcriptome, but the other side of the coin is that it is perhaps less useful to do a gene-bygene based analysis with PCA. This is why we separately performed individual gene associations and 10-gene predictive analyses. However, we have added the PC loadings for all genes and all PCs to Supplementary File 1.

(8) Avoid acronyms when possible (i.e. ELO in figures and figure legends could be spelled out to improve readability).

We appreciate this point, but because we introduced the acronym both in Figure 1 and the text and use it frequently, we believe the reader will understand this acronym. Because it is sometimes needed (especially in dense figures), we think it is best to use it consistently throughout the paper.

(9) Multiple regression: I see the most selected gene is col-20, which is also the most significantly differentially expressed from the linear mixed model (LMM). But what is the overlap between the top 300 genes in Figure 3F and the 448 identified by the LMM? And how much is the overlap in GO enrichment?

Genes that showed up in at least 4 out of 500 iterations were selected more often than expected by chance, which includes 246 genes (as indicated by the red line in Figure 3F). Of these genes, 66 genes (27%) are found in the set of 448 early brood genes. The proportion of overlap increases as the number of iterations required to consider a gene predictive increases, e.g., 34% of genes found in 5 of 500 iterations and 59% of genes found in 10 of 500 iterations overlap with the 448 early brood genes. However, likely because of the approach to identify groups of 10 genes that are predictive, we do not find significant GO terms among the 246 genes identified with this approach after multiple test correction. We think this makes sense because the LMM identifies genes that are individually associated with early brood, whereas each subsequent gene included in multiple regression affects early brood after controlling for all previous genes. These additional genes added to the multiple regression are unlikely to have similar patterns as genes that are individually correlated with early brood.  

(10) Elastic nets: prediction power is similar or better than multiple regression, but what is the overlap between genes selected by the elastic net (not presented if I am not mistaken) and multiple regression and the linear mixed model?

For the elastic net models, we used a leave-one-out cross validation approach, meaning there were separate models fit by leaving out the trait data for each worm, training a model using the trait data and transcriptomic data for the other worms, and using the transcriptomic data of the remaining worm to predict the trait data. By repeating this for each worm, the regressions shown in the paper were obtained. Each of these models therefore has its own set of genes. Of the 180 models for early brood, the median model selects 83 genes (range from 72 to 114 genes). Across all models, 217 genes were selected at least once. Interestingly, there was a clear bimodal distribution in terms of how many models a given gene was selected for: 68 genes were selected in over 160 out of 180 models, while 114 genes were selected in fewer than 20 models (and 45 genes were selected only once). Therefore, we consider the set of 68 genes as highly robustly selected, since they were selected in the vast majority of models. This set of 68 exhibits substantial overlap with both the set of 448 early brood-associated genes (43 genes or 63% overlap) and the multiple regression set of 246 genes (54 genes or 79% overlap). For ELO, the median model selected 136 genes (range of 96 to 249 genes) and a total of 514 genes were selected at least once. The distribution for ELO was also bimodal with 78 genes selected over 160 times and 255 genes selected fewer than 20 times. This set of 78 included 6 of the 11 significant ELO genes identified in the LMM.  We have added tabs to Supplementary File 1 that include the list of genes selected for the elastic net models as well as a count of how many times they were selected out of 180 models.

(11) In other words, do these different approaches yield similar sets of genes, or are there some differences?

In the end, which approach is actually giving the best predictive power? From the perspective of R2, both the multiple regression and elastic net models are similarly predictive for early brood, but elastic net is more predictive for ELO. However, in presenting multiple approaches, part of our goal was identifying predictive genes that could be considered the ‘best’ in different contexts. The multiple regression was set to identify exactly 10 genes, whereas the elastic net model determined the optimal number of genes to include, which was always over 70 genes. Thus, the elastic net model is likely better if one has gene expression data for the entire transcriptome, whereas the multiple regression genes are likely more useful if one were to use reporters or qRTPCR to measure a more limited number of genes.  

(12) Line 252: "Within this curated set, genes causally affected early brood in 5 of 7 cases compared to empty vector (Figure 4A).

" It seems to me 4 out of 7 from Figure 4A. In Figure 4A the five genes are (1) cin-4, (2) puf5; puf-7, (3) eef-1A.2, (4) C34C12.8, and (5) tir-1. We did not count nex-2 (p = 0.10) or gly-13 (p = 0.07), and empty vector is the control.

(13) Do puf-5 and -7 affect total brood size or only early brood size? Not clear. What's the effect of single puf-5 and puf-7 RNAi on brood?

We only measured early brood in this paper, but a previous report found that puf-5 and puf-7 act redundantly to affect oogenesis, and RNAi is only effective if both are knocked down together(2). We performed pilot experiments to confirm that this was the case in our hands as well.  

(14)  To truly understand if the noise in expression of Puf-5 and /or -7 really causes some of the observed difference in early brood, could the author use a reporter and dose response RNAi to reduce the level of puf-5/7 to match the lower physiological noise range and observe if the magnitude of the reduction of early brood by the right amount of RNAi indeed matches the observed physiological "noise" effect of puf-5/7 on early brood?

We agree that it would be interesting to do the dose response of RNAi, measure early brood, and get a readout of mRNA levels to determine the true extent of gene knockdown in each worm (since RNAi can be noisy) and whether this corresponds to early brood when the knockdown is at physiological levels. While we believe we have shown that a dose response of gene knockdown results in a dose response of early brood, this additional analysis would be of interest for future experiments.

(15) Regulated soma genes (enriched in H3K27me3) are negatively correlated with early brood. What would be the mechanism there? As mentioned before, it is more likely that these genes are just indicative of variation in somatic vs germline age (maybe due to latent differences in parental perception of pheromone).

We can think of a few potential mechanisms/explanations, but at this point we do not have a decisive answer. Regulated somatic genes marked with H3K27me3 (facultative heterochromatin) are expressed in particular tissues and/or at particular times in development. In this study and others, genes marked with H3K27me3 exhibit more gene expression noise than genes with other marks. This could suggest that there are negative consequences for the animal if genes are expressed at higher levels at the wrong time or place, and one interpretation of the negative association is that higher expressed somatic genes results in lower fitness (where early brood is a proxy for fitness). Another related interpretation is that there are tradeoffs between somatic and germline development and each individual animal lands somewhere on a continuum between prioritizing germline or somatic development, where prioritizing somatic integrity (e.g. higher expression of somatic genes) comes at a cost to the germline resulting in fewer progeny. Additional experiments, including measurements of histone marks in worms measured for the early brood trait, would likely be required to more decisively answer this question.  

(16) Line 151: "Among significant genes for both traits, β2 values were consistently lower than β1 (Figures 2CD), suggesting some of the total effect size was driven by environmental history rather than pure noise".

We are interpreting this quote as part of point 17 below.

(17) It looks like most of the genes associated with phenotypes from the univariate model have a decreased effect once you account for life history, but have you checked for cases where the life history actually masks the effect of a gene? In other words, do you have cases where the effect of gene expression on a phenotype is only (or more) significant after you account for the effect of life history (β2 values higher than β1)?

This is a good question and one that we did not explicitly address in the paper because we focused on beta values for genes that were significant in the univariate analysis. Indeed, for the sets of 448 early brood genes ad 11 ELO genes, there are no genes for which β2 is larger than β1. In looking at the larger dataset of 8824 genes, with a Bonferroni-corrected p-value of 0.05, there are 306 genes with a significant β2 for early brood. The majority (157 genes) overlap with the 448 genes significant in the univariate analysis and do not have a higher β2 than β1. Of the remaining genes, 72 of these have a larger β2 than β1. However, in most cases, this difference is relatively small (median difference of 0.025) and likely insignificant. There are only three genes in which β1 is not nominally significant, and these are the three genes with the largest difference between β1 and β2 with β2 being larger (differences of 0.166, 0.155, and 0.12). In contrast, the median difference between β1 and β2 the 448 genes (in which β1 is larger) is 0.17, highlighting the most extreme examples of β2 > β1 are smaller in magnitude than the typical case of β1 > β2. For ELO, there are no notable cases where β2 > β1. There are eight genes with a significant β2 value, and all of these have a β1 value that is nominally significant. Therefore, while this phenomenon does occur, we find it to be relatively rare overall. For completeness, we have added the β1 and β2 values for all 8824 genes as a tab in Supplementary File 1.

Reviewer #3 (Public review):

Summary:

This is an interesting paper by Lechler and colleagues describing the transcriptomic signature and fate of intermediate cells (ICs), a transient and poorly defined embryonic cell type in the skin. ICs are the first suprabasal cells in the stratifying skin and unlike later-developing suprabasal cells, ICs continue to divide. Using bulk RNA seq to compare ICs to spinous and granular transcriptomes, the authors find that IC-specific gene signatures include hallmarks of granular cells, such as genes involved in lipid metabolism and skin barrier function that are not expressed in spinous cells. ICs were assumed to differentiate into spinous cells, but lineage tracing convincingly shows ICs differentiate directly into granular cells without passing through a spinous intermediate. Rather, basal cells give rise to the first spinous cells. They further show that transcripts associated with contractility are also shared signatures of ICs and granular cells, and overexpression of two contractility inducers (Spastin and ArhGEF-CA) can induce granular and repress spinous gene expression. This contractility-induced granular gene expression does not appear to be mediated by the mechanosensitive transcription factor, Yap. The paper also identifies new markers that distinguish IC and spinous layers, and shows the spinous signature gene, MafB, is sufficient to repress proliferation when prematurely expressed in ICs.

Strengths:

Overall this is a well-executed study, and the data are clearly presented and the findings convincing. It provides an important contribution to the skin field by characterizing the features and fate of ICs, a much understudied cell type, at a high levels of spatial and transcriptomic detail. The conclusions challenge the assumption that ICs are spinous precursors through compelling lineage tracing data. The demonstration that differentiation can be induced by cell contractility is an intriguing finding, and adds a growing list of examples where cell mechanics influence gene expression and differentiation.

Weaknesses:

A weakness of the study is an over-reliance on overexpression and sufficiency experiments to test the contributions of MafB, Yap, and contractility in differentiation. The inclusion of loss-of-function approaches would enable one to determine if, for example, contractility is required for the transition of ICs to granular fate, and whether MafB is required for spinous fate. Second, whether the induction of contractility-associated genes is accompanied by measurable changes in the physical properties or mechanics of the IC and granular layers is not directly shown. Inclusion of physical measurements would bolster the conclusion that mechanics lies upstream of differentiation.

Finally, the role of ICs in epidermal development remains unclear. Although not essential to support the conclusions of this study, insights into the function of this transient cell layer would strengthen the overall impact.

Author Response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review): 

Summary: 

The authors address a fundamental question for cell and tissue biology using the skin epidermis as a paradigm and ask how stratifying self-renewing epithelia induce differentiation and upward migration in basal dividing progenitor cells to generate suprabasal barrier-forming cells that are essential for a functional barrier formed by such an epithelium. The authors show for the first time that an increase in intracellular actomyosin contractility, a hallmark of barrier-forming keratinocytes, is sufficient to trigger terminal differentiation. Hence the data provide in vivo evidence of the more general interdependency of cell mechanics and differentiation. The data appear to be of high quality and the evidences are strengthened through a combination of different genetic mouse models, RNA sequencing, and immunofluorescence analysis. 

To generate and maintain the multilayered, barrier-forming epidermis, keratinocytes of the basal stem cell layer differentiate and move suprabasally accompanied by stepwise changes not only in gene expression but also in cell morphology, mechanics, and cell position. Whether any of these changes is instructive for differentiation itself and whether consecutive changes in differentiation are required remains unclear. Also, there are few comprehensive data sets on the exact changes in gene expression between different states of keratinocyte differentiation. In this study, through genetic fluorescence labeling of cell states at different developmental time points the authors were able to analyze gene expression of basal stem cells and suprabasal differentiated cells at two different stages of maturation: E14 (embryonic day 14) when the epidermis comprises mostly two functional compartments (basal stem cells and suprabasal socalled intermediate cells) and E16 when the epidermis comprise three (living) compartments where the spinous layer separates basal stem cells from the barrier-forming granular layer, as is the case in adult epidermis. Using RNA bulk sequencing, the authors developed useful new markers for suprabasal stages of differentiation like MafB and Cox1. The transcription factor MafB was then shown to inhibit suprabasal proliferation in a MafB transgenic model. 

The data indicate that early in development at E14 the suprabasal intermediate cells resemble in terms of RNA expression, the barrier-forming granular layer at E16, suggesting that keratinocytes can undergo either stepwise (E16) or more direct (E14) terminal differentiation. 

Previous studies by several groups found an increased actomyosin contractility in the barrierforming granular layer and showed that this increase in tension is important for epidermal barrier formation and function. However, it was not clear whether contractility itself serves as an instructive signal for differentiation. To address this question, the authors use a previously published model to induce premature hypercontractility in the spinous layer by using spastin overexpression (K10-Spastin) to disrupt microtubules (MT) thereby indirectly inducing actomyosin contractility. A second model activates myosin contractility more directly through overexpression of a constitutively active RhoA GEF (K10-Arhgef11CA). Both models induce late differentiation of suprabasal keratinocytes regardless of the suprabasal position in either spinous or granular layer indicating that increased contractility is key to induce late differentiation of granular cells. A potential weakness of the K10-spastin model is the disruption of MT as the primary effect which secondarily causes hypercontractility. However, their previous publications provided some evidence that the effect on differentiation is driven by the increase in contractility (Ning et al. cell stem cell 2021). Moreover, the data are confirmed by the second model directly activating myosin through RhoA. These previous publications already indicated a role for contractility in differentiation but were focused on early differentiation. The data in this manuscript focus on the regulation of late differentiation in barrier-forming cells. These important data help to unravel the interdependencies of cell position, mechanical state, and differentiation in the epidermis, suggesting that an increase in cellular contractility in most apical positions within the epidermis can induce terminal differentiation. Importantly the authors show that despite contractility-induced nuclear localization of the mechanoresponsive transcription factor YAP in the barrier-forming granular layer, YAP nuclear localization is not sufficient to drive premature differentiation when forced to the nucleus in the spinous layer. 

Overall, this is a well-written manuscript and a comprehensive dataset. Only the RNA sequencing result should be presented more transparently providing the full lists of regulated genes instead of presenting just the GO analysis and selected target genes so that this analysis can serve as a useful repository. The authors themselves have profited from and used published datasets of gene expression of the granular cells. Moreover, some of the previous data should be better discussed though. The authors state that forced suprabasal contractility in their mouse models induces the expression of some genes of the epidermal differentiation complex (EDC). However, in their previous publication, the authors showed that major classical EDC genes are actually not regulated like filaggrin and loricrin (Muroyama and Lechler eLife 2017). This should be discussed better and necessitates including the full list of regulated genes to show what exactly is regulated. 

We thank the reviewers for their suggestions and comments.

Thank you for the suggestion to include gene lists. We had an excel document with all this data but neglected to upload it with the initial manuscript. This includes all the gene signatures for the different cell compartments across development. We also include a tab that lists all EDC genes and whether they were up-regulated in intermediate cells and cells in which contractility was induced. Further, we note that all the RNA-Seq datasets are available for use on GEO (GSE295753).  

In our previous publication, we indeed included images showing that loricrin and filaggrin were both still expressed in the differentiated epidermis in the spastin mutant. Both Flg and Lor mRNA were up in the RNA-Seq (although only Flg was statistically significant), though we didn’t see a notable change in protein levels. It is unclear whether this is just difficult to see on top of the normal expression, or whether there are additional levels of regulation where mRNA levels are increased but protein isn’t. That said, our data clearly show that other genes associated with granular fate were increased in the contractile skin. 

Reviewer #2 (Public review): 

Summary: 

The manuscript from Prado-Mantilla and co-workers addresses mechanisms of embryonic epidermis development, focusing on the intermediate layer cells, a transient population of suprabasal cells that contributes to the expansion of the epidermis through proliferation. Using bulk-RNA they show that these cells are transcriptionally distinct from the suprabasal spinous cells and identify specific marker genes for these populations. They then use transgenesis to demonstrate that one of these selected spinous layer-specific markers, the transcription factor MafB is capable of suppressing proliferation in the intermediate layers, providing a potential explanation for the shift of suprabasal cells into a non-proliferative state during development. Further, lineage tracing experiments show that the intermediate cells become granular cells without a spinous layer intermediate. Finally, the authors show that the intermediate layer cells express higher levels of contractility-related genes than spinous layers and overexpression of cytoskeletal regulators accelerates the differentiation of spinous layer cells into granular cells. 

Overall the manuscript presents a number of interesting observations on the developmental stage-specific identities of suprabasal cells and their differentiation trajectories and points to a potential role of contractility in promoting differentiation of suprabasal cells into granular cells. The precise mechanisms by which MafB suppresses proliferation, how the intermediate cells bypass the spinous layer stage to differentiate into granular cells, and how contractility feeds into these mechanisms remain open. Interestingly, while the mechanosensitive transcription factor YAP appears deferentially active in the two states, it is shown to be downstream rather than upstream of the observed differences in mechanics. 

Strengths: 

The authors use a nice combination of RNA sequencing, imaging, lineage tracing, and transgenesis to address the suprabasal to granular layer transition. The imaging is convincing and the biological effects appear robust. The manuscript is clearly written and logical to follow. 

Weaknesses: 

While the data overall supports the authors' claims, there are a few minor weaknesses that pertain to the aspect of the role of contractility, The choice of spastin overexpression to modulate contractility is not ideal as spastin has multiple roles in regulating microtubule dynamics and membrane transport which could also be potential mechanisms explaining some of the phenotypes. Use of Arghap11 overexpression mitigates this effect to some extent but overall it would have been more convincing to manipulate myosin activity directly. It would also be important to show that these manipulations increase the levels of F-actin and myosin II as shown for the intermediate layer. It would also be logical to address if further increasing contractility in the intermediate layer would enhance the differentiation of these cells. 

We agree with the reviewer that the development of additional tools to precisely control myosin activity will be of great use to the field. That said, our series of publications has clearly demonstrated that ablating microtubules results in increased contractility and that this phenocopies the effects of Arhgef11 induced contractility. Further, we showed that these phenotypes were rescued by myosin inhibition with blebbistatin. Our prior publications also showed a clear increase in junctional acto-myosin through expression of either spastin or Arhgef11, as well as increased staining for the tension sensitive epitope of alpha-catenin (alpha18).  We are not aware of tools that allow direct manipulation of myosin activity that currently exist in mouse models.  

The gene expression analyses are relatively superficial and rely heavily on GO term analyses which are of course informative but do not give the reader a good sense of what kind of genes and transcriptional programs are regulated. It would be useful to show volcano plots or heatmaps of actual gene expression changes as well as to perform additional analyses of for example gene set enrichment and/or transcription factor enrichment analyses to better describe the transcriptional programs 

We have included an excel document that lists all the gene signatures. In addition, a volcano plot is included in the new Fig 2, Supplement 1. All our NGS data are deposited in GEO for others to perform these analyses. As the paper does not delve further into transcriptional regulation, we do not specifically present this information in the paper.  

Claims of changes in cell division/proliferation changes are made exclusively by quantifying EdU incorporation. It would be useful to more directly look at mitosis. At minimum Y-axis labels should be changed from "% Dividing cells" to % EdU+ cells to more accurately represent findings 

We changed the axis label to precisely match our analysis. We note that Figure 1, Supplement 1 also contains data on mitosis.  

Despite these minor weaknesses the manuscript is overall of high quality, sheds new light on the fundamental mechanisms of epidermal stratification during embryogenesis, and will likely be of interest to the skin research community. 

Reviewer #3 (Public review): 

Summary: 

This is an interesting paper by Lechler and colleagues describing the transcriptomic signature and fate of intermediate cells (ICs), a transient and poorly defined embryonic cell type in the skin. ICs are the first suprabasal cells in the stratifying skin and unlike later-developing suprabasal cells, ICs continue to divide. Using bulk RNA seq to compare ICs to spinous and granular transcriptomes, the authors find that IC-specific gene signatures include hallmarks of granular cells, such as genes involved in lipid metabolism and skin barrier function that are not expressed in spinous cells. ICs were assumed to differentiate into spinous cells, but lineage tracing convincingly shows ICs differentiate directly into granular cells without passing through a spinous intermediate. Rather, basal cells give rise to the first spinous cells. They further show that transcripts associated with contractility are also shared signatures of ICs and granular cells, and overexpression of two contractility inducers (Spastin and ArhGEF-CA) can induce granular and repress spinous gene expression. This contractility-induced granular gene expression does not appear to be mediated by the mechanosensitive transcription factor, Yap. The paper also identifies new markers that distinguish IC and spinous layers and shows the spinous signature gene, MafB, is sufficient to repress proliferation when prematurely expressed in ICs. 

Strengths: 

Overall this is a well-executed study, and the data are clearly presented and the findings convincing. It provides an important contribution to the skin field by characterizing the features and fate of ICs, a much-understudied cell type, at high levels of spatial and transcriptomic detail. The conclusions challenge the assumption that ICs are spinous precursors through compelling lineage tracing data. The demonstration that differentiation can be induced by cell contractility is an intriguing finding and adds a growing list of examples where cell mechanics influence gene expression and differentiation. 

Weaknesses: 

A weakness of the study is an over-reliance on overexpression and sufficiency experiments to test the contributions of MafB, Yap, and contractility in differentiation. The inclusion of loss-offunction approaches would enable one to determine if, for example, contractility is required for the transition of ICs to granular fate, and whether MafB is required for spinous fate. Second, whether the induction of contractility-associated genes is accompanied by measurable changes in the physical properties or mechanics of the IC and granular layers is not directly shown. The inclusion of physical measurements would bolster the conclusion that mechanics lies upstream of differentiation. 

We agree that loss of function studies would be useful. For MafB, these have been performed in cultured human keratinocytes, where loss of MafB and its ortholog cMaf results in a phenotype consistent with loss of spinous differentiation (Pajares-Lopez et al, 2015). Due to the complex genetics involved, generating these double mutant mice is beyond the scope of this study. Loss of function studies of myosin are also complicated by genetic redundancy of the non-muscle type II myosin genes, as well as the role for these myosins in cell division and in actin cross linking in addition to contractility. In addition, we have found that these myosins are quite stable in the embryonic intestine, with loss of protein delayed by several days from the induction of recombination. Therefore, elimination of myosins by embryonic day e14.5 with our current drivers is not likely possible. Generation of inducible inhibitors of contractility is therefore a valuable future goal. 

Several recent papers have used AFM of skin sections to probe tissue stiffness. We have not attempted these studies and are unclear about the spatial resolution and whether, in the very thin epidermis at these stages, we could spatially resolve differences. That said, we previously assessed the macro-contractility of tissues in which myosin activity was induced and demonstrated that there was a significant increase in this over a tissue-wide scale (Ning et al, Cell Stem Cell, 2021).  

Finally, whether the expression of granular-associated genes in ICs provides them with some sort of barrier function in the embryo is not addressed, so the role of ICs in epidermal development remains unclear. Although not essential to support the conclusions of this study, insights into the function of this transient cell layer would strengthen the overall impact.  

By traditional dye penetration assays, there is no epidermal barrier at the time that intermediate cells exist. One interpretation of the data is that cells are beginning to express mRNAs (and in some cases, proteins) so that they are able to rapidly generate a barrier as they become granular cells. In addition, many EDC genes, important for keratinocyte cornification and barrier formation, are not upregulated in ICs at E14.5. We have attempted experiments to ablate intermediate cells with DTA expression - these resulted in inefficient and delayed death and thus did not yield strong conclusions about the role of intermediate cells. Our findings that transcriptional regulators of granular differentiation (such as Grhl3 and Hopx) are also present in intermediate cells, should allow future analysis of the effects of their ablation on the earliest stages of granular differentiation from intermediate cells. In fact, previous studies have shown that Grhl3 null mice have disrupted barrier function at embryonic stages (Ting et al, 2005), supporting the role of ICs in being important for barrier formation. (?)

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

Overall, this is a well-written manuscript and a comprehensive dataset. Only the RNA sequencing result should be presented more transparently providing the full lists of regulated genes instead of presenting just the GO analysis and selected target genes so that this analysis can serve as a useful repository. The authors themselves have profited from and used the published dataset of gene expression of the granular cells. Moreover, some of the previous data should be better discussed though. The authors state that forced suprabasal contractility in their mouse models induces the expression of some genes of the epidermal differentiation complex (EDC). However, in their previous publication, the authors showed that major classical EDC genes are actually not regulated like filaggrin and loricrin (Muroyama and Lechler eLife 2017). This should be discussed better and necessitates including the full list of regulated genes to show what exactly is regulated. 

A general point regarding statistics throughout the manuscript. It seems like regular T-tests or ANOVAs have been used assuming Gaussian distribution for sample sizes below N=5 which is technically not correct. Instead, non-parametric tests like e.g. the Mann-Whitney test should be used. Since Graph-Pad was used for statistics according to the methods this is easy to change. 

Figure 1: It would be good to show the FACS plot of the analyzed and sorted population in the supplementary figures. 

If granular cells can be analyzed and detected by FACS, why were they not included in the RNA sequencing analysis? 

Figure 1 supplement 1c: cell division numbers are analyzed from only 2 mice and the combined 5 or 4 fields of view are used for statistics using a test assuming normal distribution which is not really appropriate. Means per mice should be used or if accumulated field of views are used, the number should be increased using more stringent tests. Otherwise, the p-values here clearly overstate the significance. 

Granular cells could not be specifically isolated in the approach we used. The lectin binds to both upper spinous and granular cells. For this reason, we relied on a separate granular gene list as described.

For Figure 1 Supplement 1, we removed the statistical analysis and use it simply as a validation of the data in Figure 1.  

Figure 2: It is not completely clear on which basis the candidate genes were picked. They are described to be the most enriched but how do they compare to the rest of the enriched genes. The full list of regulated genes should be provided. 

Some markers for IC or granular layer are verified either by RNA scope or immunofluorescence. Is there a technical reason for that? It would be good to compare protein levels for all markers.  Figure 2-Supplement 1: There is no statement about the number of animals that these images are representative for. 

We have included a volcano plot to show where the genes picked reside. We have also included the full gene lists for interested readers. 

When validated antibodies were available, we used them. When they were not, we performed RNA-Scope to validate the RNA-Seq dataset. 

We have included animal numbers in the revised Fig 2-Supplement 2 legend (previously Fig 2Supplement 1).  

Figure 4b: It would be good to include the E16 spinous cells to get an idea of how much closer ICs are to the granular population. 

We have included a new Venn diagram showing the overlap between each of the IC and spinous signatures with the granular cell signature in Fig 4B. Overall, 36% of IC signature genes are in common with granular cells, while just 20% of spinous genes overlap.  

Reviewer #2 (Recommendations for the authors): 

(1)  Figure 6B is confusing as y-axis is labeled as EdU+ suprabasal cells whereas basal cells are also quantified. 

We have altered the y-axis title to make it clearer.  

(2)  Not clear why HA-control is sometimes included and sometimes not. 

We include the HA when it did not disrupt visualization of the loss of fluorescence. As it was uniform in most cases, we excluded it for clarity in some images. HA staining is now included in Fig 3C.

(3)  The authors might reconsider the title as it currently is somewhat vague, to more precisely represent the content of the manuscript. 

We thank the reviewer for the suggestion. We considered other options but felt that this gave an overview of the breadth of the paper.  

Reviewer #3 (Recommendations for the authors): 

(1)  ICs are shown to express Tgm1 and Abca12, important for cornified envelope function and formation of lamellar bodies. Do ICs provide any barrier function at E14.5? 

By traditional dye penetration assays, there is no epidermal barrier at the time that intermediate cells exist. One interpretation of the data is that cells are beginning to express mRNAs (and in some cases, proteins) so that they are able to rapidly generate a barrier as they become granular cells.  

(2)  Genes associated with contractility are upregulated in ICs and granular cells. And ICs have higher levels of F-actin, MyoIIA, alpha-18, and nuclear Yap. Does this correspond to a measurable difference in stiffness? Can you use AFM to compare to physical properties of ICs, spinous, and granular cells? 

Several recent papers have used AFM of skin sections to probe tissue stiDness. We have not attempted these studies and are unclear about the spatial resolution and whether in the very thin epidermis at these stages whether we could spatially resolve diDerences. It is also important to note that this tissue rigidity is influenced by factors other than contractility. That said, we previously assessed the macro-contractility of tissues in which myosin activity was induced and demonstrated that there was a significant increase in this over a tissue-wide scale (Ning et al, Cell Stem Cell, 2021).

(3)  Overexpression of two contractility inducers (spastin and ArhGEF-CA) can induce granular gene expression and repress spinous gene expression, suggesting differentiation lies downstream of contractility. Is contractility required for granular differentiation? 

This is an important question and one that we hope to directly address in the future. Published studies have shown defects in tight junction formation and barrier function in myosin II mutants. However, a thorough characterization of differentiation was not performed.  

(4)  ICs are a transient cell type, and it would be important to know what is the consequence of the epidermis never developing this layer. Does it perform an important temporary structural/barrier role, or patterning information for the skin?

We have attempted experiments to ablate intermediate cells with DTA expression - this resulted in ineDicient and delayed death and thus did not yield strong conclusions. Our findings that transcriptional regulators of granular diDerentiation (such as Grhl3 and Hopx) are also present in intermediate cells, should allow future analysis of the eDects of their ablation on the earliest stages of granular diDerentiation from intermediate cells.

Reviewer #2 (Public review):

Summary:

This study investigates a low abundance microRNA signature in extracellular vesicles to subtype pancreatic cancer and for early diagnosis. In this revision, there remain several major and minor issues.

Strengths:

The authors did a comprehensive job with numerous analyses of moderately sized cohorts to describe the clinical and translational significance of their miRNA signature.

Weaknesses:

The weaknesses of the study largely revolve around a lack of clarity about the methodology used and the validation of their findings.

(1) The WGCNA analysis was critical to identify the EV miRNAs associated with imaging features, but the "cut-off criteria" for MM and GS have no clear justification. How were these cut-offs determined? How sensitive were the results to these cut-offs?

(2) The authors now clarify that patients for the sub-study on differentiating early stage from benign pancreatic lesions were matched by age and that the benign pancreatic lesions were predominantly IPMNs. This scientific design is flawed. The CT features extracted likely differentiate solid from cystic pancreatic lesions, and the miRNA signature is doing the same. The authors need to incorporate the following benign controls into their imaging analysis and their EV miRNA analysis: pancreatitis and normal pancreata.

(3) For the radiomics features, the authors should include an additional external validation set to better support the ability to use these features reproducibly, especially given that the segmentation was manual and reliant on specific people.

(4) The DF selection process still lacks cited references as originally requested in the first review.

(5) In Figure 2, more quantitative details are needed in the manuscript. The reviewers failed to incorporate this and only responded in their rebuttal. Add details to the manuscript as originally requested.

(6) It is still not clear what Figure 4A is illustrating as regards to model performance. The authors need to state in the manuscript very clearly what they are showing in the figure and what the modules represent.

(7) Figure 5 and the descriptions for the public serum miRNA datasets need more details. Were these pancreatic cancers all adenocarcinoma, what stage, age range, sex distribution, comorbid conditions were the cases? Were the controls all IPMNs or were there other conditions in the controls?

(8) The subtype results in figures 6 and 7 are not convincing. An association on univariate analysis is not sufficient. The explanation that clinical data is not available to do a multivariable analysis indicates that the authors do not have the ability to claim that they have identified unique subtypes that have clinical relevance. A thorough evaluation of the prognostic significance and the associated molecular features of these tumors is needed.

Summary:

There remain key details and validation experiments to better support the conclusions of the study.

Author Response:

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

Reviewer #1 (Public review):

Summary:

The manuscript by Shi et al, has utilized multiple imaging datasets and one set of samples for analyzing serum EV-miRNAs & EV-RNAs to develop an EV miRNA signature associated with disease-relevant radiomics features for early diagnosis of pancreatic cancer. CT imaging features (in two datasets (UMMD & JHC and WUH) were derived from pancreatic benign disease patients vs pancreatic cancer cases), while circulating EV miRNAs were profiled from samples obtained from a different center (DUH). The EV RNA signature from external public datasets (GSE106817, GSE109319, GSE113486, GSE112264) were analyzed for differences in healthy controls vs pancreatic cancer cases. The miRNAs were also analyzed in the TCGA tissue miRNA data from normal adjacent tissue vs pancreatic cancer.

Strengths:

The concept of developing EV miRNA signatures associated with disease relevant radiomics features is a strength.

