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Full text is: Zoni American High School offers diverse course options, including electives, credit recovery, AP, and honors courses, providing tailored learning experiences for students.

Full text is: Learn English while obtaining your High School Diploma. Zoni Live classes included Work at your own pace from anywhere

Please note that "Learn English while obtaining your High School Diploma.", "Zoni Live classes included" and "Work at our own pace from anywhere" are different sentences and they are located one below the other one.

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North Exposure Studios. “THE KAYAPO IDENTITY — North Exposure Studios,” n.d. https://northexposurestudios.com/thekayapoidentity.

Zimmerman, Barbara. “Rain Forest Warriors: How Indigenous Tribes Protect the Amazon.” Science, December 23, 2013. https://www.nationalgeographic.com/science/article/131222-amazon-kayapo-indigenous-tribes-deforestation-environment-climate-rain-forest.

WWF. “Toxic Mercury Poisoning the Amazon,” n.d. https://wwf.panda.org/wwf_news/?338470/Toxic-mercury-poisoning-the-Amazon.

“Ethiopia’s Abiy Ahmed issues Warning over Renaissance Dam,” Al Jazeera, October 22, 2019.

Harry Verhoeven, “The Grand Ethiopian Renaissance Dam: Africa’s Water Tower, Environmental Justice & Infrastructural Power,” Daedalus 150, no. 4 (2021): 159-80.

Emanuele Fatini and Wondwosen Seide, “Emotions in Water Diplomacy: Negotiations on the Grand Ethiopian Renaissance Dam,” Water Alternatives 16, no. 3 (2023): 912-929.

Hagai Erlich, The Cross and the River: Ethiopia, Egypt, and the Nile, (Boulder: Lynne Rienner Publishers), 7.

Nixon, R. (2011). Slow violence and the environmentalism of the poor. Harvard University Press

Vareties of Neolibralism in brazil (2003-2019) by Alredo Saad Filho

Karim Morsy, et. al., “Comprehensive Assessment for the Potential Environmental Impacts of the Grand Ethiopian Renaissance Dam on the Downstream Countries: Itaipu Dam in the Rearview Mirror,” Air, Soil, and Water Research 14, (2021).

Eman Abdel-Aal, et. al., “Ecological status of Lake Nasser Khors, Egypt, before operating the Grand Ethiopian Renaissance Dam,” Stochastic Environmental Research and Risk Assessment 37, no. 4 (2023): 1229-1245.

Eman Abdel-Aal, et. al., “Ecological status of Lake Nasser Khors, Egypt, before operating the Grand Ethiopian Renaissance Dam,” Stochastic Environmental Research and Risk Assessment 37, no. 4 (2023): 1229-1245.

Eman Abdel-Aal, et. al., “Ecological status of Lake Nasser Khors, Egypt, before operating the Grand Ethiopian Renaissance Dam,” Stochastic Environmental Research and Risk Assessment 37, no. 4 (2023): 1229-1245.

Tinfoil hat theory: This is the stage Google Search is at in April 2024. Not looking good bruv.

Hazem Eldeeb, et. al., “Flood Propagation Modeling: Case Study the Grand Ethiopian Renaissance Dam Failure,” Alexandria Engineering Journal 71 (2023): 227-237.

Hazem Eldeeb, et. al., “Flood Propagation Modeling: Case Study the Grand Ethiopian Renaissance Dam Failure,” Alexandria Engineering Journal 71 (2023): 227-237.

Karim Morsy, et. al., “Comprehensive Assessment for the Potential Environmental Impacts of the Grand Ethiopian Renaissance Dam on the Downstream Countries: Itaipu Dam in the Rearview Mirror,” Air, Soil, and Water Research 14, (2021).

James Scott, “Nature and Space,” in Seeing like a State, (Connecticut: Yale University Press, 1998), 11-52.

“Filling of Grand Renaissance Dam on the Nile complete, Ethiopia says,” Al Jazeera, September 10, 2023.

“Filling of Grand Renaissance Dam on the Nile complete, Ethiopia says,” Al Jazeera, September 10, 2023.

Saud@ções a todos os colegas!

Ora, Pontos-chave que retiro após uma leitura um bocado diagonal donde "vejo" como mais importantes no que concerne ao tema das e-actividades.

As e-actividades devem ser cuidadosamente projetadas para: * Alcançar os objetivos de aprendizagem da unidade/tópico em questão. * Motivar os alunos e envolvê-los ativamente no processo de aprendizagem. * Considerar as características individuais dos alunos e seus ritmos de aprendizagem. * Utilizar recursos digitais adequados e promover a interação entre os participantes.

As e-actividades podem ser classificadas em diferentes categorias, como: * Socialização: para quebrar o gelo e promover a interação entre os participantes. * Aquisição de conceitos: para aprender novos conceitos ou vocabulário específico. * Aprofundamento: para aprofundar o conhecimento sobre um determinado tema. * Transferência: para aplicar o conhecimento aprendido em diferentes contextos. * Aplicação: para aplicar o conhecimento aprendido em contextos profissionais.

As e-actividades podem ser realizadas de forma assíncrona (cada um no seu ritmo) ou síncrona (em tempo real, com todos online ao mesmo tempo).

O papel do professor nas e-actividades é fundamental para:

• Orientar e acompanhar os alunos durante todo o processo.
• Fornecer feedback construtivo sobre o desempenho dos alunos.
• Facilitar a interação entre os participantes.
• Avaliar o aprendizagem dos alunos.

Ao projetar e-atividades, é importante considerar os seguintes aspectos:

• Contexto: a contextualização da atividade nos conteúdos a serem aprendidos.
• Planeamento: o período em que a atividade está inserida no calendário do curso e sua duração.
• Objetivos: os objetivos da atividade e as competências que se espera que os alunos alcancem.
• Estrutura: como a atividade será desenvolvida, em quantas fases e qual o produto final esperado.
• Recursos: os recursos que serão utilizados para o desenvolvimento da atividade.
• Ações: o tipo e os momentos das intervenções dos participantes.
• Avaliação: os critérios e a ponderação da avaliação da atividade.

Finalizo destacando a importância da seleção criteriosa de e-atividades que atendam às necessidades dos alunos e promovam uma aprendizagem ativa, significativa e colaborativa. Ricardo Silveira

Boa tarde a todos e todas. O professor/formador deve planificar as suas e-atividades, mas tem de ter sempre em atenção: * objetivo da e-atividade - o que se pretende que os alunos/formandos aprendam * contributo da e-atividade no processo de aquisição de conhecimentos da unidade/tema * motivação dos alunos/formandos * limitações decorrentes da formação e do manuseamento da tecnologia Saudações académicas. Paula Nogueira

Boa tarde a todos e todas. Desta forma o professor tem de abandonar o seu papel de mero transmissor de conteúdos/conhecimento para ter um papel de moderador, dinamizador e facilitador das aprendizagens, assumindo o seu papel de membro da comunidade virtual da aprendizagem. Saudações académicas Paula Nogueira

O modelo dos cinco estágios de Gilly Salmon é uma estrutura amplamente reconhecida para projetar e facilitar a aprendizagem online em ambientes digitais em rede, porque oferece uma abordagem estruturada para envolver os alunos e promover uma experiência de aprendizagem significativa. A importância de criar e desenhar "boas" atividades de aprendizagem em ambientes digitais em rede reside na necessidade de proporcionar uma experiência de aprendizagem eficaz e envolvente. Ao seguirem estas práticas e ao adaptarem as atividades de aprendizagem ao contexto específico do ambiente digital em rede, os professores podem criar experiências de aprendizagem mais eficazes e envolventes para os alunos.

Também em jeito de conclusão, saliento que as e-atividades são muito mais do que ferramentas digitais; elas são uma continuidade do ambiente pedagógico que promove uma aprendizagem ativa. A avaliação contínua (especialmente o feedback adequado, atempado e significativo) dessas atividades é essencial para assegurar que elas sejam relevantes e eficazes.

Ao planear e-atividades, é crucial considerar a diversidade dos estilos de aprendizagem dos alunos (há os que aprendem melhor a ler, ver, ouvir, fazer, colaborar, ...). Estratégias como aprendizagem baseada em projetos, resolução de problemas, casos de estudo ou gamificação podem ser incorporadas para aumentar o envolvimento e a aprendizagem.

Este modelo realça a importância da progressão estruturada nas atividades online, sugerindo que uma abordagem faseada e bem planeada pode melhorar significativamente a autonomia dos alunos e a eficácia da aprendizagem online. Creio que um dos principais objetivos, concomitante com a promoção da aprendizagem dos conteúdos é o desenvolvimento da autonomia dos aprendentes.

É fundamental que ao desenhar e-atividades, consideremos não apenas a interatividade e a flexibilidade que proporcionam, mas também como podem ser alinhadas com os objetivos de aprendizagem específicos e adaptadas para atender às diversas necessidades dos aprendentes, garantindo a inclusão, acessibilidade e navegação. Este último, de acordo com a minha experiência, tem que se ter atenção redobrada, pois com o potencial interativo das ferramentas, é fácil cair no erro de criar um nível demasiado complexo de interatividade que prejudica a navegação, especialmente em aprendentes com baixa literacia digital.

Pelo que é necessário conhecer muito bem o público-alvo de cada formação. A atividade/função que cada formando ocupa bem como os conhecimentos que a priori já tem,

Olá a todos/as, creio ser este um ponto central na conceção de atividades de ensino em geral, em especial as e-atividades. Quando da construção de um planeamento de ensino - aprendizagem, as estratégias utilizadas devem considerar o perfil dos estudantes, os conteúdos previstos, as competências a serem desenvolvidas, mas, acima de tudo, devem fazer sentido ao estudante. As e-atividades devem ser fatores de motivação para a participação e desenvolvimento do estudante, motivação esta que desenvolve a partir das referências do estudante, ancorando a aprendizagem nestas referências. A aprendizagem ocorrerá, bem como a motivação do estudante para este movimento, a partir do significado que esta e-atividade terá para o estudante. Uma e-atividade perde seu sentido se passar a ser mais uma atividade por parte do estudante que será somente mais uma forma de obtenção de "notas" para aprovação, pouco trazendo de aprendizagem e de desenvolvimento de competências. Esta discussão tem especial importãncia na Educação de Adultos, na qual a utilizade do processo de aprendizagem tem maior ligação a aplicação dos conhecimentos e competências devem ter com a aplicabilidade no mundo do trabalho. Ricardo Rodrigues

Secorun, Laura. “​'I can't abandon my land': the livelihoods threatened by Kenya's Tana river​ plans​.” The Guardian, 12 December 2016, https://www.theguardian.com/sustainable-business/2016/dec/12/tana-river-kenya-dam-water-business. Accessed 2 April 2024.

Secorun, Laura. “​'I can't abandon my land': the livelihoods threatened by Kenya's Tana river​ plans​.” The Guardian, 12 December 2016, https://www.theguardian.com/sustainable-business/2016/dec/12/tana-river-kenya-dam-water-business. Accessed 2 April 2024.

Secorun, Laura. “​'I can't abandon my land': the livelihoods threatened by Kenya's Tana river​ plans​.” The Guardian, 12 December 2016, https://www.theguardian.com/sustainable-business/2016/dec/12/tana-river-kenya-dam-water-business. Accessed 2 April 2024.

Secorun, Laura. “​'I can't abandon my land': the livelihoods threatened by Kenya's Tana river​ plans​.” The Guardian, 12 December 2016, https://www.theguardian.com/sustainable-business/2016/dec/12/tana-river-kenya-dam-water-business. Accessed 2 April 2024.

Leauthaud, Crystele, Stéphanie Duvail, Olivier Hamerlynck, Jean-Luc Paul, Hubert Cochet, Judith Nyunja, Jean Albergel, and Olivier Grünberger. “Floods and Livelihoods: The Impact of Changing Water Resources on Wetland Agro-Ecological Production Systems in the Tana River Delta, Kenya.” Global Environmental Change 23, no. 1 (February 1, 2013): 252–63. doi:10.1016/j.gloenvcha.2012.09.003.

BARCLAY--present at the excavation, not much other info, six foot, 190--big by standards of the day.

Dr. Copper is a physician, thus expert in serums and other medical treatments.

Leauthaud, Crystele, Stéphanie Duvail, Olivier Hamerlynck, Jean-Luc Paul, Hubert Cochet, Judith Nyunja, Jean Albergel, and Olivier Grünberger. “Floods and Livelihoods: The Impact of Changing Water Resources on Wetland Agro-Ecological Production Systems in the Tana River Delta, Kenya.” Global Environmental Change 23, no. 1 (February 1, 2013): 252–63. doi:10.1016/j.gloenvcha.2012.09.003.

Leauthaud, Crystele, Stéphanie Duvail, Olivier Hamerlynck, Jean-Luc Paul, Hubert Cochet, Judith Nyunja, Jean Albergel, and Olivier Grünberger. “Floods and Livelihoods: The Impact of Changing Water Resources on Wetland Agro-Ecological Production Systems in the Tana River Delta, Kenya.” Global Environmental Change 23, no. 1 (February 1, 2013): 252–63. doi:10.1016/j.gloenvcha.2012.09.003.

Leauthaud, Crystele, Stéphanie Duvail, Olivier Hamerlynck, Jean-Luc Paul, Hubert Cochet, Judith Nyunja, Jean Albergel, and Olivier Grünberger. “Floods and Livelihoods: The Impact of Changing Water Resources on Wetland Agro-Ecological Production Systems in the Tana River Delta, Kenya.” Global Environmental Change 23, no. 1 (February 1, 2013): 252–63. doi:10.1016/j.gloenvcha.2012.09.003.

Parker, James D. “Ecologies of Development: Ecophilosophies and Indigenous Action on the Tana River.” History in Africa: A Journal of Method 49 (June 1, 2022): 65–96. doi:10.1017/hia.2022.11.

https://typepals.com/

Pen pals with typewriters. Pre-Twitter/Facebook social media modality.

Okuku, Eric O. “Role of a Cascade of Reservoirs in Regulating Downstream Transport of Sediment, Carbon and Nutrients; Case Study of Tropical Arid Climate Tana River Basin.” Lakes and Reservoirs 23, no. 1 (March 1, 2018): 43–55. doi:10.1111/lre.12206.

Maingi, John K., and Stuart E. Marsh. “Quantifying Hydrologic Impacts Following Dam Construction along the Tana River, Kenya.” Journal of Arid Environments 50, no. 1 (January 1, 2002): 53. doi:10.1006/jare.2000.0860.

Okoko, Anita Nyapala. “The Legacy of Vulnerability to Floods in the Tana River, Kenya.” International Journal of Disaster Risk Reduction 71 (March 1, 2022): N.PAG. doi:10.1016/j.ijdrr.2022.102833.

Maingi, John K., and Stuart E. Marsh. “Quantifying Hydrologic Impacts Following Dam Construction along the Tana River, Kenya.” Journal of Arid Environments 50, no. 1 (January 1, 2002): 53. doi:10.1006/jare.2000.0860.

Maingi, John K., and Stuart E. Marsh. “Quantifying Hydrologic Impacts Following Dam Construction along the Tana River, Kenya.” Journal of Arid Environments 50, no. 1 (January 1, 2002): 53. doi:10.1006/jare.2000.0860.

Estaba pensando que tal vez podriamos graficar cuartiles mejor, en una serie de tiempo, prevalencias mensuales y 4 lineas que serian los cuartiles

Faltaria el cuadro de frecuencias y prevalencias de grupo etareo por olas

faltan las mortalidades de cada grupo

Andeso, Albert. “British Firm to Build Sh250bn High Grand Falls Dam | CK.” Construction Kenya, 24 November 2022, https://www.constructionkenya.com/933/high-grand-falls-dam-kenya/. Accessed 2 April 2024.

Leauthaud, Crystele, Stéphanie Duvail, Olivier Hamerlynck, Jean-Luc Paul, Hubert Cochet, Judith Nyunja, Jean Albergel, and Olivier Grünberger. “Floods and Livelihoods: The Impact of Changing Water Resources on Wetland Agro-Ecological Production Systems in the Tana River Delta, Kenya.” Global Environmental Change 23, no. 1 (February 1, 2013): 252–63. doi:10.1016/j.gloenvcha.2012.09.003.

Secorun, Laura. “​'I can't abandon my land': the livelihoods threatened by Kenya's Tana river​ plans​.” The Guardian, 12 December 2016, https://www.theguardian.com/sustainable-business/2016/dec/12/tana-river-kenya-dam-water-business.

“About Vision 2030.” Kenya Vision 2030, https://vision2030.go.ke/about-vision-2030/. Accessed 2 April 2024.

Secorun, Laura. “​'I can't abandon my land': the livelihoods threatened by Kenya's Tana river​ plans​.” The Guardian, 12 December 2016, https://www.theguardian.com/sustainable-business/2016/dec/12/tana-river-kenya-dam-water-business. Accessed 2 April 2024.

Abbink, Jon. “Dam Controversies: Contested Governance and Developmental Discourse on the Ethiopian Omo River Dam.” Social Anthropology/Anthropologie Sociale 20, no. 2 (2012): 125–144.

Maingi, John K., and Stuart E. Marsh. “Quantifying Hydrologic Impacts Following Dam Construction along the Tana River, Kenya.” Journal of Arid Environments 50, no. 1 (January 1, 2002): 53. doi:10.1006/jare.2000.0860.

Maingi, John K., and Stuart E. Marsh. “Quantifying Hydrologic Impacts Following Dam Construction along the Tana River, Kenya.” Journal of Arid Environments 50, no. 1 (January 1, 2002): 53. doi:10.1006/jare.2000.0860.

This doesn't seem true. I know I am weaker than Conner McGregor but I don't feel especially alone because of it. Maybe she means a specific other man? I still don't see how loneliness would be the result rather than just fear or shame.

Can't the slave still think her own thoughts, as traumatized and brutally oppressed as she may be?

eLife assessment

The authors examined whether the frequency of alternative splicing across entire genomes correlates with measures of complexities across prokaryotes and eukaryotes. Although the question is very interesting and important for our general understanding of the evolution of life forms, the work is inadequate: the methods, data, and analyses do not support the primary claims. The measure of alternative splicing frequency used by the authors is problematic; the method is inappropriate; the observed correlations may also be explained by known population genetics principles; and parts of the manuscript are difficult to understand.

Reviewer #1 (Public Review):

Summary:

The authors collected genomic information from public sources covering 423 eukaryote genomes and around 650 prokaryote genomes. Based on pre-computed CDS annotation, they estimated the frequency of alternative splicing (AS) as a single average measure for each genome and computed correlations with this measure and other genomic properties such as genome size, percentage of coding DNA, gene and intergenic span, etc. They conclude that AS frequency increases with genome complexity in a somewhat directional trend from "lower" organisms to "higher" organisms.

Strengths:

The study covers a wide range of taxonomic groups, both in prokaryotes and eukaryotes.

Weaknesses:

The study is weak both methodologically and conceptually. Current high throughput sequencing technologies, coupled with highly heterogeneous annotation methods, can observe cases of AS with great sensitivity, and one should be extremely cautious of the biases and rates of false positives associated with these methods. These issues are not addressed in the manuscript. Here, AS measures seem to be derived directly from CDS annotations downloaded from public databases, and do not account for differing annotation methods or RNA sequencing depth and tissue sample diversity.

There is no mention of the possibility that AS could be largely caused by random splicing errors, a possibility that could very well fit with the manuscript's data. Instead, the authors adopt early on the view that AS is regulated and functional, generally citing outdated literature.

There is no question that some AS events are functional, as evidenced by strongly supported studies. However, whether all AS events are functional is questionable, and the relative fractions of functional and non-functional AS are unknown. With this in mind, the authors should be more cautious in interpreting their data. The "complexity" of organisms also correlates well (negatively) with effective population size. The power of selection to eliminate (slightly) deleterious mutations or errors decreases with effective population size. The correlation observed by the authors could thus easily be explained by a non-adaptive interpretation based on simple population genetics principles.

The manuscript contains evidence that the authors might benefit from adopting a more modern view of how evolution proceeds. Sentences such as "... suggests that only sophisticated organisms optimize alternative splicing by increasing..." (L113), or "especially in highly evolved groups such as mammals" (L130), or the repeated use of "higher" and "lower" organisms need revising.

Because of the lack of controls mentioned above, and because of the absence of discussion regarding an alternative non-adaptive interpretation, the analyses presented in the manuscript are of very limited use to other researchers in the field. In conclusion, the study does not present solid conclusions.

Reviewer #2 (Public Review):

Summary:

In this contribution, the authors investigate the degree of alternative splicing across the evolutionary tree and identify a trend of increasing alternative splicing as you move from the base of the tree (here, only prokaryotes are considered) towards the tips of the tree. In particular, the authors investigate how the degree of alternative splicing (roughly speaking, the number of different proteins made from a single ORF (open reading frame) via alternative splicing) relates to three genomic variables: the genome size, the gene content (meaning the fraction of the genome composed of ORFs), and finally, the coding percentage of ORFs, meaning the ratio between exons and total DNA in the ORF. When correlating the degree of alternative splicing with these three variables, they find that the different taxonomic groups have a different correlation coefficient, and identify a "progressive pattern" among metazoan groups, namely that the correlation coefficient mostly increases when moving from flowering plants to arthropods, fish, birds, and finally mammals. They conclude that therefore the amount of splicing that is performed by an organismal group could be used as a measure of its complexity.

Weaknesses:

While I find the analysis of alternative splicing interesting, I also find that it is a very imperfect measure of organismal complexity and that the manuscript as a whole is filled with unsupported statements. First, I think it is clear to anyone studying evolution over the tree of life that it is the complexity of gene regulation that is at the origin of much of organismal structural and behavioral complexity. Arguably, creating different isoforms out of a single ORF is just one example of complex gene regulation. However, the complexity of gene regulation is barely mentioned by the authors. Further, it is clear that none of their correlation coefficients actually show a simple trend (see Table 3). According to these coefficients, birds are more complex than mammals for 3 out of 4 measures. It is also not clear why the correlation coefficient between alternative splicing ratio and genome length, gene content, and coding percentage should display such a trend, rather than the absolute value. There are only vague mechanistic arguments.

Much more troubling, however, is the statement that the data supports "lineage-specific trends" (lines 299-300). Either this is just an ambiguous formulation, or the authors claim that you can see trends *within* lineages. The latter is clearly not the case. In fact, within each lineage, there is a tremendous amount of variation, to such an extent that many of the coefficients given in Table 3 are close to meaningless. Note that no error bars or p-values are presented for the values shown in Table 3. Figure 2 shows the actual correlation, and the coefficient for flowering plants there is given as 0.151, with a p-value of 0.193. Table 3 seems to quote r=0.174 instead. It should be clear that a correlation within a lineage or species is not a sign of a trend.

There are several wrong or unsupported statements in the manuscript. Early on, the authors state that the alternative splicing ratio (a number greater or equal to one that can be roughly understood as the number of different isoforms per ORF) "quantifies the number of different isoforms that can be transcribed using the same amount of information" (lines 51-52). But in many cases, this is incorrect, because the same sequence can represent different amounts of information depending on the context. So, if a changed context gives rise to a different alternative splice, it is because the genetic sequence has a different meaning in the changed context: the information has changed. In line 149, the authors state that "the energetic cost of having large genomes is high". No citation is given, and while such a statement seems logical, it does not have very solid support. If there was indeed a strong selective force to reduce genome size, we would not see the stunning diversity of genome sizes even within lineages. This statement is repeated (without support) several times in the manuscript, apparently in support of the idea that mammals had "no choice" to increase complexity via alternative splicing because they can't increase it by having longer genomes. I don't think this reasoning can be supported. Even more problematic is the statement that "the amount of protein-coding DNA seems to be limited to a size of about 10MB" (line 219). There is no evidence whatsoever for this statement. The reference that is cited (Choi et al 2020) suggests that there is a maximum of 150GB in total genome size due to physiological constraints. In lines 257-258, the authors write that "plants are less restricted in terms of storing DNA sequences compared to animals" (without providing evidence or a citation). I believe this statement is made due to the observation that plants tend to have large intergenic regions. But without examining the functionality of these interagency regions (they might host long non-coding RNA stretches that are used to regulate the expression of other genes, for example) it is quite adventurous to use such a simple measure as being evidence that plants "are less restricted in terms of storing DNA sequences", whatever that even means. I do not think the authors mean that plants have better access to -80 freezers. The authors conclude that "plant's primary mechanism of genome evolution is by expanding their genome". This statement itself is empty: we know that plants are prone to whole genome duplication, but this duplication is not, as far as we understand, contributing to complexity. It is not a "primary mechanism of genome evolution". In lines 293-294, the authors claim that "alternative splicing is maximized in mammalian genomes". There is no evidence that this ratio cannot be increased. So, to conclude (on lines 302-303) that alternative splicing ratios are "a potential candidate to quantify organismal complexity" seems, based on this evidence, both far-fetched and weak at the same time.

I am also not very comfortable with the data analysis. The authors, for example, say that they have eliminated from their analysis a number of "outlier species". They mention one: Emmer wheat because it has a genome size of 900 Mb (line 367). Since 900MB does not appear to be extreme, perhaps the authors meant to write 900 Gb. When I consulted the paper that sequenced Triticum dicoccoides, they noted that 14 chromosomes are about 10GB. Even a tetraploid species would then not be near 900Gb. But more importantly, such a study needs to state precisely which species were left out, and what the criteria are for leaving out data, lest they be accused of selecting data to fit their hypothesis.

I understand that Methods are often put at the end of a manuscript, but the measures discussed here are so fundamental to the analysis that a brief description of what the different measures are (in particular, the "alternative splicing ratio") should be in the main text, even when the mathematical definition can remain in the Methods.

Finally, a few words on presentation. I understand that the following comments might read differently after the authors change their presentation. This manuscript was at the border of being comprehensible. In many cases, I could discern the meaning of words and sentences in contexts but sometimes even that failed (as an example above, about "species-specific trends", illustrates). The authors introduced jargon that does not have any meaning in the English language, and they do this over and over again.

Note that I completely agree with all the comments by the other reviewer, who alerted me to problems I did not catch, including the possible correlation with effective population size: a possible non-adaptive explanation for the results.

Author response:

Reviewer #1 (Public Review):

Summary:

The authors collected genomic information from public sources covering 423 eukaryote genomes and around 650 prokaryote genomes. Based on pre-computed CDS annotation, they estimated the frequency of alternative splicing (AS) as a single average measure for each genome and computed correlations with this measure and other genomic properties such as genome size, percentage of coding DNA, gene and intergenic span, etc. They conclude that AS frequency increases with genome complexity in a somewhat directional trend from "lower" organisms to "higher" organisms.