Weaknesses:

While the overall concept of developing EV miRNA signature associated with radiomics features is interesting, the findings reported are not convincing for the reasons outlined below:

(1) Discrepant datasets for analyzing radiomic features with EV-miRNAs: It is not justified how CT images (UMMD & JHC and WUH) and EV-miRNAs (DUH) on different subjects and centers/cohorts shown in Figures 1 &2 were analyzed for association. It is stated that the samples were matched according to age but there is no information provided for the stages of pancreatic cancer and the kind of benign lesions analyzed in each instance.

Thank you to the reviewer for the valuable comments. We acknowledge that the radiomics data and EV-miRNA data were derived from different patient cohorts. The primary aim of this study was to explore the integration of data from different omics sources in an exploratory manner to identify potential shared biological features.

We have revised the Methods section accordingly. Regarding the imaging data, we mainly performed batch effect correction on CT images from different centers to eliminate variability. As you correctly pointed out, the EV-miRNA data and CT images from DUH were matched by age. Since all the patients we included had early-stage pancreatic cancer, and the benign pancreatic lesions were predominantly IPMN, we did not specifically highlight this aspect. However, we have now clarified this approach in the data collection section. Thank you for your attention.

(2) The study is focused on low-abundance miRNAs with no adequate explanation of the selection criteria for the miRNAs analyzed.

We used MAD (Median Absolute Deviation) to filter low-abundance miRNAs in the manuscript, as this concept was introduced by us for the first time in this context, and we acknowledge that there is still considerable room for refinement and improvement.

(3) While EV-miRNAs were profiled or sequenced (not well described in the Methods section) with two different EV isolation methods, the authors used four public datasets of serum circulating miRNAs to validate the findings. It would be better to show the expression of the three miRNAs in the additional dataset(s) of EV-miRNAs and compare the expressions of the three EV-miRNAs in pancreatic cancer with healthy and benign disease controls.

Thank you for your suggestion. We have attempted to identify available EV-miRNA datasets; however, due to current limitations in data access, we opted to use serum samples for validation. In our follow-up studies, we are already in the process of collecting relevant EV samples for further validation.

(4) It is not clear how the 12 EV-miRNAs in Figure 4C were identified.

These 12 EV-miRNAs were identified through WGCNA analysis and are associated with the high-risk group.

(5) Box plots in Figures 4D-F and G-I of three miRNAs in serum and tissue should show all quantitative data points.

We have completed the revisions. Kindly review them at your convenience.

(6) What is the GBM model in Figure 5?

Thank you to the reviewer for raising this question. The "GBM model" referred to in Figure 5 is a classification model built using the Gradient Boosting Machine (GBM) algorithm, designed to predict the diagnostic status of pancreatic cancer by integrating EV-miRNA expression and radiomics features. We implemented the model using the `GradientBoostingClassifier` from the scikit-learn library (version 1.2.2), and optimized the model’s hyperparameters—including learning rate, maximum depth, and number of trees—within a five-fold cross-validation framework. The training process and performance evaluation of the model, including the ROC curve and AUC values, are presented in Figure 5.

(7) What are the AUCs of individual EV-miRNAs integrated as a panel of three EV-miRNAs?

Thanks for your comments, Our GBM model integrates the panel of these three EV-miRNAs.

(8) The authors could have compared the performance of CA19-9 with that of the three EV-miRNAs.

Since our main focus is on the panel of three EV-miRNAs, we did not present the AUC for each individual miRNA separately. However, we have included the performance of CA19-9 in our dataset as a reference. The predictive AUC for CA19-9 is 0.843 (95% CI, 0.762–0.924).

(9) How was the diagnostic performance of the three EV-miRNAs in the two molecular subtypes identified in Figure 6&7? Do the C1 & C2 clusters correlate with the classical/basal subtypes, staging, and imaging features?

Thank you to the reviewer for raising this important question. In fact, our EV panel is primarily designed to distinguish between normal and tumor samples, whereas both C1 and C2 represent tumor subtypes, and thus the panel is not applicable for diagnostic purposes in this context. Additionally, our subtypes are novel and do not align with the conventional classical and basal-like gene expression profiles. Furthermore, the C1 subtype is more frequently observed in stage III tumors (Figure 6J) and is associated with distinct imaging features such as higher texture heterogeneity and lower CT density.

Reviewer #2 (Public review):

Summary:

This study investigates a low abundance microRNA signature in extracellular vesicles to subtype pancreatic cancer and for early diagnosis. There are several major questions that need to be addressed. Numerous minor issues are also present.

Strengths:

The authors did a comprehensive job with numerous analyses of moderately sized cohorts to describe the clinical and translational significance of their miRNA signature.

Weaknesses:

There are multiple weaknesses of this study that should be addressed:

(1) The description of the datasets in the Materials and Methods lacks details. What were the benign lesions from the various hospital datasets? What were the healthy controls from the public datasets? No pancreatic lesions? No pancreatic cancer? Any cancer history or other comorbid conditions? Please define these better.

We sincerely thank the reviewer for the detailed and important suggestions regarding sample definition. Indeed, the source of the datasets and the definition of control groups are critical for ensuring the rigor and interpretability of the study. In response to this comment, we have added clarifications in the revised "Materials and Methods" section.

First, for the benign lesion group derived from various clinical centers (DUH, UMMD, WUH, etc.), we have carefully reviewed the pathological and clinical records and defined these samples as histologically confirmed non-malignant pancreatic lesions, primarily IPMN. All patients in the benign lesion group had no diagnosis of pancreatic cancer at the time of sample collection, and for cohorts with available follow-up data, no evidence of malignant progression was observed within at least six months.

Second, the healthy control group from public databases was derived from healthy individuals.

Finally, to eliminate potential confounding factors, we excluded any samples with a history of other malignancies (e.g., breast cancer, colorectal cancer, etc.) from all datasets with available clinical information, to ensure the specificity of the EV-miRNA expression analysis.

(2) It is unclear how many of the controls and cases had both imaging for radiomics and blood for biomarkers.

Due to limitations in resource availability, our study does not include samples with both CT imaging and serological data from the same individuals. Instead, we integrated blood samples and CT imaging data collected from different clinical centers.

(3) The authors should define the imaging methods and protocols used in more detail. For the CT scans, what slice thickness? Was a pancreatic protocol used? What phase of contrast is used (arterial, portal venous, non-contrast)? Any normalization or pre-processing?

Thank you to the reviewer for the professional suggestions regarding the imaging section. We have added detailed technical information on CT imaging in the revised Materials and Methods section. All CT images were acquired using a 64-slice multidetector spiral CT scanner, with a standard slice thickness of 1.0–1.5 mm and a reconstruction interval of 1 mm. All pancreatic cancer patients underwent a standard pancreatic protocol triphasic contrast-enhanced CT examination, which included non-contrast, arterial phase (approximately 25–30 seconds), and portal venous phase (approximately 65–70 seconds) imaging.

For the radiomics analysis, images from the portal venous phase were selected, as this phase provides consistent clarity in delineating tumor boundaries and surrounding vasculature. To ensure data consistency, all imaging data underwent preprocessing, including resampling, intensity normalization of grayscale values (standardized using z-score normalization to a mean of 0 and a standard deviation of 1), and N4 bias field correction to address potential low-frequency signal inhomogeneities.

(4) Who performed the segmentation of the lesions? An experienced pancreatic radiologist? A student? How did the investigators ensure that the definition of the lesions was performed correctly? Raidomics features are often sensitive to the segmentation definitions.

All lesion segmentations were performed on portal venous phase contrast-enhanced CT images. Manual delineation was conducted using 3D Slicer (version 4.11) by two radiologists with extensive experience in pancreatic tumor diagnosis. A consensus was reached between the two radiologists on the ROI definition criteria prior to analysis.

To further assess the robustness of radiomic features to segmentation boundary variations, we selected a subset of representative cases and created “expanded/shrunk ROIs” by adding or subtracting a 2-pixel margin at the lesion boundary. Feature extraction was then repeated, and the coefficient of variation (CV) for the main features included in the model was found to be below 10%, indicating that the model is stable with respect to minor boundary fluctuations.

(5) Figure 1 is full of vague images that do not convey the study design well. Numbers from each of the datasets, a summary of what data was used for training and for validation, definitions of all of the abbreviations, references to the Roman numerals embedded within the figure, and better labeling of the various embedded graphs are needed. It is not clear whether the graphs are real results or just artwork to convey a concept. I suspect that they are just artwork, but this remains unclear.

We thank the reviewer for the detailed feedback on Figure 1. We would like to clarify that Figure 1 is a conceptual schematic intended to visually illustrate the overall design of the study, the relationships among different data modules, and the logical sequence of the analytical strategy. It is not meant to present actual results or quantitative details.

Regarding the reviewer’s concerns about sample sizes, the division between training and validation cohorts, explanations of specific abbreviations, and the precise meaning of each panel, we have provided comprehensive and detailed clarifications in Figure 2.

(6) The DF selection process lacks important details. Please reference your methods with the Boruta and Lasso models. Please explain what machine learning algorithms were used. There is a reference in the "Feature selection.." section of "the model formula listed below" but I do not see a model formula below this paragraph.

We thank the reviewer for the thoughtful and detailed comments on the feature selection strategy. We first applied the Boruta algorithm (based on random forests, implemented using the Boruta R package) to the original feature set—which included both radiomics and EV-miRNA features—to identify variables that consistently demonstrated importance across multiple rounds of random resampling.

Subsequently, we used LASSO regression with five-fold cross-validation to further reduce the dimensionality of the Boruta-selected features and to construct the final feature set used for modeling. The formula for the model is as follows: each regression coefficient is multiplied by the corresponding feature expression level, and the resulting products are summed to generate the Risk Score.

(7) In Figure 2, more quantitative details are needed. How are patients dichotomized into non-obese and obese? What does alcohol/smoking mean? Is it simply no to both versus one or the other as yes? These two risk factors should be separated and pack years of smoking should be reported. The details of alcohol use should also be provided. Is it an alcohol abuse history? Any alcohol use, including social drinking? Similarly, "diabetes" needs to be better explained. Type I, type II, type 3c? P values should be shown to demonstrate any statistically significant differences in the proportions of the patients from one dataset to another.

Our definition of obesity was based on the standard BMI threshold (30 kg/m²). A history of smoking or alcohol consumption was defined as continuous use for more than one year. Specific details regarding smoking and alcohol use were recorded at baseline under the category of “smoking/alcohol history”; unfortunately, we did not collect follow-up data on these variables. As for diabetes, only type II diabetes was documented. Statistically significant p-values have been added. Thank you.

(8) In the section "Different expression radiomic features between pancreatic benign lesions and aggressive tumors", there is a reference to "MUJH" for the first time. What is this? There is also the first reference to "aggressive tumors" in the section. Do the authors just mean the cases? Otherwise there is no clear definition of "aggressive" (vs. indolent) pancreatic cancer. This terminology of tumor "aggressiveness" either needs to be removed or better defined.

We have corrected the abbreviation (MUJH); it should in fact be JHC. Additionally, regarding the term "aggressive," we have reviewed the literature and used it to convey the highly malignant nature of pancreatic cancer.

(9) Figure 3 needs to have the specific radiomic features defined and how these features were calculated. Labeling them as just f1, f2, etc is not sufficient for another group to replicate the results independently.

We have presented these features in Supplementary Table 1. Kindly refer to it for details.

(10) It is not clear what Figure 4A illustrates as regards model performance. What do the different colors represent, and what are the models used here? This is very confusing.

This represents the correlation between WGCNA modules and miRNAs. Different module colors indicate distinct miRNA clusters—for example, the green module contains 12 miRNAs grouped together. The colors themselves do not carry any intrinsic meaning.

(11) Figure 5 shows results for many more model runs than the described 10, please explain what you are trying to convey with each row. What are "Test A" and "Test B"? There is no description in the manuscript of what these represent. In the figure caption, there is a reference to "our center data" which is not clear. Be more specific about what that data is.

We have indicated this using arrows in Figure 5 from Test A/B/C. Please check.

(12) Figure 6 describes the subtypes identified in this study, but the authors do not show a multi-variable cox proportional hazards model to show that this subtype classification independently predicts DFS and OS when incorporating confounding variables. This is essential to show the subtypes are clinically relevant. In particular, the authors need to account for the stage of the patients, and receipt of chemotherapy, surgery, and radiation. If surgery was done, we need to know whether they had R1 or R0 resection. The details about the years in which patients were included is also important.

We sincerely thank the reviewer for this critical comment. We fully agree that incorporating a multivariate Cox proportional hazards model to control for potential confounding factors would provide a more robust validation of the independent prognostic value of our proposed subtypes for DFS and OS.

However, as the clinical data used in this study were retrospectively collected and access to certain variables is currently restricted, we were only able to obtain limited clinical information. At this stage, we are unable to systematically include key variables such as tumor staging, adjuvant chemoradiotherapy regimens, and resection margin status (R0 vs. R1), which prevents us from performing a rigorous multivariate Cox analysis.

Similarly, regarding the postoperative resection status, after reviewing the original surgical reports and pathology records, we regret to confirm that margin status (R0 vs. R1) is missing in a substantial portion of cases, making it unsuitable for reliable statistical analysis.

We fully acknowledge this as a limitation of the current study and have explicitly addressed it in the Discussion section. To address this gap, we are currently designing a more comprehensive prospective cohort study, which will allow us to validate the clinical independence and utility of the proposed subtypes in future research.

(13) How do these subtypes compare to other published subtypes?

We sincerely thank the reviewer for raising this important point. Clusters 1 and 2 represent a novel molecular classification proposed for the first time in this study, driven by EV-miRNA profiles. This classification approach is conceptually independent from traditional transcriptome-based subtyping systems, such as the classical/basal-like subtypes, as well as other existing classification schemes. Comparisons with previously reported subtypes and validation of clinical relevance will require further investigation in future studies.

Reviewer #3 (Public review):

Summary:

The authors appear to be attempting to identify which patients with benign lesions will progress to cancer using a liquid biomarker. They used radiomics and EV miRNAs in order to assess this.

Strengths:

It is a strength that there are multiple test datasets. Data is batch-corrected. A relatively large number of patients is included. Only 3 miRNAs are needed to obtain their sensitivity and specificity scores.

Weaknesses:

This manuscript is not clearly written, making interpretation of the quality and rigor of the data very difficult. There is no indication from the methods that the patients in their cohorts who are pancreatic cancer patients (from the CT images) had prior benign lesions, limiting the power of their analysis. The data regarding the cluster subtypes is very confusing. There is no discussion or comparison if these two clusters are just representing classical and basal subtypes (which have been well described).

Sorry,we don’t have the data of record from patients, in addition, Regarding the relationship between Cluster 1/Cluster 2 and classical subtypes:We are very grateful for the reviewer’s insightful question. We would like to clarify that Clusters 1 and 2, as shown in Figures 6 and 7, are derived from a novel EV-miRNA–driven molecular classification proposed for the first time in this study. This classification system is constructed independently of the traditional transcriptome-based classical/basal-like subtypes.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

There are errors in reference citations and several typos, misspellings, and grammatical errors throughout the manuscript.

We have made the necessary revisions.

Reviewer #2 (Recommendations for the authors):

(1) Were the radiomic features associated with the subtypes and prognostic in the subset of patients who had CT scans?

Unfortunately, there are no corresponding CT imaging results available for these cases, as the genes were identified based on predicted miRNA targets and were not derived from patients who had undergone CT scans.

(2) There is a whole body of literature on prognostic imaging-based subtypes of pancreatic cancer that needs to be cited.

Thank you for your suggestion. We have cited the relevant references accordingly in the manuscript.

(3) Similarly, the authors should be more comprehensive about prognostic and early detection markers for miRNAs for pancreatic cancer. Early detection markers really should be described separately from prognostic markers. The authors did not do a PROBE phase 3 study, so early detection is not really relevant. Please see https://edrn.nci.nih.gov/about-edrn/five-phase-approach-and-prospective-specimen-collection-retrospective-blinded-evaluation-study-design/

The primary objective of our study is early detection. We acknowledge the absence of third-phase validation results, which we will address in the limitations section. Additionally, the subtype classification represents our secondary objective.

(4) If they want to couch this as a PROBE phase 2 study, then they should review the PROBE guidelines and ensure they are meeting standards. Many of the comments above regarding methodologies, definitions, and patient cohort descriptions would address this concern.

We have revised the Methods section accordingly. Please kindly review the updated version.

(5) The entire manuscript needs to have a review for the use of the English language. There are numerous typos and grammatical errors that make this manuscript difficult to follow and hard to interpret.

We have revised the Methods section accordingly. Please kindly review the updated version.

(6) In the section on "Definition and identification of low abundance EV-derived miRNA transcripts", provide a reference for the "edger" function.

We have revised the Methods section accordingly. Please kindly review the updated version.

(7) In the Abstract: The purpose section only mentions early diagnosis as the goal of this study. It seems subtyping is also a major goal, but it is not mentioned.

The primary objective of our study is early detection.Additionally, the subtype classification represents our secondary objective.so,we didn’t add it in the purpose.

(8) The experimental design fails to describe any of the 8 datasets that were used. How many patients? What were the ethnic and racial backgrounds, which is one of the key aspects of this study and mentioned in the title? What range of stages? When were the images and the blood collected in relation to diagnosis? Over what time frame were the patients included? What patients were excluded, if any? These details are important to understand the materials used, along with the methods to design the signatures and models.

We have revised the Methods section accordingly. Please kindly review the updated version.

(9) Again, the purpose section of the abstract does not align with the rest of the study, including the description of the experimental design. The last sentence of the experimental design section mentions predicting drug sensitivity and survival, which is unrelated to the aim of early diagnosis.

We have revised the Methods section accordingly. Please kindly review the updated version.

(10) The results section lacks key details to indicate the impact of the work. Vague descriptions of the findings are not sufficient. The performance of the biomarkers to differentiate benign from malignant lesions, hazard ratios, survival times, and p values should be reported for key results.

Our aim was to develop an integrated panel for diagnostic purposes; therefore, we provided the AUC to evaluate its performance. However, since this is a diagnostic model, we did not include hazard ratios or survival time data.

(11) What are "tow" molecular subtypes of pancreatic cancer? Did you mean "two"? What system was used to subtype the pancreatic cancers? Is some new subtyping or a previously published method to subtype the disease?

Yes, it means two, previously published method.In method part, we have describe it.

Reviewer #3 (Recommendations for the authors):

The writing of this manuscript needs extensive re-wording and clarification to increase the readability and interpretability of the data presented. The authors could include a dataset of pancreatic cancer patient imaging data where the status of prior benign lesions was detected (as opposed to patients with benign lesions that do not develop pancreatic cancer). The authors could also address if their clusters 1 and 2 are representing (or are correlated with) the classical and basal subtypes that have been well described for pancreatic cancer.

Thank you to the reviewer for the constructive comments. We sincerely appreciate your careful review, particularly regarding language clarity, data interpretability, and subtype correlation. To enhance the readability and scientific precision of the manuscript, we have conducted a thorough revision and language polishing throughout the text, improving logical structure, terminology consistency, and clarity in result descriptions. We have especially reinforced the Methods and Discussion sections to better explain key analytical steps and data interpretation.

We fully understand the reviewer’s suggestion to include information on “the presence of benign lesions prior to pancreatic cancer diagnosis.” However, due to the retrospective nature of our study, the current imaging and EV-miRNA datasets do not contain systematically collected follow-up annotations of this type. Therefore, it is not feasible to incorporate such data into the present manuscript.

That said, we fully recognize the importance of this direction. In future studies, we plan to evaluate longitudinal samples to investigate the dynamic changes in EV-miRNAs and imaging features during the progression from premalignant to malignant states, aiming to clarify their potential value for early cancer warning.

Regarding the relationship between Cluster 1/Cluster 2 and classical subtypes:We are very grateful for the reviewer’s insightful question. We would like to clarify that Clusters 1 and 2, as shown in Figures 6 and 7, are derived from a novel EV-miRNA–driven molecular classification proposed for the first time in this study. This classification system is constructed independently of the traditional transcriptome-based classical/basal-like subtypes.

Although we attempted a cross-comparison with existing TCGA subtypes, differences in data origin, analysis modality (EV-miRNA vs. tissue transcriptome), and limitations in sample matching prevent us from establishing a direct correspondence. In the revised Discussion, we have emphasized that these two classification approaches are complementary rather than equivalent, reflecting different dimensions of tumor heterogeneity. Further integrative multi-omics studies will be needed to validate their biological significance and clinical utility.

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Reply to the reviewers

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

The authors provide a detailed characterization of the tumor microenvironment (TME) of 91 ovarian cancer patients, broken down in long and short-term survivors (post 5 years). The focus on the role of a subgroup of T cells, gamma/delta γδ) T cells with reported anti but also pro tumorigenic properties, Prior work of the lab has established a link between a subgroup of γδ T cells expressing CD73 and poor prognosis, due to the ability of these cells to produce immunosuppressive cytokines, such as IL10 or IL8 and the production of adenosine, by CD73, in the micromilieu. The data is further backed up by the analysis of fresh tumor specimens and tissue culture work.

Here they continue this story by investigating the TME using tumor microarrays (91 samples), single cell RNA seq (12 patients), imaging mass cytometry (> 30 samples) and flow cytometry (form confirmatory purposes) to define cellular neighborhoods of CD73+ and CD73- γδ T cells. This revealed differences in cellular composition and spatial transcriptome analysis further helped to define the transcriptomes in γδ T cells, cancer cells and cancer associated fibroblasts.

The authors conclude the in ovarian cancer γδ T cells expressing CD73 dampen anti-tumor immunity and propose detection and evaluation of CD73+ γδ T cells as prognostic marker.

The manuscript is well written, and despite its descriptive nature, easy to follow. Data is presented in a clear and easy to read fashion.

Reviewer #1 (Significance (Required)):

Using a well characterized cohort of ovarian cancer patients with detailed clinical follow up the authors report on the predictive power of a subset of γδ T cells expressing CD73, with immune suppressive / regulatory capacity, reading out patient survival in high grade serous ovarian cancer, a still deadly disease. As such the identification of reliable markers predicting survival is a clear medical need. These findings contrast others made in different solid cancers, suggesting tumor type specific differences, which are only starting to emerge, but are of clear clinical relevance.

What is unclear to me and needs to be addressed, is if these patient specimens were taken before or after initial therapy, whether the samples have been stratified according the treatment that they got, assuming it will be mostly platinum compounds (but maybe not), and that the p53 status of the tumors are (if genetics are available this would help to add some granularity to the study that, as it stands is largely descriptive, even though with extremely high resolution. This data should be available and could be integrated.

We thank the reviewer for this insightful and constructive comment. We agree that clinical context and treatment stratification are essential to strengthen the interpretation and translational value of our findings.

We confirm that all tumor samples used in this study were obtained prior to any systemic treatment, i.e., before first-line chemotherapy, during the Biopsy realized for the diagnosis. This information has now been clearly stated in the Methods and Results section (page 4, line 103) and also in Table S1.

Although our primary aim was not to evaluate correlations with mutational status, we recognize the critical role that tumor genetics play in shaping the immune microenvironment. Using available clinical genomics data, we found that the TP53 mutational status of our cohort aligns with that of previous analyses. As expected for high-grade serous ovarian cancer (HGSOC), nearly all tumors exhibited TP53 mutations (present in 95% of patients). Due to the lack of variability in TP53 status, no meaningful stratification was observed based on this factor. This information has been added in the Materials and methods part (page 4 lines 104 to 106)

Some minor issues

• I would stick to CD73, and not mix it with NT5E, which is confusing at first (Fig 2).

We appreciate this suggestion. To clarify the nomenclature and avoid confusion, we have consistently indicated throughout the text and figure legends that NT5E refers to the CD73 gene.

• I would ask to compare the overall survival of CD73+ between densities - is it still significantly different in fig 1 - meaning is it about density, CD73 expression, or both. Comparing survival of tumors with a low density of CD73+ γδ T cells does not seem to be different from those having a low density of CD73- γδ T cells, which could be considered in data interpretation. Same for high density tumors.

In the manuscript, the term “density” specifically refers to the density of γδ____ T cells and not the density of CD73 molecules expressed by these cells. Additionally, it is not feasible to conduct a density analysis of molecules using the data obtained from immunofluorescence (IF) staining of sample sections.

Kaplan-Meier analyses were performed to assess patient survival based on the density of total γδ____ T cells, as well as the subsets of CD73⁺ and CD73⁻ γδ T cells. The results indicate that a higher density of γδ____ T cells is associated with poorer patient survival, with a more pronounced effect seen in those with a high density of CD73⁺ γδ T cells compared to those with CD73⁻ γδ T cells.

As the reviewer pointed out, patients with a low density of CD73⁺ γδ T cells do not show significantly different survival outcomes compared to those with a low density of CD73⁻ γδ T cells (IC50 for low CD73⁺ = 6.0 years vs. IC50 for low CD73⁻ = 6.2 years). In response, we have revised the corresponding sentence in the text and included the IC50 values for greater clarity and informativeness (page 9).

• figure 1, caption should include the word "patients" at the end, I guess.

The modification has been done.

• labelling and font can be improved in many panels, eg. the dot plots in Fig 2, panel B, right, same for panel C and D

We appreciate the feedback on figure presentation. We have now updated Figure 2 with improved labeling, consistent font size, and enhanced resolution to ensure better readability across all panels, particularly panels B–D. The revised figure has been updated in the main manuscript.

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

In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ γδ T cells in ovarian cancer. CD73+ γδ T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg γδ T cells in ovarian cancers with a particular focus on the cells surrounding the γδ T cells in the tumour.

Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

General comments:

• The data provided in this manuscript are descriptive/correlative, and as such, causation cannot be inferred. Therefore, the language needs to reflect this; statements like "we investigated the impact of CD73+ regulatory γδ T cells in ovarian cancer" (L89) and "CD73+ γδ T cells were in close contact with more aggressive tumor cells" (L426), among others, are incorrect without functional data. The authors are advised to adjust the text throughout.

We thank the reviewer for this thoughtful point. We have amended the text to make it consistent with the data.

• Please make the figure legends self-explanatory without the need to search for the information in the M&M. For example, the graphs in fig1 and 3 contain many dots, but it is not explained what these dots represent. Please also add n for each experiment shown and state how often the experiment was performed independently.

As requested by the reviewer, we have revised the figure legends to make them more explicit. We have indicated the number of biological replicates (n) and how many times each experiment was performed independently. This information has been added to each legend where consistent and relevant, to ensure clarity and reproducibility.

• It would be helpful for the reader if abbreviations introduced in the M&M were also explained the first time they appeared in the results section.

This point has been addressed as requested by the reviewer.

• Please explain all abbreviations, e.g. FIGO, CST, NT5E, etc.
• L235: typo 'that' instead of 'than'; L258 'reduced'; L259 'fig1d-f'; L451f twice 'CD73+'; 'naive' instead of 'naïve' throughout; SF2 legend: '2f' instead of '3f', SF9 legend: '1.105'.
• L280ff: "Tumor cells ... were the most important cell type" - it may be clearer to use 'most frequent';

All these points have been addressed.

M&M - Please be consistent, if you provide catalogue numbers or dilutions (antibody, reagents) [which is good, maybe even adding the RRID number], do so for all items.

This point has been addressed as requested by the reviewer.

• The M&M does not state for the CAFs how long they were cultured before the supernatant was taken for the cytokine measurements.

This point has been added in M&M section.

• For the IL-6 ELISA, it is stated that the "cells were harvested"; what happened to them, and how do you get any SN from these cells?

We have amended the protocol of IL-6 Elisa in M&M section for clarification.

Figures Fig.1: - The authors used the word 'predict' in the heading, which seems not appropriate for a retrospective study; something like 'correlate' seems better.

The word “predict” has been replaced by “correlate” as suggested by the reviewer.

• Similarly, the title of the figure legends claims that the 'impact of γδ T cells' is shown, while only a correlation is presented.

The title of the figure has been modified

• For Fig1a-c, only summary data are presented. Please add exemplary pictures as well.

Pictures of IF have been added as Supplementary Fig 1.

• For Fig1d-f, the label for the x-axis is missing.

The figure has been corrected.

Fig.2 - It seems funny to call the patients 'naïve', maybe 'untreated' is clearer.

We appreciate this suggestion and agree that ‘untreated’ is a clearer and more appropriate term in this context. We have replaced all instances of ‘naïve’ with ‘untreated’ throughout the manuscript to avoid ambiguity.

• The graph in Fig2e does not allow comparing the cell frequencies properly. This would require either bar graphs or a table. Furthermore, the statistical analysis is missing. Without that, a statement like "associated with higher proportion of CAFs" (L265) is not supported.

We thank the reviewer for this valuable observation. In response, we have replaced the original visualization in Figure 2E with grouped bar graphs showing the mean ± SEM of the relative proportions of each major cell type in the NT5E_low and NT5E_high groups, based on the median split. This format allows for clearer visual comparison of cell frequencies across conditions.

Furthermore, we performed statistical comparisons using a t-test (a parametric test) on each population to evaluate differences in cell type proportions between the two groups. The results indicate a significantly higher proportion of CAFs and γδ T cells in the NT5E_high tumor profile. The corresponding p-values are provided in the figure legend. We hope this revised analysis and clearer presentation address the reviewer’s concerns.

Fig.3 - For Fig3b+c, the IMC are derived from 4 patients (not clear for the flow data)

As stated in both the figure legend and the text, the IMC analysis was conducted on 38 ROIs from four patient samples, while the flow cytometry analysis was performed on tumor samples from seven ovarian cancer patients.

• did the authors noticed differences between patients?

"As shown in new Figures 3b and 3c, no significant differences were observed between patients. Each individual patient is represented by a different color."

• For Fig3e, the description in the text does not reflect the figure, e.g. cluster 1 does not show LAG3 expression, but this is claimed in the text (other descriptions are off as well).

The text describing Fig. 3e has been amended in the new version of the manuscript.

• In Fig3h, the authors stain cytokines in γδ T cells purified from ovarian cancer samples. The text seems to imply that the cytokine staining was performed directly ex vivo, without an in vitro stimulation of the cells, e.g. with PMA/ionomycin (if so, the description is missing). In any case, the values appear surprisingly high. Exemplary data are needed to clarify how the gating was done (for γδ T cells and the cytokines) and what the primary data looked like.

The protocol has been amended in the “Materials and Methods” section. A gating strategy and primary data analysis from one representative patient are included in a supplementary Figure 4c.

We agree with the reviewer’s comments that it is surprising that γδ T cell stimulation is not required for IL-8, IL10 and IFNγ production. However, one possible explanation is the high reactivity of γδ T cells compared to other T cell subsets, as well as their localization in the tumor microenvironment rather than in healthy tissue or blood.

• In Fig3h, it is not clear what is meant with "IL-8 / IL-10", please explain.

This analysis shows the percentage of cells that are positive for both IL-8 and IL-10.

The figure and its legend have been amended for clarity.

Fig.4 - Please provide the values and the statistical analyses for all cell populations.

We performed statistical analyses (Wilcoxon signed-rank test) for all cell populations and provide the data in the Supplementary Fig. 5A. However, due to the heterogeneity of ROIs, a significant difference was observed for tumor cells, which were more prevalent more in the neighborhood of CD73- than CD73+ γδ T cells (p

Fig.5/6 - In Fig5, the authors state that 8 cell populations were differentially enriched around CD73+ or -neg γδ T cells. However, in Fig4, only 4 of these populations are mentioned. Please add the remaining 4 to fig4 and name the 8 clusters in fig5 in line with the gating strategy used in fig4.

We thank the reviewer for highlighting that the description of Figure 5 in our text was unclear. We have revised the text for clarification and specify that based on Supplementary Figure 7, which shows the number of cells for each cell type found in the neighborhood of all γδ T cell subsets (CD73- and CD73+) in all ROIs. We decided to perform phenotypic analysis on only four cell types (those with a sufficient cell counts), setting the cutoff at 700 cells.

The four cell types are analyzed in Figures 5 and 6. Figure 5A shows tumor cells, with eight clusters identified, while Figure 5B represents fibroblasts, with seven clusters identified. Figure 6A shows CD4 T cells, with eight clusters, and Figure 6B CD8 T cells, with ten clusters.

• Furthermore, the authors want to show in fig5 how the phenotype of these 8 cell populations differs depending on whether they are close to CD73+/- γδT cells. tSNE plots do not allow illustrating this (BTW: the plots lack the colour code). The frequencies of the cell types/phenotypes in the vicinity of CD73+/- γδ T cells need to be depicted differently (e.g. bar graphs). Furthermore, the claim that differences are observed, needs to be supported by showing the statistical values obtained. The same argument applies to Fig6 and SF8.

We have added the code color of tSNE plots in Figures 5, 6, and SF9. The tables in Supplementary Figure 8 show the percentage of cells in each cluster within the vicinity of CD73+/- γδ T cells, allowing for an investigation of the neighborhood of each γδ T cell subset.