Strengths:

The study covers a wide range of taxonomic groups, both in prokaryotes and eukaryotes.

Weaknesses:

The study is weak both methodologically and conceptually. Current high throughput sequencing technologies, coupled with highly heterogeneous annotation methods, can observe cases of AS with great sensitivity, and one should be extremely cautious of the biases and rates of false positives associated with these methods. These issues are not addressed in the manuscript. Here, AS measures seem to be derived directly from CDS annotations downloaded from public databases, and do not account for differing annotation methods or RNA sequencing depth and tissue sample diversity.

We are aware of the bias that may exist in annotation files. Since the source of noise can be highly variable, we have assumed that most of the data has a similar bias. However, we agree with the reviewer that we could perform some analysis to test for these biases and their association to different methodologies. Thus, we will measure the uncertainty present in the data. From one side, we will be more explicit about the data limitations and the biases it can generate in the results. On the other side, while analyzing the false positives in the data is out of our scope, we will perform a statistical test to detect possible biases regarding different methods of sequencing and annotation, and types of organisms (model or non-model organisms). If positive, we will proceed, as far as possible, to normalize the data or to estimate a confidence interval.

Here, AS measures seem to be derived directly from CDS annotations downloaded from public databases, and do not account for differing annotation methods or RNA sequencing depth and tissue sample diversity.

Beyond taking into account the differential bias that may exist in the data, we do not consider that our AS measure is problematic. The NCBI database is one of the most reliable databases that we have to date and is continuously updated from all scientific community. So, the use of this data and the corresponding procedures for deriving the AS measure are perfectly acceptable for a comparative analysis on such a huge global scale. Furthermore, the proposal of a new genome-level measure of AS that allows to compare species spanning the whole tree of life is part of the novelty of the study. We understand that small-scale studies require a high specificity about the molecular processes involved in the study. However, this is not the case, where we are dealing with a large-scale problem. On the other side, as we have previously mention, we agree with the reviewer to analyze the degree of uncertainty in the data to better interpret the results.

There is no mention of the possibility that AS could be largely caused by random splicing errors, a possibility that could very well fit with the manuscript's data. Instead, the authors adopt early on the view that AS is regulated and functional, generally citing outdated literature.

There is no question that some AS events are functional, as evidenced by strongly supported studies. However, whether all AS events are functional is questionable, and the relative fractions of functional and non-functional AS are unknown. With this in mind, the authors should be more cautious in interpreting their data.

Many studies suggest that most of the AS events observed are the result of splicing errors and are therefore neither functional nor conserved. However, we still have limited knowledge about the functionality of AS. Just because we don’t have a complete understanding of its functionality, doesn’t mean there isn’t a fundamental cause behind these events. AS is a highly dynamic process that can be associated with processes of a stochastic nature that are fundamental for phenotypic diversity and innovation. This is one of the reasons why we do not get into a discussion about the functionality of AS and consider it as a potential measure of biological innovation. Nevertheless, we agree with the reviewer’s comments, so we will add a discussion about this issue with updated literature and look at any possible misinterpretation of the results.

The "complexity" of organisms also correlates well (negatively) with effective population size. The power of selection to eliminate (slightly) deleterious mutations or errors decreases with effective population size. The correlation observed by the authors could thus easily be explained by a non-adaptive interpretation based on simple population genetics principles.

We appreciate the observation of the reviewer. We know well the M. Lynch’s theory on the role of the effective population size and its eventual correlation with genomic parameters, but we want to emphasize that our objective is not to find an adaptive or non-adaptive explanation of the evolution of AS, but rather to reveal it. Nevertheless, as the reviewer suggests, we will look at the correlation between the AS and the effective population size and discuss about a possible non-adaptive interpretation.

The manuscript contains evidence that the authors might benefit from adopting a more modern view of how evolution proceeds. Sentences such as "... suggests that only sophisticated organisms optimize alternative splicing by increasing..." (L113), or "especially in highly evolved groups such as mammals" (L130), or the repeated use of "higher" and "lower" organisms need revising.

As the reviewer suggests, we will proceed with the corresponding linguistic corrections.

Because of the lack of controls mentioned above, and because of the absence of discussion regarding an alternative non-adaptive interpretation, the analyses presented in the manuscript are of very limited use to other researchers in the field. In conclusion, the study does not present solid conclusions.

Reviewer #2 (Public Review):

Summary:

In this contribution, the authors investigate the degree of alternative splicing across the evolutionary tree and identify a trend of increasing alternative splicing as you move from the base of the tree (here, only prokaryotes are considered) towards the tips of the tree. In particular, the authors investigate how the degree of alternative splicing (roughly speaking, the number of different proteins made from a single ORF (open reading frame) via alternative splicing) relates to three genomic variables: the genome size, the gene content (meaning the fraction of the genome composed of ORFs), and finally, the coding percentage of ORFs, meaning the ratio between exons and total DNA in the ORF. When correlating the degree of alternative splicing with these three variables, they find that the different taxonomic groups have a different correlation coefficient, and identify a "progressive pattern" among metazoan groups, namely that the correlation coefficient mostly increases when moving from flowering plants to arthropods, fish, birds, and finally mammals. They conclude that therefore the amount of splicing that is performed by an organismal group could be used as a measure of its complexity.

Weaknesses:

While I find the analysis of alternative splicing interesting, I also find that it is a very imperfect measure of organismal complexity and that the manuscript as a whole is filled with unsupported statements. First, I think it is clear to anyone studying evolution over the tree of life that it is the complexity of gene regulation that is at the origin of much of organismal structural and behavioral complexity. Arguably, creating different isoforms out of a single ORF is just one example of complex gene regulation. However, the complexity of gene regulation is barely mentioned by the authors.

We disagree with the reviewer with that our measure of AS is imperfect. Just as we responded to the first reviewer, we will quantify the uncertainty in the data and correct for differential biases caused by annotation and sequencing methods. Thus, beyond correcting relevant biases in the data, we consider that our measure is adequate for a comparative analysis at a global scale. A novelty of our study is the proposal of a genome-level measure of AS that takes into account data from the entire scientific community. 

We want also to emphasize that we assume from the beginning that AS may reflect some kind of biological complexity, it is not a conclusion from the results. An argument in favor of such an assumption is that AS is associated with stochastic processes that are fundamental for phenotypic diversity and innovation. Of course, we agree with the reviewer that it is not the only mechanism behind biological complexity, so we will emphasize it in the manuscript. On the other side, we will be more explicit about the assumptions and objectives, and will correct any unsupported statement.

Further, it is clear that none of their correlation coefficients actually show a simple trend (see Table 3). According to these coefficients, birds are more complex than mammals for 3 out of 4 measures.

An evolutionary trend is broadly defined as the gradual change in some characteristic of organisms as they evolve or adapt to a specific environment. Under our context, we define an evolutionary trend as the gradual change in genome composition and its association with AS across the main taxonomic groups. If we look at Figure 4 and Table 3 we can conclude that there is a progressive trend. We will be more precise about how we define an evolutionary trend and correct any possible misinterpretation of the results. On the other side, we do not assume that mammals should be more complex than birds. First, we will emphasize that our results show that birds have the highest values of such a trend. Second, after reading the reviewer’s comments, we have decided that we will perform an additional analysis to correct for differences in the taxonomic group sizes, which will allow us to have more confidence in the results.

It is also not clear why the correlation coefficient between alternative splicing ratio and genome length, gene content, and coding percentage should display such a trend, rather than the absolute value. There are only vague mechanistic arguments.

The study analyzes the relationship of AS with genomic composition for the large taxonomic groups. We assume that significant differences in these relationships are indicators of the presence of different mechanisms of genome evolution. However, we agree with the reviewer that a correlation does not imply a causal relation, so we will be more cautious when interpreting the results.

To quantify the relationships we use correlation coefficients, the slopes of such correlations, and the relation of variability. Although the absolute values of AS are also illustrated in Table 4, we consider that they are less informative than if we include how it relates to the genomic composition. For example, we observe that plants have a different genome composition and relation with AS if compared to animals, which suggest that they follow different mechanisms of genome evolution. On the other hand, we observe a trend in animals, where high values of AS are associated to a large percentage of introns and a percentage of intergenic DNA of about the 50% of genomes.

Much more troubling, however, is the statement that the data supports "lineage-specific trends" (lines 299-300). Either this is just an ambiguous formulation, or the authors claim that you can see trends *within* lineages.

We agree with the reviewer that this statement is not correct, so we will proceed to correct it.

The latter is clearly not the case. In fact, within each lineage, there is a tremendous amount of variation, to such an extent that many of the coefficients given in Table 3 are close to meaningless. Note that no error bars or p-values are presented for the values shown in Table 3. Figure 2 shows the actual correlation, and the coefficient for flowering plants there is given as 0.151, with a p-value of 0.193. Table 3 seems to quote r=0.174 instead. It should be clear that a correlation within a lineage or species is not a sign of a trend.

The reviewer is not understanding correctly the results in Table 3. It is precisely the variation of the genome variables what we are measuring. Given the standardization of these values by the mean values, we have proceeded to compare the variability between groups, which is the result shown in Table 3. In this case there are no error bars or p-values associated. On the other hand, we agree that a correlation is not a sign of a trend. But the relations of variability, together with the results obtained in Figure 3, are indicators of a trend. As we mentioned before, we will proceed to analyze whether the variation in the group sizes is causing a bias in the results.

There are several wrong or unsupported statements in the manuscript. Early on, the authors state that the alternative splicing ratio (a number greater or equal to one that can be roughly understood as the number of different isoforms per ORF) "quantifies the number of different isoforms that can be transcribed using the same amount of information" (lines 51-52). But in many cases, this is incorrect, because the same sequence can represent different amounts of information depending on the context. So, if a changed context gives rise to a different alternative splice, it is because the genetic sequence has a different meaning in the changed context: the information has changed.

We agree that there are not well supported statements, so we will proceed to revise them.

In line 149, the authors state that "the energetic cost of having large genomes is high". No citation is given, and while such a statement seems logical, it does not have very solid support.

We will also revise the bibliography and support our statements with updated references.

If there was indeed a strong selective force to reduce genome size, we would not see the stunning diversity of genome sizes even within lineages. This statement is repeated (without support) several times in the manuscript, apparently in support of the idea that mammals had "no choice" to increase complexity via alternative splicing because they can't increase it by having longer genomes. I don't think this reasoning can be supported.

We agree with the reviewer in this issue, so we will carefully revise the statements that indirectly (or directly) assume the action of selective forces on the genome composition.

Even more problematic is the statement that "the amount of protein-coding DNA seems to be limited to a size of about 10MB" (line 219). There is no evidence whatsoever for this statement.

In Figure 1A we observe a one-to-one relationship between the genome size and the amount of coding. However, in multicellular organisms, although the genome size increases we observe that the amount of coding does not increase by more than 10Mb, which suggest the presence of some genomic limitation. Of course, this is not an absolute or general statement, but rather a suggestion. We are only describing our results.

The reference that is cited (Choi et al 2020) suggests that there is a maximum of 150GB in total genome size due to physiological constraints. In lines 257-258, the authors write that "plants are less restricted in terms of storing DNA sequences compared to animals" (without providing evidence or a citation).

We will revise the bibliography and add updated references.

I believe this statement is made due to the observation that plants tend to have large intergenic regions. But without examining the functionality of these interagency regions (they might host long non-coding RNA stretches that are used to regulate the expression of other genes, for example) it is quite adventurous to use such a simple measure as being evidence that plants "are less restricted in terms of storing DNA sequences", whatever that even means. I do not think the authors mean that plants have better access to -80 freezers. The authors conclude that "plant's primary mechanism of genome evolution is by expanding their genome". This statement itself is empty: we know that plants are prone to whole genome duplication, but this duplication is not, as far as we understand, contributing to complexity. It is not a "primary mechanism of genome evolution".

We will revise these statements.

In lines 293-294, the authors claim that "alternative splicing is maximized in mammalian genomes". There is no evidence that this ratio cannot be increased. So, to conclude (on lines 302-303) that alternative splicing ratios are "a potential candidate to quantify organismal complexity" seems, based on this evidence, both far-fetched and weak at the same time.

Our results show the highest values of AS in mammals, but we understand that the results are limited to the availability and accuracy of data, which we will emphasize in the manuscript. As we previously mention, we will also proceed to analyze the uncertainty in data and carry out the appropriate corrections.

I am also not very comfortable with the data analysis. The authors, for example, say that they have eliminated from their analysis a number of "outlier species". They mention one: Emmer wheat because it has a genome size of 900 Mb (line 367). Since 900MB does not appear to be extreme, perhaps the authors meant to write 900 Gb. When I consulted the paper that sequenced Triticum dicoccoides, they noted that 14 chromosomes are about 10GB. Even a tetraploid species would then not be near 900Gb. But more importantly, such a study needs to state precisely which species were left out, and what the criteria are for leaving out data, lest they be accused of selecting data to fit their hypothesis.

The reviewer is right, we wanted to say 900Mb, which is approximately 7.2Gb. We had a mistake of nomenclature. This value is extreme compared to the typical values, so it generates large deviations when applying measures of central tendency and dispersion. We want to obtain mean values that are representative of the most species composing the taxonomic groups, so we find appropriate to exclude all outlier values in the study. Nevertheless, we will specify the criteria that we have used to select the data in a rigorous way.

I understand that Methods are often put at the end of a manuscript, but the measures discussed here are so fundamental to the analysis that a brief description of what the different measures are (in particular, the "alternative splicing ratio") should be in the main text, even when the mathematical definition can remain in the Methods.

We agree with the reviewer, so we will add a brief description of the genomic variables at the beginning of the Results section.

Finally, a few words on presentation. I understand that the following comments might read differently after the authors change their presentation. This manuscript was at the border of being comprehensible. In many cases, I could discern the meaning of words and sentences in contexts but sometimes even that failed (as an example above, about "species-specific trends", illustrates). The authors introduced jargon that does not have any meaning in the English language, and they do this over and over again.

Note that I completely agree with all the comments by the other reviewer, who alerted me to problems I did not catch, including the possible correlation with effective population size: a possible non-adaptive explanation for the results.

No, setting up the proper metric may or may not be qualitative work after all

So you do not think physical quantities exist out of our mind?

Should look up this constitutional spending limit.

Derisking!

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Specific comments to improve the quality of the work:

(1) The choice of subunits to tag are really not ideal. In the available structures of the human proteasome, The C-terminus of Rpn3/PSMD3 points directly toward the ATPase pore and is likely to disrupt the structure and/or dynamics of the proteasome during proteolysis (see comments regarding controls for functionality below). Similarly, the C-terminal tail of Rpt1/PSMC2 has a key role in the opening of the 20S core particle gate for substrate translocation and processing (see 2018 Nature Communications, 9:1360 and 2018 Cell Reports 24:1301-1315), and Alpha3/PSMA4 can be substituted by a second copy of Alpha4/PSMA7 in some conditions (although tagging Alpha3/PSMA4 would admittedly provide a picture of the canonical proteasome interactome while actively excluding the interactome of the non-canonical proteasomes that form via replacement of Alpha3/PSMA4). Comparison of these cell lines with lines harboring tags on subunits that are commonly used for tagging in the field because of a lack of impacts, such as the N-terminus of Rpn1/PSMD2, the C-terminus of Rpn11/PSMD14, and the C-terminus of Beta4/PSMB2 would help instill confidence that the interactome reported largely arises from mature, functional proteasomes rather than subcomplexes, defective proteasomes, or other species that may occur due to tagging at these positions.

We thank the reviewer for pointing this out. The original purpose of our strategy was to establish proximity labeling of proteasomes to enable applications both in cell culture and in vivo. The choice of PSMA4 and PSMC2 was dictated by previous successful tagging with GFP in mammalian cells (Salomons et al., Exp Cell Res 2010)(Bingol and Schuman, Nature 2006). However, the choice of C-terminal PSMC2 might have been not optimal. HEK293 cells overexpressing PSMC2-BirA show slower growth and the BioID data retrieve higher enrichment of assembly factors suggesting slower assembly of this fusion protein in proteasome. Although we did not observe a negative impact on overall proteasome activity and PSMC2-BirA was (at least in part) incorporated into fully assembled proteasomes as indicated by enrichment of 20S proteins.We apologize for not making it clear that we labeled the N-terminus of PSMD3/Rpn3 and not the C-terminus (Figure 1a and S1a). Therefore, we included in Figure S1a of the revised manuscript structures of the proteasome where the tagged subunit termini are highlighted: C-terminus for PSMA4 and PSMC2 and N-terminus for PSMD3. Additionally, we would like to point out that, differently from PSMC2-BirA, cells expressing BirA-PSMD3 did not show slower growth, and BioID data showed a more homogenous enrichment of both 19S and 20S proteins, as compared to PSMC2-BirA (Figure 1D and 1E). However, the overall level of enrichment of proteasome subunits was not comparable to PSMA4-BirA and, therefore, we opted for focusing the rest of the manuscript on this construct.

In support of this point, the data provided in Figure 1E in which the change in the abundances of each proteasome subunit in the tagged line vs. the BirA control line demonstrates substantial enrichment of the subcomplexes of the proteasome that are tagged in each case; this effect may represent the known feedback-mediated upregulation of new proteasome subunit synthesis that occurs when proteasomal proteolysis is impaired, or alternatively, the accumulation of subcomplexes containing the tagged subunit that cannot readily incorporate into mature proteasomes. Acknowledging this limitation in the text would be valuable to readers who are less familiar with the proteasome.

We would like to clarify that the data shown in Figure 1E do not represent whole proteome data, but rather log2 fold changes vs. BirA* control calculated on streptavidin enrichment samples. The differences in the enrichment of the various subcomplexes between cell lines derives from the fact that the effect size of the enrichment depends on both protein abundance in the isolated complexes, but also on the efficiency of biotinylation. The latter will be higher for proteins located in closer proximity to the bait. A similar observation was pointed out in a recent publication (PMID:36410438) that compared BioID and Co-IP for the same bait. When a component of the nuclear pore complex (Nup158) was analyzed by BioID only the more proximal proteins were enriched as compared to the whole complex in Co-IP data (Author response image 1):

Author response image 1.

Proteins identified in the NUP158 BioID or pulldown experiments are filled in red or light red for significance intervals A or B, respectively. The bait protein NUP158 is filled in yellow. Proteins enriched in the pulldown falling outside the SigA/B cutoff are filled in gray. NPC, nuclear pore complex. SigA, significant class A; SigB, significant class B. Reproduced from Figure 6 of PMID: 36410438.

However, we would like to point out that despite quantitative differences between different proteasome subunits, both 19S and 20S proteins were found to be strongly enriched (typically >2 fold) in all the constructs compared to BirA* control line (Figure 1E). This indicates that at least a fraction of all the tagged subunits are incorporated into fully assembled proteasomes.

Regarding the upregulation of proteasome subunits as a consequence of proteasome dysfunction, we did not find evidence of this, at least in the case of PSMA4. The immunoblot shown in Figure 2A and its quantification in S3A indicate no increased abundance of endogenous PSMA4 upon tetracycline induction of PSMA4-BirA*.

(2) The use of myc as a substrate of the proteasome for demonstration that proteolysis is unaffected is perhaps not ideal. Myc is known to be degraded via both ubiquitin-dependent and ubiquitin-independent mechanisms, such that disruption of one means of degradation (e.g., ubiquitin-dependent degradation) via a given tag could potentially be compensated by another. A good example of this is that the C-terminal tagging of PSMC2/Rpt1 is likely to disrupt interaction between the core particle and the regulatory particle (as suggested in Fig. 1D); this may free up the core particle for ubiquitin-independent degradation of myc.

Aside from using specific reporters for ubiquitin-dependent vs. independent degradation or a larger panel of known substrates, analysis of the abundance of K48-ubiquitinated proteins in the control vs. tag lines would provide additional evidence as to whether or not proteolysis is generally perturbed in the tag lines.

We thank the reviewer for this suggestion. We have included an immunoblot analysis showing that the levels of K48 ubiquitylation (Figure S3d) are not affected by the expression of tagged PSMA4.

(3) On pg. 8 near the bottom, the authors accidentally refer to ARMC6 as ARMC1 in one instance.

We have corrected the mistake.

(4) On pg. 10, the authors explain that they analyzed the interactome for all major mouse organs except the brain; although they explain in the discussion section why the brain was excluded, including this explanation on pg. 10 here instead of in the discussion might be a better place to discuss this.

We moved the explanation from the discussion to the results part.

Reviewer #2 (Recommendations For The Authors):

(1) Perhaps the authors can quantify the fraction of unassembled PSMA4-BirA* from the SEC experiment (Fig. 2b) to give the readers a feeling for how large a problem this could be.

The percentages based on Area Under the Curve calculations have been added to Figure S3b.

(2) Do the authors observe any difference in the enrichment scores between proteins that are known to interact with the proteasome vs proteins that the authors can justify as "interactors of interactors" vs the completely new potential interactors? This could be an interesting way to show that the potential new interactors are not simply because of poor false positive rate calibration, but that they behave in the same way as the other populations.

We thank the reviewer for this suggestion. We analyzed the enrichment scores for 20S proteasome subunits, known PIPs, first neighbors and the remaining enriched proteins. The remaining proteins (potential new interactors) have very similar scores as the first neighbors of known interactors. This plot has been added to Figure S3g.

(3) Did the authors try to train a logistic model for the miniTurbo experiments, like it was done for the BirA* experiments? Perhaps combining the results of both experiments would yield higher confidence on the proteasome interactors.

Following the reviewers suggestion, we applied the classifier on the dataset of the comparison between miniTurbo and PSMA-miniTurbo. We found a clear separation between the FPR and the TPR with 136 protein groups enriched in PSMA-miniTurbo. We have added the classifier and corresponding ROC curve to Figure S4f and S4g.

75 protein groups were found to be enriched for both PSMA4-BirA* and PSMA4-miniTurbo (Author response image 2), including the proteasome core particles, regulatory particles, known interactors and potential new interactors. As we focused more on the identification of substrates with PSMA4-miniTurbo, we did not pursue these overlapping protein groups further, but rather used the comparison to the mouse model to identify potential new interactors.

Author response image 2.

Overlap between ProteasomeID enriched proteins (fpr<0.05) between PSMA4-BirA* and PSMA4-miniTurbo.

(4) Perhaps this is already known, but did the authors check if MG132 affect proteasome assembly? The authors could for example repeat their SEC experiments in the presence of MG132.

We thank the reviewer for the suggestion, however to our knowledge there are no reports that MG132 has an effect on the assembly of the proteasome. MG132 is one of the most used proteasome inhibitors in basic research and as such has been extensively characterized in the last 3 decades. The small peptide aldehyde acts as a substrate analogue and binds directly to the active site of the protease PSMB5/β5. We therefore think it is unlikely that MG132 is interfering with the assembly of the proteasome.

(5) Minor comment: at the bottom of page 8, the authors probably mean ARMC6 and not ARMC1.

We have corrected the mistake.

(6) It would be interesting to expand the analysis of the already acquired in vivo data to try to identify tissue-specific proteasome interactors. Can the authors draw a four-way Venn diagram with the interactors of each tissue?

We thank the reviewer for this suggestion. We have generated an UpSet plot showing the overlap of ProteasomeID enriched proteins in the four tissues that gave us meaningful results (Author response image 3). In order to investigate whether the observed differences in ProteasomeID enriched proteins could be meaningful in terms of proteasome biology, we have highlighted proteins belonging to the UPS that show tissue specific enrichments. We found proteasome activators such as PSME1/PA28alpha and PSME2/PA28beta to enrich preferentially in kidney and liver, respectively, as well as multiple deubiquitinases to enrich preferentially in the heart. These differences might be related to the specific cellular composition of the different tissues, e.g., number of immune cells present, or the tissue-specific interaction of proteasomes with enzymes involved in the ubiquitin cycle. Given the rather preliminary nature of these findings, we have opted for not including this figure in the main manuscript, but rather include it only in this rebuttal letter.

Author response image 3.

Upset plot showing overlap between ProteasomeID enriched proteins in different mouse organs.

Reviewer #3 (Recommendations For The Authors):

(1) In the first paragraph of the Introduction, the authors link cellular senescence caused by partial proteasome inhibition with the efficacy of proteasome inhibitors in cancer therapy. Although this is an interesting hypothesis, I am not aware of any direct evidence for this; rather, I believe the efficacy of bortezomib/carfilzomib in haematological malignancies is most commonly attributed to these cells having adapted to high levels of proteotoxic stress (e.g., chronic unfolded protein response activation). I would suggest rephrasing this sentence.

We thank the reviewer for the comment and have amended the introduction.

(2) For the initial validation experiments (e.g., Fig. 1B), have the authors checked what level of Streptavidin signal is obtained with "+ bio, - tet" ? Although I accept that the induction of PSMA4-BirA* upon doxycycline addition is clear from the anti-Flag blots, it would still be informative to ascertain what level of background labelling is obtained without induction (but in the presence of exogenous biotin).

We tested four different conditions +/- tet and +/- biotin (24h) in PSMA4-BirA* cell lines (Author response image 4). As expected, biotinylation was most pronounced when tet and biotin were added. When biotin was omitted, streptavidin signal was the lowest regardless of the addition of tet. Compared to the -biotin conditions, a slight increase of streptavidin signal could be observed when biotin was added but tet was not added. This could be either due to the promoter leaking (PMID: 12869186) or traces of tetracycline in the FBS we used, as we did not specifically use tet-free FBS for our experiments.

Author response image 4.

Streptavidin-HRP immunoblot following induction of BirA fusion proteins with tetracycline (+tet) and supplementation of biotin (+bio). For the sample used as expression control tetracycline was omitted (-tet). To test background biotinylation, biotin supplementation was omitted (-bio). Immunoblot against BirA and PSMA was used to verify induction of fusion proteins, while GAPDH was used as loading control.

(3) For the proteasome structure models in Fig. 1D, a scale bar would be useful to inform the reader of the expected 10 nm labelling radius (as the authors have done later, in Fig. 2D).

We have added 10 nm scale bars to Figure 1d.

(4) In the "Identification of proteasome substrates by ProteasomeID" Results subsection, I believe there is a typo where the authors refer to ARMC1 instead of ARMC6.

We have corrected the mistake.

(5) I think Fig. S5 was one of the most compelling in the manuscript. Given the interest in confirming on-target efficacy of targeted degradation modalities, as well as identifying potential off-target effects early-on in development, I would consider promoting this out of the supplement.