• Fig6: This reviewer disagrees with the notion that the expression of HLA-DR or CD279 is enough to imply a functional state of the cell.

As requested by the reviewer, we have amended the text to clarify that: “Cluster analysis revealed that CD4+ T cells in contact with effector γδ T cells (i.e., the CD73- subset) express HLA-DR and/or PD-1, both activation markers.”

Supplements - SF2a: please check the labels; how can CD8+ CD4+ cells be labelled 'CD8 T cells' and why do the authors exclude the possibility that e.g. B cells could express HLA-DR?

We thank the reviewer for pointing out the error in Figure 2a, which has now been corrected. The CD8+ cells have been relabeled as 'CD8 T cells,' and the B cells are now shown expressing HLA-DR.

• SF7 is not clear to this reviewer. If the clusters represent different cell types, how can e.g. tumours be found in all of them?

We believe the reviewer is referring to SF9 rather than SF7 in this comment. SF9 analyzes γδ T cells in proximity to CD73+ and CD73- γδ T cells. As in Figures 5 and 6, γδ T cell neighbors of CD73+ and CD73- γδ T cells were identified, and a clustering analysis revealed five distinct clusters. Tumor cells was not analyzed in this figure. We have clarified the text to prevent confusion

• SF9b lacks a negative control and a statistical analysis, and SF9c lacks the summary data and statistical analysis.

As requested by the reviewer, we have performed statistical analysis for SF9b and added a negative control. Additionally, we have included summary data with a statistical analysis in SF9c.

• In the text, the authors state, "We and others reported that in ovarian tumors, IL-6 is mainly produced by CAFs and induces CD73 expression by γδ T cells (Extended Data Fig. 9 and 15)." The data in SF9b are not enough to make this claim and reference 15 is a review article that does not even mention 'IL-6'. This needs to be corrected.

We have updated Supplementary Figure 9B to provide more robust data. We thank the reviewer for pointing out our error. The publication we intend to cite is a research article, not a review.” Hu G, Cheng P, Pan J, Wang S, Ding Q, Jiang Z, et al. An IL6-Adenosine Positive Feedback Loop between CD73+ γδ Tregs and CAFs Promotes Tumor Progression in Human Breast Cancer. Cancer Immunol Res. 2020;8:1273–86.” we made the correction in the manuscript.

Reviewer #2 (Significance (Required)):

In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ γδ T cells in ovarian cancer.

CD73+ γδ T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg γδ T cells in ovarian cancers with a particular focus on the cells surrounding the γδ T cells in the tumour.

Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

To enable a full evaluation of the data, we have added new figures, amended others, and clarified certain points in the text, hoping that the reviewer will find these modifications sufficient to consider our manuscript for publication.

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

In this article, Chabab et al. analyze sample from ovarian cancer patients, with a specific focus on gamma-delta T cells (Tγδ). The authors claim that CD73+ cells are associated with poor prognosis in ovarian cancer, and that CD73 expression is correlated with the composition and polarization of the microenvironment. Using imaging mass cytometry data, they also claim that the neighborhoods of CD73+ and CD73- Tγδ cells differs in composition.

Major comments: - The prognostic value of CD73/NT5E is analyzed in TCGA-Ovarian RNAseq data. In the context of this article, it is implied that this should reflect CD73 expression by Tγδ but it is likely that other cell types are contributing to bulk CD73 expression.

We appreciate the reviewer’s insightful comment. In fact, due to low proportion of Tγδ in TME we have stratified on NT5E total expression. We agree that this signal likely includes contributions from multiple cell types beyond γδ T cells, such as cancer-associated fibroblasts and endothelial cells, which are also known to express CD73 (NT5E gene).

The stratification of patient based on NT5E total expression showed an association between high NT5E expression and poorer overall survival and increase in Tγδ gene markers (TRDC, TRGC1/2) and percentage of cells (Fig2E) in the patient cohort (Fig2C). To clarify this point, we have revised the Results and Discussion sections to explicitly state that the TCGA-based survival analysis reflects total intratumoral NT5E enrichment and cannot be attributed specifically to γδ T cells. We now refer to this analysis as an independent validation of the clinical relevance of CD73, while noting that its cell-type-specific contribution remains to be resolved in future studies using spatial transcriptomics or deconvolution approaches.

• In the analysis of scRNAseq data, multiple public datasets are aggregated and the overall level of CD73 is used for stratification. Is this stratification confounded by dataset of origin?

We thank the reviewer for raising this critical point regarding potential batch effects and dataset-driven bias in our stratification strategy. To address this, we performed additional analyses to assess whether NT5E (CD73) expression is confounded by dataset of origin.

First, we verified that all single-cell datasets (GSE147082, GSE241221, and GSE235931) were processed using a harmonized integration workflow, including SCTransform normalization and integration using Seurat’s reciprocal PCA approach, which effectively minimizes batch-related variability.

• The last part of the results discusses the role of IL6 produced by CAFs on Tγδ, but very little data is shown to support the proposed mechanisms. The authors report expression of CD73 by flow cytometry on blood-sorted Tγδ following culture with IL2, IL6, IL21. The data shown however only represents one donor and should therefore be repeated on multiple donors.

We appreciate the reviewer’s insightful comment. We have added data and updated Supplementary Figure 9 to provide more robust findings. Regarding the role of IL-6, our data in ovarian cancer are consistent with the study by Hu et al. in breast cancer, which reports an IL-6-Adenosine Positive Feedback Loop between CD73+ γδ Tregs and CAFs that promotes tumor progression in human breast cancer."

Minor comments:

• The authors stratify their cohort by Tγδ density but I could not find the threshold used for stratification

The threshold has been added in figure and text.

• Labels for CD8+ and CD4+CD8+ T cells are swapped in Extended Data Fig 2A

The correction of figure has been made.

• The legend of graphs shown in multiple panels (for instance: Fig 3F) are not very clear: is each dot representing the average expression of one cluster in one patient?
• In figure 3G there is no color scale, the authors need to add it with appropriate units so that readers can interpret the data shown

These points have all been amended and corrected in the next version of the manuscript.

Reviewer #3 (Significance (Required)):

This paper shows interesting imaging mass cytometry data of ovarian cancer specimens. The focus on CD73 expression by Tγδ is fairly specific, although the exonucleotidases pathway involving CD73 is currently extensively studied for its immunosuppressive role.

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

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

In this article, Chabab et al. analyze sample from ovarian cancer patients, with a specific focus on gamma-delta T cells (Tgd). The authors claim that CD73+ cells are associated with poor prognosis in ovarian cancer, and that CD73 expression is correlated with the composition and polarization of the microenvironment. Using imaging mass cytometry data, they also claim that the neighborhoods of CD73+ and CD73- Tgd cells differs in composition.

Major comments:

• The prognostic value of CD73/NT5E is analyzed in TCGA-Ovarian RNAseq data. In the context of this article, it is implied that this should reflect CD73 expression by Tgd but it is likely that other cell types are contributing to bulk CD73 expression.
• In the analysis of scRNAseq data, multiple public datasets are aggregated and the overall level of CD73 is used for stratification. Is this stratification confounded by dataset of origin ?
• The last part of the results discuss the role of IL6 produced by CAFs on Tgd, but very little data is shown to support the proposed mechanisms. The authors report expression of CD73 by flow cytometry on blood-sorted Tgd following culture with IL2, IL6, IL21. The data shown however only represents one donor and should therefore be repeated on multiple donors.

Minor comments:

• The authors stratify their cohort by Tgd density but I could not find the threshold used for stratification
• Labels for CD8+ and CD4+CD8+ T cells are swapped in Extended Data Fig 2A
• The legend of graphs shown in multiple panels (for instance : Fig 3F) are not very clear : is each dot representing the average expression of one cluster in one patient ?
• In figure 3G there is no color scale, the authors need to add it with appropriate units so that readers can interpret the data shown

Significance

This paper shows interesting imaging mass cytometry data of ovarian cancer specimens. The focus on CD73 expression by Tgd is fairly specific, although the exonucleotidases pathway involving CD73 is currently extensively studied for its immunosuppressive role.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ gd T cells in ovarian cancer. CD73+ gd T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg gd T cells in ovarian cancers with a particular focus on the cells surrounding the gd T cells in the tumour. Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

General comments:

• The data provided in this manuscript are descriptive/correlative, and as such, causation cannot be inferred. Therefore, the language needs to reflect this; statements like "we investigated the impact of CD73+ regulatory γδ T cells in ovarian cancer" (L89) and "CD73+ γδ T cells were in close contact with more aggressive tumor cells" (L426), among others, are incorrect without functional data. The authors are advised to adjust the text throughout.
• Please make the figure legends self-explanatory without the need to search for the information in the M&M. For example, the graphs in fig1 and 3 contain many dots, but it is not explained what these dots represent. Please also add n for each experiment shown and state how often the experiment was performed independently.
• It would be helpful for the reader if abbreviations introduced in the M&M were also explained the first time they appeared in the results section.
• Please explain all abbreviations, e.g. FIGO, CST, NT5E, etc.
• L235: typo 'that' instead of 'than'; L258 'reduced'; L259 'fig1d-f'; L451f twice 'CD73+'; 'naive' instead of 'naïve' throughout; SF2 legend: '2f' instead of '3f', SF9 legend: '1.105'.
• L280ff: "Tumor cells ... were the most important cell type" - it may be clearer to use 'most frequent';

M&M

• Please be consistent, if you provide catalogue numbers or dilutions (antibody, reagents) [which is good, maybe even adding the RRID number], do so for all items.
• The M&M does not state for the CAFs how long they were cultured before the supernatant was taken for the cytokine measurements.
• For the IL-6 ELISA, it is stated that the "cells were harvested"; what happened to them, and how do you get any SN from these cells?

Figures

Fig.1:

• The authors used the word 'predict' in the heading, which seems not appropriate for a retrospective study; something like 'correlate' seems better.
• Similarly, the title of the figure legends claims that the 'impact of gd T cells' is shown, while only a correlation is presented.
• For Fig1a-c, only summary data are presented. Please add exemplary pictures as well.
• For Fig1d-f, the label for the x-axis is missing.

Fig.2

• It seems funny to call the patients 'naïve', maybe 'untreated' is clearer.
• The graph in Fig2e does not allow comparing the cell frequencies properly. This would require either bar graphs or a table. Furthermore, the statistical analysis is missing. Without that, a statement like "associated with higher proportion of CAFs" (L265) is not supported.

Fig.3

• For Fig3b+c, the IMC are derived from 4 patients (not clear for the flow data) - did the authors noticed differences between patients?
• For Fig3e, the description in the text does not reflect the figure, e.g. cluster 1 does not show LAG3 expression, but this is claimed in the text (other descriptions are off as well).
• In Fig3h, the authors stain cytokines in gd T cells purified from ovarian cancer samples. The text seems to imply that the cytokine staining was performed directly ex vivo, without an in vitro stimulation of the cells, e.g. with PMA/ionomycin (if so, the description is missing). In any case, the values appear surprisingly high. Exemplary data are needed to clarify how the gating was done (for gd T cells and the cytokines) and what the primary data looked like.
• In Fig3h, it is not clear what is meant with "IL-8 / IL-10", please explain.

Fig.4

• Please provide the values and the statistical analyses for all cell populations.

Fig.5/6

• In Fig5, the authors state that 8 cell populations were differentially enriched around CD73+ or -neg gd T cells. However, in Fig4, only 4 of these populations are mentioned. Please add the remaining 4 to fig4 and name the 8 clusters in fig5 in line with the gating strategy used in fig4.
• Furthermore, the authors want to show in fig5 how the phenotype of these 8 cell populations differs depending on whether they are close to CD73+/- gdT cells. tSNE plots do not allow illustrating this (BTW: the plots lack the colour code). The frequencies of the cell types/phenotypes in the vicinity of CD73+/- gd T cells need to be depicted differently (e.g. bar graphs). Furthermore, the claim that differences are observed, needs to be supported by showing the statistical values obtained. The same argument applies to Fig6 and SF8.
• Fig6: This reviewer disagrees with the notion that the expression of HLA-DR or CD279 is enough to imply a functional state of the cell.

Supplements

• SF2a: please check the labels; how can CD8+ CD4+ cells be labelled 'CD8 T cells' and why do the authors exclude the possibility that e.g. B cells could express HLA-DR?
• SF7 is not clear to this reviewer. If the clusters represent different cell types, how can e.g. tumours be found in all of them?
• SF9b lacks a negative control and a statistical analysis, and SF9c lacks the summary data and statistical analysis.
• In the text, the authors state, "We and others reported that in ovarian tumors, IL-6 is mainly produced by CAFs and induces CD73 expression by γδ T cells (Extended Data Fig. 9 and 15)." The data in SF9b are not enough to make this claim and reference 15 is a review article that does not even mention 'IL-6'. This needs to be corrected.

Significance

In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ gd T cells in ovarian cancer. CD73+ gd T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg gd T cells in ovarian cancers with a particular focus on the cells surrounding the gd T cells in the tumour. Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

Author Response:

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

Reviewer #1 (Public review):  

Summary: 

Kohno et al. examined whether the anti-inflammatory cytokine IL-4 attenuates neuropathic pain by promoting the emergence of antinociceptive microglia in the dorsal horn of the spinal cord. In two models of neuropathic pain following peripheral nerve injury, intrathecal administration of IL-4 once a day for 3 days from day 14 to day 17 after injury, attenuates hypersensitivity to mechanical stimuli in the hind paw ipsilateral to nerve injury. Such an antinociceptive effect correlates with a higher number of CD11c+microglia in the dorsal horn of the spinal cord which is the termination area for primary afferent fibres injured in the periphery. Interestingly, CD11c+ microglia emerge spontaneously in the dorsal horn in concomitance with the resolution of pain in the spinal nerve model of pain, but not in the spared nerve injury model where pain does not resolve, confirming that this cluster of microglia is involved in resolution pain. 

Based on existing evidence that the receptor for IL-4, namely IL-4R, is expressed by microglia, the authors suggest that IL-4R mediates IL-4 effect in microglia including up-regulation of Igf1 mRNA. They have previously reported that IGF-1 can attenuate pain neuron activity in the spinal cord. 

Strengths:

This study includes cutting-edge techniques such as flow cytometry analysis of microglia and transgenic mouse models. 

Weaknesses:

The conclusion of this paper is supported by data, but the interpretation of some data requires clarification.  

We appreciate the reviewer's careful reading of our paper.  According to the reviewer's comments, we have performed new immunohistochemical experiments and added some discussion in the revised manuscript (please see the point-by-point responses below).

Reviewer #2 (Public review):

Summary:

The authors aimed to investigate how IL-4 modulates the reactive state of microglia in the context of neuropathic pain. Specifically, they sought to determine whether IL-4 drives an increase in CD11c+ microglial cells, a population associated with anti-inflammatory responses and whether this change is linked to the suppression of neuropathic pain. The study employs a combination of behavioral assays, pharmacogenetic manipulation of microglial populations, and characterization of microglial markers to address these questions. 

Strengths: 

The methodological approach in this study is robust, providing convincing evidence for the proposed mechanism of IL-4-mediated microglial regulation in neuropathic pain. The experimental design is well thought out, utilizing two distinct neuropathic pain models (SpNT and SNI), each yielding different outcomes. The SpNT model demonstrates spontaneous pain remission and an increase in the CD11c+ microglial population, which correlates with pain suppression. In contrast, the SNI model, which does not show spontaneous pain remission, lacks a significant increase in CD11c+ microglia, underscoring the specificity of the observed phenomenon. This design effectively highlights the role of the CD11c+ microglial population in pain modulation. The use of behavioral tests provides a clear functional assessment of IL-4 manipulation, and pharmacogenetic tools allow for precise control of microglial populations, minimizing off-target effects. Notably, the manipulation targets the CD11c promoter, which presumably reduces the risk of non-specific ablation of other microglial populations, strengthening the experimental precision. Moreover, the thorough characterization of microglial markers adds depth to the analysis, ensuring that the changes in microglial populations are accurately linked to the behavioral outcomes. 

Weaknesses: 

One potential limitation of the study is that the mechanistic details of how IL-4 induces the observed shift in microglial populations are not fully explored. While the study demonstrates a correlation between IL-4 and CD11c+ microglial cells, a deeper investigation into the specific signaling pathways and molecular processes driving this population shift would greatly strengthen the conclusions. Additionally, the paper does not clearly integrate the findings into the broader context of microglial reactive state regulation in neuropathic pain.  

We thank the reviewer for these insightful comments on our paper.  As the reviewer's suggested, further investigation of the specific signaling pathways and molecular processes by which IL-4 induces a transition of spinal microglia to the CD11c+ state would strengthen our conclusion and also provide important clues to discovering new therapeutic targets.  In revising the manuscript, we have included this in the Discussion section (line 264-267), and we hope that future studies clarify these points.  As for the additional comment, we have added a brief summary of existing research on microglial function in neuropathic pain at the beginning of the Discussion section (line 188–196).

Reviewer #1 (Recommendations for the authors):

The conclusions of this paper are supported by data, but the interpretation of some data requires clarification. 

(1) In Figure 1D and Figure 7 C, CD11c+ microglia numbers are higher in contralateral dorsal horns after IL-4 administration despite IL-4 having no effect on pain thresholds. The authors should discuss these findings.  

As the reviewer pointed out, IL-4 increased the number of CD11c⁺ microglia in the contralateral spinal dorsal horn (SDH) but did not affect pain thresholds in the contralateral hindpaw.  The data seem to be related to the selective effect of CD11c+ microglia and their factors (especially IGF1) on nerve injury-induced pain hypersensitivity.  In fact, depletion of CD11c+ spinal microglia and intrathecal administration of IGF1 do not elevate pain threshold of the contralateral hindpaw (Science 376: 86–90, 2022).  We have added above statement in the Discussion section (line 208– 213).

(2)  Do monocytes infiltrate the dorsal horn and DRG after intrathecal injections?

To address this reviewer's comment, we performed new immunohistochemical experiments to analyze monocytes in the SDH using an antibody for CD169 (a marker for bone marrow-derived monocytes/macrophages, but not for resident microglia) (J Clin Invest 122: 3063– 3087, 2012; Cell Rep 3: 605–614, 2016) and found no CD169+ monocytes in the SDH parenchyma after SpNT.  Consistent with this data, we have previously demonstrated that few bone marrow-derived monocytes/macrophages are recruited to the SDH after SpNT (Sci Rep 6: 23701, 2016).  Similarly, no CD169+ monocytes in the SDH parenchyma were observed in SpNT mice treated intrathecally with PBS or IL-4 (Figure 1—figure supplement 1A).

In the DRG, CD169 is constitutively expressed in macrophages.  Thus, in accordance with a recent report showing that monocytes infiltrating the DRG are positive for chemokine (C-C motif) receptor 2 (CCR2) (J Exp Med 221: e20230675, 2024), we analyzed CCR2+ cells and found that CCR2+ IBA1dim monocytes were observed in the capsule and parenchyma of the DRG of naive mice (Figure 1—figure supplement 1B).  After SpNT, CCR2+ IBA1dim monocytes in the DRG parenchyma increased.  Intrathecal treatment of IL-4 increased CCR2+ IBA1dim cells in the DRG capsule.  However, the involvement of these monocytes in the DRG in IL-4-induced alleviation of neuropathic pain is unclear and warrants further investigation.  In revising the manuscript, we have included additional data (Figure 1—figure supplement 1) and corresponding text in the Results (line 112–114) and Discussion section (line 218–222).

(3) In Figure 4, depletion of CD11c+ cells in dorsal root ganglia (DRG) ameliorates neuropathic thresholds but does not alter the anti-nociceptive effect of IL-4 injected intrathecal. It appears that CD11c+ macrophages in DRG have an opposite role to CD11c+ microglia in the spinal cord. Please discuss this result. 

We apologize for the confusion.  The aim of the experiments in Figure 4 was to examine the contribution of CD11c+ cells in the DRG to the pain-alleviating effect of intrathecal IL-4.  For this aim, we depleted CD11c+ cells in the DRG (but not in the SDH) by intraperitoneal injection of diphtheria toxin (DTX) immediately after the behavioral measurements performed on day 17 (Fig. 4A, B).  On day 18, the paw withdrawal threshold of DTX-treated mice was almost similar to that of PBS-treated mice, indicating that the depletion of CD11c+ cells in the DRG does not affect the pain-alleviating effect of IL-4.  These data are in stark contrast to those obtained from mice with depletion of CD11c+ cells in the SDH by intrathecal DTX (the depletion canceled the IL-4's effect) (Figure 2A).  Thus, it is conceivable that CD11c+ cells in the DRG are not involved in the IL-4-induced alleviating effect on neuropathic pain.  Because the confusion might be related to the statement in this paragraph of the initial version, we thus modified our statements to make this point more clearly (line 133–139).

Reviewer #2 (Recommendations for the authors):

A discussion addressing how these results fit into existing research on microglial function in pain would enhance the study's impact.

A brief summary of existing research on microglial function in neuropathic pain has been included at the beginning of the Discussion section (line 188–196).

It would be helpful for the authors to elaborate on the implications of their findings within the larger landscape of immune regulation in neuropathic pain.

Our present findings showed an ability of IL-4, known as a T-cell-derived factor, to increase CD11c+ microglia and to control neuropathic pain.  Furthermore, recent studies have also indicated that immune cells such as CD8+ T cells infiltrating into the spinal cord (Neuron 113: 896-911.e9, 2025), and regulatory T cells (eLife 10: e69056, 2021; Science 388: 96–104, 2025) and MRC1+ macrophages in the spinal meninges (Neuron 109: 1274–1282, 2021) have important roles in regulating microglial states and neuropathic pain.  Thus, these findings provide new insights into the mechanisms of the neuro-immune interactions that regulate neuropathic pain.  In revising the manuscript, we have added above statement in the Discussion section (line 254–260).

Furthermore, a discussion on how these findings could inform the development of targeted therapies that modulate microglial populations in a controlled, disease-specific manner would be valuable. Exploring how these insights could lead to novel treatment strategies for neuropathic pain could provide important future directions for the research and broader clinical applications.

We appreciate the reviewer's valuable suggestion.  Our current data, demonstrating that IL-4 increases CD11c+ microglia without affecting the total number of microglia, could open a new avenue for developing strategies to modulate microglial subpopulations through molecular targeting, which may lead to new analgesics.  However, given IL-4's association with allergic responses, targeting microglia-selective molecules involved in shifting microglia toward the CD11c+ state—such as intracellular signaling molecules downstream of IL-4 receptors—may offer a more selective and safer therapeutic approach.  Moreover, since CD11c+ microglia have been implicated in other CNS diseases [e.g., Alzheimer disease (Cell 169: 1276–1290, 2017), amyotrophic lateral sclerosis (Nat Neurosci 25: 26–38, 2022), and multiple sclerosis (Front Cell Neurosci 12: 523, 2019)], further investigations into the mechanisms driving CD11c+ microglia induction could provide insights into novel therapeutic strategies not only for neuropathic pain but also for other CNS diseases.  In revising the manuscript, we have added above statement in the Discussion section (line 260–271).

multiplicada también por ρ g

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What is this?

from: https://dev.to/search?utf8=%E2%9C%93&q=%22open+construct%22

Itt nekem fura, hogy egy képpel indítunk, ami beleolvad a szövegbe. Így nem annyira érthető. Kellene egy kis szöveg, hogy így érhető el, és csak az a kis részlet a keresővel meg a gombbal akár a szöveggel egy sorban.

Illetve ha utána market data-val folytatjuk, akkor legyen az a gombos példa is.

reunificar con el tema pertenencias. Link pertenencias.

si no está detenido, cuando se cumpla cualquiera de las siguientes condiciones: - 72 horas - hasta el primer contacto con fuerzas de seguridad y que sea indicado por las mismas - hasta que el paciente y la familia lo requiera

idée intéresante à creuser.

on passe de la plateforme à la plateformisation sans explication

se demander si la plateforme offre une ou plusieurs interfaces. Ma suggestion est que précisément, ce qui fait la fonction plateforme c'est de s'appuyer sur plusieurs interfaces qui vont s'adresser à des catégories d'usagers différents et la fonction de la plateforme est d'intermédier ces différents catégories d'usagers en articulant les différentes interfaces entre elles.

La phrase est ambigüe : "c'est le cas de" semble s'appuyer sur "bibliothèque numérique" qui précède et donc qualifier OpenEdition de bibliothèque numérique alors que c'est le contraire qui veut être dit (si j'ai bien suivi)

idem à ma remarque précédente. + rien n'est vraiment fait de cette qualification en passant : si on dit qu'un collectif est "composé d'ingénieurs", quelle conséquence en tire-t-on `?

Ingénieurs est une méta-catégorie qui ne dit pas grand-chose si on ne précise pas. Surtout en France où ce terme peut renvoyer à des choses très différentes (diplômé d'une école d'ingénieurs ou personnel d'appui à la recherche par exemple (ingénieur d'études, ingénieur de recherche))

As I mentioned in the pre-lecture, I believe that communication skills are essential to new grads!! They are the starting factor in employment now.

Crowned with a prize for direction, that he however had to share with the Coen brothers for The Barber: The man Who was not there.

The jury presided by Liv Ullman preferred The Son's Room by Nanni Moretti.

It is not superfluous to add that the composition of casting is

Another great film on the demystification of Hollywood

This is where the filmmaker joins Billy Wilder on Sunset Boulevard

Witness a bygone age, the image of Ann Miller, Ex queen of the tap dancing musical comedy ( A day in New York with Stanley Donen and Gene Kelly, 1949), Made up as an eccentric old lady.

It is all a piece of reality and mythology of seventh art that fascinates lynch.

Of the mafia inlaid in the world of cinema to compromises linked to contract signatures.

In Elephant Man, Human distress transformed into a fairground phenomenon was a metaphor of the cruelty of the commodification of the live show.

Since the crossing of the mirror of Orpheus by Jean Marais

Discovery that is only equaled to the vision of the black lodge in Twin Peaks.

The pinnacle of trouble is reached here during the discovery of the box that corresponds to a key associated with money

Peter Deming for photography, Mary Sweeney for editing and above all Angelo Badalamenti, composer of an already cult musical score

Results here are a impressive vertigo that is equal to the first vision of Vertigo

The formula, already used in Lost highway

Narrative in the last half hour.

Which presents the actors in different characters from those played since the start of the movie.

Admittedly, this story of a naive starlet (the revelation Naomi Watts) forming a friendly and then romantic relationship with a renowned actress with whom she will lead a police investigation

Revelation

Without achieving the radical experimentation of his two previous works cited (The narrative is not completely destructed) Lynch creates an enchanting voyage.

With her help, Rita tries to find her memory and her identity.

She meets Betty Elms, An actress in the making who just arrived in Los Angeles.

In Hollywood, at night, Rita, A young woman, becomes amnesic after a car accident driving on Mulholland drive.

On the road of Mulholland drive

Editior's note

Mulholland Drive: The film review

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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

DOI: 10.21769/BioProtoc.5367

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DOI: 10.1038/s41556-019-0281-x

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DOI: 10.1016/j.devcel.2019.02.003

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DOI: 10.12688/f1000research.160217.2

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What is this?

• Item nuevo: Cuándo abrir la faja pélvica en TAC y cuando no

• Item nuevo: mapeo en la atención inicial. En qué casos se puede plantear

• Embarazada inestable e inconsciente: igual (TAC). Inestable: TAC, insistiendo a la paciente la importancia de la prueba para mujer y feto.
• Estable y consciente: se podrá valorar la realización de una ecografía dirigida (+- con contraste) que tenga aceptable definición orgánica, en vez de la TAC.

• 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)

Plantear el supuesto en el que no se realiza Rx tórax en el BAI (ej paciente estables en los que se va a realizar TAC body/torácico y en los que no se ha realizado ningún procedimiento invasivo torácico).

Point of care radiológicos para la toma de decisiones

https://archive.org/search?query=creator%3A%22Burroughs+Adding+Machine+Company%22

diferencia a lepromatoso.

Management of incidental gallbladder polyps: Do not sacrifice the gallbladder - try ursodiol first! technical success, safety, and clinical outcomes. Am J Roentgenol. 2018;210(5):1164-71. Doi:10.2214/AJR.17.18690. 14. Favero GD, Mario FD, Meggiato T, et al. Disappearance of gallbladder wall lesions after oral bile acids treatment—a pilot study. Current Therapeutic Research. 1993;54(4):384-8. Doi:10.1016/s0011-393x(05)80640-x. 15. Il'chenko AA, Orlova Iu N. [efficacy of ursodeoxycholic acid in gallbladder cholesterosis accompanied by cholecystolithiasis]. Eksp Klin Gastroenterol. 2003(1):46-50, 182.

Document de Synthèse : L'Importance des Compétences Sociales et Comportementales en Éducation

Source : Extraits de "Approches expérimentales en éducation (4) - Agir pour l'éducation (2023-2024)" - Conférence d'Élise Huillery.

Introduction : Un Déficit Français en Compétences Clés

La conférence d'Élise Huillery, chercheuse affiliée à J-PAL et professeure à Dauphine, met en lumière l'importance croissante des compétences sociales et comportementales dans la réussite scolaire et professionnelle.

Ces compétences, souvent désignées sous d'autres termes tels que "soft skills", "compétences non cognitives" (bien que ce terme soit considéré comme impropre) ou "compétences socio-émotionnelles", sont cruciales non seulement pour l'acquisition de connaissances, mais aussi pour le bien-être général des individus et leur insertion dans la société.

Un constat alarmant est dressé concernant la France : les élèves français présentent un déficit "très singulier" de ces compétences par rapport aux autres pays de l'OCDE.

Ce déficit se manifeste à travers plusieurs indicateurs psychologiques et comportementaux, qui ont des répercussions significatives sur leur parcours.

I. Définition et Mesure des Compétences Sociales et Comportementales

Élise Willer distingue deux grandes catégories de compétences :

Compétences Comportementales (rapport à soi) : Confiance en soi / Auto-efficacité : Le sentiment d'être capable de réussir des tâches.

Optimisme : La capacité à voir le positif et à croire en ses propres capacités.

État d'esprit de développement (Growth Mindset) : La conviction que l'intelligence et les capacités peuvent progresser avec l'effort et l'entraînement, par opposition à un état d'esprit fixiste où l'intelligence est perçue comme innée et immuable.

"L'intelligence c'est comme un muscle ça progresse en s'entraînant on va connecter des neurones en s'entraînant et donc on va développer son intelligence".

Locus de contrôle (interne/externe) : La perception de la source de contrôle de sa propre vie.

Un locus de contrôle interne signifie que l'individu pense que ce qui lui arrive dépend de ses propres actions, tandis qu'un locus externe attribue la réussite ou l'échec à des facteurs extérieurs incontrôlables (chance, destin).

Contrôle de soi / Autodiscipline : La capacité à différer une gratification immédiate pour un bénéfice futur plus important, à s'imposer des efforts.

Persévérance : La capacité à maintenir ses efforts face aux difficultés, étroitement liée à l'état d'esprit de développement.

Compétences Sociales (rapport aux autres) :

• Coopération : La capacité à travailler ensemble efficacement pour atteindre un objectif commun.

• Empathie : La capacité à prendre en considération le point de vue d'autrui et l'impact de son propre comportement sur les autres.

Respect et Tolérance.

Sentiment d'appartenance : Le sentiment de faire partie d'un groupe, d'être soutenu et de contribuer à la réussite collective.

Ces compétences sont mesurées principalement via des questionnaires (avec de multiples items pour construire des indices synthétiques) et parfois par des tâches comportementales (jeux de confiance ou de coopération) considérées comme plus fiables car mettant les individus en situation réelle.

II. Le Constat d'un Déficit Français Particulier

Les données des enquêtes internationales comme PISA (Programme international pour le suivi des acquis des élèves) de 2012 et 2018 révèlent un déficit marqué des élèves français :

État d'esprit de développement : La France est "plutôt dans le bas" par rapport à la moyenne des pays de l'OCDE.

Anxiété : Les élèves français sont "au-dessus de la moyenne des pays de l'OCDE" en termes d'anxiété.

Perception de ses compétences : Alors que le niveau en mathématiques des élèves français est proche de la moyenne de l'OCDE, ils se sentent "moins compétents en moyenne que la moyenne", indiquant un "problème de perception de ses compétences".

Persévérance : Les élèves français "lâchent davantage l'affaire quand ils y arrivent pas", un déficit "très net".

Ouverture à la résolution de problème : Ils ont "moins plaisir qu'ailleurs à découvrir des problèmes et à essayer de les résoudre".

Locus de contrôle : Il est "beaucoup plus externe que la moyenne", signifiant une perception que leur réussite dépend de facteurs extérieurs qu'ils ne contrôlent pas. Coopération :

La France affiche le "niveau de coopération le plus faible de tous les pays de l'OCDE".