We thank the reviewer for the comment and share the excitement about using ProteasomeID for targeted degradation screening. We have moved the data on PROTACs (Figure S5) into a new main Figure 5.

In addition, in relation to the comment of this reviewer regarding the detection of endogenous substrates, we have now included validation for one more hit emerging from our analysis (TIGD5) and included the results in Figure 4f, 4g and S4j.

eLife assessment

This study presents an important method and resource in cell lines and in mice for mass spectrometry-based identification of interactors of the proteasome, a multi-protein complex with a central role in protein turnover in almost all tissues and cell types. The method presented, including the experimental workflow and analysis pipeline, as well as the several lines of validation provided throughout, is convincing. Given the growing interest in protein aggregation and targeted protein degradation modalities, this work will be of interest to a broad spectrum of basic cell biologists and translational researchers.

Reviewer #2 (Public Review):

Summary

In this work, Bartolome and colleagues develop a new approach to identify proteasome interacting proteins and substrates. The approach is based on fusing proteasome subunits with a biotin ligase that will label proteins that come in close physical distance of the ligase. These biotin-labeled proteins (or their resulting tryptic peptides) can be affinity purified using streptavidin and identified by mass spectrometry.

This elegant solution was able to identify a large proportion of known proteasome interactors, as well as multiple potential new interactors. Combining this approach with a proteasome inhibitor allowed also for the enrichment of substrates, due to increased contact time between substrates and the proteasome. Again, the authors were able to identify novel substrates. Finally, the authors implemented this strategy in vivo, providing the hints for potential tissue-specific proteasome interactors.<br /> This novel strategy provides an additional approach to identify new proteasome substrates, which can be particularly powerful for low abundant proteins, e.g., transcription factors. The possibility to implement it in vivo in specific cell types opens the possibility for identifying proteasome interactors in small cell subpopulations or in subpopulations involved in disease.

Strengths

The authors carefully characterized their genetically engineered proteasome-biotin ligase fusions to ensure that proteasome structure and activity was not altered. This is key to ensure that the proteins identified to interact with the proteasome reflect interactions that occur under physiological conditions.

The authors implemented an algorithm that controls the false positive rate of the identified interactors of the proteasome. This is an important aspect to avoid spending time on the characterization of potential interactors that are just an artifact of the experimental setup.

The addition of a proteasome inhibitor allowed the authors to identify substrates of the proteasome. Although there are other strategies to do this (e.g., affinity purification of Gly-Gly modified peptides, which is a marker for ubiquitination), this additional approach can highlight currently unknown substrates. One example are low abundance proteins, such as transcription factors.

The overall strategy developed by the authors can be implemented in vivo, which opens for the possibility of determining cell type-specific proteasome interactors (and perhaps substrates).

Weaknesses

There is a proportion (approximately 38%) of the PSMA4-biotin ligase fusion that remains unassembled (i.e., not part of the functional proteasome) and that can contribute to a small proportion of false positive interactions.

Reviewer #3 (Public Review):

Summary:

Bartolome et al. present ProteasomeID, a novel method to identify components, interactors, and (potentially) substrates of the proteasome in cell lines and mouse models. As a major protein degradation machine that is highly conserved across eukaryotes, the proteasome has historically been assumed to be relatively homogeneous across biological scales (with few notable exceptions, e.g., immunoproteasomes and thymoproteasomes). However, a growing body of evidence suggests that there is some degree of heterogeneity in the composition of proteasomes across cell tissues, and can be highly dynamic in response to physiologic and pathologic stimuli. This work provides a methodological framework for investigating such sources of variation. The authors start by adapting the increasingly popular biotin ligation strategy for labelling proteins coming into close proximity with one of three different subunits of the proteasome, before proceeding with PSMA4 for further development and analysis based on their preliminary labelling data. In a series of well-constructed and convincing validation experiments, the authors go on to show that the tagged PSMA4 construct can be incorporated into functional proteasomes, and is able to label a broad set of known proteasome components and interacting proteins in HEK293T cells. They also attempt to identify novel proteasomal degradation substrates with ProteasomeID; while this was convincing for known substrates with particularly short half-lives, the results for substrates with longer half-lives were less clear. One of the most compelling results was from a similar experiment to confirm proteasomal degradation induced by a BRD-targeting PROTAC, which I think is likely to be of keen interest to the targeted degradation community. Finally, the authors establish a ProteasomeID mouse model, and demonstrate its utility across several tissues.

Strengths:

(1) ProteasomeID itself is an important step forward for researchers with an interest in protein turnover across biological scales (e.g., in sub-cellular compartments, in cells, in tissues, and whole organisms). I especially see interest from two communities: those studying fundamental proteostasis in physiological and pathologic processes (e.g., ageing; tissue-specific protein aggregation diseases), and those developing targeted protein degradation modalities (e.g., PROTACs; molecular glues). All the datasets generated and deposited here are likely to provide a rich resource to both. The HEK293T cell line data are a valuable proof-of-concept to allow expansion into more biologically-relevant cell culture settings; however, I envision the greatest innovation here to be the mouse model. For example, in the targeted protein degradation space, two major hurdles in early-stage pre-clinical development are (i) evaluation of degradation efficacy across disease-relevant tissues, and (ii) toxicity and safety implications caused by off-target degradation, e.g., of newly-identified molecular glues and/or in particularly-sensitive tissues. The ProteasomeID mouse allows early in vivo assessment of both these questions. The results of the BRD PROTAC experiment in 293T cells provides an excellent in vitro proof-of-concept for this approach.

(2) The mass-spectrometry-based proteomics workflows used and presented throughout the manuscript are robust, rigorous, and convincing. For example, the algorithm the authors use for defining enrichment score cut-offs are logical and based on rational models, rather than on arbitrary cut-offs that are common for similar proteomics studies. The construction (and subsequent validation) of both BirA*- and miniTurbo- tagged PSMA4 variants also increases the utility of the method, allowing researchers to choose the variant with the labelling time-scale required for their particular research question.

(3) The optimised BioID and TurboID protocol the authors develop (summarised in Fig. S2A) and validate (Fig. S2B-D) is likely to be of broad interest to cell and molecular biologists beyond the protein degradation field, given that proximity labelling is a current gold-standard in global protein:protein interaction profiling.

Limitations:

I think the authors do an excellent job in highlighting the limitations of ProteasomeID throughout the Results and Discussion. I do have some specific comments that might provide additional context for the reader.

(1) The authors do a good job in showing that a substantial proportion of PSMA4-BirA* is incorporated into functional proteasome particles; however, it is not immediately clear to me how much background (false-positive IDs) might be contributed by the ~40 % of PSMA4-BirA* that is not incorporated into the mature core particle (based on the BirA* SEC-MS traces in Fig. 2b and S3b, i.e., the large peak ~ fraction 20). Are there any bands lower down in the native gel shown in Fig. 2c, i.e., corresponding to lower molecular weight complexes or monomeric PSMA4-BirA*? The enrichment of proteasome assembly factors in all the ProteasomeID experiments might suggest the presence of assembly intermediates, which might themselves become substrates for proteasomal degradation (as has been shown for other incompletely-assembled protein complexes, e.g., the ribosome, TRiC/CCT).

(2) Although the authors attempt to show that BirA* tagging of PSMA4 does not interfere with proteasome activity (Fig. 2e-f), I think the experimental evidence for this is incomplete. They show that the overall chymotrypsin-like activity (attributable to PSMB5) in cells expressing PSMA4-BirA* is not markedly reduced compared with control BirA*-expressing cells. However, they do not show that the activity of the specific proteasome sub-population that contains PSMA4-BirA* is unaffected (e.g., by purifying this sub-population via the Flag tag). The proteasome activity of the sub-population of wild-type proteasome complexes that do not contain the PSMA4-BirA* (~50%, based on the earlier immunoblots) could account for the entire chymotrypsin-like activity-especially in the context of HEK293T cells, where steady-state proteasome levels are unlikely to be limiting. It would also be useful to assess any changes in tryspin- and caspase- like activities, especially as tagging of PSMA4 could conceivably interfere with the activity of some PSMB subunits, but not others.

(3) I was left slightly unsure as to the general utility of ProteasomeID for identifying novel proteasomal substrates in homeostatic conditions--especially for proteins with longer half-lives. The cycloheximide chases in Fig. 4g/S4j are clear for MYC and TIGD5 (which have short half-lives), but are not so clear for ARMC6 and BRAT1: the reduction in the bands are modest, and might have been clearer with longer "chase" time-points. Furthermore, classifying candidates based on enrichment following proteasome inhibition with MG-132 have the potential to lead to a high number of false positives. ProteasomeID's utility in identifying potential substrates in more targeted settings (e.g., molecular glues, off-target PROTAC substrates) is far more apparent.

Author response:

Overall recommendations.

A brief summary of the main reviewers' recommendations that should be prioritized is listed below. Detailed recommendations as suggested by each individual reviewer are also included.

-Better justification of the choice of the substitutions for the mutations should be added. In addition, authors should strongly consider adding more mutations to enable mechanistic tests of the proposed model for lipid conduction.

We will characterize more mutations to the key residues at the TM4-TM6 interface. In addition to the TM4 lysine mutations shown in the original manuscript, we will include mutations to alanine and glutamate, and justify our choice of the substitutions in the revised manuscript. Furthermore, we will also test if introducing lysine mutations in TM6 will convert the ion channels into lipid scramblases. These additional experiments will greatly strengthen our conclusion.

-Blockers to validate the concern that the recorded currents indeed arise from TMEM16A or OSCA/TMEM63 channels should be tested. Do the pore blockers also block scramblase activity in the gating mutants?

TMEM16A and OSCA1.2 are readily expressed on cell surface. OSCA1.2 also has large conductance. This is the reason why we can record huge current even with inside-out patches. We will include TMEM16A inhibitor Ani9 and a non-specific inhibitor of OSCA channels to further validate. We have reported that Ani9 can inhibit a TMEM16A-derived lipid scramblase (L543K in TM4) in our previo3us publication (PMID: 31015464). We will test if Ani9 can also inhibit other TMEM16A scramblases reported in this study. We will also examine if Gd3+ is capable of blocking lipid scrambling of the OSCA1.2 gating mutations.

-Include details of missing experimental conditions for scramblase activity.

We will conduct a thorough revision to include detailed experimental conditions for scramblase activity measurement.

-Additional mutants above and below the putative lysine gate as suggested by reviewer 3 to better assess the model.

As we explained in Response #1, we will extend our mutations around the putative activation gate.

-Concern about whether osmolarity changes are in fact activating OSC and TMEM63. As suggested by reviewers 1 and 3. This could be addressed by assessing scramblase activity and currents at different osmolarity levels.

We will test the engineered OSCA1.2 scramblases in response to solutions with different osmolarity.

Reviewer #1 (Public Review):

Summary:

TMEM16, OSCA/TMEM63, and TMC belong to a large superfamily of ion channels where TMEM16 members are calcium-activated lipid scramblases and chloride channels, whereas OSCA/TMEM63 and TMCs are mechanically activated ion channels. In the TMEM16 family, TMEM16F is a well-characterized calcium-activated lipid scramblase that plays an important role in processes like blood coagulation, cell death signaling, and phagocytosis. In a previous study, the group demonstrated that lysine mutation in TM4 of TMEM16A can enable the calcium-activated chloride channel to permeate phospholipids too. Based on this they hypothesize that the energy barrier for lipid scramblase in these ion channels is low, and that modification in the hydrophobic gate region by introducing a charged side chain between the TM4/6 interface in TMEM16 and OSCA/TMEM63 family can allow lipid scramblase. In this manuscript, using scramblase activity via Annexin V binding to phosphatidylserine, and electrophysiology, the authors demonstrate that lysine mutation in TM4 of TMEM16F and TMEM16A can cause constitutive lipid scramblase activity. The authors then go on to show that analogous mutations in OSCA1.2 and TMEM63A can lead to scramblase activity.

Strengths:

Overall, the authors introduce an interesting concept that this large superfamily can permeate ions and lipids.

Weaknesses:

The electrophysiology data does not entirely support their claims.

We appreciate your positive comments. We will conduct more experiments including more electrophysiology characterizations as suggested.

Reviewer #2 (Public Review):

This concise and focused study by Lowry and colleagues identifies a motif in the pores of three families of channel/scramblase proteins that regulate exclusive ion permeation and lipid transport. These three ion channel families, which include the TMEM16s, the plant-expressed and stress-gated cation channel OSCA, and the mammalian homolog and mechanosensitive cation channel, TMEM63 share low sequence similarity between them and have seemingly differing functions, as anion (TMEM16s), or stress-activated cation channels (OSCA/TMEM63). The study finds that in all three families, mutating a single hydrophobic residue in the ion permeation pathway of the channels confers lipid transport through the pores of the channels, indicating that TMEM16 and the related OSCA and TMEM63 channels have a conserved potential for both ion and lipid permeation. The authors interpret the findings as revealing that these channel/scramblase proteins have a relatively low "energetic barrier for scramblase" activity. The experiments themselves seem to be done with a high level of rigor and the paper is well written. A weakness is the limited scope of the experiments which, if fixed, could open up a new line of inquiry.

We appreciate the positive comments from the reviewer. We will conduct more experiments listed in our responses to the Overall Recommendations to improve the scope and quality of our study.

Reviewer #3 (Public Review):

This study was focused on the conserved mechanisms across the Transmembrane Channel/Scramblase superfamily, which includes members of the TMEM16, TMEM63/OSCA, and TMC families. The authors show that the introduction of lysine residues at the TM4-TM6 interface can disrupt gating and confer scramblase activity to non-scramblase proteins. Specifically, they show this to be true for conserved TM4 residues across TMEM16F, TMEM16A, OSCA1.2, and TMEM63A proteins. This breadth of data is a major strength of the paper and provides strong evidence for an underlying linked mechanism for ion conduction and phospholipid transport. Overall, the confocal imaging experiments, patch clamping experiments, and data analysis are performed well.

However, there are several concerns regarding the scope of experiments supporting some claims in the paper. Although the authors propose that the TM4/TM6 interface is critical to ion conduction and phospholipid scramblase activity, in each case, there is very narrow evidence of support consisting of 1-3 lysine substitutions at specific residues on TM4. Given that the authors postulate that the introduction of a positive charge via the lysine side chain is essential to the constitutive activity of these proteins, additional mutation controls for side chain size (e.g. glutamine/methionine) or negative charge (e.g. glutamic acid), or a different positive charge (i.e. arginine) would have strengthened their argument. To more comprehensively understand the TM4/TM6 interface, mutations at locations one turn above and one turn below could be studied until there is no phenotype. In addition, the equivalent mutations on the TM6 side should be explored to rule out the effects of conformational changes that arise from mutating TM4 and to increase the strength of evidence for the importance of side-chain interactions at the TM6 interface. The experiments for OSCA1.2 osmolarity effects on gating and scramblase in Figure 4 could be improved by adding different levels of osmolarity in addition to time in the hypotonic solution.

We appreciate the positive and constructive comments from the reviewer. As we outlined in our responses to the Overall Recommendations, we will include more mutations at the TM4 and TM6 interface to further strengthen our conclusion.

Reviewer #3 (Public Review):

This study was focused on the conserved mechanisms across the Transmembrane Channel/Scramblase superfamily, which includes members of the TMEM16, TMEM63/OSCA, and TMC families. The authors show that the introduction of lysine residues at the TM4-TM6 interface can disrupt gating and confer scramblase activity to non-scramblase proteins. Specifically, they show this to be true for conserved TM4 residues across TMEM16F, TMEM16A, OSCA1.2, and TMEM63A proteins. This breadth of data is a major strength of the paper and provides strong evidence for an underlying linked mechanism for ion conduction and phospholipid transport. Overall, the confocal imaging experiments, patch clamping experiments, and data analysis are performed well.

However, there are several concerns regarding the scope of experiments supporting some claims in the paper. Although the authors propose that the TM4/TM6 interface is critical to ion conduction and phospholipid scramblase activity, in each case, there is very narrow evidence of support consisting of 1-3 lysine substitutions at specific residues on TM4. Given that the authors postulate that the introduction of a positive charge via the lysine side chain is essential to the constitutive activity of these proteins, additional mutation controls for side chain size (e.g. glutamine/methionine) or negative charge (e.g. glutamic acid), or a different positive charge (i.e. arginine) would have strengthened their argument. To more comprehensively understand the TM4/TM6 interface, mutations at locations one turn above and one turn below could be studied until there is no phenotype. In addition, the equivalent mutations on the TM6 side should be explored to rule out the effects of conformational changes that arise from mutating TM4 and to increase the strength of evidence for the importance of side-chain interactions at the TM6 interface. The experiments for OSCA1.2 osmolarity effects on gating and scramblase in Figure 4 could be improved by adding different levels of osmolarity in addition to time in the hypotonic solution.

eLife assessment

This manuscript finds evidence for a latent capability in several members of the TMEM16 and OSCA/TMEM family of ion channels for lipid scramblase activity. The authors demonstrate that the introduction of lysine mutations in evolutionarily conserved areas of TM4 can confer constitutive ion conduction and scramblase activity. Although the significance and scope of the work are important, the strength of the evidence is incomplete and could be improved.

Reviewer #1 (Public Review):

Summary:

TMEM16, OSCA/TMEM63, and TMC belong to a large superfamily of ion channels where TMEM16 members are calcium-activated lipid scramblases and chloride channels, whereas OSCA/TMEM63 and TMCs are mechanically activated ion channels. In the TMEM16 family, TMEM16F is a well-characterized calcium-activated lipid scramblase that plays an important role in processes like blood coagulation, cell death signaling, and phagocytosis. In a previous study, the group demonstrated that lysine mutation in TM4 of TMEM16A can enable the calcium-activated chloride channel to permeate phospholipids too. Based on this they hypothesize that the energy barrier for lipid scramblase in these ion channels is low, and that modification in the hydrophobic gate region by introducing a charged side chain between the TM4/6 interface in TMEM16 and OSCA/TMEM63 family can allow lipid scramblase. In this manuscript, using scramblase activity via Annexin V binding to phosphatidylserine, and electrophysiology, the authors demonstrate that lysine mutation in TM4 of TMEM16F and TMEM16A can cause constitutive lipid scramblase activity. The authors then go on to show that analogous mutations in OSCA1.2 and TMEM63A can lead to scramblase activity.

Strengths:

Overall, the authors introduce an interesting concept that this large superfamily can permeate ions and lipids.

Weaknesses:

The electrophysiology data does not entirely support their claims.

Reviewer #2 (Public Review):

This concise and focused study by Lowry and colleagues identifies a motif in the pores of three families of channel/scramblase proteins that regulate exclusive ion permeation and lipid transport. These three ion channel families, which include the TMEM16s, the plant-expressed and stress-gated cation channel OSCA, and the mammalian homolog and mechanosensitive cation channel, TMEM63 share low sequence similarity between them and have seemingly differing functions, as anion (TMEM16s), or stress-activated cation channels (OSCA/TMEM63). The study finds that in all three families, mutating a single hydrophobic residue in the ion permeation pathway of the channels confers lipid transport through the pores of the channels, indicating that TMEM16 and the related OSCA and TMEM63 channels have a conserved potential for both ion and lipid permeation. The authors interpret the findings as revealing that these channel/scramblase proteins have a relatively low "energetic barrier for scramblase" activity. The experiments themselves seem to be done with a high level of rigor and the paper is well written. A weakness is the limited scope of the experiments which, if fixed, could open up a new line of inquiry.

Author response:

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

Reviewer #1 (Public Review):

In this study, the authors examined the role of IBTK, a substrate-binding adaptor of the CRL3 ubiquitin ligase complex, in modulating the activity of the eiF4F translation initiation complex. They find that IBTK mediates the non-degradative ubiquitination of eiF4A1, promotes cap-dependent translational initiation, nascent protein synthesis, oncogene expression, and tumor cell growth. Correspondingly, phosphorylation of IBTK by mTORC1/ S6K1 increases eIF4A1 ubiquitination and sustains oncogenic translation.

Strengths:

This study utilizes multiple biochemical, proteomic, functional, and cell biology assays to substantiate their results. Importantly, the work nominates IBTK as a unique substrate of mTORC1, and further validates eiF4A1 (a crucial subunit of the ei44F complex) as a promising therapeutic target in cancer. Since IBTK interacts broadly with multiple members of the translational initial complex - it will be interesting to examine its role in eiF2alpha-mediated ER stress as well as eiF3-mediated translation. Additionally, since IBTK exerts pro-survival effects in multiple cell types, it will be of relevance to characterize the role of IBTK in mediating increased mTORC1 mediated translation in other tumor types, thus potentially impacting their treatment with eiF4F inhibitors.

Limitations/Weaknesses:

The findings are mostly well supported by data, but some areas need clarification and could potentially be enhanced with further experiments:

(1) Since eiF4A1 appears to function downstream of IBTK1, can the effects of IBTK1 KO/KD in reducing puromycin incorporation (in Fig 3A), cap-dependent luciferase reporter activity (Fig 3G), reduced oncogene expression (Fig 4A) or 2D growth/ invasion assays (Fig 4) be overcome or bypassed by overexpressing eiF4A1? These could potentially be tested in future studies.

We appreciate the reviewer for bringing up this crucial point. As per the reviewer's suggestion, we conducted experiments where we overexpressed Myc-eIF4A1 in IBTK-KO SiHa cells. Our findings indicate that increasing levels of eIF4A1 through ectopic overexpression is unable to reverse the decrease in puromycin incorporation (Fig. S3C) and protein expression of eIF4A1 targets caused by IBTK ablation (Fig. S4E). These results clearly demonstrate that IBTK ablation-induced eIF4A1 dysfunctions cannot be rescued by simply elevating eIF4A1 protein levels. Given the above results are negative, the impacts of eIF4A1 overexpression on the 2D growth/invasion capacities of IBTK-KO cells were not further examined. We sincerely appreciate the reviewer's understanding regarding this matter.

(2) The decrease in nascent protein synthesis in puromycin incorporation assays in Figure 3A suggest that the effects of IBTK KO are comparable to and additive with silvesterol. It would be of interest to examine whether silvesterol decreases nascent protein synthesis or increases stress granules in the IBTK KO cells stably expressing IBTK as well.

We appreciate the reviewer for bringing up this crucial point. We have showed that silvestrol treatment still decreased nascent protein synthesis in IBTK-KO cells overexpressing FLAG-IBTK as well (Fig. S3B).

(3) The data presented in Figure 5 regarding the role of mTORC1 in IBTK- mediated eiF4A1 ubiquitination needs further clarification on several points:

• It is not clear if the experiments in Figure 5F with Phos-tag gels are using the FLAG-IBTK deletion mutant or the peptide containing the mTOR sites as it is mentioned on line 517, page 19 "To do so, we generated an IBTK deletion mutant (900-1150 aa) spanning the potential mTORC1-regulated phosphorylation sites" This needs further clarification.

We appreciate the reviewer for bringing up this crucial point. The IBTK deletion mutant used in Fig. 5F is FLAG-IBTK900-1150aa. We have annotated it with smaller font size in the panel (red box) in Author response image 1.

Author response image 1.

• It may be of benefit to repeat the Phos tag experiments with full-length FLAG- IBTK and/or endogenous IBTK with molecular weight markers indicating the size of migrated bands.

We appreciate the reviewer for bringing up this crucial point. We attempted to perform Phos-tag assays to detect the overexpressed full-length FLAG-IBTK or endogenous IBTK. However, we encountered difficulties in successfully transferring the full-length FLAG-IBTK or endogenous IBTK onto the nitrocellulose membrane during Phos-tag WB analysis. This is likely due to the limitations of this technique. Based on our experience, phos-tag gel is less efficient in detecting protein motility shifts with large molecular weights. As the molecular weight of IBTK protein is approximately 160 kDa, it falls within this category. Considering these technical constraints, we did not include Phos-tag assay results for full-length IBTK in our study. We sincerely appreciate the reviewer's understanding regarding this matter.

The binding of Phos-tag to phosphorylated proteins induces a mobility shift during gel electrophoresis or protein separation techniques. This shift allows for the visualization and quantification of phosphorylated proteins separately from non-phosphorylated proteins. It's important to note that these mobility shifts indicate phosphorylation status, rather than actual molecular weights. pre- stained protein markers are typically used as a reference to assess the efficiency of protein transfer onto the membrane [Ref: 1]. Considering the aforementioned reasons, we did not add molecular weights to the WB images.

Reference [1]. FUJIFILM Wako Pure Chemical Corporation, https://www.wako- chemicals.de/media/pdf/c7/5e/20/FUJIFILM-Wako_Phos-tag-R.pdf

• Additionally, torin or Lambda phosphatase treatment may be used to confirm the specificity of the band in separate experiments.

We appreciate the reviewer for bringing up this crucial point. Torin1 is a synthetic mTOR inhibitor by preventing the binding of ATP to mTOR, leading to the inactivation of both mTORC1 and mTORC2, whereas rapamycin primarily targets mTORC1 activity and may inhibit mTORC2 in certain cell types after a prolonged treatment. We have identified that the predominant mediator of IBTK phosphorylation is the mTORC1/S6K1 complex. Therefore, in this context, we think that rapamycin is sufficient to inactivate the mTORC1/S6K1 pathway. As shown in Fig. 5F, the phosphorylated IBTK900-1150aa was markedly decreased while the non-phosphorylated form was simultaneously increased in rapamycin- treated cells. As per the reviewer's suggestion, we treated FLAG-IBTK900-1150aa overexpressed cells with lambda phosphatase. As shown in Fig. 5G, lambda phosphatase treatment completely abolished the mobility shifts of phosphorylated FLAG-IBTK900-1150aa. Additionally, the lowest band displayed an abundant accumulation of the non-phosphorylated form of FLAG-IBTK900-1150aa. These findings confirm that the mobility shifts observed in WB analysis correspond to the phosphorylated forms of FLAG-IBTK900-1150aa.

• Phos-tag gels with the IBTK CRISPR KO line would also help confirm that the non-phosphorylated band is indeed IBTK.

We appreciate the reviewer for bringing up this crucial point. As we state above, we performed Phos-tag assays to detect the mobility shifts of phosphorylated FLAG-IBTK900-1150aa. Anti-FLAG antibody, but not the anti-IBTK antibody was used for WB detection. This antibody does not exhibit cross-reactivity with endogenous IBTK.

• It is unclear why the lower, phosphorylated bands seem to be increasing (rather than decreasing) with AA starvation/ Rapa in Fig 5H.