Sentiment d'appartenance : Il est "plutôt négatif" à l'école.

Résolution collaborative de problèmes : Le score est inférieur à la moyenne.

Ce tableau psychologique des élèves français est jugé "pas très réjouissant".

Inégalités Sociales : Ce déficit global est exacerbé par les inégalités sociales.

À niveau scolaire égal, les élèves d'origine sociale défavorisée se perçoivent comme "moins bons que les élèves favorisés".

Le même phénomène est observé chez les filles par rapport aux garçons.

III. Les Effets Multiples de ces Compétences : Le "Triple Dividende"

La recherche montre que ces compétences ne sont pas de simples "traits culturels" sans impact, mais qu'elles jouent un rôle "important" sur :

• La Réussite Scolaire :
• État d'esprit de développement : Des études en Norvège (2019), aux États-Unis (Yeager, 2019) et une étude menée en France par Élise Willer (Chouet, Allan) montrent qu'une intervention ciblée sur la plasticité cérébrale et l'état d'esprit de développement peut augmenter les notes scolaires et réduire le redoublement.

Le programme "Énergie Jeune" (France) :

Mis en place dans des collèges REP et REP+, ce programme de 12 séances sur 4 ans (de la 6ème à la 3ème), animé par des bénévoles, vise à déconstruire l'idée fixiste du niveau scolaire et à renforcer le locus de contrôle interne.

• Résultats : Une augmentation des notes de 7% d'un écart-type, un effet "très important relativement à l'investissement que ça demande".

Cet effet est comparable à des dispositifs beaucoup plus coûteux comme le dédoublement des classes de CP.

Le programme change les perceptions des chances de réussite, améliore les comportements en classe (diminution des retards, absences, sanctions en 3ème) et a un "petit effet sur également donc les ambitions scolaires à plus long terme".

• Limites : L'effet est plus fort chez les élèves ayant des comportements disciplinés à l'entrée en 6ème, et moins chez les élèves les plus difficiles, qui sont pourtant ceux "à risque de décrochage".

L'Insertion Professionnelle :

• Programme longitudinal de Montréal (démarré dans les années 80) : Visant des garçons défavorisés avec des comportements disruptifs pour renforcer le contrôle de soi et les habiletés sociales.

• Effets : Amélioration de l'autonomie et de la confiance entre pairs.

À très long terme (jusqu'à 37 ans), ce programme a montré des effets "très forts" sur : * Augmentation des revenus du travail (+20%). * Baisse du taux d'inactivité (-10 points). * Baisse de la criminalité (-33% d'arrestations). * Baisse de la dépendance aux aides sociales.

Perry Preschool Project :

Un autre programme axé sur les interactions de groupe et l'autodiscipline, montrant également des hausses de salaire et des études plus longues à l'âge adulte.

Autres Bénéfices Publics :

La réduction des coûts liés * aux redoublements, * aux programmes d'éducation spéciale, * à la criminalité et * aux aides sociales.

• Analyse coût-bénéfice : Le programme de Montréal a rapporté "11 dollars" à la société et à l'individu pour "1 euro investi" à l'âge de 7-9 ans, démontrant que ces programmes doivent être perçus comme des "investissements sociaux".

IV. Recommandations pour l'École et Perspectives

La bonne nouvelle est que ces compétences "ne sont pas fixes" et qu'il est "facile à faire évoluer" ces traits.

Pour rendre l'école plus performante, il est recommandé d'agir sur trois piliers, de manière plus systématique :

• L'apprentissage coopératif : Enseigner aux élèves à apprendre ensemble. Le coût est faible (formation des enseignants) pour un impact potentiellement "substantiel" (près de 40% d'un écart-type selon l'Education Endowment Foundation).

• Le développement de l'état d'esprit de développement :

Continuer les efforts pour faire comprendre aux élèves que leurs capacités ne sont pas figées.

• Les méthodes d'évaluation : Les méthodes actuelles françaises sont jugées comme accentuant un "état d'esprit fixiste" et un "sentiment d'incompétence".

Il est nécessaire de "retravailler nos modes d'évaluation" pour des approches qui "expliquent ce qu'il a bien fait, ce qu'il a mal fait et donne des conseils", sans stigmatiser par des "points rouges".

• Ces changements passent "forcément par la formation des enseignants", à la fois initiale et continue.

Un nouveau programme expérimental, Motiveaction, est en cours de développement avec les collègues d'Élise Willer.

Il vise à former 750 enseignants sur ces trois modules (état d'esprit de développement, évaluation pour favoriser les progrès, apprentissage coopératif) afin de "généraliser motivation énergie jeune grâce à une auprès des enseignants" et d'amplifier les gains en compétences et en apprentissages scolaires des élèves. Les résultats de cette expérimentation sont attendus pour 2025.

En synthèse, la conférence insiste sur un paradoxe français : un fort déficit en compétences sociales et comportementales, mais une grande facilité à les faire évoluer avec des investissements raisonnables, générant des bénéfices considérables à long terme.

L'école est identifiée comme un levier essentiel pour cette transformation.

Ce document de synthèse analyse en profondeur l'importance des interventions de soutien à la parentalité pour le développement cognitif et socio-émotionnel des enfants, ainsi que pour la réduction des inégalités sociales.

Il se base principalement sur les recherches et les méta-analyses présentées par Carlo Barone, sociologue et professeur à Sciences Po.

Thèmes Principaux et Idées Clés

1. L'impact Précoce et Cumulatif des Inégalités de Développement

Manifestation Précoce des Inégalités: Les inégalités de développement se manifestent dès les premières années, voire les premiers mois de vie des enfants, et ont des répercussions à long terme sur leur réussite scolaire.

Par exemple, le vocabulaire réceptif des enfants de 4 ans en France est fortement lié au niveau d'éducation des parents.

Ce constat est "préoccupant parce que nous savons que le vocabulaire réceptif avec la conscience phonologique est un des deux prédicteurs les plus importants des apprentissages en lecture et en écriture à l'école primaire".

Apprentissage Cumulatif et Plasticité Cérébrale: Tout apprentissage est cumulatif, et la plasticité cérébrale des enfants est maximale pendant les premières années de vie, soulignant "l'intérêt d'intervenir dès le plus jeune âge pour favoriser l'égalité des chances dans l'éducation"**.

2. Les Limites des Interventions Éducatives Scolaires Seules

• Concentration Exclusive sur l'Environnement Scolaire: Les politiques éducatives en France (dédoublement des classes, dispositifs d'aide aux devoirs) bien que produisant des effets, sont parfois "moins efficaces que l'on espérait notamment par rapport aux moyens financiers humains importants qui sont alloués".

Une limite majeure est qu'elles "se concentrent uniquement sur l'environnement scolaire et n'interviennent pas sur les inégalités flagrantes entre les environnements familiaux".

• Complémentarité des Approches: L'implicite est souvent qu'on ne peut ou ne devrait pas intervenir sur ce qui se passe à la maison, alors que "les deux stratégies d'intervention peuvent être complémentaires".

3. Le Rôle Crucial et Souvent Sous-Estimé des Parents

• Temps Passé avec les Enfants: Les parents passent un temps considérable avec leurs enfants, un temps qui a "augmenté au fil du temps", y compris le "temps de qualité" (activités de jeu, lecture).

Cette augmentation est plus marquée pour les parents des catégories socio-professionnelles favorisées.

• Influence Permanente: Contrairement aux enseignants et camarades de classe qui changent, les parents "constituent une influence permanente et à long terme".

• Aspirations Éducatives Élevées: Les parents, y compris ceux issus de milieux défavorisés ou immigrés, ont des aspirations éducatives élevées pour leurs enfants.

Une implication apparemment moindre de la part de familles socialement défavorisées "reflète probablement surtout un ensemble de barrières sociales auquel ces familles sont confrontées plutôt que un manque d'intérêt des parents pour le développement et la réussite scolaire de leurs enfants".

• Manque de Connaissance et Barrières Informationnelles: Beaucoup de parents "ne réalisent pas à quel point ce qui se passe à la maison a des conséquences importantes pour ce qui se passe à l'école".

Des activités comme la lecture, les jeux de société, la cuisine partagée sont des opportunités d'apprentissage informel, mais "tous les parents ne sont pas conscients du potentiel de ces apprentissages informels".

Les parents des milieux populaires ont moins accès aux informations d'experts et à leur circulation dans leurs cercles sociaux.

4. Typologie des Interventions de Soutien à la Parentalité

Une revue de 109 études randomisées identifie quatre stratégies principales :

• Compétences Langagières et Cognitives (36%): Encourager des activités stimulant les apprentissages informels (lecture, puzzles, jeux de société).

• Développement Socio-émotionnel (25%): Stimuler la réceptivité et la réactivité des parents aux besoins développementaux des enfants.

• Règles à la Maison (Moins Développé): Pratiques et routines familiales (usage des écrans, sommeil, devoirs) influençant les capacités d'autorégulation des enfants.

• Approches Hybrides/Holistiques (20%): Combinaison des stratégies précédentes.

5. Exemples d'Interventions et Leurs Leçons

• Ready4K (États-Unis): Intervention par SMS ciblant les parents d'enfants de 2 à 4 ans.

Envoi de trois SMS par semaine (information, suggestion, encouragement).

"Les enfants avec des compétences langagières moins développées ont connu une amélioration importante de leur vocabulaire et leurs compétences langagère". Points forts :

• Stratégie de Communication Innovante: Utilisation des SMS pour toucher un maximum de parents, y compris ceux qui ne peuvent pas se rendre aux réunions scolaires.

• Adaptation aux Contraintes Parentales: Messages simples, en plusieurs langues, ne demandant pas un investissement de temps important, mais intégrant des activités ludiques dans les routines quotidiennes.

"Concevoir des interventions qui prennent en compte l'ensemble des contraintes parentales et propose des conseils réalisables pour tous les parents".

• Coût-Bénéfice Favorable: Environ 5 € par enfant par an.

Intervention sur la Lecture Partagée (France):

Étude menée par Carlo Barone et ses collègues.

Distribution de flyers informatifs et prêt gratuit de livres adaptés à l'âge, avec des conseils pour rendre l'activité agréable et l'intégrer au rituel du coucher. Suivi par SMS et appels téléphoniques.

• Résultats: Augmentation significative de la fréquence de lecture parentale, particulièrement chez les parents moins éduqués, et amélioration du vocabulaire des enfants issus de familles moins éduquées et bilingues.

Ces effets ont persisté 6 mois après l'intervention.

• Limites: Pas d'impact sur les familles complètement allophones.

Les effets à long terme n'ont pas pu être observés.

Méta-analyse sur la Lecture Parentale:

Sur 30 études randomisées (0-6 ans), les interventions de "lecture dialogique" ont des impacts importants sur le développement langagier (25-26% d'écart type), tandis que les autres interventions ont des impacts très faibles.

Cependant, les impacts positifs des interventions en lecture dialogique se concentrent principalement sur les familles socialement favorisées, suggérant un risque d'augmentation des inégalités.

Les effets ont tendance à diminuer rapidement après l'intervention.

6. Leçons des Méta-Analyses Générales

Méta-analyse de Jeong et al. (2021) - 102 études (0-3 ans):

• Deux Catégories d'Intervention: Messages de stimulation d'apprentissages informels et promotion de styles parentaux autoritatifs (chaleur et soutien élevés, attentes claires et cohérentes).

• Impacts Multiples:

• Effets positifs importants sur le développement cognitif (32% d'écart type) et langagier,

• encourageants sur le développement socio-émotionnel et moteur, et sur la qualité de la relation parents-enfants.

• Moins d'effet sur la santé mentale des parents.

• Variabilité des Impacts: Grande variabilité dans l'efficacité des interventions, certains étant très efficaces, d'autres non.

• Contextes Nationaux: Impacts moyens plus importants dans les pays en développement, mais restent significatifs dans les pays riches. Une autre méta-analyse (Francis Steves et al.) suggère une "transportabilité élevée" des interventions entre pays riches.

• Combinaison des Approches: Les interventions les plus efficaces combinent la stimulation cognitive et la réceptivité parentale.

• Format: Pas de différences systématiques d'efficacité entre les visites à domicile et les espaces communautaires, ni entre les programmes individuels et collectifs.

C'est "une excellente nouvelle parce qu'évidemment les les coûts de ces formats sont très différents".

• Méta-analyse de Prime et al. (0-6 ans): Confirme les conclusions de Jeong et al.

• Hétérogénéité Sociale: Ces interventions se révèlent "plus efficaces sur les familles socialement favorisées".

Cela pose un "dilemme potentiel entre l'objectif d'améliorer le niveau moyen de développement [...] et la réduction des inégalités". Une solution est de cibler les interventions sur les familles défavorisées, mais des interventions universalistes qui réduisent les inégalités seraient préférables.

• Importance des Pères: Les interventions ciblant spécifiquement les pères sont encore rares et leurs résultats peu concluants, mais c'est une piste de recherche prometteuse.

• Effets à Long Terme: La principale limite de ces méta-analyses est qu'elles ne considèrent que les effets à court terme.

La durabilité des effets reste une question ouverte. "Nous ne sommes pas encore en mesure d'indiquer quels interventions produisent les effets les plus durables".

7. Barrières Comportementales et Stratégies pour les Surmonter

• Défis de l'Implication Parentale: Les parents peuvent être fatigués, stressés, manquer de temps, et nos messages "rivalisent avec toutes ces préoccupations pour attirer leur attention". Le passage de l'intention à l'action est difficile.

• Nécessité de Plus que de Simples Informations: Fournir des informations et des conseils est utile mais souvent "insuffisant".

Il faut "réussir à attirer l'attention des parents, rendre les contenus des interventions accessibles et pertinents par rapport au contexte de vie des parents, faire des demandes de temps raisonnables qui s'inscrivent dans les routines parentales, faire face au phénomène d'autocensure des parents notamment les parents plus socialement défavorisés et réussir à maintenir les parents impliqués dans la durée".

• Incredible Years: Programme de groupe (10-14 parents, 3 mois) testé dans 8 pays européens (pas la France).

Encourage des relations chaleureuses, le jeu interactif et une discipline constructive. Utilise des méthodes basées sur la pratique (résolution de problèmes, discussion de vidéos, jeux de rôles).

L'accès aux réunions est facilité (garde d'enfants, transport). Résultats robustes et similaires pour les familles favorisées et défavorisées (30-38% d'écart type sur le développement cognitif et socio-émotionnel).

8. Faciliter l'Accès aux Services de la Petite Enfance

• Effets Positifs des Services de Qualité: L'accès à des services de petite enfance de bonne qualité a des effets positifs, surtout pour les enfants défavorisés.

• Inégalités d'Accès en France: L'accès aux crèches et assistantes maternelles est "très inégalitaire en France", avec une sous-représentation des enfants de familles défavorisées et immigrées.

Barrières à l'Accès:

• Accessibilité: Critères d'éligibilité, distribution territoriale de l'offre.
• Économiques: Coûts directs et coûts d'opportunité.
• Informationnelles: Connaissance des coûts, critères, modalités de candidature.
• Administratives: Difficultés à interagir avec la bureaucratie, remplir des formulaires.
• Interventions Possibles: Atténuer les barrières informationnelles et administratives par des interventions qui "apportent des informations aux familles sur le fonctionnement de ces services [...] et qui les accompagnent dans le processus de candidature". Une étude en Allemagne a montré l'efficacité de ces dispositifs à réduire les inégalités d'accès.

Conclusion Générale

• Les interventions de soutien à la parentalité représentent un levier important et "trop souvent négligé par les décideurs politiques" pour favoriser le développement des enfants et réduire les inégalités.

Elles sont "peu coûteuses" et offrent une grande flexibilité. L'accessibilité effective est primordiale pour leur efficacité et la durabilité de la réduction des inégalités.

Cependant, ces interventions doivent être "complétées par d'autres types d'action de nature plus structurelle" (critères d'éligibilité, répartition de l'offre, levée des barrières linguistiques) et prendre en compte les inégalités socio-économiques plus larges (pauvreté, chômage, insécurité économique) qui peuvent entraver la réceptivité des parents.

En somme, il est essentiel de comprendre et de cibler les défis spécifiques auxquels les familles sont confrontées pour concevoir des interventions pertinentes et efficaces.

Author response:

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

Reviewer #1 (Public review):  

Summary: 

The paper describes the cryoEM structure of RAD51 filament on the recombination intermediate. In the RAD51 filament, the insertion of a DNA-binding loop called the L2 loop stabilizes the separation of the complementary strand for the base-pairing with an incoming ssDNA and the non-complementary strand, which is captured by the second DNA-binding channel called the site II. The molecular structure of the RAD51 filament with a recombination intermediate provides a new insight into the mechanism of homology search and strand exchange between ssDNA and dsDNA. 

Strengths: 

This is the first human RAD51 filament structure with a recombination intermediate called the D-loop. The work has been done with great care, and the results shown in the paper are compelling based on cryo-EM and biochemical analyses. The paper is really nice and important for researchers in the field of homologous recombination, which gives a new view on the molecular mechanism of RAD51-mediated homology search and strand exchange. 

Weaknesses: 

The authors need more careful text writing. Without page and line numbers, it is hard to give comments. 

We would like to thank the reviewer for their kind words of appreciation of our work.

Reviewer #2 (Public review):  

Summary: 

Homologous recombination (HR) is a critical pathway for repairing double-strand DNA breaks and ensuring genomic stability. At the core of HR is the RAD51-mediated strand-exchange process, in which the RAD51-ssDNA filament binds to homologous double-stranded DNA (dsDNA) to form a characteristic D-loop structure. While decades of biochemical, genetic, and single-molecule studies have elucidated many aspects of this mechanism, the atomic-level details of the strand-exchange process remained unresolved due to a lack of atomic-resolution structure of RAD51 D-loop complex. 

In this study, the authors achieved this by reconstituting a RAD51 mini-filament, allowing them to solve the RAD51 D-loop complex at 2.64 Å resolution using a single particle approach. The atomic resolution structure reveals how specific residues of RAD51 facilitate the strand exchange reaction. Ultimately, this work provides unprecedented structural insight into the eukaryotic HR process and deepens the understanding of RAD51 function at the atomic level, advancing the broader knowledge of DNA repair mechanisms. 

Strengths: 

The authors overcame the challenge of RAD51's helical symmetry by designing a minifilament system suitable for single-particle cryo-EM, enabling them to resolve the RAD51 D-loop structure at 2.64 Å without imposed symmetry. This high resolution revealed precise roles of key residues, including F279 in Loop 2, which facilitates strand separation, and basic residues on site II that capture the displaced strand. Their findings were supported by mutagenesis, strand exchange assays, and single-molecule analysis, providing strong validation of the structural insights. 

Weaknesses: 

Despite the detailed structural data, some structure-based mutagenesis data interpretation lacks clarity. Additionally, the proposed 3′-to-5′ polarity of strand exchange relies on assumptions from static structural features, such as stronger binding of the 5′-arm-which are not directly supported by other experiments. This makes the directional model compelling but contradicts several well-established biochemical studies that support a 5'-to-3' polarity relative to the complementary strand (e.g., Cell 1995, PMID: 7634335; JBC 1996, PMID: 8910403; Nature 2008, PMID: 18256600). 

Overall: 

The 2.6 Å resolution cryoEM structure of the RAD51 D-loop complex provides remarkably detailed insights into the residues involved in D-loop formation. The high-quality cryoEM density enables precise placement of each nucleotide, which is essential for interpreting the molecular interactions between RAD51 and DNA. Particularly, the structural analysis highlights specific roles for key domains, such as the N-terminal domain (NTD), in engaging the donor DNA duplex. 

This structural interpretation is further substantiated by single-molecule fluorescence experiments using the KK39,40AA NTD mutant. The data clearly show a significant reduction in D-loop formation by the mutant compared to wild-type, supporting the proposed functional role of the NTD observed in the cryoEM model. 

However, the strand exchange activity interpretation presented in Figure 5B could benefit from a more rigorous experimental design. The current assay measures an increase in fluorescence intensity, which depends heavily on the formation of RAD51-ssDNA filaments. As shown in Figure S6A, several mutants exhibit reduced ability to form such filaments, which could confound the interpretation of strand exchange efficiency. To address this, the assay should either: (1) normalize for equivalent levels of RAD51-ssDNA filaments across samples, or (2) compare the initial rates of fluorescence increase (i.e., the slope of the reaction curve), rather than endpoint fluorescence, to better isolate the strand exchange activity itself. 

We agree with the reviewer that the reduced filament-forming ability of some of the RAD51 mutants complicates a straightforward interpretation of their strand-exchange assay. Interestingly, the RAD51 mutants that appear most impaired are the esDNA-capture mutants that do not contact the ssDNA in the structure of the pre-synaptic filament. However, the RAD51 NTD mutants, that display the most severe defect in strand-exchange, have a near-WT filament forming ability.

Based on the structural features of the D-loop, the authors propose that strand pairing and exchange initiate at the 3'-end of the complementary strand in the donor DNA and proceed with a 3'-to-5' polarity. This conclusion, drawn from static structural observations, contrasts with several well-established biochemical studies that support a 5'-to-3' polarity relative to the complementary strand (e.g., Cell 1995, PMID: 7634335; JBC 1996, PMID: 8910403; Nature 2008, PMID: 18256600). While the structural model is compelling and methodologically robust, this discrepancy underscores the need for further experiments. 

We would like to thank the reviewer for highlighting the importance of our findings to our understanding of the mechanism of homologous recombination.

The reviewer correctly points out that the polarity of strand exchange by RecA and RAD51 is an extensively researched topic that has been characterised in several authoritative studies. In our paper, we simply describe the mechanistic insights obtained from the structural D-loop models of RAD51 (our work) and RecA (Yang et al, PMID: 33057191).The structures illustrate a very similar mechanism of Dloop formation that proceeds with opposite polarity of strand exchange for RAD51 and RecA. Comparison of the D-loop structures for RecA and RAD51 provides an attractive explanation for the opposite polarity, as caused by the different positions of their dsDNA-binding domains in the filament structure. 

We agree with the reviewer that further investigation will be needed for an adequate rationalisation of the available evidence. We will mention the relevant literature in the revised version of the manuscript.

Reviewer #3 (Public review):  

Summary: 

Built on their previous pioneer expertise in studying RAD51 biology, in this paper, the authors aim to capture and investigate the structural mechanism of human RAD51 filament bound with a displacement loop (D-loop), which occurs during the dynamic synaptic state of the homologous recombination (HR) strand-exchange step. As the structures of both pre- and post-synaptic RAD51 filaments were previously determined, a complex structure of RAD51 filaments during strand exchange is one of the key missing pieces of information for a complete understanding of how RAD51 functions in the HR pathway. This paper aims to determine the high-resolution cryo-EM structure of RAD51 filament bound with the D-loop. Combined with mutagenesis analysis and biophysical assays, the authors aim to investigate the D-loop DNA structure, RAD51-mediated strand separation and polarity, and a working model of RAD51 during HR strand invasion in comparison with RecA. 

Strengths: 

(1) The structural work and associated biophysical assays in this paper are solid, elegantly designed, and interpreted.  These results provide novel insights into RAD51's function in HR. 

(2) The DNA substrate used was well designed, taking into consideration the nucleotide number requirement of RAD51 for stable capture of donor DNA. This DNA substrate choice lays the foundation for successfully determining the structure of the RAD51 filament on D-loop DNA using single-particle cryo-EM. 

(3) The authors utilised their previous expertise in capping DNA ends using monomeric streptavidin and combined their careful data collection and processing to determine the cryo-EM structure of full-length human RAD51 bound at the D-loop in high resolution. This interesting structure forms the core part of this work and allows detailed mapping of DNA-DNA and DNA-protein interaction among RAD51, invading strands, and donor DNA arms (Figures 1, 2, 3, 4). The geometric analysis of D-loop DNA bound with RAD51 and EM density for homologous DNA pairing is also impressive (Figure S5). The previously disordered RAD51's L2-loop is now ordered and traceable in the density map and functions as a physical spacer when bound with D-loop DNA. Interestingly, the authors identified that the side chain position of F279 in the L2_loop of RAD51_H differs from other F279 residues in L2-loops of E, F, and G protomers. This asymmetric binding of L2 loops and RAD51_NTD binding with donor DNA arms forms the basis of the proposed working model about the polarity of csDNA during RAD51-mediated strand exchange. 

(4) This work also includes mutagenesis analysis and biophysical experiments, especially EMSA, singlemolecule fluorescence imaging using an optical tweezer, and DNA strand exchange assay, which are all suitable methods to study the key residues of RAD51 for strand exchange and D-loop formation (Figure 5). 

Weaknesses: 

(1) The proposed model for the 3'-5' polarity of RAD51-mediated strand invasion is based on the structural observations in the cryo-EM structure. This study lacks follow-up biochemical/biophysical experiments to validate the proposed model compared to RecA or developing methods to capture structures of any intermediate states with different polarity models. 

(2) The functional impact of key mutants designed based on structure has not been tested in cells to evaluate how these mutants impact the HR pathway. 

The significance of the work for the DNA repair field and beyond: 

Homologous recombination (HR) is a key pathway for repairing DNA double-strand breaks and involves multiple steps. RAD51 forms nucleoprotein filaments first with 3' overhang single-strand DNA (ssDNA), followed by a search and exchange with a homologous strand. This function serves as the basis of an accurate template-based DNA repair during HR. This research addressed a long-standing challenge of capturing RAD51 bound with the dynamic synaptic DNA and provided the first structural insight into how RAD51 performs this function. The significance of this work extends beyond the discovery of biology for the DNA repair field, into its medical relevance. RAD51 is a potential drug target for inhibiting DNA repair in cancer cells to overcome drug resistance. This work offers a structural understanding of RAD51's function with the D-loop and provides new strategies for targeting RAD51 to improve cancer therapies. 

We thank the reviewer for their positive comments on the significance of our work. Concerning the proposed polarity of strand exchange based on our structural finding, please see our reply to the previous reviewer; we agree with the reviewer that further experimentation will be needed to to reach a settled view on this.

Testing the functional effects of the RAD51 mutants on HR in cells was not an aim of the current work but we agree that it would be a very interesting experiment, which would likely provide further important insights into the mechanism of strand exchange at the core of the HR reaction.

Reviewer #1 (Recommendations for the authors):

Major points:

(1) Structural analysis showed a critical role of F279 in the L2 loop. However, the biochemical study showed that the F279A substitution did not provide a strong defect in the in vitro strand exchange, as shown in Figure 5B. Moreover, a previous study by Matsuo et al. FEBS J, 2006; ref 43) showed human RAD51-F279A is proficient in the in vitro strand exchange. These suggest that human RAD51 F279 is not critical for the strand exchange. The authors need more discussions of the role of F279 or the L2 for the RAD51-mediated reactions in the Discussion.

In the strand-exchange essay of Figure 5B, the F279A mutant shows the mildest phenotype, in agreement with the findings of Matsuo et al. Accordingly, in the text we describe the F279A mutant as having a “modest impact” on strand-exchange.

We have now added a brief comment to the relevant text, pointing out that the result of the strand exchange assay for F279A are in agreement with the previous findings by Matsuo et al., and adding the reference.

(2) In some parts, the authors cited the newest references rather than the paper describing the original findings. For RAD51 paralogs, why are these three (refs 21,22, 23) selected here? For FIGNL1, why is only one (ref 24) chosen?

The cited publications were chosen to acquaint the reader with the latest structural and mechanistic advances about the function of some of the most important and well-studied recombination mediator proteins. For completeness, we have now added a further reference for FIGNL1 - Ito, Masaru et al, Nat Comm, 2023 – in the Introduction, to provide the reader with an additional pointer to our current knowledge about the mechanism of FIGNL1 in Homologous Recombination.

Minor points:

(1) Page 3, line 1 in the second paragraph, the reaction of "HR": HR should be homology search and strand exchange. HR is used incorrectly throughout the text, please check them. Remove "strandexchange" from ATPases in line 2.

We believe that HR is used correctly in this context, as we refer to the biochemical reactions of HR, which includes the search for homology and strand exchange.

We have removed “strand-exchange” from ATPases in line 2, as requested by the reviewer.

(2) Supplementary Figure 1B, C, "EMSA" experiment: Please indicate an experimental condition in the legend: how ssDNA and dsDNA were mixed with RAD51. In (B), this is not an actual EMSA result, but rather a native gel analysis of reaction products with the D-loop. In (C), was the binding of RAD51 to the pre-formed D-loop examined? Which is correct here? Moreover, why do the authors need streptavidin in this experiment? Please explain why this is necessary for the EMSA assay. Please show where is Cy3 or Cy5 labels on the DNAs should be shown in the schematic drawing.

The conditions for the experiments of Supplementary figure 1B, C are reported in the Methods section.

Panel B shows the mobility shifts of the ssDNA and dsDNA sequences in panel A, so it is appropriate to describe it as an EMSA.

We did not examine the binding of RAD51 to a pre-formed D-loop.

We used streptavidine in the experiment of Supplementary Figure 1C to show that streptavidine binding did not interfere with D-loop reconstitution.

The position of the Cy3, Cy5 labels in the DNAs is reported in Table S1.

(3) Figure S4B, page 6, line 6 from the top, 5'-arm and 3'-arm: please add them to the figure. And also, please explain what 5'-arm and 3'-arm are here in the text, as shown in lines 3-5 in the second paragraph of the same page.

We thank the reviewer for spotting this slight incongruity. We have removed the reference to 5’- and 3’arms of the donor DNA in the initial description of the D-loop (first paragraph of the “D-loop structure” section, 6 lines from the top), as the nomenclature for the arms of the donor DNA is introduced more appropriately in the following paragraph. Thus, there is no need to re-label Figure S4B; we note that the 5’- and 3’-labels are added to the arms of the donor DNA in Figure S4D.

(4) Page 7, line 4, and Figure 2E, "C24": C24 should be C26 here (Figure 2D shows that position 24 in esDNA is "T").

We thank the reviewer for spotting this typo, that is now corrected in the revised version of Figure 2 and in the text.

(5) Page 8, line 1, K284: It would be nice to show "K284" in Figure 3F.

We have added the side chain of K284 to Figure 3F, as suggested by the reviewer.

(6) Page 8, second paragraph, line 3 from the bottom, "5'-arm" should be "3'-arm" for the binding of RAD51A NTD to ds DNA (Figure 4D).

We thank the reviewer for spotting this typo, that is now corrected in the revised version of the text.

Reviewer #2 (Recommendations for the authors):

I understand that the strand exchange polarity of RAD51 should be opposite to that of RecA. But in the RecA manuscript (Nature 2020), it states (in the extended figure 1) " Because the mini-filament consists of fused RecA protomers, it does not reflect the effects a preferential polarity of RecA polymerization might have on the directionality of strand exchange. Also, our strand exchange reactions do not include the single-stranded DNA binding protein SSB that is involved in strand exchange in vivo and may sequester released DNA strands."

We are aware that the findings by Yang et al, 2020 were obtained with a multi-protomeric RecA chimera and that their construct might not therefore recapitulate a potential effect of RecA polymerisation on the directionality of strand-exchange. 

Comparison of the RecA and RAD51 D-loop structures shows that RecA and RAD51 adopt the same asymmetric mechanism of D-loop formation, which begins at one arm of the donor DNA and proceeds with donor unwinding and strand invasion until the second arm is captured, completing D-loop formation. However, the cryoEM structures provide compelling evidence that, after engagement with the donor DNA, RecA and RAD51 proceed to unwind the donor with opposite polarity; the structures provide a clear rationale for this, because of the different position of their dsDNA-binding domains relative to the ATPase domain.

We acknowledge that there exists an extensive body of literature that has investigated the polarity of strand exchange by RecA and RAD51 under a variety of experimental conditions, and we have added a brief comment to the text to reflect this, as well as some of the key citations. Undoubtedly, and as we also mention in our reply to the public reviews, further experimental work will be needed for a full reconciliation of the available evidence.

Reviewer #3 (Recommendations for the authors):

(1) I have a minor comment regarding the DNA shown in the structural figures in this work. The authors have used different colours to differentiate between isDNA, esDNA, and csDNA for easier interpretation. However, these colour codes are inconsistent across Figures 1, 2, 3, and 5. This inconsistency makes it difficult to interpret which strand is which, particularly for readers unfamiliar with D-loops and strand invasion. A consistent colour scheme for the DNA strands would enhance the quality of the structural figures.

We appreciate the reviewer’s comment about the colour scheme of the strands in the D-loop. We chose a unique colour scheme for each figure, to help the reader focus on the particular structural features that we wanted to highlight in the figure. So for instance, in figure 1D we chose to highlight the relationship (complementary vs identical) of the donor DNA strands with the the invading strand; in figure 2, the emphasis is on distinguishing the homologously paired dsDNA (pink) from the exchanged strand (magenta), as a consequence of L2 loop binding; etc.

(2) I have another comment regarding the rationale behind naming the RAD51 protomers (A to H) within the structure, which could confuse general readers if not clearly explained. In this paper, the RAD51 protomer is RAD51_A when closest to the 3' end of the isDNA. I assume the authors chose this order because HR generates a 3' ssDNA overhang before strand invasion. It would be beneficial for the introduction and results sections to mention this property of the 3' ssDNA overhang and the reasoning behind this naming strategy. This explanation will help readers understand how it differs from other naming orders used in RecA/RAD51 with ssDNA, where protomer A is closer to the 5' ssDNA.

We thank the reviewer for their insightful comment. We chose to name as chain A the RAD51 protomer nearest to the 3’-end of the isDNA to be consistent with the naming scheme that we use for all our published RAD51 filament structures.

(3) I have highlighted some text within this paper that has contradicting parts for authors to clarify and correct:

"Overall, the structural features of the RAD51 D-loop provide a strong indication that strand pairing and exchange begins at the 3'-end of the complementary strand in the donor DNA and progresses with 3'-to5' polarity (Fig. 5F)"

"The observed 5'-to-3' polarity of strand-exchange by RAD51 is opposite to the 3'-to-5' polarity of bacterial RecA (Fig. S8), that was determined based on cryoEM structures of RecA D-loops".

We thank the reviewer for alerting us to this inconsistency that has now been corrected in the revised manuscript.

(4) Figure S8 last model: NTD should be CTD in the title; Figure 2B: resolution scale bar needs A unit. We thank the reviewer for spotting this typo that has now been corrected in the revised version of figure S8. 

We couldn’t find a missing resolution scale bar in Figure 2B; however, we have added a missing resolution bar with A unit to Fig. S3B.

Author Response:

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

Reviewer #1 (Public Review):

In this study, the authors developed three case studies:

(1) transcriptome profiling of two human cell cultures (HEK293 and HeLa)

(2) identification of experimentally enriched transcripts in cell culture (RiboMinus and RiboPlus treatments)

(3) identification of experimentally manipulated genes in yeast strains (gene knockouts or strains transformed with plasmids containing the deleted gene for overexpression). Sequencing was performed using the Oxford Nanopore Technologies (ONT), the only technology that allows for real-time analysis. The real-time transcriptomic analysis was performed using NanopoReaTA, a recent toolbox for comparative transcriptional analyses of Nanopore-seq data, developed by the group (Wierczeiko and Pastore et al. 2023). The authors aimed to show the use of the tool developed by them in data generated by ONT, evidencing the versatility of the tool and the possibility of cost reduction since the sequencing by ONT can be stopped at any time since enough data were collected.

Strengths: 

Given that Oxford Nanopore Technologies offers real-time sequencing, it is extremely useful to develop tools that allow real-time data analysis in parallel with data generation. The authors demonstrated that this strategy is possible for both human cell lines and yeasts in the case studies presented. It is a useful strategy for the scientific community, and it has the potential to be integrated into clinical applications for rapid and cost-effective quality checks in specific experiments such as overexpression of genes.

Weaknesses:

In relation to the RNA-Seq analyses, for a proper statistical analysis, a greater number of replicates should have been performed. The experiments were conducted with a minimal number of replicates (2 replicates for case study 1 and 2 and 3 replicates for case study 3).

We have addressed this issue by performing two new sets of experiments: similar HEK293 vs HeLa with 10 replicates per condition and heatshocked vs non-heat shock with 6 replicates per condition. In the case of HEK293 vs HeLa comparison, we kept the 2 replicates per condition comparison to demonstrate the effect of limited replication number, simulating an early-stage evaluation of the experimental approach to obtain valuable quality control metrics. Nevertheless, we show that relevant and reproducible data can be obtained even with a lower replication number (2 replicates per condition), compared to a higher replication number (10 replicates), across both PromethION and MinION sequencing platforms.

Regarding the experimental part, some problems were observed in the conversion to doublestranded and loading for Nanopore-Seq, which were detailed in Supplementary Material 2. This fact is probably reflected in the results where a reduction in the overall sequencing throughput and detected gene number for HEK293 compared to HeLa were observed (data presented in Supplementary Figure 2). It is necessary to use similar quantities of RNA/cDNA since the sequencing occurs in real-time. The authors should have standardized the experimental conditions to proceed with the sequencing and perform the analyses.

We completely agree with the reviewer. In the 10-replicate HEK vs HeLa experiment, we collected similar data to what was presented in Supplementary Material 2. We chose to include this information to highlight the experimental variability that can arise during Nanopore-seq library preparation, particularly with cDNA synthesis. This type of information is not often highlighted in Nanoporebased studies, yet it is crucial to be aware of such differences. Despite these variations, we identified a consistent set of DEGs across comparisons of low versus high replicate numbers. Importantly, NanopoReaTA successfully provided realtime monitoring (e.g. detected number of genes per replicate/condition) as it allows for informed decision-making regarding the next steps in sequencing-based experiments.

Reviewer #2 (Public Review):

Transcriptomics technologies play important roles in biological studies. Technologies based on second-generation sequencing, such as mRNA-seq, face some serious obstacles, including isoform analysis, due to short read length. Third-generation sequencing technologies perfectly solve these problems by having long reads, but they are much more expensive. The authors presented a useful real-time strategy to minimize the cost of sequencing with Oxford Nanopore Technologies (ONT). The authors performed three sets of experiments to illustrate the utility of the real-time strategy. However, due to the problems in experimental design and analysis, their aims are not completely achieved. If the authors can significantly improve the experiments and analysis, the strategy they proposed will guide biologists to conduct transcriptomics studies with ONT in a fast and cost-effective way and help studies in both basic research and clinical applications.

Strengths:

The authors have recently developed a computational tool called NanopoReaTA to perform real-time analysis when cDNA/RNA samples are sequenced with ONT (Wierczeiko et al., 2023). The advantage of real-time analysis is that the sequencing can be stopped once enough data is collected to save cost. Here, they described three sets of experiments: a comparison between two human cell lines, a comparison among RNA preparation procedures, and a comparison between genetically modified yeasts. Their results show that the real-time strategy works for different species and different RNA preparation methods.

Weaknesses:

However, especially considering that the computational tool NanopoReaTA is their previous work, the authors should present more helpful guidelines to perform real-time ONT analysis and more advanced analysis methods. There are four major weaknesses:

(1) For all three sets of experiments, the authors focused on sample clustering and gene-level differential expression analysis (DEA), and only did little analysis on isoform level and even nothing in any figures in the main text. Sample clustering and gene-level DEA can be easily and well done using mRNA-seq at a much cheaper cost. Even for initial data quality checking, mRNA-seq can be first done in Illumina MiSeq/NextSeq which is quick, before deep sequencing in HiSeq/NovaSeq. The real power of third-generation RNA sequencing is the isoform analysis due to the long read length. At least for now, PacBio Iso-seq is very expensive and one cannot analyze the data in real-time. Thus, the authors should focus on the real-time isoform analysis of ONT to show the advantages.

We are aware that isoform analysis is one of the powers of real-time monitoring of long-read data, especially with Nanopore-seq. That is why we have included pipelines such as DRIM-seq and DEX-seq, which could provide valuable information about the differential transcript usage (i.e. isoforms). However, interpreting the results in a biologically meaningful context, particularly regarding the role of specific isoforms, remains challenging. This is especially relevant as our main goal is to demonstrate NanopoReaTA's utility as a real-time transcriptomic tool that offers valuable quality control and meaningful insights. Nevertheless, in the heat-shock experiments, we have identified one isoform that was differentially expressed and included it in the main figure. We hope that with the right experimental setup, users could use the incorporated tools for meaningful analyses for isoforms identification.

(2) The sample sizes are too small in all three sets of experiments: only two for sets 1 and 2, and three for set 3. For DEA, three is the minimal number for proper statistics. But a sample size of three always leads to very poor power. Nowadays, a proper transcriptomics study usually has a larger sample size. Besides the power issue, biological samples always contain many outliers due to many reasons. It is crucial to show whether the real-time analysis also works for larger sample sizes, such as 10, i.e., 20 samples in total. Will the performance still hold when the sample number is increasing? What is the maximum sample number for an ONT run? If the samples need to be split into multiple runs, how the real-time analysis will be adjusted? These questions are quite useful for researchers who plan to use ONT.

We thank the reviewer for their suggestion. We performed the suggested experiment in the HEK293 vs HeLa, taking 10 replicates per condition and acquired the data during the sequencing. As you can see in the results (Figure 2), the performance held very well, from the first hour up until the 24hour mark. In theory, the maximum number of barcodes that can be integrated in a sequencing run can be used for the pair-wise comparison. We are using 24 barcoding kit (provided by ONT) therefore we can include up to 12 replicates per condition. We are aware that there is a 96 barcoding kit that could be used as well. However, it is important to note that with more samples integrated in the sequencing run, less reads will be generated per sample. Therefore, it is important to plan properly the number of replicates used per sequencing run.

(3) According to the manuscript, real-time analysis checks the sequencing data in a few time points, this is usually called sequential analysis or interim analysis in statistics which is usually performed in clinical trials to save cost. Care must be taken while performing these analyses, as repeated checks on the data can inflate the type I error rate. Thus, the authors should develop a sequential analysis procedure for real-time RNA sequencing.

We would like to respond to this comment by addressing two points: 1) Quality control: During the analysis we offer two main statistics, which enable scientists to assess the experimental development. For each iteration the change in relative gene counts per sample is computed to assess the convergence towards 0. Moreover, for each iteration the number of detected genes per sample is computed to assess whether the number of detected reads is saturated. These metrics allow the user to independently assess whether samples within the experimental development reach a stable state, to reveal a meaningful timepoint of data evaluation. 

Sequential analysis: One solution to lower the type 1 error during sequential analysis is using the Pocock boundary, a systematic lowering of the p-value threshold depending on the number of interim analyses. We offer in NanopoReaTA a custom choice of the p-value threshold during the analysis. This allows researchers to set their parameters as needed.  

(4) The experimental set 1 (comparison between two completely different human cell lines) and experimental set 2 (comparison among RNA preparation procedures) are not quite biologically meaningful. If it is possible, it is better for the authors to perform an experiment more similar to a real situation for biological discovery. Then the manuscript can attract more researchers to follow its guidelines.

We took the suggestion of reviewer 2 (from recommendation for authors) to perform heat-shock experimental comparison between heatshocked and non-heat shocked cells from the same cell line (HEK293). We sequenced the sample (6 replicates per condition) and one-hour postsequencing initiation, we already identified three DEGs (including HSPA1A, DNAJB1, and HSP90AA1) known to be upregulated in heat shock conditions (Yonezawa and Bono 2023, Sanchez-Briñas et al. 2023). Therefore, we illustrate how NanopoReaTA can capture biologically relevant insights in real time.

Reviewer #1 (Recommendations for The Authors):

(1) The comparison between two different human cell lines doesn't have much biological relevance. It would be more interesting and useful to evaluate the genes and transcripts expressed from the same cell in different conditions.

As mentioned previously, we conducted a heat-shock experimental comparison between heat-shocked and non-heat-shocked within the same cell line HEK293. We observed reliable results already within one hour of initiating the sequencing.

(2) Increase the number of replicates to give greater confidence in the results.

We have addressed the replicate issue by performing two new sets of experiments: HEK293 vs HeLa with 10 replicates per condition and heatshocked vs non-heat shock with 6 replicates per condition. In both cases, we obtained reliable and reproducible results (even when comparing with lower replicate number).

(3) One of the advantages of performing Nanopore sequencing is the possibility of sequencing RNA molecules directly. It would be interesting to test the real-time analysis strategy in parallel using direct RNA sequencing if it is possible.

That is a great point. In theory, it would be possible to perform realtime differential gene expression on direct RNA data (since the pipeline for such analysis is already integrated in NanopoReaTA), however the limiting factor is the lack of multiplexing. To perform real-time transcriptomic analysis with direct RNA-seq data, one would need to sequence at least 4 flow cells (MinION or PromethION), each containing one sample (2 flow cells per condition to perform pairwise transcriptomic analyses). Despite the possibility of such an analysis, this scenario will not be cost-effective as this will increase significantly the costs for the amount of data gathered. We are aware that ONT is planning to release a multiplexing option to direct RNA-seq in the unforeseen future. We have integrated the option of direct RNA-seq analyses for the day that such option will be available, and the users will be able to perform real-time transcriptomic analysis with dRNA-seq data.  

Some minor weakneses are below:

(4) With respect to the text as a whole, the authors should be more careful with standardization, such as mL/ml and uL/ul, Ribominus/RiboMinus.

We have standardized the nomenclature to µL, mL and Ribominus (due to trademark).  

(5) Set up paragraphs on page 9 and throughout the text when necessary.

We have set the suggested paragraphs on page 9 and throughout the text.

(6) Please, check the word form in the sentence: "To isolate the RNA form the

RiboMinus{trade mark, serif} supernatant.."

The word has been corrected.

(7) In order to make clear to the reader at the outset, I suggest including in the methodology how many biological replicates were performed for each cell type studied (cell lines and yeast strains).

_For cell line w_e have included now the number of replicates used for each replicate. We have included this also for yeast setups. 

(8) Please, check the Supplementary Tables as the word VERDADEIRO has not been translated (TRUE) in Supplementary Table 1.

This issue appears to be influenced by the language settings configured on the viewer's computer.

(9) On page 17, I suggest including the absorbance used to measure RNA concentration in HEK293 and HeLa cell lines. Also, I suggest including how the quality of the RNA extracted from the cell cultures and yeast strains was determined. Was the ratio 260/280 and 260/230 calculated? Given that the material was extracted with Trizol, which has phenol and chloroform in its composition, it would be important to evaluate the quality of the RNA, especially by calculating the 260/230 ratio.

We have included a statement regarding the concentrations and quality of RNA in the “RNA isolation” section within the material and methods.

(10) On page 18, the topic of Selective purification of ribosomal-depleted (RiboMinus) and ribosomal-enriched (RiboPlus) transcripts needs to be better detailed, especially in the last two sentences. For example: "The pooled bead samples (containing the rRNA) were further processed with Trizol RNA isolation to complete the purification." This sentence should be detailed to make it clear that this procedure is what you call ribosomal-enriched (RiboPlus).

Qualitative analysis of the material was performed after rRNA depletion and enrichment.

We have made these sentences clearer.

(9) On the topic of Direct cDNA-native barcoding Nanopore library preparation and sequencing, in the following sentences: "Concentration determination (1 μl) and adapter ligation using 5 μL NA, 10 μL NEBNext Quick Ligation Reaction Buffer (5X), and 5 μL Quick T4 DNA Ligase (NEB, cat # E6056) were performed. Pooled library purification with 0.7X AMPure XP Beads resulted in a final elution volume of 33 μl EB. Concentration of the pooled barcoded library was determined using Qubit (1 μl)."

Two concentration determinations were performed, before and after adapter ligation. I suggest writing one sentence for concentration determination and another for adapter ligation.

We applied the reviewer’s suggestion. 

(11) In the section Experimental Design in Results, the first sentences are part of the methodology and are described in materials and methods. I suggest removing it from the results and rewriting the text. Results of the RNA extraction methodology and library preparation were shown in supplementary material. Thus, the authors could mention that the results were presented in supplementary material.

We have revised this section to remove the details of RNA extraction and library preparation, focusing instead on the pipeline and experimental setups. The methodology is outlined in Figure 1, as well as in the materials and methods and the supplementary figures for each experimental setup.

Reviewer #2 (Recommendations For The Authors):

For major weakness 4 described in the Public Review, the authors could try experiments like:

(1) comparison between females and males of tissues or primary cells; or

(2) comparison between cell lines before and after heat shock.

They are easy to perform and much more similar to real experimental designs for discovery, and the authors may actually have some new findings because usually people do not do much investigation on the isoform level using mRNA-seq.

We thank the reviewer for their suggestions. We performed the heat-shock experimental comparison between heat-shocked and non-heat shocked cells from the same cell line (HEK293). We sequenced the sample (6 replicates per condition) and already one-hour post-sequencing initiation, we identified three DEGs including HSPA1A, DNAJB1, and HSP90AA1 reported to be upregulated heat shock conditions (Yonezawa and Bono 2023, Sanchez-Briñas et al. 2023). We have identified one differentially expressed isoform and included it in the main figure.

There are two minor weaknesses:

(1) Many figure numbers in the main text are wrong, including:

Page 4, "similarity plot and principal component analysis (PCA) (Figure 1B, 1C)";

Page 7, "same intervals as mentioned earlier (Figure 1A)", and "Next, we inspected the PCA and dissimilarity plots (Figure 2B";

Page 10, "process (Supplementary Figure 19A) until the 24-hour PSI mark point (Figure 9B", and "NEW1 was the sole differentially expressed gene (Figure 9D)".

The authors should be more careful about this. It is very confusing for readers.

We have addressed these points in the text. 

(2) The texts in the figures are too small to recognize, especially in Figures 4 and 5. The reason is that there are too many sub-figures in one figure. Is that really necessary to put more than 20 sub-figures in one? The authors should better summarize their results. For example, remove sub-figures with little information; do not show figures with the same styles again and again in the main text and just summarize them instead.

We thank the reviewer for the suggestion. We have updated the figure to focus on the most relevant comparisons (new1Δ-pEV vs. WT-pEV and rkr1Δ-pEV vs. WT-pEV), providing a clearer and more realistic comparison between mutant and wild-type conditions in the main figure. Additionally, a summary and all related comparisons are included in Supplementary Documents S4 and S5. We believe these supplementary figures are essential to demonstrate NanopoReaTA's capabilities as a quality control tool, effectively detecting expected transcriptomic alterations in real-time.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors have developed self-amplifying RNAs (saRNAs) encoding additional genes to suppress dsRNA-related inflammatory responses and cytokine release. Their results demonstrate that saRNA constructs encoding anti-inflammatory genes effectively reduce cytotoxicity and cytokine production, enhancing the potential of saRNAs. This work is significant for advancing saRNA therapeutics by mitigating unintended immune activation.

Strengths:

This study successfully demonstrates the concept of enhancing saRNA applications by encoding immune-suppressive genes. A key challenge for saRNA-based therapeutics, particularly for non-vaccine applications, is the innate immune response triggered by dsRNA recognition. By leveraging viral protein properties to suppress immunity, the authors provide a novel strategy to overcome this limitation. The study presents a well-designed approach with potential implications for improving saRNA stability and minimizing inflammatory side effects.

We thank Reviewer #1 for their thorough review and for recognizing both the significance of our work and the potential of our strategy to expand saRNA applications beyond vaccines.

Weaknesses:

(1) Impact on Cellular Translation:

The authors demonstrate that modified saRNAs with additional components enhance transgene expression by inhibiting dsRNA-sensing pathways. However, it is unclear whether these modifications influence global cellular translation beyond the expression of GFP and mScarlet-3 (which are encoded by the saRNA itself). Conducting a polysome profiling analysis or a puromycin labeling assay would clarify whether the modified saRNAs alter overall translation efficiency. This additional data would strengthen the conclusions regarding the specificity of dsRNA-sensing inhibition.

We thank the Reviewer for this insightful suggestion. We performed a puromycin labeling assay to assess global translation rates (Figure 3—figure supplement 1c). This experiment revealed that the E3 construct significantly reduces global protein synthesis, despite driving high levels of saRNAencoded transgene expression (Figure 1d, e). In contrast, the E3-NSs-L* construct mitigated this reduction in global translation while maintaining moderate transgene expression. These findings support our hypothesis that E3 enhances transgene output in part by activating RNase L, which degrades host mRNAs and thereby reduces ribosomal competition. We appreciate the Reviewer’s recommendation of this experiment, which has strengthened the manuscript.

(2) Stability and Replication Efficiency of Long saRNA Constructs:

The saRNA constructs used in this study exceed 16 kb, making them more fragile and challenging to handle. Assessing their mRNA integrity and quality would be crucial to ensure their robustness.

Furthermore, the replicative capacity of the designed saRNAs should be confirmed. Since Figure 4 shows lower inflammatory cytokine production when encoding srIkBα and srIkBαSmad7-SOCS1, it is important to determine whether this effect is due to reduced immune activation or impaired replication. Providing data on replication efficiency and expression levels of the encoded anti-inflammatory proteins would help rule out the possibility that reduced cytokine production is a consequence of lower replication.

We thank the Reviewer for these valuable suggestions.

To assess the integrity of the saRNA constructs, we performed denaturing gel electrophoresis (Supplemental Figure 6c). The native saRNA, E3, and E3-NSs-L* constructs each migrated as a single band. The moxBFP, srIκBα, and srIκBα-Smad7-SOCS1 constructs showed both a full-length transcript and a lower-abundance truncated band (Supplemental Figure 6d), suggestive of a cryptic terminator sequence introduced in a region common to these three constructs.

To evaluate replicative capacity, we performed qPCR targeting EGFP, which is encoded by all constructs. This analysis revealed that the srIκBα-Smad7-SOCS1 construct exhibited lower replication efficiency than both native saRNA and E3. Several factors may contribute to this difference, including the longer transcript length, reduced molar input when equal mass was used for transfection, prevention of host mRNA degradation due to RNase L inhibition, or the presence of truncated transcripts.

Given these confounding variables, we revised our approach to analyzing cytokine production. Rather than comparing all six constructs together, we split the analysis into two parts: (1) the effects of dsRNA-sensing pathway inhibition (Figure 4a), and (2) the effects of inflammatory signalling inhibition (Figure 4c). For the latter, we compared srIκBα and srIκBα-Smad7-SOCS1 to moxBFP, as these three constructs are more comparable in size, share the same truncated transcript, and all encode L* to inhibit RNase L. This strategy minimizes the likelihood that differences in the cytokine responses are due to variation in replication efficiency.

(3) Comparative Data with Native saRNA:

Including native saRNA controls in Figures 5-7 would allow for a clearer assessment of the impact of additional genes on cytokine production. This comparison would help distinguish the effect of the encoded suppressor proteins from other potential factors.

We thank the Reviewer for this helpful suggestion. We have added the native saRNA condition to Figure 5 as a visual reference. However, due to the presence of truncated transcripts in the constructs designed to inhibit inflammatory signalling pathways, the actual amount of full-length saRNA delivered in these conditions is likely lower than expected, despite using equal total RNA mass for transfection. This complicates direct comparisons with constructs targeting dsRNAsensing pathways, which do not show transcript truncation. For this reason, native saRNA was included only as a visual reference and was not used in statistical comparisons with the inflammatory signalling inhibitor constructs.

(4) In vivo Validation and Safety Considerations:

Have the authors considered evaluating the in vivo potential of these saRNA constructs? Conducting animal studies would provide stronger evidence for their therapeutic applicability. If in vivo experiments have not been performed, discussing potential challenges - such as saRNA persistence, biodistribution, and possible secondary effectswould be valuable.

(5) Immune Response to Viral Proteins:

Since the inhibitors of dsRNA-sensing proteins (E3, NSs, and L*) are viral proteins, they would be expected to induce an immune response. Analyzing these effects in vivo would add insight into the applicability of this approach.

We appreciate the Reviewer’s points regarding in vivo validation and safety considerations. While in vivo studies are beyond the scope of the present investigation, we agree that evaluating therapeutic potential, biodistribution, persistence, and secondary effects will be essential for future translation. We have now included a brief discussion of these considerations at the end of the revised discussion. In ongoing work, we are planning follow-up studies incorporating in vivo imaging and functional assessments of saRNA-driven cargo delivery in preclinical models of inflammatory joint pain.

Regarding the immune response to viral proteins, we agree that this is an important consideration and have now included a clearer discussion of this limitation in the revised manuscript. Specifically, we highlight that encoding multiple viral inhibitors (E3, NSs, and L*), in combination with the VEEV replicase, may increase the likelihood of adaptive immune recognition via MHC class I presentation. This could lead to cytotoxic T cell–mediated clearance of saRNA-transfected cells, thereby limiting therapeutic durability. We emphasize that addressing both intrinsic cytotoxicity and immune-mediated clearance will be essential for advancing the clinical potential of this platform.

(6) Streamlining the Discussion Section:

The discussion is quite lengthy. To improve readability, some content - such as the rationale for gene selection-could be moved to the Results section. Additionally, the descriptions of Figure 3 should be consolidated into a single section under a broader heading for improved coherence.

Thank you for these helpful suggestions. We have streamlined the Discussion to improve readability and have moved the rationale for gene selection to the results section, as recommended. In addition, we have consolidated the Figure 3 descriptions to improve coherence and to simplify the presentation.

Reviewer #2 (Public review):

Summary:

Lim et al. have developed a self-amplifying RNA (saRNA) design that incorporates immunomodulatory viral proteins, and show that the novel design results in enhanced protein expression in vitro in mouse primary fibroblast-like synoviocytes. They test constructs including saRNA with the vaccinia virus E3 protein and another with E3, Toscana virus NS protein and Theiler's virus L protein (E3 + NS + L), and another with srIκBα-Smad7SOCS1. They have also tested whether ML336, an antiviral, enables control of transgene expression.

Strengths:

The experiments are generally well-designed and offer mechanistic insight into the RNAsensing pathways that confer enhanced saRNA expression. The experiments are carried out over a long timescale, which shows the enhance effect of the saRNA E3 design compared to the control. Furthermore, the inhibitors are shown to maintain the cell number, and reduce basal activation factor-⍺ levels.

We thank Reviewer #2 for their thoughtful and detailed assessment of our manuscript, and for recognizing the mechanistic insights provided by our study. We also appreciate their positive comments on the experimental design, the extended timescale, and the observed effects on transgene expression, cell viability, and basal fibroblast activation factor-α levels.

Weaknesses:

One limitation of this manuscript is that the RNA is not well characterized; some of the constructs are quite long and the RNA integrity has not been analyzed. Furthermore, for constructs with multiple proteins, it's imperative to confirm the expression of each protein to confirm that any therapeutic effect is from the effector protein (e.g. E3, NS, L). The ML336 was only tested at one concentration; it is standard in the field to do a dose-response curve. These experiments were all done in vitro in mouse cells, thus limiting the conclusion we can make about mechanisms in a human system.

Thank you for your detailed feedback. We have added new experiments and clarified limitations in the revised manuscript to address these concerns:

RNA integrity: We performed denaturing gel electrophoresis on the in vitro transcribed saRNA constructs (Supplemental Figure 7c). Constructs targeting dsRNA-sensing pathways migrated as a single band, while those targeting inflammatory signalling pathways showed both a full-length product and a common, lower-abundance truncated transcript. This suggests that the actual amount of full-length RNA delivered for the constructs inhibiting inflammatory signalling was overestimated. To account for this, we avoided direct comparisons between the two types of constructs and instead focused on comparisons within each type to ensure more meaningful interpretation.

Confirmation of protein expression: While we acknowledge that direct measurement of each protein would provide additional insight, we believe the functional assays presented offer strong evidence that the encoded proteins are expressed and exert their intended biological effects. Additionally, IRES functionality was confirmed visually using fluorescent protein reporters, supporting the successful expression of downstream genes.

ML336 concentration–response: We have now performed a concentration–response analysis for ML336 (Figure 8a and b), which demonstrates its ability to modulate transgene expression in a concentration-dependent manner.

Use of human cells: We agree that testing these constructs in human cells is essential for future translational applications and are actively exploring opportunities to evaluate them in patientderived FLS. However, previous studies have shown that Theiler’s virus L* does not inhibit human RNase L (Sorgeloos et al., PLoS Pathog 2013). As a result, it is highly likely that the E3-NSs-L* construct will not function as intended in human systems. Addressing this limitation will be a priority in our future work, where we aim to develop constructs incorporating inhibitors specific to human RNase L to ensure efficacy in human cells.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Figure 2c is not indicated.

Thank you for pointing out this error. It has now been corrected in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) The Graphical Abstract is a bit confusing; suggest modifying it to represent the study and findings more accurately.

We have revised the graphical abstract to improve clarity and better reflect the study’s design and main findings. Thank you for the suggestion.

(2) The impact of this paper would be greatly improved if these experiments were repeated, at least partially, in human cells. The rationale for mouse cells in vitro is unclear.

The rationale for developing constructs targeting mouse cells is based on our intention to utilize these constructs in mouse models of inflammatory joint pain in future studies.

We recognize that incorporating data from human cells would significantly enhance the translational relevance of our work, and we are actively pursuing collaborations to test these constructs in patient-derived FLS. However, a key component of our saRNA constructs—Theiler’s virus L*—has been shown to inhibit mouse, but not human, RNase L (Sorgeloos et al., PLoS Pathog 2013). Consequently, the E3-NSs-L* polyprotein may not function as intended in human cells. To address this limitation, future work will focus on developing constructs that incorporate inhibitors specific to human RNase L, thereby facilitating more effective translation of our findings to human systems.

(3) The ML336 was only tested at one concentration and works mildly well, but would be more impactful if tested in a dose-response curve.

We have now performed a concentration–response analysis for ML336 (Figure 8a and b), which demonstrates its concentration-dependent effects on transgene expression and saRNA elimination. Thank you for the suggestion.

(4) Overall, there is not a cohesive narrative to the story, instead it comes off as we tried these three different approaches, and they worked in different contexts.

We have revised the graphical abstract, results, and discussion to improve the cohesiveness of the manuscript’s narrative and to better integrate the mechanistic rationale linking the different approaches. We appreciate the feedback.

(5) The title is not supported by the data; the saRNA is still somewhat cytotoxic, immunostimulatory and the antiviral minimally controls transgene expression; suggest making this reflect the data.

We have revised the title to better reflect the scope of the data and the mechanistic focus of the study. The updated title emphasizes the pathways targeted and the outcomes demonstrated, while avoiding overstatement. Thank you for this helpful recommendation.

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Referee #2

Evidence, reproducibility and clarity

The authors describe a modified version of single molecule Fluorescence In-situ Hybridization (smFISH) method they have adapted to successfully measure RNA levels in isolated human donor T cells, that are very hard to grow on glass and have small amounts of cytoplasm relative to cell size, a challenge for all researchers working with small cells that only grow in suspension cultures. Using this methodology, the authors have queried transcription status and mRNA localization and fate of the two cytokines, IFNG and TNF, upon T-cell activation. The main findings of the study are: (1) activation of T-cells results in rapid accumulation of IFNG an TNF mRNA; there is differential distribution of the cytokine mRNAs between the nucleus and cytoplasm with greater accumulation in the cytoplasm as activation progresses resulting in increased protein production. There is significant transcriptional heterogeneity in response to T-cell activation. (2) The cytokine mRNA turnover appears to be controlled by translation. (3) HUR, an RBP appears to control poly(A) tail length of TNF mRNA in response to T-cell activation. The successful implementation of a modified smFISH protocol used in this study is a welcome resource for all labs that want to study small human primary cells that are difficult to culture on glass coverslips and grow as suspension cultures. Although the authors have very exciting observations, they have shied away from discussing their results in the context of the biology of T-cell activation and how their observations may explain prior studies on cytokine gene expression patterns during T-cell activation.

In my opinion, the authors should discuss their observations in depth from the context of T-cell activation and cytokine expression. I have enumerated several specific comments that may help the authors in revising the manuscript if they choose to do so.