We appreciate the reviewer for bringing up this crucial point. We think the panel the reviewer mentioned is Fig. 5F. According to the principle of Phos-tag assays, proteins with higher phosphorylation levels have slower migration rates on SDS-PAGE, while proteins with lower phosphorylation levels have faster migration rates.

As shown in Author response image 2, the green box indicates the most phosphorylated forms of FLAG-IBTK900-1150aa, the red box indicates the moderately phosphorylated forms of FLAG-IBTK900-1150aa, and the yellow box indicates the non-phosphorylated forms of FLAG-IBTK900-1150aa. AA starvation or Rapamycin treatment reduced the hyperphosphorylated forms of FLAG-IBTK900-1150aa (green box), while simultaneously increasing the hypophosphorylated (red box) and non- phosphorylated (yellow box) forms of FLAG-IBTK900-1150aa. Thus, we conclude that AA starvation or Rapamycin treatment leads to a marked decrease in the phosphorylation levels of FLAG-IBTK900-1150aa.

Author response image 2.

Reviewer #2 (Public Review):

Summary:

This study by Sun et al. identifies a novel role for IBTK in promoting cancer protein translation, through regulation of the translational helicase eIF4A1. Using a multifaceted approach, the authors demonstrate that IBTK interacts with and ubiquitinates eIF4A1 in a non-degradative manner, enhancing its activation downstream of mTORC1/S6K1 signaling. This represents a significant advance in elucidating the complex layers of dysregulated translational control in cancer.

Strengths:

A major strength of this work is the convincing biochemical evidence for a direct regulatory relationship between IBTK and eIF4A1. The authors utilize affinity purification and proximity labeling methods to comprehensively map the IBTK interactome, identifying eIF4A1 as a top hit. Importantly, they validate this interaction and the specificity for eIF4A1 over other eIF4 isoforms by co- immunoprecipitation in multiple cell lines. Building on this, they demonstrate that IBTK catalyzes non-degradative ubiquitination of eIF4A1 both in cells and in vitro through the E3 ligase activity of the CRL3-IBTK complex. Mapping IBTK phosphorylation sites and showing mTORC1/S6K1-dependent regulation provides mechanistic insight. The reduction in global translation and eIF4A1- dependent oncoproteins upon IBTK loss, along with clinical data linking IBTK to poor prognosis, support the functional importance.

Weaknesses:

While these data compellingly establish IBTK as a binding partner and modifier of eIF4A1, a remaining weakness is the lack of direct measurements showing IBTK regulates eIF4A1 helicase activity and translation of target mRNAs. While the effects of IBTK knockout/overexpression on bulk protein synthesis are shown, the expression of multiple eIF4A1 target oncogenes remains unchanged.

Summary:

Overall, this study significantly advances our understanding of how aberrant mTORC1/S6K1 signaling promotes cancer pathogenic translation via IBTK and eIF4A1. The proteomic, biochemical, and phosphorylation mapping approaches established here provide a blueprint for interrogating IBTK function. These data should galvanize future efforts to target the mTORC1/S6K1-IBTK-eIF4A1 axis as an avenue for cancer therapy, particularly in combination with eIF4A inhibitors.

Reviewer #1 (Recommendations For The Authors):

(1) Certain references should be provided for clarity. For e.g.,: Page 15, line 418 " The C-terminal glycine glycine (GG) amino acid residues are essential for Ub conjugation to targeted proteins".

We appreciate the reviewer for bringing up this crucial point. We have taken two fundamental review papers (PMID: 22524316, 9759494) on the ubiquitin system as references in this sentence.

(2) Please describe the properties of the ΔBTB mutant on page 15 when first describing it. What motifs does it lack and has it been described before in functional studies?

We appreciate the reviewer for bringing up this crucial point. We added a sentence to describe the properties of the ΔBTB mutant. This mutant lacks the BTB1 and BTB2 domains (deletion of aa 554–871), which have been previously demonstrated to be essential for binding to CUL3. The original reference has been added to the revised manuscript.

(3) In Figure 2G how do the authors explain the fact that co-expression of the Ub K-ALLR mutant, which is unable to form polyubiquitin chains, formed only a moderate reduction in IBTK-mediated eIF4A1 ubiquitination?

We appreciate the reviewer for bringing up this crucial point. The Ub K-ALLR mutant can indeed conjugate to substrate proteins, but it cannot form chains due to its absence of lysine residues, resulting in mono-ubiquitination. Multi- mono-ubiquitination refers to the attachment of single ubiquitin molecules to multiple lysine residues on a substrate protein. It's worth noting that a poly- ubiquitinated protein and a multi-mono-ubiquitinated protein appear strikingly similar in Western blot. Our findings demonstrated that the co-expression of the Ub K-ALL-R mutant resulted in only a modest reduction in IBTK-mediated eIF4A1 ubiquitination (Fig. 2G), and that eIF4A1 was ubiquitinated at twelve lysine residues when co-expressed with IBTK (Fig. S2F). As such, we conclude that the CRL3IBTK complex primarily catalyzes multi-mono-ubiquitination on eIF4A1. .

(4) In Figure 5, The identity of the seven sites in the IBTK 7ST A mutants should be specified.

We appreciate the reviewer for bringing up this crucial point. We have specified the seven mutation sites in the IBTK-7ST A mutant (Fig. 6A).

(5) In Figure 5, the rationale for generating antibodies only to S990/992/993, as opposed to the other mTORC1/S6K motifs should be specified.

We appreciate the reviewer for bringing up this crucial point. Upon demonstrating that IBTK can be phosphorylated—with evidence from positive Phos-tag and in vitro phosphorylation assays—we sought to directly detect changes in the phosphorylation levels using an antibody specific to IBTK phosphorylation. However, the expense of generating seven phosphorylation- specific antibodies for each site is significant. Recognizing that S990/992/993 are three adjacent sites, we deemed it appropriate to generate a single antibody to recognize the phospho-S990/992/993 epitope. Moreover, out of the seven phosphorylation sites, S992 perfectly matches the consensus motif for S6K1 phosphorylation (RXRXXS). Utilizing this antibody allowed us to observe a substantial decrease in the phosphorylation levels of these three adjacent Ser residues in IBTK following either AA deprivation or Rapamycin treatment (Fig. 5L). We have specified these points in the manuscript.

Reviewer #2 (Recommendations For The Authors):

The following suggestions would strengthen the study:

(1) Directly examine the effects of IBTK modulation (knockdown/knockout/ overexpression) on eIF4A1 helicase activity.

We appreciate the reviewer for bringing up this crucial point. We agree with the reviewer's suggestion that evaluating IBTK's influence on eIF4A1 helicase activity directly would enhance the strength of our conclusion. However, the current eIF4A1 helicase assays, as described in previous publications [Ref: 1, 2], can only be conducted using in vitro purified recombinant proteins. For instance, it is feasible to assess the varying levels of helicase activity exhibited by recombinant wild-type or mutant EIF4A1 proteins [Ref: 2]. Importantly, there is currently no reported methodology for evaluating the helicase activity of EIF4A1 in vivo, as mentioned by the reviewer in gene knockdown, knockout, or overexpression cellular contexts. Therefore, we have not performed these assays and we sincerely appreciate the reviewer's understanding in this regard. We sincerely appreciate the reviewer's understanding regarding this matter.

Reference:

[1] Chu J, Galicia-Vázquez G, Cencic R, Mills JR, Katigbak A, Porco JA, Pelletier J. CRISPR-mediated drug-target validation reveals selective pharmacological inhibition of the RNA helicase, eIF4A. Cell reports. 2016 Jun 14;15(11):2340-7.

[2] Chu J, Galicia-Vázquez G, Cencic R, Mills JR, Katigbak A, Porco JA, Pelletier J. CRISPR-mediated drug-target validation reveals selective pharmacological inhibition of the RNA helicase, eIF4A. Cell reports. 2016 Jun 14;15(11):2340-7.

(2) Justify why the expression of some but not all eIF4A1 target oncogenes is affected in IBTK-depleted/overexpressing cells. This is important if IBTK should be considered as a therapeutic target. The authors should consider which of the eIF4A1 targets are most impacted by IBTK KO. This would provide a more focused therapeutic approach in the future.

We appreciate the reviewer for bringing up this crucial point. As the reviewer has pointed out, we assessed the protein levels of ten reported eIF4A1 target genes across three cancer cell lines (Fig.4, Fig. S4A, C）. We observed that IBTK depletion led to a substantial reduction in the protein levels of most eIF4A1- regulated oncogenes upon IBTK depletion, although there were some exceptions. For instance, IBTK KO in H1299 cells exerted minimal influence on the protein levels of ROCK1 (Fig. S4A). Several possible explanations might account for this observation: firstly, given that our list of eIF4A1 target genes collected from previous studies conducted using distinct cell lines, it is not unexpected for different lines to exhibit subtle differences in regulation of eIF4A1 target genes. Secondly, as a CRL3 adaptor, IBTK potentially performs other biological functions via ubiquitination of specific substrates; dysregulation of these could buffer the impact of IBTK KO on the protein expression of some eIF4A1 target genes. We added these comments to the Discussion section of the revised manuscript.

(3) Expand mTOR manipulation experiments (inhibition, Raptor knockout, activation) and evaluate impacts on IBTK phosphorylation, eIF4A1 ubiquitination, and translation.

The mTORC1 signaling pathway is constitutively active under normal culture conditions. In order to inhibit mTORC1 activation, we employed several approaches including AA starvation, Rapamycin treatment, or Raptor knockout. Our results have demonstrated that both AA starvation and rapamycin treatment led to a reduction in eIF4A1 ubiquitination (Fig. 5M). Moreover, we have included new findings in the revised manuscript, which highlight that Raptor knockout specifically decreases eIF4A1 ubiquitination (Fig. 5N). It is worth mentioning that the impacts of mTOR inhibition or activation on protein translation have been extensively investigated and documented in numerous studies. Therefore, in our study, we did not feel it necessary to examine these treatments further.

(4) Although not absolutely necessary, it would be nice to see if some of these findings are true in other cancer cell types.

We appreciate the reviewer for bringing up this crucial point. We concur with the reviewer's suggestion that including data from other cancer cell types would enhance the strength of our conclusion. While the majority of our data is derived from two cervical cancer cell lines, we have corroborated certain key findings— such as the impact of IBTK on eIF4A1 and its target gene expression—in H1299 cells (human lung cancer) (Fig. 2C, Fig. S4A, B) and in CT26 cells (murine colon adenocarcinoma) (Fig. S4C, D). Additionally, we demonstrated that IBTK promotes IFN-γ-induced PD-L1 expression and tumor immune escape in both the H1299 and CT26 cells (Fig. S6A-K).

eLife assessment

This study reports a novel substrate and a mediator of oncogenesis downstream of mTORC1, fundamentally advancing our understanding of the mechanistic basis of mTORC1-regulated cap-dependent translation and protein synthesis. Using an array of biochemical, proteomic and functional assays, the authors provide compelling evidence for a novel mTORC1/S6K1-IBTK-eIF4A1 signaling axis that promotes cancer pathogenic translation. This work is of broad interest and significance, given the importance of aberrant protein synthesis in cancer.

Reviewer #1 (Public Review):

In this study, the authors examined the role of IBTK, a substrate-binding adaptor of the CRL3 ubiquitin ligase complex, in modulating the activity of the eiF4F translation initiation complex. They find that IBTK mediates the non-degradative ubiquitination of eiF4A1, promotes cap-dependent translational initiation, nascent protein synthesis, oncogene expression, and tumor cell growth. Correspondingly, phosphorylation of  IBTK by mTORC1/ S6K1 increases eIF4A1 ubiquitination and sustains oncogenic translation.

Strengths:

This study utilizes multiple biochemical, proteomic, functional and cell biology assays to substantiate their results.  Importantly, the work nominates IBTK as a unique substrate of mTORC1, and further validates eiF4A1 ( a crucial subunit of the ei44F complex) as a promising therapeutic target in cancer. Since IBTK interacts broadly with multiple members of the translational initial complex- it will be interesting to examine its role in eiF2alpha-mediated ER stress as well as eiF3-mediated translation. Additionally, since IBTK exerts pro-survival effects in multiple cell types, it will be of relevance to characterize the role of IBTK in mediating increased mTORC1 mediated translation in other tumor types, thus potentially impacting their treatment with eiF4F inhibitors.

Limitations/Weaknesses:

The findings are mostly well supported by data, but some areas need clarification and could potentially be enhanced with further experiments:

(1) Since eiF4A1 appears to function downstream of IBTK1, can the effects of IBTK1 KO/KD in reducing puromycin incorporation ( in Fig 3A),  cap-dependent luciferase reporter activity (Fig 3G), reduced oncogene expression ( Fig 4A) or 2D growth/ invasion assays (Fig 4) be overcome or bypassed by overexpressing eiF4A1? These could potentially be tested in future studies. <br /> (2) The decrease in nascent protein synthesis in puromycin incorporation assays in Figure 3A suggests that the effects of IBTK KO are comparable to and additive with silvesterol. It would be of interest to examine whether silvesterol decreases nascent protein synthesis or increases stress granules in the IBTK KO cells stably expressing IBTK as well. <br /> (3) The data presented in Figure 5 regarding the role of mTORC1 in IBTK-mediated eiF4A1 ubiquitination needs further clarification on several points:<br /> - It is not clear if the experiments in Figure 5F with Phos-tag gels are using the FLAG-IBTK deletion mutant or the peptide containing the mTOR sites as it is mentioned on line 517, page 19 "To do so, we generated an IBTK deletion mutant (900-1150 aa) spanning the potential mTORC1-regulated phosphorylation sites" This needs further clarification.<br /> -It may be of benefit to repeat the Phos tag experiments with full length FLAG-IBTK and/or endogenous IBTK with molecular weight markers indicating size of migrated bands.<br /> -Additionally, torin or Lambda phosphatase treatment may be used to confirm the specificity of the band in separate experiments.<br /> -Phos-tag gels with the IBTK CRISPR KO line would also help confirm that the non-phosphorylated band is indeed IBTK. <br /> -It is unclear why the lower, phosphorylated bands seem to be increasing ( rather than decreasing) with AA starvation/ Rapa in Fig 5H.

Reviewer #2 (Public Review):

Summary:

This study by Sun et al. identifies a novel role for IBTK in promoting cancer protein translation, through regulation of the translational helicase eIF4A1. Using a multifaceted approach, the authors demonstrate that IBTK interacts with and ubiquitinates eIF4A1 in a non-degradative manner, enhancing its activation downstream of mTORC1/S6K1 signaling. This represents a significant advance in elucidating the complex layers of dysregulated translational control in cancer.

Strengths:

A major strength of this work is the convincing biochemical evidence for a direct regulatory relationship between IBTK and eIF4A1. The authors utilize affinity purification and proximity labeling methods to comprehensively map the IBTK interactome, identifying eIF4A1 as a top hit. Importantly, they validate this interaction and the specificity for eIF4A1 over other eIF4 isoforms by co-immunoprecipitation in multiple cell lines. Building on this, they demonstrate that IBTK catalyzes non-degradative ubiquitination of eIF4A1 both in cells and in vitro through the E3 ligase activity of the CRL3-IBTK complex. Mapping IBTK phosphorylation sites and showing mTORC1/S6K1-dependent regulation provides mechanistic insight. The reduction in global translation and eIF4A1-dependent oncoproteins upon IBTK loss, along with clinical data linking IBTK to poor prognosis, support the functional importance. Finally, the impact of IBTK on eIF4A1 target gene expression in colon and lung cancer cell lines, strengthens these findings.

Weaknesses:

While the effects of IBTK knockout/over-expression on bulk protein synthesis are shown, the expression of several eIF4A1 target oncogenes remains unchanged.

Summary:

Overall, this study significantly advances our understanding of how aberrant mTORC1/S6K1 signaling promotes cancer pathogenic translation via IBTK and eIF4A1. The proteomic, biochemical and phosphorylation mapping approaches established here provide a blueprint for interrogating IBTK function. These data should galvanize future efforts to target the mTORC1/S6K1-IBTK-eIF4A1 axis as an avenue for cancer therapy, particularly in combination with eIF4A inhibitors.

https://www.derstandard.at/story/3000000215556/franzoesischer-badeort-muss-uebersiedeln-das-meer-ist-immer-staerker

la etica tiene que ver con bridar algo a la sociedad, de ser conciente de como las desiciones afectan a los demas.

en el ambito academico es lo que quiero aporr¿tar desde la traspariencia, atenticidad,. min2:24 lo que no se autentico y etico no tiene futuro...

debe ser claro el objetivo con el que se hacel el doctorado, tambien se optar por estudiar no porque le gusta sino porque es la manera de poder tener mejores condiciones de vida. la oferta y la demanada. es lo que el autor menciona. el conocimiento sin etica, talvez no es suficiente.

anteligencia y valores humanos...

can't wait for the pizza ;)

No estoy de acuerdo con esta idea porque aunque haya un caracter normativo, la etica tambien debe ser una desicion personal y quien quiera pertenecer o esta en determiando grupo social debe hacerlo con la conviccion de entender como funcionan las relaciones internas.

mencionar reflexion sobre la libertad

asumir un reto académico debe ser una decisión totalmente consciente, que genere motivacion, compromiso, disciplina, autonomia, esfuerxo, responsabilidad. La etica podria enternder se como un pilar fundamental en el ejercicio academico e investigativo, teniendo en cuenta a demaas que se esta generando conocimiento cientifico que fdebra ser autentico y libre de todo fraude.

mencionar algunas practicas de fraude que usualmente suceden en una educación a distancia. buscar referencias del tema

Ir a la fuente primaria para retomar concepto de fraude académico.

en contraposicion a la etica, aparece el fraude, que aparece como una practica que transgrede la integridad del proceso academico.

a ética en el ámbito académico se define como una práctica reglamentaria o normativa que estructura las políticas educativas, planes institucionales y escenarios pedagógicos donde se configuran las discusiones sobre el proceder práctico de los miembros del entorno educativo, Cita textual

En los diferentes grupos sociales se configuran acuerdos sobre lo que se considera correcto e incorrecto, las relaciones se configuran en esa dualidad de lograr definir lo que se considera que esta bien o no, para permitir que se cumplan los objetivos comunes.

ampliar mas informacion sobre los pricipios de la etica academica

La etica se concibe como algo aprendido que modifica la conducta humana y permite enterder la manera qen qu el ser humano debe relacionarse con el mundo. Ir a la fuente primaria.

La etica ha transitado de ser un saber normativo orientado a la busqueda de la felicidad a una valor fundamental orientado a la búsqueda de la justicia. tal citando a tal

add graphs!

I am wondering if we could format the Pressbook to have a colored text box specific to "Indigenous Perspectives"

In general all images included so far appear very large. Can these be made smaller. Works on my large screen at home but less so on my laptop screen. Considering some students will be using thier phones can view be made more flexible??

Maria to look at original draft, as I thought I had included this.

Image of family does not appear to have citation?? Do you think we could find a more "diverse" family image?

eLife assessment

This valuable study is of relevance for those interested in the mechanism required for infections of humans by Klebsiella pneumoniae. The authors apply TraDIS (high-density TnSeq) to K. pneumoniae with the goal of identifying genes required for survival under various infection-relevant conditions and the gene sets identified, together with the raw sequence data, will be resources for the Klebsiella research community. The evidence to support the lists of essential and conditionally-essential genes is convincing. The study provides strong evidence that some genes are conditionally essential in urine because of iron limitation, but there is less mechanistic insight for genes that are conditionally essential in serum.

Author response:

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

The reviewer comments have been helpful, and we have revised the manuscript to address the concerns of reviewer 2. In addition to text changes, we also added a negative control to Figure 1 to address concerns about photobleaching or DNA unwrapping.

Reviewer #1:

This manuscript presents an extremely exciting and very timely analysis of the role that the nucleosome acidic patch plays in SWR1-catalyzed histone exchange. Intriguingly, SWR1 loses activity almost completely if any of the acidic patches are absent. To my knowledge, this makes SWR1 the first remodeler with such a unique and pronounced requirement for the acidic patch. The authors demonstrate that SWR1 affinity is dramatically reduced if at least one of the acidic patches is absent, pointing to a key role of the acidic patch in SWR1 binding to the nucleosome. The authors also pinpoint a specific subunit - Swc5 - that can bind nucleosomes, engage the acidic patch, and obtain a cryo-EM structure of Swc5 bound to a nucleosome. They also identify a conserved arginine-rich motif in this subunit that is critical for nucleosome binding and histone exchange in vitro and for SWR1 function in vivo. The authors provide evidence that suggests a direct interaction between this motif and the acidic patch.

Strengths:

The manuscript is well-written and the experimental data are of outstanding quality and importance for the field. This manuscript significantly expands our understanding of the fundamentally important and complex process of H2A.Z deposition by SWR1 and would be of great interest to a broad readership.

We thank the reviewer for their enthusiastic and positive comments on our work.

Reviewer #2:

Summary:

In this study, Baier et al. investigated the mechanism by which SWR1C recognizes nucleosomal substrates for the deposition of H2A.Z. Their data convincingly demonstrate that the nucleosome's acidic patch plays a crucial role in the substrate recognition by SWR1C. The authors presented clear evidence showing that Swc5 is a pivotal subunit involved in the interaction between SWR1C and the acidic patch. They pared down the specific region within Swc5 responsible for this interaction. However, two central assertions of the paper are less convincing. First, the data supporting the claim that the insertion of one Z-B dimer into the canonical nucleosome can stimulate SWR1C to insert the second Z-B dimer is somewhat questionable (see below). Given that this claim contradicts previous observations made by other groups, this hypothesis needs further testing to eliminate potential artifacts. Secondly, the claim that SWR1C simultaneously recognizes the acidic patch on both sides of the nucleosome also needs further investigation, as the assay used to establish this claim lacks the sensitivity necessary to distinguish any difference between nucleosomal substrates containing one or two intact acidic patches.

Strengths:

As mentioned in the summary, the authors presented clear evidence demonstrating the role of Swc5 in recognition of the nucleosome acidic patch. The identification of the specific region in Swc5 responsible for this interaction is important.

We thank the reviewer for their careful critique of our work. Below we address each major concern.

Major comments: (1) Figure 1B: It is unclear how much of the decrease in FRET is caused by the bleaching of fluorophores. The authors should include a negative control in which Z-B dimers are omitted from the reaction. In the absence of ZB dimers, SWR1C will not exchange histones. Therefore, any decrease in FRET should represent the bleaching of fluorophores on the nucleosomal substrate, allowing normalization of the FRET signal related to A-B eviction.

In this manuscript, as well as in our two previous publications (Singh et al., 2019; Fan et al.,2022), we have presented the results of no enzyme controls, +/- ZB dimers, no ATP controls, or AMP-PNP controls for our FRET-based, H2A.Z deposition assay (see also Figure S3). We do not observe significant levels of photobleaching in this assay, either during ensemble measurements or in an smFRET experiment. To aid the reader, we have added the AMP-PNP data for the experiment shown in Figure 1B. The results show there is less than a 10% decrease in FRET over 30’, and the signal from the double acidic patch disrupted nucleosome is identical to this negative control.

(2) Figure S3: The authors use the decrease in FRET signal as a metric of histone eviction. However, Figure S3 suggests that the FRET signal decrease could be due to DNA unwrapping. Histone exchange should not occur when SWR1C is incubated with AMP-PNP, as histone exchange requires ATP hydrolysis (10.7554/eLife.77352). And since the insertion of Z-B dimer and the eviction of A-B dimer are coupled, the decrease of FRET in the presence of AMP-PNP is unlikely due to histone eviction or exchange. Instead, the FRET decrease is likely due to DNA unwrapping (10.7554/eLife.77352). The authors should explicitly state what the loss of FRET means.

We agree with the reviewer, that loss of FRET can be due to DNA unwrapping from the nucleosome. We have previously demonstrated this activity by SWR1C in our smFRET study (Fan et al., 2022). However, DNA unwrapping is highly reversible and has a time duration of only 1-3 seconds. We and others have not observed stable unwrapping of nucleosomes by SWR1C, but rather the stable loss of FRET reports on dimer eviction. We assume the reviewer is concerned about the rather large decrease in FRET signal shown in the AMP-PNP controls for Figure S3, panels A and D. For the other 7 panels, the decrease in FRET with AMP-PNP are minimal. In fact, if we average all of the AMP-PNP data points, the rate of FRET loss is not statistically different from no enzyme control reactions (nucleosome plus ZB dimers).

Data for panels A and D used a 77NO nucleosomal substrate, with Cy3 labeling the linker distal dimer. This is our standard DNA fragment, and it was used in Figure 1B. The only difference between data sets is that the data shown in Fig 1B used nucleosome reconstituted with a Cy5-labelled histone octamer, rather than the hexasome assembly method used for Fig S3. Three points are important. First, for all of these substrates, we assembled 3 independent nucleosomes, and the results are highly reproducible. Two, we performed a total of 6 experiments for the 77NO-Cy5 substrates to ensure that the rates were accurate (+/-ATP). Third, and most important, we do not see this decrease in FRET signal in the absence of SWR1C (no enzyme control). This data was included in the data source file. Thus, it appears that there is significant SWR1C-induced nucleosome instability for these two hexasome-assembled substrates. We now note this in the legend to Figure S3. Key for this work, however, is that there is a large increase in the rate of FRET loss in the presence of ATP, and this rate is faster when a ZB dimer was present at the linker proximal location. In response to the last point, we state in the first paragraph of the results: “The dimer exchange activity of SWR1C is monitored by following the decrease in the 670 nm FRET signal due to eviction of the Cy5-labeled AB-Cy5 dimer (Figure 1A).”

(3) Related to point 2. One way to distinguish nucleosomal DNA unwrapping from histone dimer eviction is that unwrapping is reversible, whereas A-B eviction is not. Therefore, if the authors remove AMP-PNP from the reaction chamber and a FRET signal reappears, then the initial loss of FRET was due to reversible DNA unwrapping. However, if the removal of AMP-PNP did not regain FRET, it means that the loss of FRET was likely due to A-B eviction. The authors should perform an AMP-PNP and/or ATP removal experiment to make sure the interpretation of the data is correct.

See response to item 2 above

(4) The nature of the error bars in Figure 1C is undefined; therefore, the statistical significance of the data is not interpretable.

We apologize for not making this more explicit for each figure. The error bars report on 95% confidence intervals from at least 3 sets of experiments. This statement has been added to the legend.

(5) The authors claim that the SWR1C requires intact acidic patches on both sides of the nucleosomes to exchange histone. This claim was based on the experiment in Figure 1C where they showed mutation of one of two acidic patches in the nucleosomal substrate is sufficient to inhibit SWR1C-mediated histone exchange activity. However, one could argue that the sensitivity of this assay is too low to distinguish any difference between nucleosomes with one (i.e., AB/AB-apm) versus two mutated acidic patches (i.e., AB-apm/AB-apm). The lack of sensitivity of the eviction assay can be seen when Figure 1B is taken into consideration. In the gel-shift assay, the AB-apm/AB-apm nucleosome exhibited a 10% SWR1C-mediated histone exchange activity compared to WT. However, in the eviction assay, the single AB/AB-apm mutant has no detectable activity. Therefore, to test their hypothesis, the authors should use the more sensitive in-gel histone exchange assay to see if the single AB/AB-apm mutant is more or equally active compared to the double AB-apm/AB-apm mutant.

Our pincher model is based on three, independent sets of data, not just Figure 1C. First, as noted by the reviewer, we find that disruption of either acidic patch cripples the dimer exchange activity of SWR1C in the FRET-based assay. Whether the defect is identical to that of the double APM mutant nucleosome does not seem pertinent to the model. In a second set of assays, we used fluorescence polarization to quantify the binding affinity of SWR1C for wildtype nucleosomes, a double APM nucleosome, or each single APM nucleosome. Consistent with the pincher model, each single APM disruption decreases binding affinity at least 10-fold (below the sensitivity of the assay). Finally, we monitored the ability of different nucleosomes to stimulate the ATPase activity of SWR1C. Consistent with the pincher model, a single APM disruption was sufficient to eliminate nucleosome stimulation.

(6) The authors claim that the AZ nucleosome is a better substrate than the AA nucleosome. This is a surprising result as previous studies showed that the two insertion steps of the two Z-B dimers are not cooperative (10.7554/eLife.77352 and 10.1016/J.CELREP.2019.12.006). The authors' claim was based on the eviction assay shown in Fig 1C. However, I am not sure how much variation in the eviction assay is contributed by different preparations of nucleosomes. The authors should use the in-gel assay to independently test this hypothesis.

For all data shown in our manuscript, at least three different nucleosome preparations were used. The impact of a ZB dimer on the rates of dimer exchange was highly reproducible among different nucleosome preparations and experiments. We also see reproducible ZB stimulation for three different substrates – with ZB on the linker proximal side, the linker distal side, and on one side of a core particle. We do not believe that our data are inconsistent with previous studies. First, the previous work referenced by the reviewer performed dimer exchange reactions with a large excess of nucleosomes to SWR1C (catalytic conditions), whereas we used single turnover reactions. Secondly, our study is the first to use a homogenous, ZA heterotypic nucleosome as a substrate for SWR1C. All previous studies used a standard AA nucleosome, following the first and second rounds of dimer exchange that occur sequentially. And finally, we observe only a 20-30% increase in rate by a ZB dimer (e.g. 77N0 substrates), and such an increase was unlikely to have been detected by previous gel-based assays.

Minor comments:

(1) Abstract line 4: To say 'Numerous' studies have shown acidic patch impact chromatin remodeling enzymes activity may be too strong.

Removed

(2) Page 15, line 15: The authors claim that swc5∆ was inviable on formamide media. However, the data in Figure 8 shows cell growth in column 1 of swc5∆.

The term ‘inviable’ has been replaced with ‘poor’ or ‘slow growth’

(3) The authors should use standard yeast nomenclature when describing yeast genes and proteins. For example, for Figure 8 and legend, Swc5∆ was used to describe the yeast strain BY4741; MATa; his3Δ1; leu2Δ0; met15Δ0; ura3Δ0; YBR231c::kanMX4. Instead, the authors should describe the swc5∆ mutant strain as BY4741 MAT a his3∆1 leu2∆0 met15∆0 ura3∆0 swc5∆::kanMX4. Exogenous plasmid should also be indicated in italics and inside brackets, such as [SWC5-URA3] or [swc5(R219A)-URA3].

We apologize for missing this mistake in the Figure 8 legend. We had inadvertently copied this from the euroscarf entry and forgot to edit the entry. We decided not to add all the plasmid names to the figure, as it was too cluttered. We state in the figure legend that the panels show growth of swc5 deletion strains harboring the indicated swc5 alleles on CEN/ARS plasmids.

(4) According to Lin et al. 2017 NAR (doi: 10.1093/nar/gkx414), there is only one Swc5 subunit per SWR1C. Therefore, the pincher model proposed by the authors would suggest that there is a missing subunit that recognizes the second acidic patch. The authors should point out this fact in the discussion. However, as mentioned in Major comment 6, I am not sure if the pincer model is substantiated.

In our discussion, we had noted that the published cryoEM structure had suggested that the Swc2 subunit likely interacts with the acidic patch on the dimer that is not targeted for replacement, and we proposed that Swc5 interacts with the acidic patch on the exchanging H2A/H2B dimer. We have now made this more clear in the text.

Reviewer #1 (Public Review):

This manuscript presents an extremely exciting and very timely analysis of the role that the nucleosome acidic patch plays in SWR1-catalyzed histone exchange. Intriguingly, SWR1 loses activity almost completely if any of the acidic patches are absent. To my knowledge, this makes SWR1 the first remodeler with such a unique and pronounced requirement for the acidic patch. The authors demonstrate that SWR1 affinity is dramatically reduced if at least one of the acidic patches is absent, pointing to a key role of the acidic patch in SWR1 binding to the nucleosome. The authors also pinpoint a specific subunit - Swc5 - that can bind nucleosomes and engage the acidic patch and obtain a cryo-EM structure of Swc5 bound to a nucleosome. They also identify a conserved arginine-rich motif in this subunit that is critical for nucleosome binding and histone exchange in vitro and for SWR1 function in vivo. The authors provide evidence that suggests a direct interaction between this motif and the acidic patch.

Strengths:

The manuscript is well-written and the experimental data are of outstanding quality and importance for the field. This manuscript significantly expands our understanding of the fundamentally important and complex process of H2A.Z deposition by SWR1 and would be of great interest for a broad readership.

eLife assessment

This manuscript presents an important analysis of the role that the nucleosome acidic patch plays in SWR1-catalyzed histone exchange. This manuscript contains convincing data which significantly expands our understanding of the complex process of H2A.Z deposition by SWR1 and therefore would be of interest to a broad readership.

Randomly Accessible Medium. A medium whose parts are uniquely referenced and hence can be pointed at from any location. In opposition to a scheme where the access of a particular location is given as the product of a sequence of traversals through the medium.

eLife assessment

This paper addresses an important topic (normative trajectory modelling), seeking to provide a method aiming to accurately reflect the individual deviation of longitudinal/temporal change compared to the normal temporal change characterized based on a pre-trained population normative model. The evidence provided for the new methods is, however, inadequate. There is a lack of simulation studies to formally evaluate the performance of the proposed method in making accurate estimations and inferences about the longitudinal changes, the novelty of the method is not sufficiently described, and the example provided is unsatisfactory.

Reviewer #1 (Public Review):<br /> <br /> Summary:

This paper provides a methodology for normative trajectory modeling, using cross-sectional data to set the "norms," and then applying these norms to longitudinal brain observations. An example of schizophrenia trajectories (two time points) is provided. The method assumes a Bayesian mixed effects model, which included some hyperparameters that need to be tuned. The longitudinal assumption is essentially a random intercept model, assuming that the age-based quantiles do not shift, and if they do that is a sign of disease-like trajectories.

Strengths:

Normative modeling of brain feature trajectories is an important topic. Bayesian models are a promising alternative to modeling these. Leveraging large-scale data to provide norms is also potentially useful.

Weaknesses:

The models described are not fundamentally novel, essentially a random intercept model (with a warping function), and some flexible covariate effects using splines (i.e., additive models). The assumption of constant quantiles is very strong, and limits the utility of the model to very short term data. The schizophrenia example leads to a counter-intuitive normalization of trajectories, which leads to suspicions that this is driven by some artifact of the data modeling/imaging pipelines. The method also assumes that the cross-sectional data is from a "healthy population" without describing what this population is (there is certainly every chance of ascertainment bias in large scale studies as well as small scale studies). This issue is completely elided over in the manuscript.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors provide a method aiming to accurately reflect the individual deviation of longitudinal/temporal change compared to the normal temporal change characterized based on pre-trained population normative model (i.e., a Bayesian linear regression normative model), which was built based on cross-sectional data. This manuscript aims to solve a recently identified problem of using normative models based on cross-sectional data to make inferences about longitudinal change.

Although the proposed method was implemented with real data and shown to be more sensitive in capturing the differences confirmed by previous studies than conventional methods, there is still a lack of simulation studies to formally evaluate the performance of the proposed method in making accurate estimations and inferences about the longitudinal changes.

Strengths:

The efforts of this work make a good contribution to addressing an important question of normative modeling. With the greater availability of cross-sectional studies for normative modeling than longitudinal studies, and the inappropriateness of making inferences about longitudinal subject-specific changes using these cross-sectional data-based normative models, it's meaningful to try to address this gap from the perspective of methodological development.

Weaknesses:

• The organization and clarity of this manuscript need enhancement for better comprehension and flow. For example, in the first few paragraphs of the introduction, the wording is quite vague. A lot of information was scattered and repeated in the latter part of the introduction, and the actual challenges/motivation of this work were not introduced until the 5th paragraph.

• There are no simulation studies to evaluate whether the adjustment of the cross-sectional normative model to longitudinal data can make accurate estimations and inferences regarding the longitudinal changes. Also, there are some assumptions involved in the modeling procedure, for example, the deviation of a healthy control from the population over time is purely caused by noise and constant variability of error/noise across x_n, and these seem to be quite strong assumptions. The presentation of this work's method development would be strengthened if the authors can conduct a formal simulation study to evaluate the method's performance when such assumptions are violated, and, ideally, propose some methods to check these assumptions before performing the analyses.

• The proposed "z-diff score" still falls in the common form of z-score to describe the individual deviation from the population/reference level, but now is just specifically used to quantify the deviation of individual temporal change from the population level. The authors need to further highlight the difference between the "z-score" and "z-diff score", ideally at its first mention, in case readers get confused (I was confused at first until I reached the latter part of the manuscript). The z-score can also be called a measure of "standardized difference" which kind of collides with what "z-diff" implies by its name.

• Explaining that one component of the variance is related to the estimation of the model and the other is due to prediction would be helpful for non-statistical readers.

• It would be easier for the non-statistical reader if the authors consistently used precision or variance for all variance parameters. Probably variance would be more accessible.

• The functions psi were never explicitly described. This would be helpful to have in the supplement with a reference to that in the paper.

• What is the goal of equations (13) and (14)? The authors should clarify what the point of writing these equations is prior to showing the math. It seems like it is to obtain an estimate of \sigma_{\ksi}^2, which the reader only learns at the end.

• What is the definition of "adaption" as used to describe equation (15)? In this equation, I think norm on subsample was not defined.

• "(the sandwich part with A)" - maybe call this an inner product so that it is not confused with a sandwich variance estimator. This is a bit unclear. Equation (8) does have the inner product involving A and \beta^{-1} does include variability of \eta. It seems like you mean that equation (8) incorrectly includes variability of \eta and does not have the right term vector component of the inner product involving A, but this needs clarifying.

• One challenge with the z-diff score is that it does not account for whether a person sits above or below zero at the first time point. It might make it difficult to interpret the results, as the results for a particular pathology could change depending on what stage of the lifespan a person is in. I am not sure how the authors would address those challenges.

Author response:

We thank the reviewers for the feedback on our manuscript; we are planning to address the raised concerns in the following manner:

We will be more explicit about the novelty of this method framing it more concretely within the scope of current research. From some comments of the reviewers, we understand that it is not clear that our method is an extension of an already existing method and model that has been extensively validated with pre-trained models brought online. Consequently, the details of the model as well as the training cohort are only covered briefly, referencing relevant published works on this topic. We will improve the clarity in this respect in the full responses. Nevertheless, we agree that the work would benefit from a simulation study that formally evaluates the performance of our method compared with more traditional approaches and will add it in our full responses. We will take care specifically of investigating the effect of assumptions like the centile-stability in healthy controls as suggested by the Reviewer 2.

The novelty of this work lies in introducing a mathematically transparent method to use normative modelling for evaluating studies with a longitudinal design, using normative models trained on cross sectional data. We emphasise strongly that this is otherwise not possible using current methods. Furthermore, by building on a pre-trained model, this method enjoys the benefits of big (cross-sectional) data (by the pre-trained model being fitted on an extensive population sample) without the need to have direct access to them, or a ‘big’ longitudinal dataset from the cohort at hand. This is crucial in neuroimaging, where longitudinal data are much more scarce than cross-sectional data.

We strongly disagree with the notion raised by Reviewer 1 that after the first episode cortical thickness alterations are expected to become more severe. There is now increasing evidence that: (i) trajectories of cortical thickness are highly variable across different individuals after the first psychotic episode and (ii) that individuals treated with second-generation antipsychotics and with careful clinical follow-up can show normalisation of cortical thickness atypicalities after the first episode. Indeed, we can provide evidence for this in an independent cohort, with different analytical methodologies, where precisely this occurs (https://www.medrxiv.org/content/10.1101/2024.04.19.24306008v1, https://pubmed.ncbi.nlm.nih.gov/36805840/). In the full revision, we would be happy to provide further discussion of evidence in support of this.

We  would also like to re-emphasise  that the data were processed with the utmost rigour using state of the art processing pipelines including quality control.

We will take care to improve the flow of the manuscript with special attention to the theoretical part and sections highlighted by the Reviewer 2. 

We agree with the challenge outlined by the Reviewer 2 regarding the limitations in interpretation of overall trends when the position in the visit one is different between the subjects. However, this is a much broader challenge and is not specific to this study. The non-random sampling of large cohort studies is problematic for nearly all studies using such cohorts, and regardless of the  statistical approach used. We will explicitly acknowledge these limitations in the full response.

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

Learn more at Review Commons

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Reply to the reviewers

Manuscript number: RC-2023-02154

Corresponding author(s): Marco, Galardini

1. General Statements

We have carefully read the comments put forward by the two reviewers and we have produced a revised version of the manuscript that we believe addresses all the concerns expressed by the reviewers. In short, we have validated our approach against experimentally derived epistatic coefficients, compared our mutual information (MI) method against one that uses direct coupling analysis (DCA), and experimentally tested three interactions in the spike RBD that we have predicted and which emerged only in summer 2023, thus demonstrating the potential predictive power of this approach. We have also carefully reworded the manuscript to acknowledge the inherent limitation of a method based on MI to identify epistatic interactions. We believe that the revised manuscript is now more robust with these new in-silico and in-vitro validations, and more direct in exposing the advantages (speed) and caveats (higher false-positives) of this approach.

Note: the line numbers referenced in the responses to reviewers below refer to the document in which the changes are highlighted.

Point-by-point description of the revisions

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

Summary: The authors inferred the pairwise epistasis through the Mutual Information provided by the spydrpick algorithm. They claim that the MIs could serve as a real-time identification of the epistatic interactions with the SARS-CoV-2 genomes due to the fast inference and high sensitivities.

Major comments:

1.The authors take a data-driven approach to infer the Mutation Information as the epistatic interactions between the mutations over different sites over SARS-CoV-2 genomes. However, it would be better to specify why this metric is reliable to be used as the representation of the pairwise epistatic interactions, and any theoretical explanations to support this.

We agree that readers should be better informed on why MI can be used to estimate epistatic interactions from genomic data. We have therefore expanded the introduction (lines 93-98), methods (lines 540-543) and discussion (lines 453-457) sections to provide a proper theoretical and practical foundation on the use of a MI-based method. Furthermore, we have expanded the results section to add one additional in-silico validation (lines 244-249, Supplementary Figure 5, and updated Supplementary Figure 8) and an in-vitro one (Figure 5, see also reply to comment 2 from reviewer #2), which we believe give strong support to the MI-based method.

2.The authors claimed that the DCA method requires more computational resources and more time to complete. However, with a proper filtering procedure, the computational time could be reduced heavily. An example is Physical Review E 106 (4), 044409, 2002, in which the DCA was used to investigate the real-time pair-wise interactions (month-to-month). There the DCA results were compared with the correlation analysis. It would be nice to have comparisons of the inferred interactions between MIs and other methods.

We agree that our MI-based approach should be compared against DCA-based methods. The original manuscript had in fact one such comparison (for the 2023-03 dataset, Figure 3C), which indicated a strong correlation between the two methods. To make this result more robust we have computed the DCA values for the complete time-series dataset and measured the correlation with the MI values (Supplementary Figure 4)

We observed a relatively high correlation in estimated values between the two methods, with the exception of three time points, i.e., 2020-11, 2023-02 and 2023-03. We can explain these lower correlations with the low overall sequence diversity observed in the early phase of the pandemic (2020-11) and with the different weighting scheme of our approach, which would significantly alter the dataset when compared to the one used by the DCA method, especially towards the later timepoints (see also the reply to reviewer #2, comment 3, section iv). When those three timepoints are excluded, the two methods show a high degree of correlation, implying that they are comparably suitable in detecting coevolutionary signals.

We have also used the 2nd order coefficients derived from experimental data in Moulana et al., 2022 (10.1038/s41467-022-34506-z) to validate both approaches (see methods, lines 624-631).

The panels which we have combined to create the new Supplementary Figure 5, indicate how both approaches (MI for panel A and C, and DCA for panels B and D) correctly recover the interaction with 2nd order epistatic coefficient > 0.15, based on the odds-ratio metric. Our MI-based approach has, however, a higher recall across multiple time points, which is especially visible comparing panels A and B. The DCA-based method did correctly identify known epistatic interactions, but did so only in sporadic timepoints, even though the distribution of the underlying variants did not change significantly month to month. We believe that the higher recall of the MI-based method has a higher value for genomic epidemiology, at least for SARS-CoV-2.

3.In Figure 1C, the authors show that their spydrpick algorithm provides more pairwise MIs for longer distances, where the outliers are denser than those with short distances. How do we explain this phenomenon?

We thank the reviewer for bringing this point up; we actually think that our data shows the opposite, meaning that we observe a higher proportion of close interactions when normalizing by the number of possible interactions. If we take an arbitrary distance threshold of 1'000 bases to define "close" Vs. "distant" interactions, we observe 194 and 280 interactions, respectively. It is true that distant interactions would be more, but the space of possible interactions is orders of magnitude larger for "distant" interactions, simply by the fact that there are more sites from which interactions can originate. As a crude estimate we can use the combinations between 1,000 sites (499,500 possible interactions) Vs those between 28,903 sites (the full SARS-CoV-2 genome length 29,903 bp minus 1,000, 417,677,253). Based on these estimates we have indeed observed less "close" than "distant" interactions.

Minor comments:

4.The explanations of Fig. 1E could be in more detail. Say, the grey dots in Fig. 1E, which is marked as "other" and such "other"s are dominated here. Why?

We thank the reviewer for pointing out a section where more clarity was needed. We have added the following sentence to the figure legend: "The category "other" indicates positions which are not known to have an impact on affinity to ACE2, immune escape or otherwise flagged as MOI/MOC.". This indicates that predicted interactions involving a site classified as "other" are either false positives or previously undiscovered interactions.

5.On line 210, the authors mentioned that the weights of the old sequences are lower "at around six months (120 days)". It would be better to specify why six months is 120 days instead of 180 days,

We have corrected this mistake and indicated 4 months. We thank the reviewer for spotting this error.

Referees cross-commenting

I agree with what Reviewer #2 presented in the Consults Comments. The authors should present the reasons why MIs can be explained as the epistatic interations between sites as both of us mentioned this point. I checked the other revision points that raised by the Reviewer #2. They would be definetely helpful for enhancing the quality of the manuscript.

Reviewer #1 (Significance (Required)):

The work in the current manuscript is interesting and presented nicely. However, the theoretical foundations that the MIs could be explained as epistatic interactions should be illustrated. Otherwise, the tools would be useful for SARS-CoV-2 and other potential pandemics by different virus.

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

The manuscript proposes an approach to identify epistatic interactions in the SRAR-CoV-2 genome using the large amount of genomic data which accumulated during the COVID pandemics. They argue that due to a relatively low computational cost, this can be done online in any ongoing pandemics nowadays (i.e. in the situation where the viral spreading and evolution are closely monitored by massive sequencing). In principle, this is interesting, but in my opinion the manuscript has some strong problems and will require major rewrighting:

1) In difference to the claims of the manuscript, detected correlation does not necessarily imply epistatic couplings:

• Even in a totally neutral setting, mutations may occur by chance together, and expand due to genetic drift or when ecountering a susceptible population. Equally, to independent muations may spread in different geographic regions, without the double mutant ever arising. Both cases lead to non-zero mutual information.

• In evolution, frequently driver and passenger mutations are observed, in particular in settings of relatively high mutation rate. The passenger will rise in frequency with the driver, without any epistatic coupling.

• The very unequal sequencing across geographic areas will enhance certain variants and leave others undetected. Even if the authors avoid double counting of identical sequences, more small variation is detected when sequencing deeper. The Omicron variant illustrates an extreme case here: it combined a large number of mutations, never detected before, but epistasis is not the most likely explanation, but rather lack of monitoring of the evolutionary path from the ancestral variants to Omicron.

• MI has been criticised because it overestimates the effect of indirecrt correlations in particular in dense epistatic networks. The situation in the spike protein in Fig. 1B seems very dense.

Currently the manuscript does not make any effort to disentangle any of these effects.

Following this (and reviewer 1) comments, we have made a number of changes to the manuscript in order to provide more context into how MI can be used to estimate epistatic interactions and the inherent limitations of this approach. In particular, we have expanded the introduction (lines 93-98), methods (lines 540-543) and discussion (lines 453-457) sections in a way that we believe exposes the limitations of the approach. Despite these limitations, we still believe that a MI-based approach strikes a good balance between speed, ease of implementation, and sensitivity. To further demonstrate this point we have added two additional validations to our results: the first one (in-silico) uses estimated 2nd order epistatic coefficients derived from experimental data (Moulana et al., 2022, 10.1038/s41467-022-34506-z), and the second (in-vitro) our own experimental data on three predicted interactions. The results of the new in-vitro validation have been described in the reply to comment #2 from reviewer 1; in short they show how the MI-based method has comparable sensitivity and specificity as the DCA-based method, and most importantly they allow the recovery of known epistatic interactions across the time period in which they have appeared. The results of the in-vitro validation are discussed in the reply to the next comment from this reviewer, as they directly address the predictive power of our approach: in short, we show how we could also validate these predictions. We think that these new results clearly show how, despite its limitations, the MI-based approach is able to identify bona-fide epistatic interactions, with the advantage of being a simple method to be implemented and with the possibility to be run in real time. For a more detailed discussion of the merits of the MI-based approach over DCA, see the reply to comment #3 from this reviewer.

2) What are the predictive capacities of the approach? Mutual information is bounded from above by the individual site entropies. So high MI can be detected only in highly mutated sites - i.e. in sides for sure already under monitoring. In fact, the sites in Fig. 1B with many links reflect the overall profile of variant frequencies in single sites (i.e. a totally non-epistatic measure) available on Nextstrain, and extracted from the same data sources.

The discussion of the results is very anecdotal and it is not clear to me in how far there is any real prediction in the paper, which might surprise and trigger observation or further analyses.

There is an entire line of related research in estimating and exploiting epistatic couplings in HIV evolution (A Chakraborty, M. Kardar, J. Barton, M MacKay and others) - not cited in the manuscript but relevant for the question how to detect epistatic couplings and what they are good for.

We thank the reviewer for pointing out relevant literature we had not covered in the original manuscript, and which can be used to indicate how epistatic interaction signals can be leveraged when studying viruses. We have added citations to these studies in the introduction (lines 76-78) to provide a better background for our own study. Regarding the broader concern of showing the predictive power of our approach, we had a similar concern after the manuscript was submitted, and we had already planned a "blind" in-vitro validation to put our approach to the test. In order to make this validation as "blind" as possible, we expanded the dataset to include sequences until August 2023. We then selected interactions within the spike RBD with confidence level O4 in at least the last 4 time points and with one position already flagged as either "affinity", "escape" or "other MOI/MOC"

We then selected the top three interactions (446-460, 446-486 and 452-490) for our validation, as they have an outlier confidence O4 in at least the 4 time points, and lower or no prediction before. We also added the known 498-501 interaction as a control (Figure 5, panel B)

We then focused on selecting a set of non-synonymous substitutions to test for their potential epistatic interactions. We decided to select 6 substitutions affecting the 3 predicted interactions based on their frequency in the time points after the cutoff of the original manuscript, shown in Figure 5, panel C.

Of those, L452R/F490S and G446S/F486V are anti-correlated in their frequency and virtually never observed together in our dataset, G446S/F486S is observed at low frequency (87 samples after 2023-05), and G446S/N460H is virtually never observed (5 samples). We chose the anti-correlated pairs to test the potential of the MI method to explain these "avoidance" phenomenon, and the low frequency pairs as a way to test an early warning system for mutation signatures that might rise in the future. We then planned to test the impact of the individual variants, the double variants, both in the wild-type background and in the Q498R/N501Y background as a crude model for the Omicron variant.

We then used a pseudovirus assay to test mutated RBDs across two phenotypes: infectivity (i.e. the ability to infect Vero B4 cells) and immune escape (i.e. antibody neutralization curves). We then tested for the presence of epistatic interactions for the double mutants in both backgrounds using a simple linear model (see Methods, lines 711-727). The results of these in-vitro assays are summarized below (Figure 5, panel E for infectivity, F for immune escape).

Double mutants with a significant (p-value -10) interaction have been highlighted with an asterisk. We confirmed the epistatic interaction for the Q498R/N501H, both for its effect on infectivity and immune escape. For both anti-correlated pairs we found a significant interaction for either the infectivity assay (both) and immune escape (G446S/F486V). In particular, we found that the one hand the G446S/F486V pair induced a large drop in infectivity in the Q498R/N501H background while the double mutant was fairly similar to the immune escape profile of the single G446S variant, thus compensating for the loss of escape shown by the F486V variant alone. We observed the opposite for the L452R/F490S pair in terms of infectivity, with the pair showing a large increase in infectivity in the Q498R/N501H background, an effect we found to be significant. The double mutant had a slightly better immune escape profile than the single mutants, although not significant. From these observations we can hypothesize that the G446S/F486V is anticorrelated for their strong defect in infectivity; we cannot apply the same reasoning for the L452R/F490S pair, whose absence from circulating variants could be ascribed to stochasticity in population dynamics or interactions with other variants. We observed a similar impact of the G446S/F486S and G446S/N460H pairs on infectivity as G446S/F486V; based on these results we could estimate that variants carrying these pairs might have a fitness disadvantage. The inability of unsupervised methods (MI or DCA based) to predict the direction of the effect of course makes it difficult to inform which of the two pairs should be added to a "watchlist", but it would potentially reduce the number of interactions to be tested. We believe that the results of this admittedly small scale in-vitro validation demonstrates the potential of the MI-based approach to flag emerging interactions worthy of further studying. Recent advances in scalability of molecular assays (e.g. 10.1101/2024.03.08.584176) could then be coupled with a real-time system as the one we describe in our manuscript to filter out the more relevant interactions. We have added this forward-looking observation in the discussion as well (lines 465-474).