Specific comments:

1. Based on the data presented in Figure 1 D and E, it is clear there is depletion of IFNG and TNF mRNA 4hrs after activation and then the mRNA levels go up at 6h in both cases. However, the authors suggest that only TNF mRNA is depleted at 4hrs of activation (lines 169-172). The median number of IFNG mRNA gradually decreases after 1h of activation and reaches a low at 4h and then substantially increases by 6h. Did the authors measure gene expression of these mRNAs at later time points in the activation process? Perhaps transcription is coupled to mature mRNA levels in the cytoplasm and transcription is ramped up again once the cytoplasmic mRNA levels reach a lower threshold. Is this just an anomaly of the system or is gene expression pattern of cytokines upon T-cell activation cyclical?
2. In data presented Figure 2 and Suppl Figure 2, the authors show correlation between dual cytokine expression and biallelic expression. However, not all dual cytokine expressing cells show bi-allelic expression of both cytokines. It will be useful to know what fraction of cells are biallelic for both genes. Since the experiment was done using two color smFISH, a scatter plot will cluster those dual expressor cells for both cytokines that are also bi-allelic for both genes. Extending this further would be to systematically address protein expression in the various combination of expression patterns. Combining smFISH with immunofluorescence will help address this. Overall, these results will be helpful in getting a better understanding of gene expression patterns during T-cell activation.
3. The mRNA localization data presented in Figure 3A and the associated supplemental figure: A better analysis and representation of the data presented in 3A would be a scatter plot of individual cells for their nuclear and cytoplasmic localization of mature mRNA. The authors might also want to extend this analysis based on the data presented in Figure 2 for dual expressors and bi-allelic expression. In other words, do cells with bi-allelic expression have more mRNA localized in the cytoplasm, and does this hold true in dual expressor cells? In the context of translation dependent decay of mRNA, do the dual expressor cells with biallelic expression fare better thereby producing and secreting cytokines continuously?
4. The data presented for IFNG in Figure 4 is quite intriguing. In HuR-KO cells at 2h post induction, two of the three donors cell lines have only a small fraction of cells producing protein compared to the controls, however, they are substantially higher than the KO cells at time "0". Surprisingly, the amount of protein produced by these cells (panel B), although statistically lower than the control, is substantially higher than KO cells at "0"h. Does the lone donor cell line with higher number of protein producing cells contribute to majority of the protein produced? There appears to be substantial difference between the three donor cell lines in the number of protein producing cells and mature IFNG mRNA after activation (Suppl Figure 1G & H). The authors may wish to compare the results before combining the data of all three donor cell lines before interpreting the data.
5. Also intriguing, HuR knock out results in a significant increase in transcription of IFNG at time "0" (Figure 4, panel E). Despite this, there is a significant loss in transcription of IFNG 2h post activation. However, there is significant accumulation of mature mRNA (panel D). Combined with the protein expression data presented in panels A & B, and the fact that translation induces mRNA decay, how do the authors reconcile this data?
6. The differential effect of HuR knock out on poly(A) tail length of IFNG and TNF mRNA is of great importance and the most striking finding in this study! It is generally accepted that poly(A) tail length contributes to mRNA stability and survival. The results presented in Figures 4 and 5 argue otherwise. Only a small fraction of TNF mRNA have full length poly(A) tails, however, the number of mature TNF mRNA in KO cells is much greater than the control even at "0"h. In addition, the TNF mRNA appear to be well translocated into the cytoplasm and effectively translated. Given these conflicting observations, what possible mechanism do the authors envision that can explain this result.<br /> Again, plotting the data presented in Figure 5A as a scatter plot between # of RNA in the cytosol vs nucleus will give a better picture of the localization changes in individual cells.
7. A more elaborate discussion of the results as it relates to the biology of cytokine gene expression during T-cell activation will immensely strengthen the manuscript.

Minor comment:

1. Images of cells with smFISH data (Figures 1, 3 & 4) must be bigger for better visualization. Show images with only a couple of cells enlarged to show the mRNA spots more visibly. Include images with more cells in the supplement instead.

Referee's cross-commenting

I must confess I am not an immunologist, so my knowledge of the intricacies of gene expression in T-cells in very limited. However, I do have a fair sense of transcription regulation and use single molecule approaches, especially smFISH, to address these questions. I agree with the other reviewer the study is of significance, especially the advancement in the ability to do smFISH in primary cells, a challenge that I know first hand. I also have to agree with the other reviewer that the discussion was too short and the authors shied away from the bigger picture of being able to comment on regulation of expression of cytokines during T-cell activation. It is remarkable that they see heterogeneity in gene expression of the individual target genes and bi-allelic expression. The other point of interest is the difference in p(A) tail length and its potential role in regulating TNF gene expression.

Significance

The successful implementation of a modified smFISH protocol used in this study is a welcome resource for all labs that want to study small human primary cells that are difficult to culture on glass coverslips and grow as suspension cultures.

Overall, this work is of high quality and can be better presented to fully explore and discuss the biological implications of the observations from the study. It is not clear to me if the authors wished to present this manuscript reporting an advancement in technology tool to study gene expression during T-cell activation, or a more in-depth study of gene expression.

The study will benefit the larger community that use single molecule approaches to understand genew expression.

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Lattanzio and colleagues uses advanced single molecule FISH, adapted specifically for T cells, to examine RNA transcriptional dynamics in polyclonally stimulated human T cells. By examining the subcellular localisation of both IFNg and TNF mRNAs (nascent and mature), they are able to characterise rate things like rate of transcription and RNA stability. Key findings include the identification of bi-allelic vs mono-allelic transcription at the single cell level which maps to polyfucntion vs monofunctional T cells. Moreover, they identified distinct mechanisms regulating RNA stability and the role of RNA binding protein HuR in mediating that.

Overall, this is really a proof of concept paper that uses elegant technologies and analysis tools showing just how much information can be obtained from this approach. The ability to examine RNA dynamics and the imapct of RNA binding proteins in regulating RNA stability/translation/transriptoin at a single cell level will be an advance for the field, not just those interested in T cell biology but all cell types.

There are no specific experimental issues that came to mind that need to be addressed and it is really only some minor comments, particularly for the discussion that would strengthen the implicaitons of the study.

1. I might have missed it but it wasn't exactly clear from the results or from the methods exactly how nascent vs mature RNA was discriminated. Was this just from the subcellular localisation (i.e nuclear vs cytoplasmic)? RNA imaged close to the TSS? If so, this should be noted somewhere. If there was some other way of precisely ascribing RNA status, this should be outlined (use of primers that targeted intergenic sequences).
2. The discussion was very brief and would have benefited from a bit more speculation about the implications of their findings. Specifically, why would there be a need for different cytokine RNAs to be regulated in such distinct ways (IFNg vs IL-2)? Do the authors have any thoughts? Another point is the proposed explanations for the distinct T cell subsets observed that produce cytokines at different levels. While the authors propose three possible explanations, they are presented as being mutually exclusive, of course it could be a combination of all three. Moreover, putting the importance of higher functioning T cells (i.e those that produce more cytokines) into context is also important. Early studies from La Gruta et al., (J Immunol, 2004; doi: 10.4049/jimmunol.172.9.5553), Betts et al, (Blood, 2006 doi.org/10.1182/blood-2005-12-4818) and Darrah et al. (Nat Med, 2007 doi.org/10.1038/nm1592) linked higher cytokine production/multifunctionality to better immune outcomes, while Denton et al (PNAS, 2011, 10.1073/pnas.1112520108) linked the extent of cytokine production to cellular differentiation (and epigenetic landscape at the TNF and IFNg locus). These studies should be cited to provide a setting where this approach will be relevant.

A minor point relates to line 329, that sentence stating "Even though most activated Teff cells express cytokine mRNAs, they display a two order of magnitude difference in mRNA and protein expression."

It is not clear what this is relevant or compared to. A two order of magnitude difference compared to what?

Significance

This is a proof of concept study that demonstrates the utility of the T cell smFISH approach to delineate high resolution analysis of cytokine RNA dynamics at a single cell level, for multiple cytokine RNA species. It clearly provides interesting biology and further understanding of RNA dynamics in activated T cells. I especially appreciated the observation of bi-allelic vs mono-allelic transcription, and the ability to explore the role of RNA binding proteins in RNA regulation.

This technique will have broader applicability and hence will be of interest to those outside T cell immunology. It only requires some minor corrections/revisions.

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Reply to the reviewers

Reviewer #1 (Significance (Required)):

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This study aims to bridge a gap between the mechanisms of preeclampsia and neurodegenerative disorders, and this through the existence of misfolded proteins in the preeclamptic placenta which has been reported before, in particular the beta amyloid protein, known as operative in Alzheimer's disease (AD) in particular.

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Our response: We sincerely appreciated the reviewer’s constructive comments.

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

Minor remarks

1. It is classical now to present in extenso the WB as supplementary data for Fig 3, 4 and 5. Our response: We will include the full blots in Supplemental information.

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It seems that the beta amyloid signal is not stronger for the early onset and the late onset PE samples. Have the authors an interpretation?

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Our response: The current manuscript includes both early-onset and late-onset cases. Thus, we are certain that amyloid beta deposition is involved in both early- and late-onset PE. We will discuss this matter.

The figure 4b does not show the BeWo labeling in forskolin with or without beta amyloid peptides, why? It would be illustrative to show a decrease in the fusion processes

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Our response: In Fig. 4A, we pretreated BeWo cells with Aβ fibrils and after that, cell fusion was induced by Fsk. On the other hand, in Fig. 4b, we treated BeWo with Aβ fibrils and investigated the protein levels and subcellular localization of ZO-1 and E-cadherin. Fig.4b shows that expressions of proteins involved in cell-cell interaction were reduced by Aβ fibril treatment without Fsk. Cell-cell interaction before syncytialization is required for cell fusion, and these proteins disappear after cell fusion. Thus, our results demonstrate that elimination of cell-cell interaction by Aβ fibrils resulted in reduced cell fusion induced by Fsk. This is why we treated BeWo cells with Aβ fibrils before the induction of cell fusion by Fsk, and BeWo labeling in forskolin with or without Aβ fibrils will result in a loss of ZO-1 and E-cadherin regardless of the occurrence of cell fusion. We will discuss this matter in more detail.

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How do the authors explain that exposure to fibrils did not seem to slow down significantly the fusion process, even though markers are decreased?

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Our response: Since we previously demonstrated that loss of membrane E-cadherin slows the fusion (Iwahashi et al., Endocrinology, 2019, PMID: 30551188), we believe that reduction of membrane localization of E-cadherin also slows the fusion process. We will discuss this matter further.

• Could the authors attempt a labeling with the Di-8, an interesting quantitative marker of cell fusion (see ref PMID: 38019394).*

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Our response: We have shown that pretreatment of BeWo cells and human primary cytotrophoblasts (CTBs) inhibited induction of syncytin-1 and β-hCG. Syncytin-1 is a critical driver of syncytialization and formation of the syncytiotrophoblast layer, and β-hCG is one of the major products of syncytiotrophoblasts. Thus, induction of these proteins is widely used as syncytialization markers of trophoblasts. On the other hand, Di-8-ANEPPS is a potentiometric fluorescent dye that may be used assess cell fusion simply and economically. Although we understand the robustness of this method, we believe that the current data are sufficient to demonstrate that Aβ fibril pretreatment inhibited syncytialization of BeWo cells and CTBs.

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Reviewer #2 (Significance (Required)):

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Investigating the deposition of Aβ in the placenta could enhance our understanding of pregnancy complications such as PE, fetal growth restriction, and neurodevelopmental risks. However, further research on this topic is necessary.

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Our response: We sincerely appreciate the critical reading and constructive comments of the reviewer. We agree that further research on protein aggregation and the pathogenesis of preeclampsia is necessary. We will discuss this matter in the discussion.

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

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Major comments

• • If CTBs are treated with Aβ, and if it affects STB, what happens with EVT? Why didn't they check with EVT if the authors wanted to link with PE?*
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Our response: We thank the reviewer for the critical comment. We investigated Aβ generation by an EVT model cell, HTR8/SVneo cells. We found that HTR8/SVneo cells produced much less amount of Aβ compared to BeWo cells (unpublished). Gao et al. reported that Aβ aggregates induced autophagy in HTR8/SVneo cells and suggested that an excessive autophagy may be detrimental and be involved in the development of preeclampsia (Gao et al., J Mol Histol, 2024, PMID: 38777993). We will discuss this matter in the discussion.

On the other hand, we have already investigated the effects of Aβ monomers in EVTs, and discovered that even low levels of Aβ produced by EVTs promote EVT invasiveness and have a physiological function. Please see below. We will add these new data in the revised manuscript.

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• • Did the authors look for pathologies related to Aβ deposition on PE placentas?*
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Our response: We did not observe any pathologies near the Aβ deposition.

• • Line# 103, the IF images don't show that BACE1 is around HIF1. There are no merged images, and the results are over- or underestimated.*
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Our response: We agree with the reviewer. There is a time discrepancy between HIF activation and BACE1 induction. Our immunohistochemical analysis showed that PE placentas are in a chronic hypoxia condition and that BACE1 was increased in PE placentas. Our cell-based assay supports that HIF1α stabilization by Roxadustat increased BACE1 levels in BeWo cells. We will tone down the results section of the immunohistochemical analysis.

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• • What is the intended purpose of using Roxadustat? If it inhibits HIF1α, could you explain the reason behind the increased expression of HIF1α? Furthermore, is there evidence to support the efficacy of this compound?*
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Our response: Roxadustat inhibits the proline hydroxylation of HIF1α and thereby inhibits the ubiquitination and degradation of HIF1α via the ubiquitin proteasomal system. In this study, we used Roxadustat as a HIF1α stabilizer to investigate whether BACE1 levels are increased with hypoxia and HIF1. Our data showed that treatment of BeWo cells with Roxadustat increased HIF1α levels, supporting the efficacy of Roxadustat. We will include this information in the result section for clarity.

• • Is Aβ deposition very specific to PE, or can it also occur for other reasons during pregnancy?*
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Our response: To date, no report has been found showing Aβ deposition in placentas other than PE. The deposition of protein aggregates, including those of Aβ and transthyretin, has previously been reported in PE. However, the presence and role of these protein deposits in placentas under pathological conditions, in addition to PE, remains to be elucidated. Several stresses such as hypoxia and ER stress may lead to deposition of protein aggregates in the placenta. These points will be discussed in the discussion.

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• • BACE1 is expressed in Normal#2 and #3 but not in #1, #4, and #5. Why is this expressed in #2 and #3? Is there anything wrong with these samples? If patients had gestational hypertension or some other complications?*
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Our response: We did not find any other complications in the normal placentas. In the brain, Aβ is constitutively generated and thus, thought to play physiological roles. The amount of Aβ is determined by the balance between the production and the clearance. A sustained imbalance of Aβ production and Aβ clearance will lead Aβ aggregation and deposition. We found that BeWo cells expressed BACE1 in a normoxic condition and thus, normal placentas may express BACE1 and generate small amounts of Aβ. Our results suggested that chronic hypoxia in PE placentas resulted in increased BACE1 expression and increased Aβ production, which may eventually result in Aβ aggregation and deposition, because the aggregation process of Aβ is concentration-dependent. We will include this point in the revised manuscript.

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• • PE placentae were compared with GA matched placentae. What is the expression of BACE1 and RB4CD12 in term control placentae?*
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Our response: We used RB4CD12 as a protein aggregation marker. As shown in Table 1, the current study includes 3 placentas whose gestational ages are over 37 weeks. We did not observe RB4CD12 and Aβ deposition in gestational age-matched control and observed BACE1 expression in one 37 weeks gestational age control. We will include these points in the result section.

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• • If AB fibril deposition is hypoxia dependent, what happens at the early gestation, where oxygen conc is 1-2%?*
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Our response: At the early gestation, physiological hypoxia promotes the EVT invasion and helps the remodeling of spiral arteries for oxygen supply. Please see our response above. Severe hypoxia on the CTB side in early gestation may result in a miscarriage before PE develops.

Minor comments

• • The authors only performed IF and IHC. Please confirm and correct the methods accordingly.*
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Our response: We thank the reviewer for pointing this out. We will correct the methods.

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• • Was the BeWo-b21 clone cell line used for all the experiments in this paper? This is the only clone that can be used for BeWo-STB models.*
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Our response: We do not have information about the clone number of BeWo cells used in this study. We purchased them from the American Type Culture Collection (Manassas, VA) and they were authenticated by JCRB Cell Bank (National Institute of Biomedical Innovation Japan, report no. KBN0410). By using the same cells, we published three articles in which we successfully analyzed syncytialization of BeWo cells (Yamamoto et al., Endocrinology, 2017, PMID: 28938427; Iwahashi et al., Endocrinology, 2019, PMID: 30551188; Matsukawa et al., Biomolecules, 2022, PMID: 36008943). We would like to apologize for our mistake in the description of BeWo cells in the methods section and thank the reviewer for providing us with an opportunity to correct our mistake. We will note that BeWo cells were purchased from the American Type Culture Collection (Manassas, VA) and authenticated by JCRB Cell Bank (National Institute of Biomedical Innovation Japan, report no. KBN0410) in the methods section, and will upload the authentication report KBN0410 as a review process file.

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• • Have all the experiments on BeWo only been performed once?*
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Our response: We repeated 6 experiments (the repetitions are biological, not technical, replicates). The results are shown as means ± SEM (n = 6) as stated in the Figure legends.

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Reviewer #3 (Significance (Required)):

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While Aβ is present in human placentas and accumulates in preeclamptic placentas, the production and role of Aβ in the human placenta remain unclear. The current findings suggest that increased Aβ production in cytotrophoblast by hypoxia may lead to the formation of Aβ fibrils, which inhibit syncytiotrophoblast formation and are detrimental to pregnancy, revealing a novel role of Aβ fibrils in the pathogenesis of preeclampsia.

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

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The authors found that Amyloid β suppressed cytotrophoblasts syncytialization, which is innovative. The authors used human patient samples and human primary CTB culture which are powerful data.

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Our response: We appreciate the reviewer’s thoughtful feedback and support.

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Fig. 3. The authors used Roxadustat to stimulate HIF-1α and showed BACE1 increase. It would be better to have the cells in real hypoxia condition.

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Our response: There is a time discrepancy between the increase in HIF-1α levels by hypoxia and induction of BACE1. Because the purpose of this experiment is to show that increased HIF1-α correlated BACE1 induction, we used Roxadustat as a HIF1-α stabilizer and showed that sustained induction of HIF increased BACE1 levels. However, we do understand the reviewer’s concern. We will include data showing an increase in BACE1 in hypoxic conditions by performing new Western blotting experiments.

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Fig. 4 and 5. The authors used external Amyloid β for stimulation. Would the endogenous Amyloid β levels reach the concentration of external one? It would be better to see the quantitative levels of Amyloid β in Fig. 3b.

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Our response: Because the aggregation of Aβ requires a high concentration of a micromolar order, we used synthetic Aβ fibrils for stimulation. We propose that chronic hypoxia in preeclampsia leads to an elevated local concentration of Aβ through a sustained increase in Aβ production, which eventually results in Aβ fibrillogenesis and deposition of Aβ fibrils. Therefore, it will be difficult for the Aβ concentrations generated by BeWo cells to reach a level sufficient for fibrillogenesis. We will discuss this point in the revised manuscript. In addition, we have already performed ELISA assays to quantitatively analyze Aβ generation by BeWo cells. We will include these ELISA data in the revised manuscript.

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Reviewer #4 (Significance (Required)):

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The manuscript addresses an important theme recently identified to address the heterogeneous etiology of preeclampsia. Although the authors have used in vitro approaches, the study could have been a solid if not for some major concerns.

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The authors have focused on an already demonstrated phenomenon but have tried to validate the findings using their in vitro approaches. The manuscript is well written but some lapses for correct references.

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Our response: We thank the reviewer for the critical reading of our manuscript and his/her constructive comments. As the reviewer pointed out, recent studies suggest that preeclampsia is a proteinopathy. However, the mechanisms by which protein aggregate plays detrimental roles in placentation has not been well-understood. In the present study, we discovered a detrimental role of Aβ fibrils in syncytiotrophoblast formation.

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

Major comments:

1. • In lines 50 and 51 of Introduction, the authors provide references to two publications. However, several other articles have appeared before or after these publications that demonstrated evidence for proteinopathy in the placenta and circulation of preeclampsia patients. The reviewer has gone through the literature and found several publications. For example, Kalkunte et al were the first ones to demonstrate the etiology of proteinopathy in preeclampsia placenta and focused on a protein called transthyretin (Am J Pathol. 2013, 183(5):1425-1436). Similarly, Cheng et al demonstrated using a novel blood test that serum from early onset and late onset preeclampsia manifestations contained Aβ and transthyretin (Nature Sci Rep. 2021;11:15934). Jash et al showed the presence of cis P-tau in the placenta and serum of early and late onset preeclampsia patients (Nat Commun. 2023;14:5414). This article and another article by Cheng et al (Hypertension 79(8):1738-1754) revealed that aggregated cis P-tau and transthyretin are etiologically critical for the onset of preeclampsia. There have been several other review and original articles that have talked about Alzheimer's like etiology in preeclampsia (Olie et al, JAMA Netw Open, 2024; e2412870; Basit et al, BMJ 2018; 363:k4109; Schliep et al, Hypertension 2023; 80:257-267, Cheng et al, Am J Reprod Immunol. 2016;75:372-381).*
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Our response: We sincerely appreciate the reviewer for his/her helpful comment. We will revise the introduction by citing the references recommended by the reviewer.

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• Following up on the comments made above, the authors talk about induction of Aβ in hypoxia-treated human trophoblasts represented by an established cell line, BeWo, and primary human trophoblasts. However, it is not clear whether Aβ42 as stated in the manuscript was detected as an aggregated structure or a protein coupled with RB4CD12 aggregate marker. It would have been helpful if the authors could provide direct evidence for Aβ aggregation.*

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Our response: Based on our previous findings showing that highly sulfated domains of heparan sulfate are common components of protein aggregate deposits, we used RB4CD12, which recognizes these domains, as a marker of protein aggregate deposition. These include aggregates of Aβ in Alzheimer’s disease, transthyretin in ATTR, and p53 aggregates in p53-mutated cancers (Hoshono-fukao et al., Am J Pathol, 2012, PMID: 22429964; Kameyama et al., Am J Pathol, 2019, PMID: 30414409; Iwahashi, PNAS, 2020, PMID: 33318190). Please also see our reply to Comment 5 below. We will perform additional immunohistochemical analysis with the β0001 anti-Aβ antibody and the ProteoStat dye that recognizes protein aggregates.

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• What appeared to be more surprising is the statement on lines 162 and 163 that cultured CTBs produced Aβ40/42. Again, it is not clear whether the authors are talking about aggregated Aβ or just induction of Aβ. Why should normal CTBs produce Aβ? It is not clear whether this is a transient expression or a long-term phenomenon. The issue is distinction between normal and adverse pregnancy conditions, and the latter associated with protein aggregation as suggested in the literature.*

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Our response: BeWo cells and cultured CTBs produce Aβ peptides in a normoxic condition. In the brain, neurons constitutively produce Aβ peptides, which have physiological roles such as controlling neuronal hyperexcitability, enhancing of synaptic plasticity, and improving memory (reviewed in Kent et al., Acta Neuropathol, 2020, PMID: 32728795). The amount of Aβ in the brain is regulated by the balance between Aβ production and Aβ clearance, and the imbalance of the production and the clearance may result in an increase in Aβ local concentration and Aβ aggregation. Our results showing that hypoxia increased Aβ production in BeWo cells suggest that chronic hypoxia, which is a risk of preeclampsia, may lead to a sustained increase in Aβ production and an elevated local concentration of Aβ at or near the site of Aβ production. We will discuss these points in the discussion.

In the present study, we showed that aggregated form of Aβ (i.e., Aβ fibrils) was detrimental to the CTB differentiation. On the other hand, we already found that Aβ monomers promoted EVT invasion (please see the below). We believe that promotion of EVT invasion by Aβ monomers represent a physiological function of Aβ in the placenta. We will include these new data in the revised manuscript and we will also perform experiments with BeWo cells and Aβ monomers in order to investigate whether Aβ monomers have some roles in CTB differentiation.

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• The authors have adequately pointed to importance of hypoxia in the onset of preeclampsia-like features. As a matter of fact, Lai et al demonstrated in a mouse pre-clinical model that hypoxia could induce severe features of preeclampsia (Hypertension. 2011;57:505-514). The use of hypoxia as driver of Aβ induction is appreciated.*

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Our response: We agree with the reviewer that studies using preclinical animal models are an important topic for the future. __We will discuss this point in the discussion. __

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• In Fig. 1, although the authors have used DIC approach, it would have been helpful if they presented individual Aβ and RB4CD12 green and red channels, and a merged profile. For example, PE #4 sample does not appear to have much RB4CD12. Again, there is a question of aggregated or native protein structures. It is difficult to have a satisfactory statistical analysis. Did the authors look for Aβ in the anchoring villi region of the placenta?*

• *

Our response: We will show the green and red channel images individually. We have noticed that we detected Aβ deposition without RB4CD12 signals. Aβ is small peptides of 40 to 42 amino acid residues and is extracellularly released after the production. Non-deposited Aβ monomers are not detected by immunohistochemical analysis, because these soluble Aβ peptides are spread out in the tissue fluid. Thus, in our statistical analysis, we calculated only merged signals of Aβ and RB4CD12, which suggests that our data show the aggregated and deposited Aβ. We will note this point in the results. In addition, we will perform immunohistochemical analysis with the anti-Aβ antibody and the ProteoStat dye. Please also see our response to Comment 2 above. We did not observe Aβ deposition at the anchoring villi.

• *

• Fig. 2 does not show significant staining for HIF1-α in PE placental tissue.*

• *

Our response: In a normoxic condition, HIF1-α is constitutively expressed but degraded via the proline-hydroxylation and the subsequent ubiquitination and degradation in the proteasome. Because the proline-hydroxylation is oxygen-dependent, hypoxia induce HIF1-α accumulation. Thus, our data suggest a hypoxic environment in the preeclamptic placentas. We will note this point in the results section.

• *

• Fig. 3B, why should there be Aβ40/42 under normoxic conditions? This is the most pertinent concern and the authors are validating significant expression of Aβ40/42 under normal conditions. In normal pregnancy placenta, this protein has not been detected.*

• *

Our response: Aβ peptides are constitutively produced in BeWo cells, and the production was enhanced by hypoxia. Aβ is small peptides of 40 to 42 amino acid residues. We did not observe Aβ signals in the immunohistochemical analysis of the normal pregnancy placentas, because Aβ peptides that do not aggregate and deposit in the placenta were distributed in the tissue fluid and lost before and during the processing of the placentas for the paraffin-embedding and immunostaining. Our immunohistochemical analysis detects only Aβ deposition. Thus, the absence of Aβ signals in the immunohistochemical analysis of normal placentas does not mean that normal placenta does not produce any Aβ peptides.

• *

• Figs. 4 and 5 present the crux of the conclusions that the authors are trying to draw from their study. Aβ peptide solution was incubated for 5 days at 370C to prepare so called Aβ fibril-like structures. What is the purity of fibril structures? Does this preparation show toxic effects on cell viability? Human trophoblasts expressing E-cadherin fail to participate in endovascular cross-talk with endothelial cells, a process required for spiral arteries. It appears that either BeWo cells or primary trophoblasts used in this study represent trophoblasts from third trimester. It is not clear why should Aβ fibril like structures should inhibit ZO-1 and E-cadherin or β-hCG (Fig. 5) for that matter. In Fig. 5C, there does not seem to be a major effect of Aβ fibrils. Did the authors try synthetic Aβ as a control. These experiments could have been meaningful but for proper controls.*

• *

Our response: Synthetic Aβ was purchased from Peptide Institute (Osak, Japan). The purity is >95%. We will include the data sheet as a review process file. In case that the reviewer wants to know the fibril content of the preparation, we will calculate the fibril content by using Native PAGE followed by Western blotting. We did not observe any cytotoxicity of the preparation as shown in Supplemental Fig. S3.

We previously showed that membrane localization of cell-cell interaction proteins such as ZO-1 and E-cadherin in cytotrophoblasts is required for syncytialization (Iwahashi et al., Endocrinology, 2019, PMID: 30551188; Matsukawa et al., Biomolecules, 2022, PMID: 36008943). Because Aβ aggregates disrupt membrane localization of tight junction proteins partly by inducing excess autophagy (Marco et al., Neurosci Lett, 2006, PMID: 16644119; Chan et al., Exp Cell Res, 2012, PMID: 29856989), we hypothesized that Aβ fibrils may also disrupt membrane localization of ZO-1 and E-cadherin in BeWo cells. We are focusing on the effect of Aβ fibrils on cytotrophoblasts at the late stage of pregnancy when the remodeling of spiral arteries is completed. We understand the importance of investigating the effects of Aβ and Aβ fibrils on early pregnancy. We will cite an article showing the effects of Aβ aggregates on EVTs (Gao et al., J Mol Histol, 2024, PMID: 38777993) and include our data showing the Aβ monomer functions on EVT invasion. Please also see our reply to Comment 3 above. As for Fig. 5C, we will improve the quality of images. We will also perform experiments to investigate whether Aβ monomers alone affect syncytialization of BeWo cells.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors examine the deposition of amyloid-β (A β) peptides that accumulate in the brains of patients with Alzheimer's disease (AD). The authors demonstrated the expression of HIF-1 in the pre-eclamptic (PE) placental tissue using immunofluorescence (which is not novel), alongside the expression of BACE1. These experiments were also validated using BeWo and primary trophoblast cells cultured under hypoxia to mimic one of the characteristics of PE. However, this manuscript is quite preliminary, and many additional experiments are necessary to confirm the deposition of Aβ fibrils in PE. The authors treated CTB and observed the effects on STB, but in PE, the main cell lineage affected is extravillous trophoblast (EVT) cells, which invade the spiral artery. The defect in this invasion is one of the major causes of PE. Therefore, the authors should investigate the effect of hypoxia and Aβ deposition on EVT invasion. Overall, this work appears very incomplete, and further experiments are warranted.

Major comments

• If CTBs are treated with Aβ, and if it affects STB, what happens with EVT? Why didn't they check with EVT if the authors wanted to link with PE?
• Did the authors look for pathologies related to Aβ deposition on PE placentas?
• Line# 103, the IF images don't show that BACE1 is around HIF1. There are no merged images, and the results are over- or underestimated.
• What is the intended purpose of using Roxadustat? If it inhibits HIF1, could you explain the reason behind the increased expression of HIF1? Furthermore, is there evidence to support the efficacy of this compound?
• Is Aβ deposition very specific to PE, or can it also occur for other reasons during pregnancy?
• BACE1 is expressed in Normal#2 and #3 but not in #1, #4, and #5. Why is this expressed in #2 and #3? Is there anything wrong with these samples? If patients had gestational hypertension or some other complications?
• PE placentae were compared with GA matched placentae. What is the expression of BACE1 and RB4CD12 in term control placentae?
• If AB fibril deposition is hypoxia dependent, what happens at the early gestation, where oxygen conc is 1-2%?

Minor comments

• The authors only performed IF and IHC. Please confirm and correct the methods accordingly.
• Was the BeWo-b21 clone cell line used for all the experiments in this paper? This is the only clone that can be used for BeWo-STB models.
• Have all the experiments on BeWo only been performed once?

Significance

Investigating the deposition of Aβ in the placenta could enhance our understanding of pregnancy complications such as PE, fetal growth restriction, and neurodevelopmental risks. However, further research on this topic is necessary.

This should exclude any faulty information from outdated articles

Algo similar me pasó con Hypothesis durante la escritura doctoral. Sin embargo, fui grandemente ayudado por los sistemas de autocompleción de etiquetas en Hypothesis, de modo que, cuando estaba etiquetando una lectura, encontraba otras etiquetas que ya había usado y un orden emergente iba ocurriendo en mi forma de leer, que luego empleaba para mi forma de escribir.

Al comienzo pensaba algo así de cosas como Leo editor, donde organicé mucho de mi conocimiento en árboles con clones que podían aparecer en varias hojas. Sin embargo, dicha estructura podía tonarse complicada de acceder y preordenar (en esa época no tenía la estrategía de ordenar por fecha) y la interfaz no tenía muchas de las conveniencias de la interfaz web.

Nuestros wikis interpersonales son pensados para ser utilizados por otras personas. Sin embargo, los identificadores únicos vía NanoID, podrían tener también una jerarquía de contadores arborecentes, similares a los de Luhmann.

Había pensado en esos contadores únicos en cada wiki para TiddlyWiki por si queríamos algún tipo de enlace corto local de ese wiki y el NanoID para los indicadores únicos entre wikis, dadas las bajas probabilidades de colisión. Son ideas que se pueden combinar con otras como el control de versiones.

El carácter dinámico del wiki no tiene por qué sacrificar la trazabilidad histórica y una cierta memoria visual de los enlaces y sus conexiones. Lo clave es que el wiki sea programable con curvas progresivas entre creación de contenido y de funcionalidad, como lo hace TiddlyWiki y lo propone Cardumem

El comentario a una anotación continente habla de Monos de Markov, en el sentido de atribuirle personalidad y vida a algo inanimado, al hablar con él (tanto como se habla con un Tarot u otro juego interpretativo/narrativo). Estos serían los límites del "ghost in a box", que valdría la pena reconocer a pesar de lo útil de la interpretación animista para el archivo.

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Reply to the reviewers

General Statement

*Our lab was totally destroyed on June 15th by an Iranian missile. All stocks, equipment and reagents were lost. While we performed many of the experiments requested by the reviewers, unfortunately some were never completed. We thank you for your understanding. *

We thank the three reviewers for their thoughtful comments and useful suggestions on how to improve our paper. Some of the reviewers claimed that the paper is “preliminary”. We would like to highlight that in our opinion “preliminary” has two possible meanings in this context: 1) the data does not yet support the claims that the authors wrote; 2) the story is short and should be extended. While we totally agree that type 1 “preliminary” should be addressed (and we have addressed that to the best of our abilities), type 2 “preliminary” is a matter of scope, the length of the paper/project and the publication home. We believe that this story, which has been led by an outstanding master’s student (and as such has had a limited timespan) is worthwhile of publication in its current scope.