3) The authors say that more involved methods like the Direct Coupling Analysis with Pseudolikelihood maximisation would be too slow for the analysis, but several papers show the contrary. The paper by Zeng et al. (Ref. [39]) does so very early in the pandemics in 2020, and another uncited paper of the same authors (Physical Review 2022) uses a nearly identical approach to study the time evolution of epistatic couplings (extractions from Gisaid at several times). As one of theit results, they show that their approach is not only feasible, but delivers more stable results than simpler correlation measures like MI.

We thank the reviewer for pointing out a relevant reference we had missed in the initial manuscript. At a general level Zeng et al. take a similar approach to what we have described, namely to divide the data according to the isolation date to look for temporal trends. We however see a few differences that we think are in favor of the approach we describe:

1- Our manuscript covers the time period after the emergence of the Omicron variant, in which epistatic interactions are known and have been characterized and validated experimentally, a crucial requirement for validation. We have also conducted an in-vitro validation on a selected set of predicted interactions (see the reply to the previous comment), which indicates that the method is sound and predictive.

2- We have prepared a cumulative time-series dataset, meaning that each month introduces new sequences on top of the ones already selected from the previous time points. To the best of our knowledge the Zheng et al. dataset has "insulated" sequences at each month. We believe our approach has the advantage of allowing for a higher recall, as it includes a representation of extinct lineages, which may increase diversity at key loci and thus boost the signal. As described in the original manuscript and in the reply to this reviewer's comments "iv" and "v", we have added a weighting scheme in order to reduce the influence of older sequences and increase the relevance of smaller lineages.

3- While we have not tested the DCA implementation used by Zeng et al., and we cannot therefore directly comment on its scalability, we have encountered serious limitations when scaling up the popular plmc C implementation developed by the lab of Deborah Marks. In particular we were unable to successfully run it for datasets with more than ~300k sequences, encountering segmentation faults.

Regarding the third point, while this meant that we could not test the DCA approach on the full dataset, we could still manage to apply it on the time series data, focusing exclusively on the spike (S) gene. As shown above in the reply to reviewer's 1 comment #2, the two methods have a high correlation and are both able to recover known interactions, although with the DCA method having a lower recall. Taken together we believe that the MI-based approach we describe is robust enough to be considered when a tradeoff between speed, ease of implementation and sensitivity has to be struck, which we believe may be the case for a rapid response during a potential future pandemic. We have added more details to the part of the discussion in which the comparison with the DCA-based methods was made to point out how those are still feasible with very large collections of sequences (lines 444-448).

It would therefore be essential that the authors strongly revise their manuscript to show the relaibility of the results, the predictive value of the predicted couplings, and the originality and robustness of the approach.

We believe that our response to both reviewers have addressed these concerns, and as a result we have provided a more nuanced view on the use of MI-based methods in the prediction of epistatic interactions in pandemic viruses. Our wording has been modified to make sure that readers interested in replicating our approach are aware of its strengths (speed, ease of implementation) and limitations.

Furthermore, there are some minor issues in the formulations, which should be corrected

i) "the virus has differentiated into a number of lineages, almost all of which have taken over the whole population..." This is wrong. SARS-CoV-2 has always been very heterogeneous, with diverse variants circulating (the authors use millions of non-redundant sequences), and only very few have become VOIs or VOCs at some point. This image of competition between multiple coexisting strains is much closer to clonal interference than what the authors describe (even if clonal interference does not rely on population structure, which has always been an important element in COVID).

We thank the reviewer for pointing out this error in our observation. We have changed "almost all" to "some", which we agree is more accurate.

ii) The authors say that pseudolikelihood methods would require "aggressive subsampling". This is not true, in machine learning massive training data are frequently used in the context of batch learning, i.e. in each learning epoch a "batch" is sampled from the full data. This leads to stochasticity in learning, but all data are eventually used.

We have reformulated this sentence (lines 85-90) to indicate how batch learning could also be used to make certain methods scalable, with the caveat that they would be more complicated to implement.

iii) The authors say that the download also a phylogenetic tree, but I do not see where it is used.

As indicated in the methods section, we have used the phylogenetic tree for two purposes:

1- To single out high quality sequences from the raw MSA (line 515)

2- To compute the weight of each sequence in the final MSA, as described in line 540-549

iv)The authors use sequence weights as implemented in Ref. [31]. There a weighting at sequence similarity threshold of 90% is used. I would expect that there are no SARS-CoV-2 genomes having accumulated more than 10% of nucleotide mutations, i.e. the weighting procedure would be without any effect.

We realized that the sequence weighting scheme we have used is not described in Pensar et al. (10.1093/nar/gkz656), but rather in the implementation of the spydrpick algorithm used by the panaroo software (Tonkin-Hill et al., 10.1186/s13059-020-02090-4). This weighting scheme is based on the more granular metric that is the patristic distance of each sequence from the root of the tree, divided at each branching point by the number of its terminal leaves. In practical terms this means that sequences belonging to smaller lineages (i.e. with fewer observed samples) will have a larger weight, regardless of a discrete sequence similarity threshold, as was done in the original implementation. We have updated the methods section to clearly indicate that the weighting scheme is that first shown in the panaroo software package (line 543).

v)The authors estimate that they need 10,000-100,000 sequences to estimate MI, but find the epistatic coupling in spike residues 498-501 as soon as 6 double mutants are present, which is a frequency of about 1e-4. The corresponding entropies should be low and in consequence the MI, too.

We thank the reviewer for raising this point, which prompted us to devise a way to better illustrate the sequence weighting scheme we have used. As a side note we also discovered that the number of Omicron sequences at the 2021-11 was actually 7, and not 6 as stated throughout the original manuscript, an error we have now fixed. As described in the methods section we have combined two weights in the time-series analysis: the first one, described in the response to the previous comment, is based on the "density" of the phylogenetic tree, which deflates the contribution of "denser" regions of the tree, and the second reduces the relevance of older sequences. The two weights are then combined multiplicatively. As a result the "real" (i.e. effective) number of sequences harboring a particular double mutation will be different than by just counting their occurrences.

As shown in Supplementary Figure 3, the combination of both weights (first column) leads to an increased effective number of sequences for "younger" samples and those that come from "sparser" regions of the overall phylogenetic tree. This is particularly evident for the middle row (2021-11); the light orange dot, which indicates sequences belonging to the first Omicron lineage to appear in the dataset (BA.1), has an actual N of 7, but an effective N of ~100 (exact value 86), thanks to its "novelty" both in the tree (middle panel) and in terms of time (right panel). We again thank the reviewer for raising this point, which led us to generate this visualization, which will hopefully clarify the rationale for the weighting strategy we have used for moist readers.

vi)The authors say that the public health toll of COVID has been "balanced" by scientific discovery - I would urge the authors to avoid such formulations, which sound cynical.

We agree with the reviewer that this comment might sound cynical and tone-deaf, and have reformulated to indicate that the impact of the pandemic has coincided with an accelerated pace of applied scientific discovery.

Referees cross-commenting

Both reports bring up very similar points (points 1 of both reports, point 2 of Reviewer #1 vs. my point 3) but add partially complementary questions (point 3 of Reviewer #1, my point 2), both related to the interpretation of the data. My report is more severe, but reading the ms I am convinced that the paper requires serious revision. So reports seem coherent but with different degrees of recommendations. However, none of the comments of one reviewer is contradiction to the other reviewer.

Reviewer #2 (Significance (Required)):

While the paper asks interesting questions and wants to make use of the quite unique data which have accumulated during the COVID pandemics, the above mentioned problems raise important questions about the manuscript. It would be essential that the authors strongly revise their manuscript to show the relaibility of the results, the predictive value of the predicted couplings, and the originality and robustness of the approach.

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

The manuscript proposes an approach to identify epistatic interactions in the SRAR-CoV-2 genome using the large amount of genomic data which accumulated during the COVID pandemics. They argue that due to a relatively low computational cost, this can be done online in any ongoing pandemics nowadays (i.e. in the situation where the viral spreading and evolution are closely monitored by massive sequencing). In principle, this is interesting, but in my opinion the manuscript has some strong problems and will require major rewrighting:

1. In difference to the claims of the manuscript, detected correlation does not necessarily imply epistatic couplings:
2. Even in a totally neutral setting, mutations may occur by chance together, and expand due to genetic drift or when ecountering a susceptible population. Equally, to independent muations may spread in different geographic regions, without the double mutant ever arising. Both cases lead to non-zero mutual information.
3. In evolution, frequently driver and passenger mutations are observed, in particular in settings of relatively high mutation rate. The passenger will rise in frequency with the driver, without any epistatic coupling.
4. The very unequal sequencing across geographic areas will enhance certain variants and leave others undetected. Even if the authors avoid double counting of identical sequences, more small variation is detected when sequencing deeper. The Omicron variant illustrates an extreme case here: it combined a large number of mutations, never detected before, but epistasis is not the most likely explanation, but rather lack of monitoring of the evolutionary path from the ancestral variants to Omicron.
5. MI has been criticised because it overestimates the effect of indirecrt correlations in particular in dense epistatic networks. The situation in the spike protein in Fig. 1B seems very dense.

Currently the manuscript does not make any effort to disentangle any of these effects. 2. What are the predictive capacities of the approach? Mutual information is bounded from above by the individual site entropies. So high MI can be detected only in highly mutated sites - i.e. in sides for sure already under monitoring. In fact, the sites in Fig. 1B with many links reflect the overall profile of variant frequencies in single sites (i.e. a totally non-epistatic measure) available on Nextstrain, and extracted from the same data sources.

The discussion of the results is very anecdotal and it is not clear to me in how far there is any real prediction in the paper, which might surprise and trigger observation or further analyses. There is an entire line of related research in estimating and exploiting epistatic couplings in HIV evolution (A Chakraborty, M. Kardar, J. Barton, M MacKay and others) - not cited in the manuscript but relevant for the question how to detect epistatic couplings and what they are good for. 3. The authors say that more involved methods like the Direct Coupling Analysis with Pseudolikelihood maximisation would be too slow for the analysis, but several papers show the contrary. The paper by Zeng et al. (Ref. [39]) does so very early in the pandemics in 2020, and another uncited paper of the same authors (Physical Review 2022) uses a nearly identical approach to study the time evolution of epistatic couplings (extractions from Gisaid at several times). As one of theit results, they show that their approach is not only feasible, but delivers more stable results than simpler correlation measures like MI.

It would therefore be essential that the authors strongly revise their manuscript to show the relaibility of the results, the predictive value of the predicted couplings, and the originality and robustness of the approach.

Furthermore, there are some minor issues in the formulations, which should be corrected

i) "the virus has differentiated into a number of lineages, almost all of which have taken over the whole population..." This is wrong. SARS-CoV-2 has always been very heterogeneous, with diverse variants circulating (the authors use millions of non-redundant sequences), and only very few have become VOIs or VOCs at some point. This image of competition between multiple coexisting strains is much closer to clonal interference than what the authors describe (even if clonal interference does not rely on population structure, which has always been an important element in COVID).

ii) The authors say that pseudolikelihood methods would require "aggressive subsampling". This is not true, in machine learning massive training data are frequently used in the context of batch learning, i.e. in each learning epoch a "batch" is sampled from the full data. This leads to stochasticity in learning, but all data are eventually used.

iii) The authors say that the download also a phylogenetic tree, but I do not see where it is used.

iv)The authors use sequence weights as implemented in Ref. [31]. There a weighting at sequence similarity threshold of 90% is used. I would expect that there are no SARS-CoV-2 genomes having accumulated more than 10% of nucleotide mutations, i.e. the weighting procedure would be without any effect.

v)The authors estimate that they need 10,000-100,000 sequences to estimate MI, but find the epistatic coupling in spike residues 498-501 as soon as 6 double mutants are present, which is a frequency of about 1e-4. The corresponding entropies should be low and in consequence the MI, too.

vi)The authors say that the public health toll of COVID has been "balanced" by scientific discovery - I would urge the authors to avoid such formulations, which sound cynical.

Referees cross-commenting

Both reports bring up very similar points (points 1 of both reports, point 2 of Reviewer #1 vs. my point 3) but add partially complementary questions (point 3 of Reviewer #1, my point 2), both related to the interpretation of the data. My report is more severe, but reading the ms I am convinced that the paper requires serious revision. So reports seem coherent but with different degrees of recommendations. However, none of the comments of one reviewer is contradiction to the other reviewer.

Significance

While the paper asks interesting questions and wants to make use of the quite unique data which have accumulated during the COVID pandemics, the above mentioned problems raise important questions about the manuscript. It would be essential that the authors strongly revise their manuscript to show the relaibility of the results, the predictive value of the predicted couplings, and the originality and robustness of the approach.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary The authors inferred the pairwise epistasis through the Mutual Information provided by the spydrpick algorithm. They claim that the MIs could serve as a real-time identification of the epistatic interactions with the SARS-CoV-2 genomes due to the fast inference and high sensitivities.

Major comments:

1. The authors take a data-driven approach to infer the Mutation Information as the epistatic interactions between the mutations over different sites over SARS-CoV-2 genomes. However, it would be better to specify why this metric is reliable to be used as the representation of the pairwise epistatic interactions, and any theoretical explanations to support this.
2. The authors claimed that the DCA method requires more computational resources and more time to complete. However, with a proper filtering procedure, the computational time could be reduced heavily. An example is Physical Review E 106 (4), 044409, 2002, in which the DCA was used to investigate the real-time pair-wise interactions (month-to-month). There the DCA results were compared with the correlation analysis. It would be nice to have comparisons of the inferred interactions between MIs and other methods.
3. In Figure 1C, the authors show that their spydrpick algorithm provides more pairwise MIs for longer distances, where the outliers are denser than those with short distances. How do we explain this phenomenon?

Minor comments: 4.The explanations of Fig. 1E could be in more detail. Say, the grey dots in Fig. 1E, which is marked as "other" and such "other"s are dominated here. Why? 5.On line 210, the authors mentioned that the weights of the old sequences are lower "at around six months (120 days)". It would be better to specify why six months is 120 days instead of 180 days,

Referees cross-commenting

I agree with what Reviewer #2 presented in the Consults Comments. The authors should present the reasons why MIs can be explained as the epistatic interations between sites as both of us mentioned this point. I checked the other revision points that raised by the Reviewer #2. They would be definetely helpful for enhancing the quality of the manuscript.

Significance

The work in the current manuscript is interesting and presented nicely. However, the theoretical foundations that the MIs could be explained as epistatic interactions should be illustrated. Otherwise, the tools would be useful for SARS-CoV-2 and other potential pandemics by different virus.

Author response:

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

eLife assessment

This solid study investigates the transdifferentiation of chicken embryonic fibroblasts into muscle and fat cells in 3D to create whole-cut meat mimics. The study is important and provides a method to control muscle, fat, and collagen content within the 3D meat mimics and thus provides a new avenue for customized cultured meat production. Limitations of this study include the use of transgene for transdifferentiation and thus the creation of GMO food.

We are grateful for the substantial effort that editors and reviewers put into assessing our manuscript and providing insightful feedback. We have tried to address, as much as possible, all comments and criticisms. We believe that we have now a significantly improved manuscript. Below, there is a point-by-point response.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors presented here a novel 3D fibroblast culture and transdifferentiation approach for potential meat production with GelMA hydrogel.

Strengths:

(1) Reduced serum concentration for 3D chicken fibroblast culture and transdifferentiation is optimized.

(2) Efficient myogenic transdifferentiation and lipogenesis as well as controlled fat deposition are achieved in the 3D GelMA.

Weaknesses:

(1) While the authors stated the rationale of using fibroblasts instead of myogenic/adipogenic stem cells for meat production, the authors did not comment on the drawbacks/disadvantages of genetic engineering (e.g., forced expression of MyoD) in meat production.

Thanks for the reviewer for raise this important issue. We have now described this drawback in the discussion part.

As a proof-of-concept study, we sought to explore the potential of utilizing the transdifferentiation integrated transgene tools for overexpressing a transdifferentiation factor to achieve the maximum muscle production. However, it is important to acknowledge that genetically modified meat products derived from the genetic engineering of cultured cells will not be suitable for consumer acceptance and market viability. We are currently testing other non-genomic integrating delivery means such as modRNAs and chemical cocktails to induce myogenic transdifferentiation in fibroblasts. We believe the new non-genomic integration means would be compatible for the meat production and consumer acceptance.

Please see lines 439-445.

“As a proof-of-concept, we utilized the transgene method to achieve maximum myogenic induction and the final products still retain the foreign transgene fragment in the cells’ genome. It is therefore posing a risk of genetic modified food which is not suitable for mass production. In the next step, other non-transgenic means such as non-integrating vectors, chemical reprogramming, modified RNAs, and recombinant transgene removal techniques will be explored to develop transgene-free end products.”

(2) While the authors cited one paper to state the properties and applications of GelMA hydrogel in tissue engineering and food processing, concerns/examples of the food safety with GelMA hydrogel are not discussed thoroughly.

Thank you for pointing out this issue. We discussed the drawbacks of Gelma hydrogel applications in the meat production in the main text.

GelMA-based hydrogels have shown great potential due to their biocompatibility and mechanical tenability. It is widely used in 3D cell culture and tissue engineering for regenerative medicine, but less common in food processing and agricultural applications. Due to its special photo-crosslinking properties, biocompatibility and degradability, it allows this material to be shaped into complex tissue structures by 3D printing or modelling. Many researchers have also used Gelma hydrogel as a scaffold for culture meat production (Jeong et al., 2022; Li et al., 2021; Park et al., 2023). Later research will carefully consider Gelma hydrogen as well as other types of scaffold biomaterials for cost-effective and food-safety compliant culture meat production (Bomkamp et al., 2022).

Bomkamp, C., Skaalure, S. C., Fernando, G. F., Ben‐Arye, T., Swartz, E. W., & Specht, E. A. J. A. S. (2022). Scaffolding biomaterials for 3D cultivated meat: prospects and challenges. Advanced Science (Weinh), 9(3), 2102908.

Jeong, D., Seo, J. W., Lee, H. G., Jung, W. K., Park, Y. H., & Bae, H. (2022). Efficient Myogenic/Adipogenic Transdifferentiation of Bovine Fibroblasts in a 3D Bioprinting System for Steak-Type Cultured Meat Production. Advanced Science (Weinh), 9(31), e2202877.

Li, Y., Liu, W., Li, S., Zhang, M., Yang, F., & Wang, S. J. J. o. F. F. (2021). Porcine skeletal muscle tissue fabrication for cultured meat production using three-dimensional bioprinting technology. Journal of Future Foods, 1(1), 88-97.

Park, S., Hong, Y., Park, S., Kim, W., Gwon, Y., Jang, K.-J., & Kim, J. J. J. o. B. E. (2023). Designing Highly Aligned Cultured Meat with Nanopatterns-Assisted Bio-Printed Fat Scaffolds. Journal of Biosystems Engineering, 48(4), 503-511.

We discussed the drawbacks of GelMA hydrogel. Please see lines 445-457.

“Another food safety concern in this study is the use of GelMA hydrogel for culture meat production. Due to its excellent biocompatibility and mechanical flexibility, GelMA-based hydrogel has demonstrated significant potential in scalable 3D cell culture for creating artificial tissue ranging in sizes from millimeters to centimeters. It is widely used in 3D cell culture and tissue engineering for regenerative medicine, but less common in food processing and agricultural applications. Due to its special photo-crosslinking properties, biocompatibility and degradability, it allows this material to be shaped into complex tissue structures by 3D printing or modelling. Many researchers have also used GelMA hydrogel as a scaffold for culture meat production (Jeong et al., 2022; Li et al., 2021; Park et al., 2023). Later research will carefully consider hydrogel as well as other types of scaffold biomaterials for cost-effective and food-safety compliant culture meat production (Bomkamp et al., 2022). ”

(3) In Fig. 4C, there seems no significant difference in the Vimentin expression between Fibroblast_MyoD and Myofibroblast. The conclusion of "greatly reduced in the myogenic transdifferentiated cells" is overstated.

Thanks for pointing out this mistake.

We revised the wording accordingly. The vimentin expression was reduced in fibroblast_MyoD compare to the original fibroblast.

Please see lines 231-233.

“The fibroblast intermediate filament Vimentin (Tarbit et al., 2019) was abundantly expressed in the fibroblasts but reduced in the myogenic transdifferentiated cells (Figure 4C)”

(4) The presented cell culture platform is only applied to chicken fibroblasts and should be tested in other species such as pigs and fish.

Thank you for the suggestion.

In this pilot cultured meat study, we utilized chicken embryonic fibroblasts. These specific cells were chosen for their near-immortal nature and robustness in culture, as well as the inducible myogenic capacity. In our previous experiments (Ren et al, Cell Reports, 2022, 40:111206), we have tested the myogenic transdifferentiation potential of fibroblasts from mice, pigs, and chickens, and observed varying efficiencies of myogenesis. It is important to note that fibroblast cells derived from different species, or even different tissues within the same species, would exhibit significant variations in their capacities for myogenic and adipogenic transdifferentiation.

In this proof-of-concept study we used only one source of fibroblasts for testing culture meat production and confirmed the myogenic/adipogenic transdifferentiation could be manipulated as feasible means to precisely control muscle, fat and collagen content. We would expect that different origins of fibroblasts to display different transdifferentiation efficiencies and thus produce various muscle/fat ratios in meat mimics. That is beyond the scope of current study.

Furthermore, we are also testing myogenic/adipogenic transdifferentiation of fibroblasts from pigs through non-genomic integration approaches. We believe only the non-transgene tools are viable solutions for culture meat production in the future. We added the species information in the discussion part.

See lines 515-517.

“This approach can be readily extrapolated to other species such as pigs and presents promising avenues for the large-scale production of customized and versatile meat products that may cater to varying consumer preferences.”

Reviewer #2 (Public Review):

The manuscript by Ma et al. tries to develop a protocol for cell-based meat production using chicken fibroblasts as three-dimensional (3D) muscle tissues with fat accumulation. The authors used genetically modified fibroblasts which can be forced to differentiate into muscle cells and formulated 3D tissues with these cells and a biphasic material (hydrogel). The degrees of muscle differentiation and lipid deposition in culture were determined by immunohistochemical, biochemical, and molecular biological evaluations. Notably, the protocol successfully achieved the process of myogenic and lipogenic stimulation in the 3D tissues.

Overall, the study is reasonably designed and performed including adequate analysis. The manuscript is clearly written with well-supported figures. While it presents valuable results in the field of cultivated meat science and skeletal muscle biology, some critical concerns were identified. First, it is unclear whether some technical approaches were really the best choice for cell-based meat production. Next, more careful evaluations and justifications would be required to properly explain biological events in the results. These points include additional evaluations and considerations with regard to myocyte alignment and lipid accumulation in the differentiated 3D tissues. The present data are very suggestive in general, but further clarifications and arguments would properly support the findings and conclusions.

Thanks for the reviewer’s comments. We have performed additional experiments and analysis to address the critical questions. We also revised the text extensively to clarify or discuss some of the concerns, such as the cell alignment and cellular distribution of intramuscular fat issues. We expect the revised data and text could adequately support the conclusions of the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) In Figure 1, the authors used 1% chicken serum. Have the authors tested other lower concentrations? It will be interesting to see the lowest chicken serum concentrations in fibroblast culture and transdifferentiation;

Thank you for your suggestion.

Yes, we actually have tested the lower concentrations of serum, such as 1% FBS, and 0.5% chicken serum. However, the cells are not in a healthy state under these low levels of serum, as shown by the abnormal cell morphology and nearly no cell growth. Please see the revised Supplementary Figure S1D, in which we added the 1%FBS and 0.5% chicken serum data. Hence, the 1% chicken serum is optimal in our hands. We will also test other types of specialized serum-free medium in future experiments.

(2) In Figure 2, the authors should quantify the fold expansion of fibroblasts cultured in 3D gel after 1, 3, 5, and 9 days since this data is important for future meat manufacturing. In addition, long-term expansion (e.g., 1 month) in 3D gel should also be shown;

Thanks for the question. We have quantified the cell growth in 3D by measuring the PHK26 stained cells. Since the cells were implanted into the gel, they propagated exponentially from 1 day to 9 days. The cell proliferation data provide good reference for the future meat manufacturing (Figure 2D). We have tried the long-term expansion in 3D but failed to measure the cell proliferation. Because the 3D gel always collapsed during 12-15 days in cell culture for some unknown reasons, either the cells are grown too crowded to compromise the gel structure or the gel matrix itself is not strong enough for standing long-term. We believe the cells will grow well in long-term if we provide enough 3D attachment surface, since they grow indefinitely in 2D. We will testing different 3D matrix in the future.

Please see the revised Figure 2D for the quantification of cells.

(3) In Figure 3, please also show MyoD staining as it'll be interesting to see the expression of exogenous and endogenous MyoD expression after dox treatment. In Figure G, the hydrogel meat seems very small, please show/discuss the maximum size of hydrogel meat that may be achieved using this approach;

Thanks for asking this information. We performed the immunostaining by using the anti-MyoD and anti-Flag to show the expression of all MyoD (exogenous and endogenous) and only exogenous MyoD after dox treatment. The MyoD and 3xFlag were fused in-frame in the transgene plasmid and thus the anti-Flag staining indicate the exogenous MyoD expression and anti-MyoD staining indicate the expression of exogenous and endogenous MyoD together.

As shown in Figure S4, we found that almost 100% of cells were positive for MyoD staining and 60% of which expressed Flag, these data were consistent with our previous results (Ren et al., 2022, Cell Reports).

Author response image 1.

As for the size of the culture meat based on hydrogel, we discussed the possibilities in scalable production of hydrogel based whole-cut meat mimics. Please see lines 446-449. “Due to its excellent biocompatibility and mechanical flexibility, GelMA-based hydrogel has demonstrated significant potential in scalable 3D cell culture for creating artificial tissue ranging in sizes from millimeters to centimeters.”

(4) In Figure 5 and Supplementary Figure 6, please quantify the Oil-red O+ fat cells in the 2D and 3D lipogenic induction. Also in Fig. 6B, quantify the oil-red+MHC+ cells;

Thank you for this advice. We have quantified the oil-red O stained images in the result “Stimulate the fat deposition in chicken fibroblasts in 3D” using analysis software imageJ and the quantification of Oil-red O area was added to the corresponding graphs (Figure 5C, Figure S6C and S6F).

However, due to the unique structure of the 3D matrix, many MHC+ and Oil Red O+ double-positive cells overlap with each other across different Z-stack layers in 3D. This overlap makes it challenging to accurately position and quantify the double-positive cells as the different layers interfere with each other.

(5) In Figure 7, please show immunostaining images of collagen and other major ECMs;

Thank you for this question. We have tried to stain collagen networks the by the Picrosirius Red staining but failed. Instead, we employed the laminin immunostainings to confirm that the ECM contents in the 3D matrix is increasing steadily during cell culturation.

Please see Figure 7C. Lines 346-348.

“the laminin protein content was accumulated and increased steadily during 3D culturation (Figure 7C) “

(6) In Figure 8, please show hierarchical clustering analysis of whole transcriptomes of 3D_fibroblasts, 3D_MyoD, 3D+FI, and 3D_MyoD+FI. A Venn Diagram showing the overlap and distinct gene expression among these groups is also appreciated.

Thank you for the suggestion.

We added the hierarchical clustering analysis of whole transcriptomes of 3D_fibroblasts, 3D_MyoD, 3D+FI, and 3D_MyoD+FI using Euclidean distance with ward.D cluster method. Please see Figure 8B. The result showed that these groups formed two large clusters, in which the 3D+FI clustered separately and the 3D_fibroblasts, 3D_MyoD and 3D_MyoD+FI were more similar. Please see Figure 8B.

As the reviewer suggested, we also compared the transcriptomes of 3D_MyoD, 3D+FI, and 3D_MyoD+FI to the original 3D_fibroblasts to identify differentially expression genes (DEG) and then analyzed the overlap and distinct DEGs respectively. As shown in Figure 8D, the Venn Diagram showed that majority of DEG from 3D_MyoD+FI (3D_MyoD+FI versus 3D_fibroblasts) are overlapped with 3D_MyoD and 3D+FI, indicating that 3D_MyoD+FI are compatible with myogenic and adipogenic function.

Please see the revised Figure 8.

Reviewer #2 (Recommendations For The Authors):

In this study, the authors demonstrated a new approach for cultivated meat production using chicken fibroblasts. Specifically, the cells were cultured as 3D and induced muscle differentiation and lipid deposition. The manuscript contains a good set of data, which would be valuable to researchers in the fields of both cell-based meat and skeletal muscle biology. From the aspect of cultivated meat science, the rationale behind the idea is understandable, but it remains unclear whether the proposed approach was really the best choice to achieve their final goal. On the other hand, when we read this manuscript as a paper in skeletal muscle biology, the overall approach was not innovative enough and several uncertain issues remain. The authors should add more sufficient justifications, arguments, and discussions.

(1) When considering their goal to produce edible meat products, the current approach has some concerns. First, there are issues with the approach used for the induction of myogenesis by MyoD transgene. This makes the end products GMO foods, which are not easily acceptable to a wide range of consumers. Next, the hydrogel was used for 3D tissue formation, but it is unclear whether this matrix type is edible, safe, and bio-comparable for cell-based meat production. The authors already discussed these points by excusing that the current work remains proof-of-concept. However, more careful considerations and justifications would be required.

Thank you for the suggestion.

We acknowledge that the current transgene myogenic induction method is not suitable for mass production of culture meat because of the GMO food concerns. We utilized the MyoD transgene as the means of myogenic transdifferentiation at the first place, because of the ease of genetic manipulation and maximum efficiency. We are current testing non-genomic integration tools such as chemical cocktails and modified RNAs for myogenic transdifferentiation.

When it comes to the applications of hydrogel in the food industry, certain types of hybrid hydrogels, such as those made from pectin or sodium polyacrylate, are not only edible but also safe for consumption. While GelMA hydrogel is typically utilized in tissue engineering and subsequent implantation in patients for therapeutic regenerative medicine purposes, it has not been commonly employed in food processing. In this study, we cultivated cells within GelMA hydrogel due to its durability and ease of use in cell culture. Moving forward, we plan to investigate alternative types of matrices to develop cultured meat suitable for food applications.

We have now described the GMO and hydrogel drawbacks in the discussion part. Please see lines 439-457.

“As a proof-of-concept, we utilized the transgene method to achieve maximum myogenic induction and the final products still retain the foreign transgene fragment in the cells’ genome. It is therefore posing a risk of genetic modified food which is not suitable for mass production. In the next step, other non-transgenic means such as non-integrating vectors, chemical reprogramming, modified RNAs, and recombinant transgene removal techniques will be explored to develop transgene-free end products. Another food safety concern in this study is the use of GelMA hydrogel for culture meat production. Due to its excellent biocompatibility and mechanical flexibility, GelMA-based hydrogel has demonstrated significant potential in scalable 3D cell culture for creating artificial tissue ranging in sizes from millimeters to centimeters. It is widely used in 3D cell culture and tissue engineering for regenerative medicine, but less common in food processing and agricultural applications. Due to its special photo-crosslinking properties, biocompatibility and degradability, it allows this material to be shaped into complex tissue structures by 3D printing or modelling. Many researchers have also used GelMA hydrogel as a scaffold for culture meat production (Jeong et al., 2022; Li et al., 2021; Park et al., 2023). Later research will carefully consider hydrogel as well as other types of scaffold biomaterials for cost-effective and food-safety compliant culture meat production (Bomkamp et al., 2022). ”

(2) From the view of skeletal muscle biology, the approaches (MyoD overexpression, hydrogel-based 3D tissue formation, and lipogenic induction) have already been tested.

Thank you for the insightful comments from the perspective of skeletal muscle cell biology. We totally agree that the current approaches including MyoD overexpression, 3D cell culture and lipogenic induction, were routine experiments in muscle cell biology. However, we want to highlight that utilization of these classical and robust muscle cell approaches, combine with the unique advantages of fibroblast cells (easily accessible, immortalized, cost-effective, ...) would provide a novel and practical avenue for culture meat production. We stated these issues in the revised manuscript in the discussion part.

Please see lines 511-515.

“In conclusion, we have effectively utilized immortalized chicken fibroblasts in conjunction with classical myogenic/adipogenic transdifferentiation approaches within 3D hydrogel to establish a cultured meat model. This model allows for the precise regulation of the synthesis of key components found in conventional meat, including muscle, fat, and ECM.”

(3) The common emphasis in this manuscript is to use the advantages of 3D culture for tissue differentiation. As the authors described, skeletal muscle is a highly aligned tissue. In this study, some results successfully demonstrated advantages in terms of myocyte alignment, maturation, and lipid deposition. However, the current results cannot address whether the entire 3D tissues maintained these advantageous characteristics or not. Because the method for 3D formation does not have any additional modifications to make the cells aligned, like micropatterning, scaffolding, or bioprinting.

Thank you for the suggestion.

We agree with the reviewer that the skeletal muscle tissues are composed of well organized, directional bundles of fibers, and the cell alignment would greatly affect the meat tenderness and sensory properties. Therefore, it is a desired attribute if the cells in the culture meat matrix could be aligned together. But this alignment would require sophisticated biomaterial engineering mainly involved in the scaffold manipulation which is beyond the scope of this study. The hydrogel used in this study formed different sizes of pores at random directions and we would expect the embedded cells to be totally non-directional. But we still found localized cell alignments in some parts of the gel matrix which confirming the cell-cell interactions, please see figure 3D. We describe this feature in the results part. In the future, we will be testing the application of physical or electrical stimulations to the matrix to see if we can align the cells better to make all the muscle cells in the whole matrix to align together.

Please see lines 186-190.

“The separate XY axis views of the orthogonal projections at different depths (Figure 3D) and a multi-angle video (Supplementary Video 2) also showed the several myotubes were aligned together. Nevertheless, many myotubes were oriented in different directions, preventing the entire matrix from aligning in one direction.”

(4) In the skeletal muscle, fat accumulation mainly occurs in adipocytes between myocytes. This means that "intra-" muscular fat deposition is identified. However, lipid deposition within myocytes also occurred in this preparation (Supplementary Figure 7C). This situation is not "intra-" muscular accumulation, which sounds different from what is going on in normal skeletal muscle tissues. Please explain what happened and what biological situations accounted for this. Also, the authors should clarify better how lipogenesis was induced in the 3D tissues, such as cell types (transdifferentiated myocytes, remained/un-transdifferentiated fibroblasts, or both).

Thank you for the very insightful question. We have revised the corresponding text to further explain the intramuscular fat distribution in different cell types in culture meat.

We totally agree with the reviewer that intramuscular fat accumulation may occur mainly in the intramuscular adipocytes. However, under some pathological and physiological conditions in human and animals, the lipid droplets were also abundantly observed inside myofibers (intramyocellular lipids within myofiber cytoplasm). For instance, high intramyocellular lipid content was found in insulin resistance patients and paradoxically in endurance trained athletes, (doi.org/10.1016/j.tem.2012.05.009), as well as in some farm animals under intensive selective breeding (doi:10.2174/1876142910901010059). In the current study, with the Oil Red O staining of lipid droplets, we identified lipid deposition in both the transdifferentiated myocytes and the remained un-transdifferentiated fibroblasts in the culture meat. This lipid distribution pattern is comparable to the intramuscular fat storage pattern observed in some human and animals, in which fat accumulation occurs in both myofibers (intramyocellular lipids) and intramuscular adipocyte cells (extramyocellular lipids) which reside within the muscle tissue bundle but between myofibers. We reason that current adipogenic induction treatment caused lipogenesis in both the MyoD-transdifferentiated cells and un-transdifferentiated fibroblasts. It is difficult to compare the absolute amount of lipids between these two types of cells via the Oil Red O staining. Also, it is almost impossible to separate these two types of cells from the 3D meat mimics. Thus, we can only confirm the lipid deposition occurs in both transdifferentiated myocytes and un-transdifferentiated fibroblasts, but without knowing which one is dominant and the major contributor to the intramuscular fat content in the culture meat.

Please see lines 486-492.

“In this study, the deposition of fat in the myotubes/myofibers facilitated the storage of significant lipid quantities in transdifferentiated muscle cells, known as intramyocellular lipids. Additionally, we observed Oil Red O staining in the remaining un-transdifferentiated fibroblasts, resembling cells of intramuscular adipocytes (extramyocellular lipids) found within muscle tissue. Hence, current adipogenic induction treatment caused lipogenesis in both the MyoD-transdifferentiated cells and un-transdifferentiated fibroblasts.”

eLife assessment

This study presents an important new technology for transdifferentiation of fibroblasts into muscle cells. The data and methods used for analysis were compelling. This study will have broad interest to cellular reprogramming biologists and general public.

Reviewer #1 (Public Review):

Summary:

The authors presented here a novel 3D fibroblast culture and transdifferentiation approach for potential meat production with GelMA hydrogel.

Strengths:

(1) Reduced serum concentration for 3D chicken fibroblast culture and transdifferentiation is optimized.<br /> (2) Efficient myogenic transdifferentiation and lipogenesis as well as controlled fat deposition are achieved in the 3D GelMA.

Author response:

Reviewer #1 (Public Review):

Given that this is one of the first studies to report the mapping of longitudinal intactness of proviral genomes in the globally dominant subtype C, the manuscript would benefit from placing these findings in the context of what has been reported in other populations, for example, how decay rates of intact and defective genomes compare with that of other subtypes where known.

Most published studies are from men living with HIV-1 subtype B and the studies are not from the hyperacute infection phase and therefore a direct head-to-head comparison with the FRESH study is difficult. However, we can cite/highlight and contrast our study with a few examples from other acute infection studies as follows.

(1) Peluso et. al., JCI, 2020, showed that in Caucasian men (SCOPE study), with subtype B infection, initiating ART during chronic infection virus intact genomes decayed at a rate of 15.7% per year, while defective genomes decayed at a rate of 4% per year. In our study we showed that in chronic treated participants genomes decreased at a rate of 25% (intact) and 3% (defective) per month for the first 6 months of treatment.

(2) White et. al., PNAS, 2021, demonstrated that in a cohort of African, white and mixed-race American men treated during acute infection, the rate of decay of intact viral genomes in the first phase of decay was <0.3 logs copies in the first 2-3 weeks following ART initiation. In the FRESH cohort our data from acute treated participants shows a comparable decay rate of 0.31 log copies per month for virus intact genomes.

(3) A study in Thailand (Leyre et. al., 2020, Science Translational Medicine), of predominantly HIV-1 CRF01-AE subtype compared HIV-reservoir levels in participants starting ART at the earliest stages of acute HIV infection (in the RV254/SEARCH 010 cohort) and participants initiating ART during chronic infection (in SEARCH 011 and RV304/SEARCH 013 cohorts). In keeping with our study, they showed that the frequency of infected cells with integrated HIV DNA remained stable in participants who initiated ART during chronic infection, while there was a sharp decay in these infected cells in all acutely treated individuals during the first 12 weeks of therapy. Rates of decay were not provided and therefore a direct comparison with our data from the FRESH cohort is not possible.

(4) A study by Bruner et. al., Nat. Med. 2016, described the composition of proviral populations in acute treated (within 100 days) and chronic treated (>180 days), predominantly male subtype B cohort. In comparison to the FRESH chronic treated group, they showed that in chronic treated infection 98% (87% in FRESH) of viral genomes were defective, 80% (60% in FRESH) had large internal deletions and 14% (31% in FRESH) were hypermutated. In acute treated 93% (48% in FRESH) were defective and 35% (7%) in FRESH were hypermutated. The differences frequency of hypermutations could be explained by the differences in timing of infection specifically in the acute treated groups were FRESH participants initiate ART at a median of 1 day after infection. It is also possible that sex- or race-based differences in immunological factors that impact the reservoir may play a role.

This study also showed that large deletions are non-random and occur at hotspots in the HIV-1 genome. The design of the subtype B IPDA assay (Bruner et. al., Nature, 2019) is based on optimal discrimination between intact and deleted sequences - obtained with a 5′ amplicon in the Ψ region and a 3′ amplicon in Envelope. This suggest that Envelope is a hotspot for large while deletions in Ψ is the site of frequent small deletions and is included in larger 5′ deletions. In the FRESH cohort of HIV-1 subtype C, genome deletions were most frequently observed between Integrase and Envelope relative to Gag (p<0.0001–0.001).

(5) In 2017, Heiner et. al., in Cell Rep, also described genetic characteristics of the latent HIV-1 reservoir in 3 acute treated and 3 chronic treated male study participants with subtype B HIV. Their data was similar to Bruner et. al. above showing proportions of intact proviruses in participants who initiated therapy during acute/early infection at 6% (94% defective) and chronic infection at 3% (97% defective). In contrast the frequencies in FRESH in acute treated were 52% intact and 48% defective and in chronic infection were 13% intact and 87% defective. These differences could be attributed to the timing of treatment initiation where in the aforementioned study early treatment ranged from 0.6-3.4 months after infection.

Indeed, in the abstract, the authors indicate that treatment was initiated before the peak. The use of the term 'peak' viremia in the hyperacute-treated group could perhaps be replaced with 'highest recorded viral load'. The statistical comparison of this measure in the two groups is perhaps more relevant with regards to viral burden over time or area under the curve viral load as these are previously reported as correlates of reservoir size.

We will edit the manuscript text to describe the term peak viraemia in hyperacute treated participants more clearly. We will perform an analysis of area under the curve to compare viral burden in the two study groups.

Reviewer #2 (Public Review):

Other factors also deserve consideration and include age, and environment (e.g. other comorbidities and coinfections.)

We agree that these factors could play a role however participants in this study were of similar age (18-23), and information on co-morbidities and coinfections are not known.

Reviewer #3 (Public Review):

The word reservoir should not be used to describe proviral DNA soon after ART initiation. It is generally agreed upon that there is still HIV DNA from actively infected cells (phase 1 & 2 decay of RNA) during the first 6-12 months of ART. Only after a full year of uninterrupted ART is it really safe to label intact proviral HIV DNA as an approximation of the reservoir. This should be amended throughout.

We agree and will amend the use of the word reservoir to only refer to the proviral DNA load after full viral suppression, i.e., during undetectable viral load.

All raw, individualized data should be made available for modelers and statisticians. It would be very nice to see the RNA and DNA data presented in a supplementary figure by an individual to get a better grasp of intra-host kinetics.

We will make all relevant data available and accessible to interested parties.

The legend of Supplementary Figure 2 should list when samples were taken.

The data in this figure represents an overall analysis of all sequences available for each participant at all time points. This will be explained more clearly in the manuscript and added to the figure legend.

eLife assessment

This important, clearly written, and timely manuscript links the timing of ART with the kinetics of total and intact proviral HIV DNA. The conclusions are interesting and somewhat novel, and the importance of the work is high because the focus is on African women and clade C virus, both of which are understudied in the HIV reservoir field. The strength of the evidence is convincing though some definitions could be more precise and in some places the data could be reported slightly more clearly. Overall, this work will be of very high interest to scientists and clinicians in the HIV cure/persistence fields.

Reviewer #1 (Public Review):

The authors sought to determine the impact of early antiretroviral treatment on the size, composition, and decay of the HIV latent reservoir. This reservoir represents the source of viral rebound upon treatment interruption and therefore constitutes the greatest challenge to achieving an HIV cure. A particular strength of this study is that it reports on reservoir characteristics in African women, a significantly understudied population, of whom some have initiated treatment within days of acute HIV diagnosis. With the use of highly sensitive and current technologies, including digital droplet PCR and near full-length genome next-generation sequencing, the authors generated a valuable dataset for investigation of proviral dynamics in women initiating early treatment compared to those initiating treatment in chronic infection. The authors confirm previous reports that early antiretroviral treatment restricts reservoir size, but further show that this restriction extends to defective viral genomes, where late treatment initiation was associated with a greater frequency of defective genomes. Furthermore, an additional strength of this study is the longitudinal comparison of viral dynamics post-treatment, wherein early treatment was shown to be associated with a more rapid rate of decay in proviral genomes, regardless of intactness, over a period of one year post-treatment. While it is indicated that intact genomes were not detected after one year following early treatment initiation, caution should be taken with interpretation where sequence numbers are low. Defective genomes are more abundant than intact genomes and are therefore more likely to be sampled. Early treatment was also associated with reduced proviral diversity and fewer instances of polymorphisms associated with cytotoxic T-lymphocyte immune selection. This is expected given that rapid evolution and extensive immune selection are synonymous with HIV infection in the absence of treatment, yet points to an additional benefit of early treatment in the context of immune therapies to restrict the reservoir.

Given that this is one of the first studies to report the mapping of longitudinal intactness of proviral genomes in the globally dominant subtype C, the manuscript would benefit from placing these findings in the context of what has been reported in other populations, for example, how decay rates of intact and defective genomes compare with that of other subtypes where known. While not a primary outcome of the study, the comparisons of peak viremia in the hyperacute and chronic-treated groups may be confounded by the fact that peak viremia may have been pre-empted by early treatment i.e., the true peak was not reached in early-treated individuals. Indeed, in the abstract, the authors indicate that treatment was initiated before the peak. The use of the term 'peak' viremia in the hyperacute-treated group could perhaps be replaced with 'highest recorded viral load'. The statistical comparison of this measure in the two groups is perhaps more relevant with regards to viral burden over time or area under the curve viral load as these are previously reported as correlates of reservoir size. The analysis of clonal expansion of proviral genomes may be limited by higher sequence homogeneity in hyperacute infection i.e., cells with different proviral integration sites may have a higher likelihood of containing identical genomes than chronic infection.

Overall, these data demonstrate the distinct benefits of early treatment initiation at reducing the barrier to a functional cure for HIV, not only by restricting viral abundance and diversity but also potentially through the preservation of immune function and limiting immune escape. It therefore provides clues to curative strategies even in settings where early diagnosis and treatment may be unlikely.

Reviewer #2 (Public Review):

HIV infection is characterized by viral integration into permissive host cells - an event that occurs very early in viral-host encounter. This constitutes the HIV proviral reservoir and is a feature of HIV infection that provides the greatest challenge for eradicating HIV-1 infection once an individual is infected.

This study looks at how starting HIV treatment very early after infection, which substantially reduces the peak viral load detectable (compared to untreated infection), affects the amount and characteristics of the viral reservoir. The authors studied 35 women in South Africa who were at high risk of getting HIV. Some of these women started HIV treatment very soon after getting infected, while others started later. This study is well-designed and has as its focus a very well-characterized cohort. Comparison groups are appropriately selected to address reservoir characterization and dynamics in the context of acute and chronic treated HIV-1. The amount of HIV and various characteristics of the genetic makeup of the virus (intact/defective proviral reservoir) were evaluated over one year of treatment. Methods employed for reservoir characterization are state-of-the-art and provide in-depth insights into the reservoir in peripheral blood.

While starting treatment early didn't reduce the amount of HIV DNA at the outset, it did lead to a gradual decrease in total HIV DNA quantity over time. In contrast, those who started treatment later didn't see much change in this parameter. Starting treatment early led to a faster decrease in intact provirus (a measure of replication-competence), compared to starting treatment later. Additionally, early treatment reduced the genetic diversity of the viral DNA and resulted in fewer immune escape variants within intact genomes. This suggests that collectively having a smaller intact replication-competent reservoir, less viral variability, and less opportunity for the virus to evade the immune system - are all features that are likely to facilitate more effective clearance of viral reservoir, especially when combined with other intervention strategies.

Major strengths of the study include the cohort of very early treated persons with HIV and the depth of study. These are important findings, particularly as the study was conducted in HIV-1 subtype C infected women (more cure studies have focussed on men and with subtype B infection)- and in populations most affected by HIV and in need of HIV cure interventions. This is highly relevant because it cannot be assumed that any interventions employed for reducing/clearing the HIV reservoir would perform similarly in men and women or across different populations. Other factors also deserve consideration and include age, and environment (e.g. other comorbidities and coinfections).

Reviewer #3 (Public Review):

Summary:

This paper assesses the size and clearance kinetics of proviral HIV DNA (intact and total) in women in South Africa with clade C virus. who started ART at different time points of infection (very early vs late).

Strengths:

The cohort is excellent. The paper is easy to read. The methodology is appropriate. Some conclusions, particularly the differing kinetics of total HIV DNA despite a similar amount of virus in early vs late treated women are novel and thought-provoking. I really enjoyed reading this paper!

Weaknesses:

There are several areas in the paper that could be explicated a bit more accurately with more detailed references to past work.

(1) The word reservoir should not be used to describe proviral DNA soon after ART initiation. It is generally agreed upon that there is still HIV DNA from actively infected cells (phase 1 & 2 decay of RNA) during the first 6-12 months of ART. Only after a full year of uninterrupted ART is it really safe to label intact proviral HIV DNA as an approximation of the reservoir. This should be amended throughout.

(2) All raw, individualized data should be made available for modelers and statisticians. It would be very nice to see the RNA and DNA data presented in a supplementary figure by an individual to get a better grasp of intra-host kinetics.

(3) The legend of Supplementary Figure 2 should list when samples were taken.

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is a follow-up study to the authors' previous eLife report about the roles of an alpha-arrestin called protein thioredoxin interacting protein (Txnip) in cone photoreceptors and in the retinal pigment epithelium. The findings are important because they provide new information about the mechanism of glucose and lactate transport to cone photoreceptors and because they may become the basis for therapies for retinal degenerative diseases.

Strengths:

Overall, the study is carefully done and, although the analysis is fairly comprehensive with many different versions of the protein analyzed, it is clearly enough described to follow. Figure 4 greatly facilitated my ability to follow, understand and interpret the study. The authors have appropriately addressed a few concerns about statistical significance and the relationship between their findings and previous studies of the possible roles of Txnip on GLUT1 expression and localization on the surfaces of RPE cells.

We are delighted that Reviewer #1 is satisfied with this revised version.

Reviewer #2 (Public Review):

The hard work of the authors is much appreciated. With overexpression of a-arrestin Txnip in RPE, cones and the combined respectively, the authors show a potential gene agnostic treatment that can be applied to retinitis pigmentosa. Furthermore, since Txnip is related to multiple intracellular signaling pathway, this study is of value for research in the mechanism of secondary cone dystrophy as well.

There are a few areas in which the article may be improved through further analysis and application of the data, as well as some adjustments that should be made in to clarify specific points in the article.

Strengths

• The follow-up study builds on innovative ground by exploring the impact of TxnipC247S and its combination with HSP90AB1 knockdown on cone survival, offering novel therapeutic pathways.

• Testing of different Txnip deletion mutants provides a nuanced understanding of its functional domains, contributing valuable insights into the mechanism of action in RP treatment.

• The findings regarding GLUT1 clearance and the differential effects of Txnip mutants on cone and RPE cells lay the groundwork for targeted gene therapy in RP.

Weaknesses

• The focus on specific mutants and overexpression systems might overlook broader implications of Txnip interactions and its variants in the wider context of retinal degeneration.

Txnip is not expressed in WT or RP cones, as described in our previous study (Xue et al., 2021, eLife), so we could not perform loss of function assays. We thus chose overexpression, and assayed various alleles, based upon the literature, as we describe in our manuscript.

• The study's reliance on cell count and GLUT1 expression as primary outcomes misses an opportunity to include functional assessments of vision or retinal health, which would strengthen the clinical relevance.

In our previous study, we demonstrated that the optomotor response of Txnip-treated RP mice improved (Xue et al., 2021, eLife). Also, as described in our previous Txnip study, as well as an independent study (Xue et al., 2021, eLife; Xue et al., 2023, PNAS), ERG assays of Txnip-treated RP cones were no different than the controls. Other therapies that prolong RP cone survival and the optomotor response in our lab also failed to save the ERG, suggesting that there are other pathways that need to be addressed, e.g. the visual cycle. A combination therapy addressing multiple problems is one of our goals.

• The paper could benefit from a deeper exploration of why certain treatments (like Best1-146 Txnip.C247S) do not lead to cone rescue and the potential for these approaches to exacerbate disease phenotypes through glucose shortages.

This system is more complicated than we currently understand, and more work needs to be done.

• Minor inconsistencies, such as the missing space in text references and the need for clarification on data representation (retinas vs. mice), should be addressed for clarity and accuracy.

The missing spaces are added.