2. Point-by-point description of the revisions

Reviewers’ comments are in BLUE while our responses are in BLACK.

Reviewer 1 Summary: This study reports a role for matrix metalloproteinases (MMPs) in the developmental pruning of gamma Kenyon cells (KCs) in the fruit fly Mushroom Body during larval-pupal metamorphosis. The authors show through gene expression studies that MMP genes are upregulated in late larval stages as part of the early program for this type of neuronal pruning. They show through cell-targeted RNAi studies of both secreted MMP-1 and membrane-anchored MMP-2, that both genes are required in glial cells and to a lesser extent within KCs.

Both MMPs have secreted and membrane-anchored isoforms and we did not assess whether the secreted/anchored isoforms are involved; e.g. see LaFever et al. 2017.

The authors show that MMP secreted from glial is required for normal levels of Mushroom Body developmental neuronal pruning. They mention that MMP genes have been identified in schizophrenic patient screens in patients, and that perhaps a comparable pruning mechanism could be involved in the loss of grey matter (loss of synapses) in patients. The authors propose that MMP levels may be a potential therapeutic marker in the future.

We thank the reviewer for his comments. We find it important to clarify that we do not think our work suggests that the MMPs levels may be a potential therapeutic marker without much additional work in the future. In the original text we added a claim from another paper suggesting MMPs as therapeutic target. However, due to the arising confusion, we decided to delete this statement from the text (original line 198). We also added a general disclaimer towards the end of the discussion regarding the genetic power of Drosophila but its limited implication into human health (new lines 276-278).

Major Comments: Overall, the work is of a reasonable standard, but very preliminary

Please see general note on two types of “preliminary” – we thank the reviewer for helping us substantiate our claims and strengthen our paper but we do not plan to significantly increase its scope.

The study lacks the substance to completely convince me of any of the results. There is SUBSTANTIAL work that needs to be done to make this publishable. There are a lot of writing mistakes; so many that I do not list them in detail here

We are not absolutely sure that we understand to which mistakes this reviewer is eluding. However, we carefully rewrote the manuscript, streamlined many of our claims and added many new and more recent references.

The references citations are fairly old, but I do not list update replacements here

Thanks – we added many newer and relevant citations.

The text is very brief, and the overall writing needs to include significantly more description and detail

We have included more descriptions and details, as will be elaborated later on, but – again - this is a short report and will remain as such.

This is evident in all aspects of the manuscript, but especially notable in the Methods and Figure Legends

Thanks for raising this comment, which was reverberated also by other reviewers – we have now included more details, with a particular focus on the genotypes (Table 2), that somehow were erroneously not included in the original submission, as well as more detailed figure legends.

None of the Figure Legends include full genotypes of any of the fly lines, and these full fly lines are also not included in the Methods. This is vital to compare the experimental lines to the controls

True – our apologies for this mistake, we now added the full genotypes in Table 2.

Major points are listed below:

1. Figure 2: It is important to note of the specific age of animals in these images when talking about the loss of genes in development. Are all the animals age-matched? High levels of synaptic pruning occur post-eclosion), and it is important to understand when these pruning defects occur. It is mentioned that that overlap for the gene expression data is upregulated during 6-18h APF is this when these images are taken? This is very important in the context of pruning as SCZ symptom presentation is very late relative to these early events.

We thank the reviewer for this comment which suggests we were not clear enough in our description. We do not claim to have generated an SCZ model and have clarified this better in the text (lines 275-278). Furthermore, axon pruning happens during pupal development, but in all the main figures in this manuscript we dissected young adult flies (3-5 days post eclosion) and show the remnants of unpruned axons (as we have done in numerous studies). To make sure that initial development occurred normally, we also include larval brains in the Figure S7. We now clarified the fact that we are imaging adult brains as a readout to investigate whether pruning occurred during metamorphosis or not (line 124-126).

1. Figure 2: In the figure legend, it is indicated that the arrows are unpruned axons, however in the controls these areas appear to be highly innervated. Further explanation is needed about the context of the arrows, as there are clear visual differences between these images and the controls, but they appear to have a more expansive phenotype than "unpruned axons". The data does not match the visual representation in comparison to the control.

We apologize for this confusion. Unfortunately, the driver which we use to label the γ-axons, R71G10-QF2, is not absolutely specific to the γ type KCs but also expressed (sometimes) in the ɑ/β KCs. As the ɑ/β axons are very stereotypic in shape and also express high levels of FasII (which we stain for), we can easily distinguish between the ɑ lobe and unpruned γ axons. To clarify this point, we now clearly demarcate all lobes in the control images and specifically the ɑ lobe in all panels. Additionally, we added new schemes in Figure 2A and 2O to better clarify the anatomy and experimental design.

1. Figure 2: There needs to be more descriptive definitions and clarifications to the defects labeled in panel K. This could be done in the figure legend, but it would be more useful to label the images provided. For example, if Mmp2 is a "mild pruning affect, put that in the pie chart somewhere, to help guide the description of the phenotype to what those confocal images look like.

We understand that the pie chart in Figure 2 was confusing and therefore simplified it in the current version (Fig. 2B and 2P). Also, thanks to this great point, we now include a new Figure S3 that includes examples for the ranking categories, which were now performed by two independent investigators in a blind manner.

Figure 3: The time points of the images of the Mushroom Body (MB) are vital to understanding the process and regulation of these genes.

Please see our comment to point #1 – unless specifically stated otherwise, all images are MBs of adult flies, as now clearly mentioned in the figure legends, in the text and in the Material and Methods section.

1. Figure 3D: Significant description of this graph needs to be added for clarity. What parameters separate each phenotypic defect? Labeling the images and showing images that belong in different groups would be very helpful and improve the paper significantly.

We now included a new Figure S3 (also see our response to comment #3).

1. Figure S1: Additional experiments would help answer the strength of the phenotype for the ALG-Gal 4 driver. The authors need to perform the rescue experiment. Use a MMP-2 null and then drive it back in the ALG-GAL4 to see if this is sufficient to rescue the neuron pruning. This also isolates the mechanisms to one subtype of glia.

These are excellent suggestions that are, unfortunately, not doable. To perform a rescue experiment, one would need a viable loss-of-function phenotype of an Mmp2 mutant. There is one published Mmp2 loss-of-function null allele which is lethal during pupal development (Page-McCaw et al, 2003). Our previous data, using tissue specific (ts)CRISPR, suggested the involvement of Mmp2 in neurons for their remodeling (Meltzer et al, 2019). We therefore independently generated an Mmp2 germline mutant using CRISPR (harboring an indel resulting in a premature stop codon and predicted to encode a truncated, 77 amino-acid long protein), now described in Fig. S5A (and in the Materials and Methods). This allele is, as expected, unfortunately also lethal. We attempted to overcome lethality by generating MARCM (mosaic) clones in neurons, but as expected, because Mmp2 is largely secreted, there was no pruning defect phenotype (Fig. S5B-C). Unfortunately, it is not yet possible to generate glial clones.

Figure 3 and 4: The other glial subtypes need to be analyze to make any conclusion about their involvement, as well as the involvement of the astrocytes. Running these exact same experiments on the cortex glial and ensheathing glia will provide essential insight into what glial subtype is involved. The presumed lack of phenotypes in these other glial subtypes will also strengthen the argument that the astrocytes are specifically involved in this process. These are vital experiments.

We currently limited our analysis (and conclusions) to astrocytes. Despite the fact that this experiment is beyond our initial scope, we obtained reagents and performed preliminary experiments (using the R77A03-Gal4 driver for cortex glia, and the R83E12-Gal4 for ensheathing glia). In both cases, we observed extremely mild pruning defects, not comparable to those with Repo- or Alrm-Gal4. In these preliminary experiments we lacked a proper control, and now, unfortunately, due to the loss of our lab, we are unable to complete these experiments in a reasonable amount of time.

1. Figure 4: Again, description of the phenotypes and examples of these would improve the quality of this figure substantially.

Absolutely agree – see our response to comment #3 (and Fig. S3).

1. Figure 5: An improvement on the quantifications of these phenotypes would strengthen the paper substantially. More detailed description of the phenotypes and how they related to the control would significantly improve the overall quality of the work.

Thanks again for highlighting that we neglected to include the full genotypes that are now added (Table 2). We also thank the reviewer for raising the point regarding quantification. First, we generated a new Fig. S3A-E to show examples of the ranking by two independent rankers. Second, ranking was performed by looking at TdTomato positive vertical axons that are outside of the ɑ lobe (high FasII) – this is now better explained in the materials and methods. Additionally, while we would love to have a better scoring, and automatic, system – and even published a semi-automated scoring algorithm in Alyagor et al. 2018 (Figure 3O in the Alyagor paper), because the driver also labels vertical axons (ɑ/β) and because unpruned γ axons often express FasII, this quantification method does not always work. What we have done in previous cases, as we have also done here, is to provide independent ranking by two investigators and compare their ranking (Fig. S3F-G). Finally, we are working with our AI hub to develop automatic scoring systems that will not require human ranking – however this is beyond the scope for this manuscript.

Minor Comments: 1. Figure 1A: I would suggest labeling the KC (gamma) and potentially one of the others (a/B, a'/B') to orient the reader to the differences between these two subsets of the KCs, and to emphasize which neurons are undergoing pruning and where the cell bodies are and where the axons project.

Thanks for the suggestions – we now better annotated the scheme in Figure 1A as well as additional schematics in Figure 2 and, finally, better annotations in selected panels. Specifically, the ɑ lobe is outlined in magenta throughout all relevant panels.

1. Figure 1C: This panel needs further labeling to explain the findings in the heat map. Labeling some of the genes that were found and where they were would be helpful. This could also be done in the figure legend, however without any further labeling or context the heatmap is confusing.

We apologize for the incomplete figure. We did not want to overload the figure with data, which is why we are showing only the important clusters and did not include gene names. To keep the figure simple, but at the same time provide the complete information, we now include the full data in Fig. S1 (that includes the original heatmap with all the dynamic clusters I-IX, and including all the gene names). For the full raw data, including non-dynamic clusters, the reader is referred to look in Supplemental excel file 1. We hope this provides the clarity that this reviewer rightfully asks for.

1. Figure 3B,C: The full genotypes need to be labeled. What is the exact genotype used for the control?

The full genotypes of all figure panels are now included in Table 2 in the Materials and Methods.

1. Figure S1: The stock number for the ALG-GAL4 is missing, there are multiple different drivers, therefore this could be helpful in understanding this phenotype, as some are better than others.

Indeed, Alrm-Gal4 comes on two chromosomes – we used BDSC #67032, which is on chromosome III and this is now clearly mentioned the Materials and Methods section.

1. Figures 3 and 4: Labeling needs to remain consistent; Figure 3 "Glia-Gal4", Figure 4 "glia-gal4".

Thanks, done.

Reviewer #1 (Significance (Required)):

General Assessment: An interesting study on MMP function during an unusual type of neural development (axon pruning). Most of the MMP function appears to be in glia, although the MMP role in this context in unclear. The MMP function in the neurons being pruned is unexpected and even less clear. The study is somewhat poorly described in terse language lacking essential information, which gives the overall impression of a preliminary report.

Advance: Glial MMP function has been described for neuronal clearance mechanisms following injury. The main advance here is to describe a similar function during normal development. Audience: Developmental neuroscientists, MMP biologists, possibly schizophrenia clinician researchers

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

Neuropsychiatric conditions are often influenced by genetic factors. Schizophrenia is a complex mental disorder characterised by a mixture of hallucinations, delusions and disorganised thinking that causes lifelong problems in daily life. GWAS have identified a number of genes associated with the risk of developing schizophrenia, although genetic predisposition alone is not sufficient and additional environmental factors are required. In the current manuscript, the authors aim to exploit the strength of the Drosophila system to explore a link between schizophrenia-associated genes and neuronal remodelling during development. They focus on the mushroom body in the adult brain, where pronounced neuronal remodelling occurs during metamorphosis. To assess the potential role of the genes identified by the GWAS, they performed a targeted RNAi-based screen. They focus on the role of metalloproteases and find that they are required in neurons and in glia for the pruning of mushroom body axons. The study starts with a selection of 32 genes, 29 of which are listed (a bit hidden) in materials and methods and the identification of the Drosophila orthologs. The expression patterns of these genes in Kenyon cells are presented in Figure 1 - but unfortunately no information is given on who is expressed when

We apologize for the confusion. We attempted to keep Figure 1 simple but this resulted in the absence of critical information, as the reviewer suggests. We now include a Figure S1 that includes the entire heatmap of the dynamically expressed clusters I-IX with all the gene names. Additionally, we now augmented the information in Table 1 to include the screen phenotypes. Finally, Supplemental excel file 1, also included in our original submission, includes all the data, and is now better referred to throughout the text.

In a next step, Kenyon cell specific RNAi knockdown experiments are shown that identify a pruning phenotype for several genes. They demonstrate that Mmp2 (and similarly Mmp1) is also required in glia. Although Mmp2 was identified by neuronal RNAi-based knockdown, double knockdown experiments led the authors conclude that its primary function is in glia. The study emphasises the use of the advanced genetic model to understand complex human diseases. However, the paper does not go far enough in making use of the excellent genetics available. Basically, the report is about the identification of a few hits in a small RNAi screen, which is fine in itself, but leaves many questions unanswered. Do mmp1/2 mutants have a phenotype?

This is a very important question that cannot be answered, unfortunately. There is one published Mmp2 loss of function null allele which is lethal during pupal development (Page-MaCaw et al, 2003). Our previous data, using tissue specific (ts)CRISPR, suggested the involvement of Mmp2 in neurons for their remodeling (Meltzer et al, 2019). We therefore independently generated an Mmp2 germline mutant using CRISPR (harboring an indel resulting in a premature stop codon and predicted to encode a truncated, 77 amino-acid long protein), now described in Fig. S5A (and in the Materials and Methods). This allele is, as expected, unfortunately also lethal. We attempted to overcome lethality by generating MARCM (mosaic) clones in neurons, but as expected, because Mmp2 is largely secreted, there was no pruning defect phenotype (Fig. S5B-C). Unfortunately, it is not yet possible to generate glial clones. Additionally, available Mmp1 mutants are, sadly, also homozygous lethal. That said, in our revised manuscript we now include data demonstrating that expression of a dominant negative variant of Mmp1 inhibits pruning (Fig. 3J-K). We strengthened the evidence regarding the reliability of Mmp1 RNAi using an antibody mix (Fig. S4), and for Mmp2 – we refer to a manuscript that tested its efficiency (Harmansa et al., 2023). Lastly, we added new data using an additional RNAi line targeting Mmp2 from the VDRC collection (Fig. 3L).

Can the phenotype be rescued?

Unfortunately, without a viable mutant LOF phenotype, a rescue experiment is impossible. Regardless, in an attempt to rescue the RNAi phenotype, we designed and generated an RNAi-resistant Mmp2 overexpression transgene. Unfortunately, due to the destruction of our lab – several days after we received this transgenic line from Bestgene – this experiment is not included in the revision.

Does TIMP expression lead to similar phenotypes?

This is an interesting question which we addressed in our experiments but did not include in the text. Unfortunately, overexpression of TIMP did not have any effect on MB development. We are adding this figure here as Reviewer Figure 1, but we think that adding this information to the paper will not improve it for several reasons. The lack of phenotype by overexpression of Timp can result from a technical issue such as low expression or mislocalization of the protein, or a biological issue such as more complicated involvement of TIMP or other MMP inhibitors.

What is the temporal requirement for Mmp1/2?

This is an excellent suggestion, not an easy experiment, but one that we initiated, using a temperature sensitive Gal80 to control the expression of the RNAi only during metamorphosis. However, to the unfortunate destruction of our lab, this experiment was never completed.

What are the target proteins of Mmp2?

This is the million-dollar question – but unfortunately is beyond the scope of this short report.

Is Mmp2 still required when astrocyte motility is blocked? What is the morphology of glia after Mmp1/2 knockdown?

Thank you for this wonderful suggestion. We initiated two types of experiments using sparse labeling techniques (both MARCM and SPARC) to identify the morphology of single astrocytes in WT vs. MMP KD. However, these are complicated crosses that were not completed prior to the destruction of our lab.

Reviewer #2 (Significance (Required)):

The strength of the study is to identify a pruning phenotype after RNAi-based knockdown. The limitations is that this study is very superficial, it is the beginning of a paper. The initial claim to use Drosophila because to its advanced genetics is not met. The results section is shorter than the discussion.

While we agree with much of the reviewer’s statement this also relates to our general comment about “preliminary” type 1 and type 2 – True, this could be the beginning of a big paper and it would definitely be a more comprehensive and deep story. Most of the papers from my lab are indeed a 5 year endeavor. However, this short report (which is now longer, more detailed, and includes additional experiments) is a result of the work of an outstanding master’s student who came up with the idea for the project entirely by herself. Thus – given the data that she has acquired, and the fact that my lab will not continue to study MMPs or schizophrenia, the question needs to be whether the data supports the claims and whether this is an advance of science worthwhile of publication in a respectable journal. Our clear and decisive opinion is that the answer to that question is yes.

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

In this work, Schuldiner and colleagues explore the role of Mmp1 and Mmp2 in neuronal remodeling in the mushroom body of Drosophila. Overall, this work is very interesting, but in its current form seems quite preliminary. The biggest limitation of the study is that single RNAi lines are used with no validation that the lines are working, despite the fact that Mmp antibodies are available as are endogenously tagged Mmp lines that could have been used to validate the genetic manipulations. Specific concerns are listed below.

We thank reviewer 3 for his generally positive assessment of our work and we now performed additional experiments to strengthen and validate the original RNAi findings – for specifics see our reply to the points below.

Major concerns 1) The scoring system for pruning of mushroom body neurons seems very variable, even in controls (where scoring can range from very mild to moderate), and it is very hard to assess from the images what one is looking at (rather than using our own judgment, we rely on the authors' words). It would be necessary to have better labeling and examples of what phenotypes are considered "mild", "severe", "wild type-like". It would also help to understand how phenotype assessment is guided by the overlap between the signals from TdTomato fluorescence and FasII stain.

We thank the reviewer for raising this point, that has also been highlighted by other reviewers in some form. First, we have generated Figure S3A-E to show examples of the ranking, which was now performed by two independent investigators. Second, ranking was performed by looking at TdTomato positive vertical axons that are outside of the αlobe (high FasII) – this is now better explained in the materials and methods. Additionally, while we would love to have a better scoring, and automatic, system – and even published a semi-automated scoring algorithm in Alyagor et al. 2018 (Figure 3O in the Alyagor paper), because the driver also labels vertical axons (ɑ/β) and because unpruned γ axons often express FasII, this quantification method does not always work. What we have done in previous cases, as we have also done here, is to provide independent ranking by two investigators and compare their ranking (Fig. S3F-G). Finally, we are working with our AI hub to develop automatic scoring systems that will not require human ranking – however this is beyond the scope for this manuscript.

2) The biggest limitations of the approach are that single RNAi lines are used to screen, with no accompanying validation of the tool (see above)

We agree. Unfortunately not all RNAis are “equal” and thus not all of them work. To support the RNAi data, we have better clarified previous experiments that demonstrate the importance of neuronal Mmp2 via tissue specific (ts) CRISPR (Meltzer, et al, 2019). Unfortunately, the Mmp2 null mutant that is available is lethal during pupal development (Page-MaCaw et al, 2003). We therefore independently generated an Mmp2 germline mutant using CRISPR (harboring an indel resulting in a premature stop codon and predicted to encode a truncated, 77 amino-acid long protein), now described in Fig. S5A (and in the Materials and Methods). This allele is, as expected, unfortunately also lethal. We attempted to overcome lethality by generating MARCM (mosaic) clones in neurons, but as expected, because Mmp2 is largely secreted, there was no pruning defect phenotype (Fig. S5B-C). Unfortunately, it is not yet possible to generate glial clones. Additionally, available Mmp1 mutants are, sadly, also homozygous lethal. That said, in our revised manuscript we now include data demonstrating that expression of a dominant negative variant of Mmp1 inhibits pruning (Fig. 3J-K). We strengthened the evidence regarding the reliability of Mmp1 RNAi using an antibody mix (Fig. S4), and for Mmp2 – we refer to a manuscript that tested its efficiency (Harmansa et al., 2023). Lastly, we added new data using an additional RNAi line targeting Mmp2 from the VDRC collection (Fig. 3L).

3) RNAi-based knockdown is used to infer epistatic information-this is not appropriate as epistasis experiments need to be done with null alleles to make firm conclusions. Additional concerns: ● Even with the same driver, knockdown efficiency for 2 different genes could be variable and dependent of the specific RNAi used. ● The comparison between drivers is even harder, as driver strength varies greatly. ● The knockdown efficiency drops with increasing numbers of RNAi used. ● The specific genotypes used for this experiment should be clarified, as it would be very important to ensure that the UAS dosage is equal across conditions.

We agree that RNAi is not optimal to assess epistasis. And indeed, we did not mean to claim epistasis relationship between Mmp1 and Mmp2, nor between neurons and glia. We now use better language to clarify this. To define epistatic relationships, the use of mutants would be required, unfortunately the use of nulls is not possible because they are lethal and secreted (thus not enabling mosaic analyses). We agree that increasing the number of RNAi lines is expected to reduce their efficiency – this is why it is even more significant when we see an increased defective phenotype in the double knockdown experiments. Finally, we totally agree about the genotype comment and apologize that it was erroneously omitted in the original submission– all of which have been now added (Table 2 in materials and methods).

4) To further deepen the rigor of this work, a few simple yet important things could have been done. First, it would be important to rule out that knocking down Mmps does not affect astrocyte numbers and health (could be assessed by counting numbers and observing their morphology). Also, the authors previously showed that astrocytes actively infiltrate the axon bundle prior to pruning to facilitate axon defasciculation and pruning (Marmor-Kollet et al., 2023). It would have provided an important insight to examine if astrocytes can infiltrate the axon bundle if Mmp2 and/or Mmp1 are knocked down.

Thank you for these wonderful suggestions. We embarked on a few experiments as detailed below, unfortunately these are complicated crosses that were not completed prior to the destruction of our lab. 1) We initiated two types of experiments using sparse labeling techniques (both MARCM and SPARC) to identify the morphology of single astrocytes in WT vs. MMP KD. 2) Testing astrocytic infiltrations requires three binary systems, we obtained and generated stocks required for these experiments, but these were prematurely terminated. 3) We initiated experiments to count the number of glial nuclei in the vicinity of the degenerating axonal lobe (at the onset of pruning). Preliminary experiments with a small n (3 controls, 4 Mmp1 RNAi, and 5 Mmp2 RNAi) suggest that the number of glial nuclei is not significantly different between these conditions.

Minor The introduction puts big emphasis on the role of glia, but then to narrows down candidate genes for the screen a γ-KCs transcriptional data set is used, and the initial screen is done via knockdown of those candidates in neurons (there is a disconnect between rationale and approach).

We totally agree with this reviewer which is why we now changed the paper to include both neuronal and glial loss-of-function screens. Figure 1 is now augmented with the glial data.

Rationale for looking into axon pruning and how that translates into insights about synaptic pruning defects in schizophrenia should be more clearly stated.

Indeed, our belief that synapse pruning and axon pruning share molecular mechanisms remains yet unproven. However, both are steps during neuronal remodeling, which has been previously implicated in schizophrenia. That said, we now added an additional disclaimer to acknowledge the limitation of our findings in the context of human disease and synapse elimination (lines 275-279).

Figure 1C: data visualization for this heat map should be improved. Parts of the data are faded, and the differences between gene clusters are unclear.

We apologize for the incomplete figure. We did not want to overload the figure with data, which is why we are showing only the important clusters and did not include gene names. To keep the figure simple, but at the same time provide the complete information, we now include the full data in Fig. S1 (that includes the original heatmap with all the dynamic clusters I-IX, and including all the gene names). For the full raw data, including non-dynamic clusters, the reader is referred to look in Supplemental excel file 1. We hope this provides the clarity that this reviewer rightfully asks for.

Reviewer #3 (Significance (Required)):

In this work, Schuldiner and colleagues explore the role of Mmp1 and Mmp2 in neuronal remodeling in the mushroom body of Drosophila. Overall, this work is very interesting, but in its current form seems quite preliminary. The biggest limitation of the study is that single RNAi lines are used with no validation that the lines are working, despite the fact that Mmp antibodies are available as are endogenously tagged Mmp lines that could have been used to validate the genetic manipulations.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This study reports a role for matrix metalloproteinases (MMPs) in the developmental pruning of gamma Kenyon cells (KCs) in the fruit fly Mushroom Body during larval-pupal metamorphosis. The authors show through gene expression studies that MMP genes are upregulated in late larval stages as part of the early program for this type of neuronal pruning. They show through cell-targeted RNAi studies of both secreted MMP-1 and membrane-anchored MMP-2, that both genes are required in glial cells and to a lesser extent within KCs. The authors show that MMP secreted from glial is required for normal levels of Mushroom Body developmental neuronal pruning. They mention that MMP genes have been identified in schizophrenic patient screens in patients, and that perhaps a comparable pruning mechanism could be involved in the loss of grey matter (loss of synapses) in patients. The authors propose that MMP levels may be a potential therapeutic marker in the future.

Major Comments:

Overall, the work is of a reasonable standard, but very preliminary. The study lacks the substance to completely convince me of any of the results. There is SUBSTANTIAL work that needs to be done to make this publishable. There are a lot of writing mistakes; so many that I do not list them in detail here. The references citations are fairly old, but I do not list update replacements here. The text is very brief, and the overall writing needs to include significantly more description and detail. This is evident in all aspects of the manuscript, but especially notable in the Methods and Figure Legends. None of the Figure Legends include full genotypes of any of the fly lines, and these full fly lines are also not included in the Methods. This is vital to compare the experimental lines to the controls. Major points are listed below:

1. Figure 2: It is important to note of the specific age of animals in these images when talking about the loss of genes in development. Are all the animals age-matched? High levels of synaptic pruning occur post-eclosion), and it is important to understand when these pruning defects occur. It is mentioned that that overlap for the gene expression data is upregulated during 6-18h APF is this when these images are taken? This is very important in the context of pruning as SCZ symptom presentation is very late relative to these early events.
2. Figure 2: In the figure legend, it is indicated that the arrows are unpruned axons, however in the controls these areas appear to be highly innervated. Further explanation is needed about the context of the arrows, as there are clear visual differences between these images and the controls, but they appear to have a more expansive phenotype than "unpruned axons". The data does not match the visual representation in comparison to the control.
3. Figure 2: There needs to be more descriptive definitions and clarifications to the defects labeled in panel K. This could be done in the figure legend, but it would be more useful to label the images provided. For example, if Mmp2 is a "mild pruning affect, put that in the pie chart somewhere, to help guide the description of the phenotype to what those confocal images look like.
4. Figure 3: The time points of the images of the Mushroom Body (MB) are vital to understanding the process and regulation of these genes.
5. Figure 3D: Significant description of this graph needs to be added for clarity. What parameters separate each phenotypic defect? Labeling the images and showing images that belong in different groups would be very helpful and improve the paper significantly.
6. Figure S1: Additional experiments would help answer the strength of the phenotype for the ALG-Gal 4 driver. The authors need to perform the rescue experiment. Use a MMP-2 null and then drive it back in the ALG-GAL4 to see if this is sufficient to rescue the neuron pruning. This also isolates the mechanisms to one subtype of glia.
7. Figure 3 and 4: The other glial subtypes need to be analyze to make any conclusion about their involvement, as well as the involvement of the astrocytes. Running these exact same experiments on the cortex glial and ensheathing glia will provide essential insight into what glial subtype is involved. The presumed lack of phenotypes in these other glial subtypes will also strengthen the argument that the astrocytes are specifically involved in this process. These are vital experiments.
8. Figure 4: Again, description of the phenotypes and examples of these would improve the quality of this figure substantially.
9. Figure 5: An improvement on the quantifications of these phenotypes would strengthen the paper substantially. More detailed description of the phenotypes and how they related to the control would significantly improve the overall quality of the work.

Minor Comments:

1. Figure 1A: I would suggest labeling the KC (gamma) and potentially one of the others (a/B, a'/B') to orient the reader to the differences between these two subsets of the KCs, and to emphasize which neurons are undergoing pruning and where the cell bodies are and where the axons project.
2. Figure 1C: This panel needs further labeling to explain the findings in the heat map. Labeling some of the genes that were found and where they were would be helpful. This could also be done in the figure legend, however without any further labeling or context the heatmap is confusing.
3. Figure 3B,C: The full genotypes need to be labeled. What is the exact genotype used for the control?
4. Figure S1: The stock number for the ALG-GAL4 is missing, there are multiple different drivers, therefore this could be helpful in understanding this phenotype, as some are better than others.
5. Figures 3 and 4: Labeling needs to remain consistent; Figure 3 "Glia-Gal4", Figure 4 "glia-gal4".

Significance

General Assessment: An interesting study on MMP function during an unusual type of neural development (axon pruning). Most of the MMP function appears to be in glia, although the MMP role in this context in unclear. The MMP function in the neurons being pruned is unexpected and even less clear. The study is somewhat poorly described in terse language lacking essential information, which gives the overall impression of a preliminary report.

Advance: Glial MMP function has been described for neuronal clearance mechanisms following injury. The main advance here is to describe a similar function during normal development.

Audience: Developmental neuroscientists, MMP biologists, possibly schizophrenia clinician researchers

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Reply to the reviewers

We thank the reviewers for providing us the opportunity to revise our manuscript titled “Identifying regulators of associative learning using a protein-labelling approach in C. elegans.” We appreciate the insightful feedback that we received to improve this work. In response, we have extensively revised the manuscript with the following changes: we have (1) clarified the criteria used for selecting candidate genes for behavioural testing, presenting additional data from ‘strong’ hits identified in multiple biological replicates (now testing 26 candidates, previously 17), (2) expanded our discussion of the functional relevance of validated hits, including providing new tissue-specific and neuron class-specific analyses, and (3) improved the presentation of our data, including visualising networks identified in the ‘learning proteome’, to better highlight the significance of our findings. We also substantially revised the text to indicate our attempts to address limitations related to background noise in the proteomic data and outlined potential refinements for future studies. All revisions are clearly marked in the manuscript in red font. A detailed, point-by-point response to each comment is provided below.

1. Point-by-point description of the revisions

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

Summary:

Rahmani et al., utilize the TurboID method to characterize the global proteome changes in the worm's nervous system induced by a salt-based associative learning paradigm. Altogether, Rahmani et al., uncover 706 proteins that are tagged by the TurboID method specifically in samples extracted from worms that underwent the memory inducing protocol. Next, the authors conduct a gene enrichment analysis that implicates specific molecular pathways in salt-associative learning, such as MAP-kinase and cAMP-mediated pathways. The authors then screen a representative group of the hits from the proteome analysis. The authors find that mutants of candidate genes from the MAP-kinase pathway, namely dlk-1 and uev-3, do not affect the performance in the learning paradigm. Instead multiple acetylcholine signaling mutants significantly affected the performance in the associative memory assay, e.g., acc-1, acc-3, gar-1, and lgc-46. Finally, the authors demonstrate that the acetylcholine signaling mutants did not exhibit a phenotype in similar but different conditioning paradigms, such as aversive salt-conditioning or appetitive odor conditioning, suggesting their effect is specific to appetitive salt conditioning.

Major comments:

1. The statistical approach and analysis of the behavior assay: The authors use a 2-way ANOVA test which assumes normal distribution of the data. However, the chemotaxis index used in the study is bounded between -1 and 1, which prevents values near the boundaries to be normally distributed.

Since most of the control data in this assay in this study is very close to 1, it strongly suggests that the CI data is not normally distributed and therefore 2-way ANOVA is expected to give skewed results.

I am aware this is a common mistake and I also anticipate that most conclusions will still hold also under a more fitting statistical test.

We appreciate the point raised by Reviewer 1 and understand the importance of performing the correct statistical tests.

The statistical tests used in this study were chosen since parametric tests, particularly ANOVA tests to assess differences between multiple groups, are commonly used to assess behaviour in the C. elegans learning and memory field. Below is a summary of the tests used by studies that perform similar behavioural tests cited in this work, as examples:

Table 1 | A summary for the statistical tests performed by similar studies for chemotaxis assay data. References (listed in the leftmost column) were observed to (A) use parametric tests only or (B) performed either a parametric or non-parametric test on each chemotaxis assay dataset depending on whether the data passed a normality test. Listings for ANOVA tests are in bold to demonstrate their common use in the C. elegans learning and memory field.

Reference

Parametric test/s used in the reference

Non-parametric test/s used in the reference

Beets et al., 2020

Two-way ANOVA

None

Hiroki & Iino 2022

One-way ANOVA

None

Hiroki et al., 2022

One-way ANOVA

None

Hukema et al., 2006

T-tests

None

Hukema et al., Learn. Mem. 2008

T-tests

None

Jang et al., 2019

ANOVA

None

Kitazono et al., 2017

Two-way ANOVA and t-tests

None

Lans et al., 2004

One-way ANOVA

None

Lim et al., 2018

Two-way ANOVA

Wilcoxon rank sum test adjusted with the Benjamini–Hochberg method

Lin et al., 2010

Two-way or three-way ANOVA

None

Nagashima et al., 2019

One-way ANOVA

None

Ohno et al., 2014

None

Sakai et al., 2017

One-way ANOVA or t-tests

None

Stein & Murphy 2014

Two-way ANOVA and t-tests

None

Tang et al., 2023

One-way ANOVA or t-tests

None

Tomioka et al., 2006

T tests

None

Watteyne et al., 2020

One-way ANOVA

Two-sided Kruskal–Wallis

We note Reviewer 1's concern that this may stem from a common mistake. As stated, Two-way ANOVA generally relies on normally distributed data. We used GraphPad Prism to perform the Shapiro-Wilk normality test on our chemotaxis assay data as it is generally appropriate for sample sizes Table 2 | Shapiro-Wilk normality test results for chemotaxis assay data in Figure S8C. Chemotaxis assay data was generated to assess salt associative learning capacity for wild-type (WT) versus lgc-46(-) mutant C. elegans. Three experimental groups were prepared for each C. elegans strain (naïve, high-salt control, and trained). From top-to-bottom, the data below displays the ‘W’ value, ‘P value’, a binary yes/no for whether the data passes the Shapiro-Wilk normality test, and a ‘P value summary’ (ns = non-significant). W values measure the similarity between a normal distribution and the chemotaxis assay data. Data is considered normal in the Shapiro-Wilk normality test when a W value is near 1.0 and the null hypothesis is not rejected (i.e., P value > 0.05).*

WT naïve

WT high-salt control

WT trained

lgc-46 naïve

lgc-46 high-salt control

lgc-46 trained

W

0.9196

0.9114

0.8926

0.8334

0.8151

0.8769

P value

0.5272

0.4758

0.3705

0.1475

0.1070

0.2954

Passed normality test (alpha=0.05)?

Yes

Yes

Yes

Yes

Yes

Yes

P value summary

ns

ns

ns

ns

ns

ns

The manuscript now includes the use of the Shapiro-Wilk normality test to assess chemotaxis assay data before using two-way ANOVA on page 51.

Nevertheless an appropriate statistical analysis should be performed. Since I assume the authors would wish to take into consideration both the different conditions and biological repeats, I can suggest two options:

• Using a Generalized linear mixed model, one can do with R software.

• Using a custom bootstrapping approach. We thank Reviewer 1 for suggesting these two options. We carefully considered both approaches and consulted with the in-house statistician at our institution (Dr Pawel Skuza, Flinders University) for expert advice to guide our decision. In summary:

• Generalised linear mixed models: Generalised linear mixed models (GLMMs) are generally most appropriate for nested/hierarchal data. However, our chemotaxis assay data does not exhibit such nesting. Each biological replicate (N) consists of three technical replicates, which are averaged to yield a single chemotaxis index per N. Our statistical comparisons are based solely on these averaged values across experimental groups, making GLMMs less applicable in this context.

• __Bootstrapping: __Based on advice from our statistician, while bootstrapping can be a powerful tool, its effectiveness is limited when applied to datasets with a low number of biological replicates (N). Bootstrapping relies on resampling existing data to simulate additional observations, which may artificially inflate statistical power and potentially suggest significance where the biological effect size is minimal or not meaningful. Increasing the number of biological replicates to accommodate bootstrapping could introduce additional variability and compromise the interpretability of the results. The total number of assays, especially controls, varies quite a bit between the tested mutants. For example compare the acc-1 experiment in Figure 4.A., and gap-1 or rho-1 in Figure S4.A and D. It is hard to know the exact N of the controls, but I assume that for example, lowering the wild type control of acc-1 to equivalent to gap-1 would have made it non significant. Perhaps the best approach would be to conduct a power analysis, to know what N should be acquired for all samples.

We thoroughly evaluated performing the power analysis: however, this is typically performed with the assumption that an N = 1 represents a singular individual/person. An N =1 in this study is one biological replicate that includes hundreds of worms, which is why it is not typically employed in our field for this type of behavioural test.

Considering these factors, we have opted to continue using a two-way ANOVA for our statistical analysis. This choice aligns with recent publications that employ similar experimental designs and data structures. Crucially, we have verified that our data meet the assumptions of normality, addressing key concerns regarding the suitability of parametric testing. We believe this approach is sufficiently rigorous to support our main conclusions. This rationale is now outlined on page 51.

To be fully transparent, our aim is to present differences between wild-type and mutant strains that are clearly visible in the graphical data, such that the choice of statistical test does not become a limiting factor in interpreting biological relevance. We hope this rationale is understandable, and we sincerely appreciate the reviewer’s comment and the opportunity to clarify our analytical approach.

We hope that Reviewer 1 will appreciate these considerations as sufficient justification to retain the statistical tests used in the original manuscript. Nevertheless, to constructively address this comment, we have performed the following revisions:

1. __Consistent number of biological replicates: __We performed additional biological replicates of the learning assay to confirm the behavioural phenotypes for the key candidates described (KIN-2 , F46H5.3, ACC-1, ACC-3, LGC-46). We chose N = 5 since most studies cited in this paper that perform similar behavioural tests do the same (see the table below). Table 3 | A summary for sample sizes generated by similar studies for chemotaxis assay data. References (listed in the leftmost column) were observed to the sample sizes (N) below corresponding to biological replicates of chemotaxis assay data. N values are in bold when the study uses N ≤ 5.

Reference

N used in the study for chemotaxis assay data

Beets et al., 2020

8

Hiroki & Iino 2022

5-8

Hiroki et al., 2022

6-7

Hukema et al., 2006

≥ 4

Hukema et al., Learn. Mem. 2008

≥ 4

Jang et al., 2019

≥ 4

Kitazono et al., 2017

≥ 4

Kauffman et al., 2010

≥ 3

Kauffman et al., J. Vis. Exp. 2011

≥ 3

Lans et al., 2004

2

Lim et al., 2018

2-4

Lin et al., 2010

≥ 4

Nagashima et al., 2019

≥ 7

Ohno et al., 2014

≥ 11

Sakai et al., 2017

≥ 4

Stein & Murphy 2014

3-5

Tang et al., 2023

≥ 9

Watteyne et al., 2020

≥ 10

__Grouped presentation of behavioural data: __We now present all behavioural data by grouping genotypes tested within the same biological replicate, including wild-type controls, rather than combining genotypes tested separately. This ensures that each graph displays data from genotypes sharing the same N, also an important consideration for performing parametric tests. Accordingly, we re-performed statistical analyses using this reduced Nfor relevant graphs. As anticipated, this rendered some comparisons non-significant. All statistical comparisons are clearly indicated on each graph. Improved clarity of figure legends: __We revised figure legends for __Figures 5, 6, S7, S8, & S9 to make clear how many biological replicates have been performed for each genotype by adding N numbers for each genotype in all figures.

The authors use the phrasing "a non-significant trend", I find such claims uninterpretable and should be avoided. Examples: Page 16. Line 7 and Page 18, line 16.

This is an important point. While we were not able to find the specific phrasing "a non-significant trend" from this comment in the original manuscript, we acknowledge that referring to a phenotype as both a trend and non-significant may confuse readers, which was originally stated in the manuscript in two locations.

The main text has been revised on pages 27 & 28 when describing comparisons between trained groups between two C. elegans lines, by removing mentions of trends and retaining descriptions of non-significance.

Neuron-specific analysis and rescue of mutants:

Throughout the study the authors avoid focusing on specific neurons. This is understandable as the authors aim at a systems biology approach, however, in my view this limits the impact of the study. I am aware that the proteome changes analyzed in this study were extracted from a pan neuronally expressed TurboID. Yet, neuron-specific changes may nevertheless be found. For example, running the protein lists from Table S2, in the Gene enrichment tool of wormbase, I found, across several biological replicates, enrichment for the NSM, CAN and RIG neurons. A more careful analysis may uncover specific neurons that take part in this associative memory paradigm. In addition, analysis of the overlap in expression of the final gene list in different neurons, comparing them, looking for overlap and connectivity, would also help to direct towards specific circuits.

This is an important and useful suggestion. We appreciate the benefit in exploring the data from this study from a neuron class-specific lens, in addition to the systems-level analyses already presented.

The WormBase gene enrichment tool is indeed valuable for broad transcriptomic analyses (the findings from utilising this tool are now on page 16); however, its use of Anatomy Ontology (AO) terms also contains annotations from more abundant non-neuronal tissues in the worm. To strengthen our analysis and complement the Wormbase tool, we also used the CeNGEN database as suggested by Reviewer 3 Major Comment 1 (Taylor et al., 2021), which uses single cell RNA-Seq data to profile gene expression across the C. elegans nervous system. We input our learning proteome data into CeNGEN as a systemic analysis, identifying neurons highly represented by the learning proteome (on pages 16-20). To do this, we specifically compared genes/proteins from high-salt control worms and trained worms to identify potential neurons that may be involved in this learning paradigm. Briefly, we found:

• WormBase gene enrichment tool: Enrichment for anatomy terms corresponding to specific interneurons (ADA, RIS, RIG), ventral nerve cord neurons, pharyngeal neurons (M1, M2, M5, I4), PVD sensory neurons, DD motor neurons, serotonergic NSM neurons, and CAN.
• CeNGEN analysis: Representation of neurons previously implicated in associative learning (e.g., AVK interneurons, RIS interneurons, salt-sensing neuron ASEL, CEP & ADE dopaminergic neurons, and AIB interneurons), as well as neurons not previously studied in this context (pharyngeal neurons I3 & I6, polymodal neuron IL1, motor neuron DA9, and interneuron DVC). Methods are detailed on pages 50 & 51. These data are summarised in the revised manuscript as Table S7 & Figure 4.

To further address the reviewer’s suggestion, we examined the overlap in expression patterns of the validated learning-associated genes acc-1, acc-3, lgc-46, kin-2, and F46H5.3 across the neuron classes above, using the CeNGEN database. This was done to explore potential neuron classes in which these regulators may act in to regulate learning. This analysis revealed both shared and distinct expression profiles, suggesting potential functional connectivity or co-regulation among subsets of neurons. To summarise, we found:

• All five learning regulators are expressed in RIM interneurons and DB motor neurons.
• KIN-2 and F46H5.3 share the same neuron expression profile and are present in many neurons, so they may play a general function within the nervous system to facilitate learning.
• ACC-3 is expressed in three sensory neuron classes (ASE, CEP, & IL1).
• In contrast, ACC-1 and LGC-46 are expressed in neuron classes (in brackets) implicated in gustatory or olfactory learning paradigms (AIB, AVK, NSM, RIG, & RIS) (Beets et al., 2012, Fadda et al., 2020, Wang et al., 2025, Zhou et al., 2023, Sato et al., 2021), neurons important for backward or forward locomotion (AVE, DA, DB, & VB) (Chalfie et al., 1985), and neuron classes for which their function is yet detailed in the literature (ADA, I4, M1, M2, & M5). These neurons form a potential neural circuit that may underlie this form of behavioural plasticity, which we now describe in the main text on pages 16-20 & 34-35 and summarise in Figure 4.

OPTIONAL: A rescue of the phenotype of the mutants by re-expression of the gene is missing, this makes sure to avoid false-positive results coming from background mutations. For example, a pan neuronal or endogenous promoter rescue would help the authors to substantiate their claims, this can be done for the most promising genes. The ideal experiment would be a neuron-specific rescue but this can be saved for future works.

We appreciate this suggestion and recognise its potential to strengthen our manuscript. In response, we made many attempts to generate pan-neuronal and endogenous promoter re-expression lines. However, we faced several technical issues in transgenic line generation, including poor survival following microinjection likely due to protein overexpression toxicity (e.g., C30G12.6, F46H5.3), and reduced animal viability for chemotaxis assays, potentially linked to transgene-related reproductive defects (e.g., ACC-1). As we have previously successfully generated dozens of transgenic lines in past work (e.g. Chew et al., Neuron 2018; Chew et al., Phil Trans B 2018; Gadenne/Chew et al., Life Science Alliance 2022), we believe the failure to produce most of these lines is not likely due to technical limitations. For transparency, these observations have been included in the discussion section of the manuscript on pages 39 & 40 as considerations for future troubleshooting.

Fortunately, we were able to generate a pan-neuronal promoter line for KIN-2 that has been tested and included in the revised manuscript. This new data is shown in Figure 5B __and described on __pages 23 & 24. Briefly, this shows that pan-neuronal expression of KIN-2 from the ce179 mutant allele is sufficient to reproduce the enhanced learning phenotype observed in kin-2(ce179) animals, confirming the role of KIN-2 in gustatory learning.

To address the potential involvement of background mutations (also indicated by Reviewer 4 under ‘cross-commenting’), we have also performed experiments with backcrossed versions of several mutants. These experiments aimed to confirm that salt associative learning phenotypes are due to the expected mutation. Namely, we assessed kin-2(ce179) mutants that had been backcrossed previously by another laboratory, as well as C30G12.6(-) and F46H5.3(-) animals backcrossed in this study. Although not all backcrossed mutants retained their original phenotype (i.e., C30G12.6) (Figure 6D, a newly added figure), we found that backcrossed versions of KIN-2 and F46H5.3 both robustly showed enhanced learning (Figures 5A & 6B). This is described in the text on pages 23-26.

__Minor comments: __

1. Lack of clarity regarding the validation of the biotin tagging of the proteome. The authors show in Figure 1 that they validated that the combination of the transgene and biotin allows them to find more biotin-tagged proteins. However there is significant biotin background also in control samples as is common for this method. The authors mention they validated biotin tagging of all their experiments, but it was unclear in the text whether they validated it in comparison to no-biotin controls, and checked for the fold change difference.

This is an important point: We validated our biotin tagging method prior to mass spectrometry by comparing ‘no biotin’ and ‘biotin’ groups. This is shown in Figure S1 in the revised manuscript, which includes a western blot comparing untreated and biotin treated animals that are non-transgenic or expressing TurboID. As expected, by comparing biotinylated protein signal for untreated and treated lanes within each line, biotin treatment increased the signal 1.30-fold for non-transgenic and 1.70-fold for TurboID C. elegans. This is described on __page 8 __of the revised manuscript.

To clarify, for mass spectrometry experiments, we tested a no-TurboID (non-transgenic) control, but did not perform a no-biotin control. We included the following four groups: (1) No-TurboID ‘control’ (2) No-TurboID ‘trained’, (3) pan-neuronal TurboID ‘control’ and (4) pan-neuronal TurboID ‘trained’, where trained versus control refers to whether ‘no salt’ was used as the conditioned stimulus or not, respectively (illustrated in Figure 1A). Due to the complexity of the learning assay (which involves multiple washes and handling steps, including a critical step where biotin is added during the conditioning period), and the need to collect sufficient numbers of worms for protein extraction (>3,000 worms per experimental group), adding ‘no-biotin’ controls would have doubled the number of experimental groups, which we considered unfeasible for practical reasons. This is explained on __pages 8 & 9 __of the revised manuscript.

Also, it was unclear which exact samples were tested per replicate. In Page 9, Lines 17-18: "For all replicates, we determined that biotinylated proteins could be observed ...", But in Page 8, Line 24 : "We then isolated proteins from ... worms per group for both 'control' and 'trained' groups,... some of which were probed via western blotting to confirm the presence of biotinylated proteins".

• Could the authors specify which samples were verified and clarify how?

Thank you for pointing out these unclear statements: We have clarified the experimental groups used for mass spectrometry experiments as detailed in the response above on pages 8 &____ 9. In addition, western blots corresponding to each biological replicate of mass spectrometry data described in the main text on page 10 and have been added to the revised manuscript (as Figure S3). These western blots compare biotinylation signal for proteins extracted from (1) No-TurboID ‘control’ (2) No-TurboID ‘trained’, (3) pan-neuronal TurboID ‘control’ and (4) pan-neuronal TurboID ‘trained’. These blots function to confirm that there were biotinylated proteins in TurboID samples, before enrichment by streptavidin-mediated pull-down for mass spectrometry.

OPTIONAL: include the fold changes of biotinylated proteins of all the ones that were tested. Similar to Figure 1.C.

This is an excellent suggestion. As recommended by the reviewer, we have included fold-changes for biotinylated protein levels between high-salt control and trained groups (on pages 9 & 10 for replicate #1 and in __Table S2 __for replicates #2-5). This was done by measuring protein levels in whole lanes for each experimental group per biological replicate within western blots (__Figure 1C __for replicate #1 and __Figure S3 __for replicates #2-5) of protein samples generated for mass spectrometry (N = 5).

Figure 2 does not add much to the reader, it can be summarized in the text, as the fraction of proteins enriched for specific cellular compartments.

• I would suggest to remove Figure 2 (originally written as figure 3) to text, or transfer it to the supplementry material.

As noted in cross-comment response to Reviewer 4, there were typos in the original figure references, we have corrected them above. Essentially, this comment is referring to Figure 2.

We appreciate this feedback from Reviewer 1. We agree that the original __Figure 2 __functions as a visual summary from analysis of the learning proteome at the subcellular compartment level. However, it also serves to highlight the following:

• Representation for neuron-specific GO terms is relatively low, but even this small percentage represents entire protein-protein networks that are biologically meaningful, but that are difficult to adequately describe in the main text.
• TurboID was expressed in neurons so this figure supports the relevance of the identified proteome to biological learning mechanisms.

• Many of these candidates could not be assessed by learning assay using single mutants since related mutations are lethal or substantially affect locomotion. These networks therefore highlight the benefit in using strategies like TurboID to study learning. We have chosen to retain this figure, moving it to the supplementary material as Figure S4 in the revised manuscript, as suggested.

• OPTIONAL- I would suggest the authors to mark in a pathway summary figure similar to Figure 3 (originally written as Figure 4) the results from the behavior assay of the genetic screen. This would allow the reader to better get the bigger picture and to connect to the systemic approach taken in Figures 2 and 3.

We think this is a fantastic suggestion and thank Reviewer 1 for this input. In the revised manuscript, we have added Figure 7, which summarises the tested candidates that displayed an effect on learning, mapped onto potential molecular pathways derived from networks in the learning proteome. This figure provides a visual framework linking the behavioural outcomes to the network context. This is described in the main text on pages 32-33.

Typo in Figure 3: the circle of PPM1: The blue right circle half is bigger than the left one.

We thank the Reviewer for noticing this, the node size for PPM-1.A has been corrected in what is now Figure 2 in the revised work.

Unclarity in the discussions. In the discussion Page 24, Line 14, the authors raise this question: "why are the proteins we identified not general learning regulators?. The phrasing and logic of the argumentation of the possible answers was hard to follow. - Can you clarify?

We appreciate this feedback in terms of unclarity, as we strive to explain the data as clearly and transparently as possible. Our goal in this paragraph was to discuss why some candidates were seen to only affect salt associative learning, as opposed to showing effects in multiple learning paradigms (i.e., which we were defining as a ‘general learning regulator’). We have adjusted the wording in several places in this paragraph now on pages 36 & 37 to address this comment. We hope the rephrased paragraph provides sufficient rationalisation for the discussion regarding our selection strategy used to isolate our protein list of potential learning regulators, and its potential limitations.

***Cross-Commenting** *

Firstly, we would like to express our appreciation for the opportunity for reviewers to cross-comment on feedback from other reviewers. We believe this is an excellent feature of the peer review process, and we are grateful to the reviewers for their thoughtful engagement and collaborative input.

I would like to thank Reviewer #4 for the great cross comment summary, I find it accurate and helpful.

I also would like to thank Reviewer #4 for spotting the typos in my minor comments, their page and figure numbers are the correct ones.

We have corrected these typos in the relevant comments, and have responded to them accordingly.

Small comment on common point 1 - My feeling is that it is challanging to do quantitative mass spectrometry, especially with TurboID. In general, the nature of MS data is that it hints towards a direction but a followup validation work is required in order to assess it. For example, I am not surprised that the fraction of repeats a hit appeared in does not predict well whether this hit would be validated behavioraly. Given these limitations, I find the authors' approach reasonable.

We thank Reviewer 1 for this positive and thoughtful feedback. We also appreciate Reviewer 4’s comment regarding quantitative mass spectrometry and have addressed this in detail below (see response to Reviewer 4). However, we agree with Reviewer 1 that there are practical challenges to performing quantitative mass spectrometry with TurboID, primarily due to the enrichment for biotinylated proteins that is a key feature of the sample preparation process.

Importantly, we whole-heartedly agree with Reviewer 1’s statement that “In general, the nature of MS data is that it hints towards a direction but a follow-up validation work is required in order to assess it”. This is the core of our approach: however, we appreciate that there are limitations to a qualitative ‘absent/present’ approach. We have addressed some of these limitations by clarifying the criteria used for selecting candidate genes, based additionally on the presence of the candidate in multiple biological replicates (categorised as ‘strong’ hits). Based on this method, we were able to validate the role of several novel learning regulators (Figures 5, 6, & S7). We sincerely hope that this manuscript can function as a direction for future research, as suggested by this Reviewer.

I also would like to highlight this major comment from reviewer 4:

"In Experimental Procedures, authors state that they excluded data in which naive or control groups showed average CI 0.5499 for N2 (page 36, lines 5-7). "

This threshold seems arbitrary to me too, and it requires the clarifications requested by reviewer 4.

As detailed in our response to Reviewer 4, Major Comment 2, data were excluded only in rare cases, specifically when N2 worms failed to show strong salt attraction prior to training, or when trained N2 worms did not exhibit the expected behavioural difference compared to untrained controls – this can largely be attributed to clear contamination or over-population issues, which are visible prior to assessing CTX plates and counting chemotaxis indices.

These criteria were initially established to provide an objective threshold for excluding biological replicates, particularly when planning to assay a large number of genetic mutants. However, after extensive testing across many replicates, we found that N2 worms (that were not starved, or not contaminated) consistently displayed the expected phenotype, rendering these thresholds unnecessary. We acknowledge that emphasizing these criteria may have been misleading, and have therefore removed them from page 50 in the revised manuscript to avoid confusion and ensure clarity.

Reviewer #1 (Significance (Required)):

This study does a great job to effectively utilize the TurboID technique to identify new pathways implicated in salt-associative learning in C. elegans. This technique was used in C. elegans before, but not in this context. The salt-associative memory induced proteome list is a valuable resource that will help future studies on associative memory in worms. Some of the implicated molecular pathways were found before to be involved in memory in worms like cAMP, as correctly referenced in the manuscript. The implication of the acetylcholine pathway is novel for C. elgeans, to the best of my knowledge. The finding that the uncovered genes are specifically required for salt associative memory and not for other memory assays is also interesting.

However overall I find the impact of this study limited. The premise of this work is to use the Turbo-ID method to conduct a systems analysis of the proteomic changes. The work starts by conducting network analysis and gene enrichment which fit a systemic approach. However, since the authors find that ~30% of the tested hits affect the phenotype, and since only 17/706 proteins were assessed, it is challenging to draw conclusive broad systemic claims. Alternatively, the authors could have focused on the positive hits, and understand them better, find the specific circuits where these genes act. This could have increased the impact of the work. Since neither of these two options are satisfied, I view this work as solid, but not wide in its impact and therefore estimate the audience of this study would be more specialized.

My expertise is in C. elegans behavior, genetics, and neuronal activity, programming and machine learning.

We thank the Reviewer for these comments and appreciate the recognition of the value of the proteomic dataset and the identification of novel molecular pathways, including the acetylcholine pathway, as well as the specificity of the uncovered genes to salt-associative memory.

Regarding the reviewer’s concern about the overall impact and scope of the study, we respectfully offer the following clarification. Our aim was to establish a systems-level approach for investigating learning-related proteomic changes using TurboID, and we acknowledge that only a subset of the identified proteins was experimentally tested (now 26/706 proteins in the revised manuscript). Although only five of the tested single gene mutants showed a robust learning phenotype in the revised work (after backcrossing, more stringent candidate selection, improved statistical analysis in addressing reviewer comments), our proteomic data provides us a unique opportunity to define these candidates within protein-protein networks (as illustrated in Figure 7). Importantly, our functional testing focused on single-gene mutants, which may not reveal phenotypes for genes that act redundantly (now mentioned on pages 28-30). This limitation is inherent to many genetic screens and highlights the value of our proteomic dataset, which enables the identification of broader protein-protein interaction networks and molecular pathways potentially involved in learning.

To support this systems-level perspective, we have added Figure 7, which visually integrates the tested candidates into molecular pathways derived from the learning proteome for learning regulators KIN-2 and F46H5.3. We also emphasise more explicitly in the text (on pages 32-33) the value of our approach by highlighting the functional protein networks that can be derived from our proteomics dataset.

We fully acknowledge that the use of TurboID across all neurons limits the resolution needed to pinpoint individual neuron contributions, and understand the benefit in further experiments to explore specific circuits. Many circuits required for salt sensing and salt-based learning are highly explored in the literature and defined explicitly (see Rahmani & Chew, 2021), so our intention was to complement the existing literature by exploring the protein-protein networks involved in learning, rather than on neuron-neuron connectivity. However, we recognise the benefit in integrating circuit-level analyses, given that our proteomic data suggests hundreds of candidates potentially involved in learning. While validating each of these candidates is beyond the scope of the current study, we have taken steps to suggest candidate neurons/circuits by incorporating tissue enrichment analyses and single-cell transcriptomic data (Table S7 & Figure 4). These additions highlight neuron classes of interest and suggest possible circuits relevant to learning.

We hope this clarification helps convey the intended scope and contribution of our study. We also believe that the revisions made in response to Reviewer 1’s feedback have strengthened the manuscript and enhanced its significance within the field.

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

__Summary: __

In this study by Rahmani in colleagues, the authors sought to define the "learning proteome" for a gustatory associative learning paradigm in C. elegans. Using a cytoplasmic TurboID expressed under the control of a pan-neuronal promoter, the authors labeled proteins during the training portion of the paradigm, followed by proteomics analysis. This approach revealed hundreds of proteins potentially involved in learning, which the authors describe using gene ontology and pathways analysis. The authors performed functional characterization of some of these genes for their requirement in learning using the same paradigm. They also compared the requirement for these genes across various learning paradigms, and found that most hits they characterized appear to be specifically required for the training paradigm used for generating the "learning proteome".

Major Comments:

1. The definition of a "hit" from the TurboID approach is does not appear stringent enough. According to the manuscript, a hit was defined as one unique peptide detected in a single biological replicate (out of 5), which could give rise to false positives. In figure S2, it is clear that there relatively little overlap between samples with regards to proteins detected between replicates, and while perhaps unintentional, presenting a single unique peptide appears to be an attempt to inflate the number of hits. Defining hits as present in more than one sample would be more rigorous. Changing the definition of hits would only require the time to re-list genes and change data presented in the manuscript accordingly. We thank Reviewer 2 for this valuable comment, and the following related suggestion. We agree with the statement that “Defining hits as present in more than one sample would be more rigorous”. Therefore, to address this comment, we have now separated candidates into two categories in Table 2 __in the revised manuscript: ‘__strong’ (present in 3 or more biological replicates) and ‘weak’ candidates (present in 2 or fewer biological replicates). However, we think these weaker candidates should still be included in the manuscript, considering we did observe relationships between these proteins and learning. For example, ACC-1, which influences salt associative learning in C. elegans, was detected in one replicate of mass spectrometry as a potential learning regulator (Figure S8A). We describe this classification in the main text on pages 21-22.

We also agree with Reviewer 2 that the overlap between individual candidate hits is low between biological replicates; the inclusion of Figure S2 __in the original manuscript serves to highlight this limitation. However, it is also important to consider that there is notable overlap for whole molecular pathways between biological replicates of mass spectrometry data as shown in __Figure 2 __in the revised manuscript (this consideration is now mentioned on __pages 13-14). We have included Figure 3 to illustrate representation for two metabolic processes across several biological replicates normally indispensable to animal health, as an example to provide additional visual aid for the overlap between replicates of mass spectrometry. We provide this figure (described on pages 13 & 15) to demonstrate the strength of our approach in that it can detect candidates not easily assessable by conventional forward or reverse genetic screens.

We also appreciate the opportunity to explain our approach. The criteria of “at least one unique peptide” was chosen based on a previous work for which we adapted for this manuscript (Prikas et al., 2020). It was not intended to inflate the number of hits but rather to ensure sensitivity in detecting low-abundance neuronal proteins. We have clarified this in our Methods (page 46).

The "hits" that the authors chose to functionally characterize do not seem like strong candidate hits based on the proteomics data that they generated. Indeed, most of the hits are present in a single, or at most 2, biological replicate. It is unclear as to why the strongest hits were not characterized, which if mutant strains are publicly available, would not be a difficult experiment to perform.

We thank the reviewer for this important suggestion. To address this, we have described two molecular pathways with multiple components that appear in more than one biological replicate of mass spectrometry data in Figure 3 (main text on page 13). In addition, we have included __Figures 6 & S7 __where 9 additional single mutants corresponding to candidates in three or more biological replicates of mass spectrometry were tested for salt associative learning. Briefly, we found the following (number of replicates that a protein was unique to TurboID trained animals is in brackets):

• Novel arginine kinase F46H5.3 (4 replicates) displays an effect in both salt associative learning and salt aversive learning in the same direction (Figures 6A, 6B, & S9A, pages 31-32 & 37-38).
• Worms with a mutation for armadillo-domain protein C30G12.6 (3 replicates) only displayed an enhanced learning phenotype when non-backcrossed, not backcrossed. This suggests the enhanced learning phenotype was caused by a background mutation (Figure 6, pages 24-25).
• We did not observe an effect on salt associative learning when assessing mutations for the ciliogenesis protein IFT-139 (5 replicates), guanyl nucleotide factors AEX-3 or TAG-52 (3 replicates), p38/MAPK pathway interactor FSN-1 (3 replicates), IGCAM/RIG-4 (3 replicates), and acetylcholine components ACR-2 (4 replicates) and ELP-1 (3 replicates) (Figure S7, on pages 27-30). However, we note throughout the section for which these candidates are described that only single gene mutants were tested, meaning that genes that function in redundant or compensatory pathways may not exhibit a detectable phenotype. Because of the lack of strong evidence that these are indeed proteins regulated in the context of learning based on proteomics, including evidence of changes in the proteins (by imaging expression changes of fluorescent reporters or a biochemical approach), would increase confidence that these hits are genuine.

We thank Reviewer 2 for this suggestion – we agree that it would have been ideal to have additional evidence suggesting that changes in candidate protein levels are associated directly with learning. Ideally, we would have explored this aspect further; however, as outlined in response to Reviewer 1 Major Comment 2 (OPTIONAL), this was not feasible within the scope of the current study due to several practical challenges. Specifically, we attempted to generate pan-neuronal and endogenous promoter rescue lines for several candidates, but encountered significant challenges, including poor survival post-microinjection (likely due to protein overexpression toxicity) and reduced viability for behavioural assays, potentially linked to transgene-related reproductive defects. This information is now described on pages 39 & 40 of the revised work.

To address these limitations, we performed additional behavioural experiments where possible. We successfully generated a pan-neuronal promoter line for kin-2, which was tested and included in the revised manuscript (Figure 5B, pages 30 & 31). In addition, to confirm that observed learning phenotypes were due to the expected mutations and not background effects, we conducted experiments using backcrossed versions of several mutant lines as suggested by Reviewer 4 Cross Comment 3 (Figure 6, pages 23-24 & 24-26). Briefly, this shows that pan-neuronal expression of KIN-2 from the ce179 mutant allele is sufficient to repeat the enhanced learning phenotype observed in backcrossed kin-2(ce179) animals, providing additional evidence that the identified hits are required for learning. We also confirmed that F46H5.3 modulates salt associative learning, given both non-backcrossed and backcrossed F46H5.3(-) mutants display a learning enhancement phenotype. The revised text now describes this data on the page numbers mentioned above.

Minor Comments:

1. The authors highlight that the proteins they discover seem to function uniquely in their gustatory associative paradigm, but this is not completely accurate. kin-2, which they characterize in figure 4, is required for positive butanone association (the authors even say as much in the manuscript) in Stein and Murphy, 2014. We appreciate this correction and thank the Reviewer for pointing this out. We have amended the wording appropriately on page 31 to clarify our meaning.

2. “Although kin-2(ce179) mutants were not shown to impact salt aversive learning, they have been reported previously to display impaired intermediate-term memory (but intact learning and short-term memory) for butanone appetitive learning (Stein and Murphy, 2014).”*

Reviewer #2 (Significance (Required)):