We described the strategy of injecting the same mouse in each eye, one eye with control and one with the experimental vector. However, the following sentence has been added to the Materials and Methods to better assist the reader:

“In almost all experiments, other than as noted, one eye of the mouse was treated with control (AAV8-RedO-H2BGFP, 2.5 × 108 vg/eye), and the other eye was treated with the experimental vector plus AAV8-RedO-H2BGFP, 2.5 × 108 vg/eye.”

• The observation of promoter leakage and potential vector tropism issues raise questions about the specificity and efficiency of the gene delivery system, necessitating further discussion and validation.

The following sentences have been added to the Results. We do not think this phenomenon affects the practice of the experiments or the interpretation of the results in this study.

“To enable automated cone counting and trace the infection, we co-injected an AAV (AAV8-RedO-H2BGFP-WPRE-bGHpA) encoding an allele of GFP fused to histone 2B (H2BGFP), which localized to the nucleus. As the red opsin promoter was used to express this gene, H2BGFP was seen in cone nuclei, but not in the RPE, if AAV8-RedO-H2BGFP-WPRE-bGHpA was injected alone. However, when an AAV that expressed in the RPE, i.e. AAV8-Best1-Sv40intron-(Gene)-WPRE-bGHpA, was co-injected with AAV8-RedO-H2BGFP-WPRE-bGHpA, H2BGFP was expressed in the RPE, along with expression in cones (Figure 2A). We speculate that this is due to concatenation or recombination of the two genomes, such that the H2BGFP comes under the control of the RPE promoter. This may be due to the high copy number of AAV in the RPE, as it did not happen in the reverse combination, i.e. AAV with an RPE promoter driving GFP and a cone promoter driving another gene, perhaps due to the observation that the AAV genome copy number is »10 fold lower in cones than in the RPE (Wang et al., 2020).”

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

During the last decades, extensive studies (mostly neglected by the authors), using in vitro and in vivo models, have elucidated the five-step mechanism of intoxication of botulinum neurotoxins (BoNTs). The binding domain (H chain) of all serotypes of BoNTs binds polysialogangliosides and the luminal domain of a synaptic vesicle protein (which varies among serotypes). When bound to the synaptic membrane of neurons, BoNTs are rapidly internalized by synaptic vesicles (SVs) via endocytosis. Subsequently, the catalytic domain (L chain) translocates, a process triggered by the acidification of these organelles. Following translocation, the disulfide bridge connecting the H chain with the L chain is reduced by the thioredoxin reductase/thioredoxin system, and it is refolded by the chaperone Hsp90 on SV's surface. Once released into the cytosol, the L chains of different serotypes cleave distinct peptide bonds of specific SNARE proteins, thereby disrupting neurotransmission. In this study, Yeo et al. extensively revise the neuronal intoxication model, suggesting that BoNT/A follows a more complex intracellular route than previously thought. The authors propose that upon internalization, BoNT/A-containing endosomes are retro-axonally trafficked to the soma. At the level of the neuronal soma, this serotype then traffics to the endoplasmic reticulum (ER) via the Golgi apparatus. The ER SEC61 translocon complex facilitates the translocation of BoNT/A's LC from the ER lumen into the cytosol, where the thioredoxin reductase/thioredoxin system and HSP complexes release and refold the catalytic L chain. Subsequently, the L chain diffuses and cleaves SNAP25 first in the soma before reaching neurites and synapses. Strengths:

I appreciate the authors' efforts to confirm that the newly established methods somehow recapitulate aspects of the BoNTs mechanism of action, such as toxin binding and uptake occurring at the level of active synapses. Furthermore, even though I consider the SNAPR approach inadequate, the genome-wide RNAi screen has been well executed and thoroughly analyzed. It includes well-established positive and negative controls, making it a comprehensive resource not only for scientists working in the field of botulinum neurotoxins but also for cell biologists studying endocytosis more broadly. Weaknesses:

I have several concerns about the authors' main conclusions, primarily due to the lack of essential controls and validation for the newly developed methods used to assess toxin cleavage and trafficking into neurons. Furthermore, there is a significant discrepancy between the proposed intoxication model and existing studies conducted in more physiological settings. In my opinion, the authors have omitted over 20 years of work done in several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc.). I want to emphasize that I support changes in biological dogma only when these changes are supported by compelling experimental evidence, which I could not find in the present manuscript.

We thank the reviewer for his reading and comments and for pointing out the discrepancy between our proposed model and the existing model. However, we respectfully disagree with the phrase of “extensive studies have elucidated the five-steps mechanism of intoxication…”. This sentence and the following imply that the model is well-established and demonstrated. It also highlights how the reviewer is convinced about this previous model.

We contest this model for theoretical reasons and contest the strength of evidences that support it. We previously included references to previous work showing that the model is also being challenged by others. In light of the reviewer’s comments, we incluced more references in the introduction and we also explicit our main theoretical concern in the introduction:

“Arguably, the main problem of the model is its failure to propose a thermodynamically consistent explanation for the directional translocation of a polypeptidic chain across a biologial membrane. Other known instances of polypeptide membrane translocation such as the co-translational translocation into the ER indicate that it is an unfavorable process, which consumes significant energy (Alder and Theg 2003). ”

We also added the following text in the Discussion to address with the reviewer’s concerns: “Our study contradicts the long-established model of BoNT intoxication, which is described in several reviews specifically dedicated to the subject 1–4. In short, these reviews support the notion that BoNT are molecular machines able to mediate their own translocation across membranes; this notion has convinced some cell biologists interested in toxins and retrograde traffic, who describe BoNT mode of translocation in their reviews 5,6.

But is this notion well supported by data? A careful examination of the primary literature reveals that early studies indeed report that BonTs form ion channels at low pH values 7,8. These studies have been extended by the use of patch-clamp 9,10. These works and others lead to various suppositions on how the toxin forms a channel and translocate the LC 1,11 .

However, only a single study claims to reconstitute in vitro the translocation of BonT LC across membranes 12. In this paper, the authors report using a system of artificial membranes separating two aqueous compartments. They load the toxin in the cis compartment and measure the protease activity in the trans compartment after incubation. However, when the experimental conditions described are actually converted in terms of molarity, it appears that the cis compartment was loaded at 10e-8M BonT and that the reported translocated protease activity is equivalent to 10e-17 M (Figure 3D, 12). Thus, in this experiment, about 1 LC molecule in 100 millions has crossed the membrane. Such extremely low transfert rate does not tally with the extreme efficiency of intoxication in vivo, even while taking into account the difference between artificial and biological membranes.

In sum, a careful analysis of the primary literature indicate that while there is ample evidence that BoNTs have the ability to affect membranes and possibly create ion channels, there is actually no credible evidence that these channels mediate translocation of the LC. As mentioned earlier, it is not clear how such a self-translocation mechanism would function thermodynamically. By contrast, our model proposes a mechanism without a thermodynamic problem, is consistent with current knowledge about other protein toxins, such as PE, Shiga and Ricin, and can help explain previously puzzling features of BonT effects. It is worth noting that a similar self-translocation model was proposed for other protein toxins such as Pseudomonas exotoxin, which have similar molecular organisation as BonT (68). However, it has since been demonstrated that the PE toxins require cellular machinery, in particular in the ER, for intoxication (21,69,70).”

Reviewer #2 (Public Review):

Summary:

The study by Yeo and co-authors addresses a long-lasting issue about botulinum neurotoxin (BoNT) intoxication. The current view is that the toxin binds to its receptors at the axon terminus by its HCc domain and is internalized in recycled neuromediator vesicles just after the release of the neuromediators. Then, the HCn domain assists the translocation of the catalytic light chain (LC) of the toxin through the membrane of these endocytic vesicles into the cytosol of the axon terminus. There, the LC cleaves its SNARE substrate and blocks neurosecretion. However, other views involving kinetic aspects of intoxication suggest that the toxin follows the retrograde axonal transport up to the nerve cell body and then back to the nerve terminus before cleaving its substrate.

In the current study, the authors claim that the BoNT/A (isotype A of BoNT) not only progresses to the cell body but once there, follows the retrograde transport trafficking pathway in a retromer-dependent fashion, through the Golgi apparatus, until reaching the endoplasmic reticulum. Next, the LC dissociates from the HC (a process not studied here) and uses the translocon Sec61 machinery to retro-translocate into the cytosol. Only then, does the LC traffic back to the nerve terminus following the anterograde axonal transport. Once there, LC cleaves its SNARE substrate (SNAP25 in the case of BoTN/A) and blocks neurosecretion.

To reach their conclusion, Yeo and co-authors use a combination of engineered tools: a cell line able to differentiate into neurons (ReNcell VN), a reporter dual fluorescent protein derived from SNAP25, the substrate of BoNT/A (called SNAPR), the use of either native BoNT/A or a toxin to which three fragment 11 of the reporter fluorescent protein Neon Green (mNG) are fused to the N-terminus of the LC (BoNT/A-mNG11x3), and finally ReNcell VN transfected with mNG1-10 (a protein consisting of the first 10 beta strands of the mNG).

SNAPR is stably expressed all over in the ReNcell VN. SNAPR is yellow (red and green) when intact and becomes red only when cleaved by BoNT/A LC, the green tip being degraded by the cell. When the LC of BoNT/A-mNG11x3 reaches the cytosol in ReNcell VN transfected by mNG1-10, the complete mNG is reconstituted and emits a green fluorescence.

In the first experiment, the authors show that the catalytic activity of the LC appears first in the cell body of neurons where SNAPR is cleaved first. This phenomenon starts 24 hours after intoxication and progresses along the axon towards the nerve terminus during an additional 24 hours. In a second experiment, the authors intoxicate the ReNcell VN transfected by mNG1-10 using the BoNT/A-mNG11x3. The fluorescence appears also first in the soma of neurons, then diffuses in the neurites in 48 hours. The conclusion of these two experiments is that translocation occurs first in the cell body and that the LC diffuses in the cytosol of the axon in an anterograde fashion.

In the second part of the study, the authors perform a siRNA screen to identify regulators of BoNT/A intoxication. Their aim is to identify genes involved in intracellular trafficking of the toxin and translocation of the LC. Interestingly, they found positive and negative regulators of intoxication. Regulators could be regrouped according to the sequential events of intoxication.

Genes affecting binding to the cell-surface receptor (SV2) and internalization. Genes involved in intracellular trafficking. Genes involved in translocation such as reduction of the disulfide bond linking the LC to the HC and refolding in the cytosol. Genes involved in signaling such as tyrosine kinases and phosphatases. All these groups of genes may be consistent with the current view of BoNT intoxication within the nerve terminus. However, two sets of genes were particularly significant to reach the main conclusion of the work and definitely constitute an original finding important to the field. One set of genes consists of those of the retromer, and the other relates to the Sec61 translocon. This should indicate that once endocytosed, the BoNT traffics from the endosomes to the Golgi apparatus, and then to the ER. Ultimately, the LC should translocate from the ER lumen to the cytosol using the Sec61 translocon. The authors further control that the SV2 receptor for the BoNT/A traffics along the axon in a retromer-dependent fashion and that BoNT/A-mNG11x3 traverses the Golgi apparatus by fusing the mNG1-10 to a Golgi resident protein.

Strengths:

The findings in this work are convincing. The experiments are carefully done and are properly controlled. In the first part of the study, both the activity of the LC is monitored together with the physical presence of the toxin. In the second part of the work, the most relevant genes that came out of the siRNA screen are checked individually in the ReNcell VN / BoNT/A reporter system to confirm their role in BoNT/A trafficking and retro-translocation.

These findings are important to the fields of toxinology and medical treatment of neuromuscular diseases by BoNTs. They may explain some aspects of intoxication such as slow symptom onset, aggravation, and appearance of central effects.

Weaknesses:

The findings antagonize the current view of the intoxication pathway that is sustained by a vast amount of observations. The findings are certainly valid, but their generalization as the sole mechanism of BoNT intoxication should be tempered. These observations are restricted to one particular neuronal model and engineered protein tools. Other models such as isolated nerve/muscle preparations display nerve terminus paralysis within minutes rather than days. Also, the tetanus neurotoxin (TeNT), whose mechanism of action involving axonal transport to the posterior ganglia in the spinal cord is well described, takes between 5 and 15 days. It is thus possible that different intoxication mechanisms co-exist for BoNTs or even vary depending on the type of neurons.

Although the siRNA experiments are convincing, it would be nice to reach the same observations with drugs affecting the endocytic to Golgi to ER transport (such as Retro-2, golgicide or brefeldin A) and the Sec61 retrotranslocation (such as mycolactone). Then, it would be nice to check other neuronal systems for the same observations.

We thank the reviewer for the careful reading and comments of our manuscript. The reference to “a vast amount of observation” is a similar argument to the Reviewer 1 and used to suggest that our study may not be applicable as a general mechanism.

We respectfully disagree as described above and posit on the contrary that the model we propose is much more likely to be general than the model presented in current reviews for the several reasons cited (see added text in Introduction and Discussion). While we agree that more work is needed to confirm the proposed mechanisms of BonT translocation in other models, these experiments fall outside the perimeter of our study.

The fact that nerve/muscle preparations of BonT activity have relatively fast kinetics does not pose a contradiction to our model. Our model reveals primarily the requirement for trafficking to the ER membranes. This ER targeting requires trafficking through the Golgi complex, in turn explaining the requirement for trafficking to the soma of neurons in the experimental system we used. However, in neuronal cells in vivo, Golgi bodies can be found along the lenght of the axon, thus BonT may not always require trafficking to the soma of the affected cells. The time required for intoxication could thus vary greatly depending on the neuronal structural organisation.

TenT is proposed to transfer from excitatory neurons into inhibitory neurons before exerting its action. While the detailed mechanism of this fascinating mechanism remain to be explored, it clearly falls beyond the purview of this manuscript.

Regarding the use of drugs, we agree that it would be a nice addition; unfortunately we are unable to perform such experiments at this stage. Setting up a large scale siRNA screen for BonT mechanism of action is challenging as it requires a special facility with controlled access and police authorisation (in Singapore) given the high toxicity of this molecule. Unfortunately, the authorisations have now lapsed.

Reviewer #3 (Public Review): Summary:

The manuscript by Yao et al. investigates the intracellular trafficking of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. Contrary to the prevailing understanding of BoNT/A translocation into the cytosol, the study suggests a retrograde migration from the synapse to the soma-localized Golgi in neurons. Using a genome-wide siRNA screen in genetically engineered neurons, the researchers identified over three hundred genes involved in this process. The study employs organelle-specific split-mNG complementation, revealing that BoNT/A traffics through the Golgi in a retromer-dependent manner before moving to the endoplasmic reticulum (ER). The Sec61 complex is implicated in the retro-translocation of BoNT/A from the ER to the cytosol. Overall, the research challenges the conventional model of BoNT/A translocation, uncovering a complex route from synapse to cytosol for efficient intoxication. The findings are based on a comprehensive approach, including the introduction of a fluorescent reporter for BoNT/A catalytic activity and genetic manipulations in neuronal cell lines. The conclusions highlight the importance of retrograde trafficking and the involvement of specific genes and cellular processes in BoNT/A intoxication.

Strengths:

The major part of the experiments are convincing. They are well-controlled and the interpretation of their results is balanced and sensitive.

Weaknesses:

To my opinion, the main weakness of the paper is in the interpretation of the data equating loss of tGFP signal (when using the Red SNAPR assay) with proteolytic cleavage by the toxin. Indeed, the first step for loss of tGFP signal by degradation of the cleaved part is the actual cleavage. However, this needs to be degraded (by the proteasome, I presume), a process that could in principle be affected (in speed or extent) by the toxin.

We thank the reviewer for his comments and careful reading of our manuscript.

Regarding the read-out of the assay, we agree that the assay could be sensitive to alteration in the protein degradation pathway. We have added the following sentence in the Discussion to take it into account:

“As noted by one reviewer, the assay may be sensitive to perturbation in the general rate of protein degradation, a consideration to keep in mind when evaluating the results of large scale screens.”

While this may be valid for some hits in the general list, it is important to note that the main hits have been shown to affect toxin trafficking by an independent, orthogonal assay based on the split GFP reconstitution.

Recommendations to authors:

Reviewer #1 (Recommendations For The Authors):

(1) To assess the activity of BoNT/A in neurons, Yeo et al. have generated a neuronal stem line referred to as SNAPR. This cell line stably expresses a chimeric reporter protein that consists of SNAP25 flanked at its N-terminus with a tagRFPT and at its C-terminus with a tagGFP. After exposure to BoNT/A, SNAP25 is cleaved and, the C-terminal tGFP-containing moiety is rapidly degraded. I have many doubts about the validity of the described method. Indeed, BoNT/A activity is analysed in an indirect way by quantifying the degradation of the GFP moiety generated after toxin cleavage (Fig. 2). In this regard, the authors should consider that their approach is dependent, not only on the toxin's metalloprotease activity but also on the functionality of the proteasome in neurons. Therefore, considering the current dataset, it is impossible to rule out the possibility that the progression of GFP signal loss from the soma to the neurite terminals may be attributed to the different proteasome activity in these compartments. Is it conceivable that the GFP fragment generated upon toxin cleavage degrades more rapidly in the soma in comparison to axonal terminals? This alternative explanation could challenge the conclusion drawn in Fig. 2.

The reviewer’s alternative explanation disregards the experiments performed with the split-GFP complementation approach, which indicate translocation in the soma first. The split GFP reporter is not dependent on the proteasome activity. It also disregard the genetic data implicating many genes involved in membrane retrograde traffic, which are also not consistent with the hypothesis of the reviewer. These genes depletions not only affect SNAPR degradation but also BoNT/A-mNG11 trafficking: thus, their effect cannot be attributed to an completely hypothetical spatial heterogeneous distribution of the proteasome.

For this reason, I strongly suggest using a more physiological approach that does not depend on proteasomal degradation or on the expression of the sensor in neurons. The authors should consider performing a time course experiment following intoxication and staining BoNT/A-cleaved SNAP25 by using specific antibodies (see Antonucci F. et al., Journal of Neuroscience, 2008 or Rheaume C. et al., Toxins 2015).

For the above reason, we do not agree with the pressing importance of confirming by a third method using specific antibodies; especially considering that BonT is very difficult to detect in cells when incubated at physiological levels. By the way, the cited paper, by Antonucci F; et al. documents long distance retrograde traffic of BonT/A, which is in line with our data.

An alternative approach could involve the use of microfluidic devices that physically separate axons from cell bodies. Such a separation will allow us to test the authors' primary conclusion that SNAP25 is initially cleaved in the soma. The suggested experiments will also rule out potential overexpression artifacts that could influence the authors' conclusions when using the newly developed SNAPR approach. Without these additional experiments, the authors' main conclusion that SNAP25 is cleaved first in the neuronal soma rather than at the nerve terminal is inadequate.

As discussed above we disagree about the doubts raised by the reviewer: we present three types of evidences (SNAPR, split GFP and genetic hits) and they all point in the same direction. Thus, we respectfully doubt that a fourth approach would convince this reviewer. To note, we have attempted to use microfluidics devices as suggested by the reviewer, however, the Ren-VM neurons were not able to extend axons long enough across the device.

(2) To detect BoNT/A translocation into the cytosol, the authors have used a complementation assay by intoxicating ReNcell VM cell expressing a cytosolic HA-tagged split monomeric NeonGreen (Cyt-mNG1-10) with an engineered BoNT/A, where the catalytic domain (LC) was fused to mNG1-11. When drawing conclusions regarding the detection of cytosolic LC in the neuronal soma, the authors should highlight the limitations of this assay and explicitly describe them to the readers. Firstly, the authors need to investigate whether the addition of mNG1-11 to the LC affects the translocation process itself (by comparing with a WT, not tagged, LC).

Additionally, from the data shown in Fig. 2C, it is evident that the Cyt-mNG1-10 is predominantly expressed in the cytosol and less detected in neurites. This raises the question of whether there might be a bias for the cell soma in this assay. To address this important concern, I suggest quantifying MFI per cell (Fig. 2D) taking into consideration the amount of HA-tagged Cyt-mNG1-10. Furthermore, I strongly suggest targeting mNG1-10 to synapses and performing a similar time course experiment to observe when LC translocation occurs at nerve terminals. Alternative experiments, to prove that BoNT/A requires retrograde trafficking before it can translocate, may be done to repeat the experiments shown in Fig. 2D in the presence of inhibitors (or by KD some of the hits identified as microtubule stabilizers) that should interfere with BoNT/A trafficking to the neuronal somata. Without these additional experiments, the authors' main conclusion that the BoNT/A catalytic domain is first detected in the neuronal soma rather than at the nerve terminal is very preliminary.

Similarly as for the SNAPR assay, the reviewer is raising the level of doubt to very high levels. We respect his thoroughness and eagerness to question the new model. However, we note that a similar level of scrutiny does not apply to the prevalent competitive model. Indeed, the data supporting the self-translocation model is based on a single in vitro experiment published in one panel as we have explain din the discussion (see above).

(3) In the genome-wide RNAi screening, rather than solely assessing SV2 surface levels, it would have been beneficial to directly investigate BoNT/A binding to the neuronal membrane. For instance, this could have been achieved by using a GFP-tagged HC domain of BoNT/A. At present, the authors cannot exclude the possibility that among the 135 hits that did not affect SV2 levels, some might still inhibit BoNT/A binding to the neuronal surface. These concerns, already exemplified by B4CALT4 (which is known to be involved in the synthesis of GT1b), should be explicitly addressed in the main text.

We agree with the reviewer that perturbation of binding of BonT is possible. We added the following text:

“Network analysis reveals regulators of signaling, membrane trafficking and thioreductase redox state involved in BoNT/A intoxication

Among the positive regulators of the screen, 135 hits did not influence significantly surface SV2 levels and are thus likely to function in post-endocytic processes (Supplementary Table 2). However, we cannot formerly exclude that they could affect binding of BonT to the cell surface independently of SV2.”

(4) The authors should clearly state which reagents they have tried to use in order to explain the challenges they faced when directly testing the trafficking of BoNT/A. The accumulation of Dendra-SV2 bulbous structures at the neurite tips in VPS35-depleted cells could be interpreted as a sign of neuronal stress/death. Have the authors investigated other proteins that do not undergo retro-axonal trafficking in a retromer-dependent manner? This control is essential. In this regard, the use of a GFP-tagged HC domain of BoNT/A could prove to be quite helpful.

We tried multiple commercially available antibodies against BonT but we could not get a very good signal. The postdoc in charge of this project has now gone to greener pastures and we are not in the capacity to provide the details corresponding to these antibodies. We di dnot observe significant cell death after VPS-35 knockdown at the time of the experiment, however longe rterm treatment might result in toxicity indeed.

(5) Considering my concerns related to the SNAPR system and the complementation assay to study SNAP25 cleavage and BoNT/A trafficking, I suggest validating some of their major hits (ex. VPS34 and Sec61) by performing WB or IF analysis to examine the cleavage of endogenous SNAP25. Furthermore, the authors should test VPS35 depletion in the context of the experiments performed in Fig. 6G-H, by validating that this protein is essential for BoNT/A retrograde trafficking.

The reviewer concerns are well noted but as discussed above, the two systems we used are completely orthogonal. Thus, for the reviewer’s concerns to be valid, it would have to be two completely independent artefacts giving rise to the same result. The alternative explanation is that BonT/A translocates in the soma. The Ockham razor principle dictates that the simplest explanation is the likeliest.

(6) The introduction and the discussion section of this paper completely disregard more than 20 years of research conducted by several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc). The authors should make an effort to contextualize their data within the framework of these studies and address the significant discrepancies between their proposed intoxication model and existing research that clearly demonstrates BoNTs translocating upon the endocytic retrieval of SVs at presynaptic sites. Nevertheless, even assuming that the model proposed by the authors is accurate, numerous questions emerge. One such question is: How can the authors explain the exceptional toxicity of botulinum neurotoxin in an ex vivo neuromuscular junction preparation devoid of neuronal cell bodies (see Cesare Montecucco and Andreas Rummel's seminal studies)?

Please see above in the answer to public reviews.

(7) Scale bars should be added to all representative pictures.

This has been done. Thank you for the thorough reading of our manuscript.

Reviewer #2(Recommendations For The Authors):*

(1) The title overstates the results. It may be indicated "in differenciated ReNcell VM".

Title changed to: “Botulinum toxin intoxication requires retrograde transport and membrane translocation at the ER in RenVM neurons”

(2) In the provided manuscript there are two Figure 2 and no Figure 3. This made the reading and understanding extremely difficult and should be corrected. As a result, the Figure legends do not fit the numbering. There are also discrepancies between some Figure panels (A, B, C, etc), the text, and the Legends. All this needs to be carefully checked.

We apologize for the confusion as the manuscript as followed multiple rounds of revisions. We have carefully verified labels and legends.

(3) The BoNT/A-mNG11x3 may introduce some bias that could be discussed. Would these additional peptides block LC translocation from synaptic vesicles in the nerve termini? In addition, the mNG peptides that are unfolded before complementation may direct LC towards Sec61. These aspects should be discussed.

The comment would be valid if BoNT/A-mNG11x3 was the only approach used in the paper, however the SNAPR reporter is used with native BonT and shows data consistent with the split GFP approach.

(4) In the Figure about SV2 (Fig 3 or 4): The authors did not locate SV2. The cells seem not to have the same differentiated phenotype as in Figure 1 and Figure 2/3A.

We apologized above for the mislabeling. It is not clear what is the question here.

(5) The authors should check whether BoNT/A wt cleaves the endogeneous SNAP25 by western blot for instance in the original ReNcell VN before SNAPR engineering. This should be compared with wt SNAP25 cleavage by the BoNT/A-LC-mNG.

It is likely that BoNT/A-LC-mNG11 should have similar activity as it is only adding a small peptide at the end of the LC. At any rate, it is not clear why this is so important since both molecules translocate in the cytosol, with the same kinetics and in the same subcellular locale.

(6) Perhaps I did not understand. How can the authors exclude that what is observed is the kinetic overproduction of the reporter substrate SNAPR?

The authors could use SLO toxin (PNAS 98, 3185-3190, 2001) to permeabilize the cells all along their body and axon to introduce BoNT/A or LC (wt) and observe synchronized SNAPR cleavage throughout the cells.

The concept mentioned here is not very clear to us. The reviewer is proposing that the SNAPR is produced much more efficiently at the tips of the neurites and thus its cleavage takes longer to be detected and is apparent first in the soma?? With all due respect, this is a strange hypothesis, at odds with what we know of protein dynamics in the neurons (i.e. most proteins are largely made in the soma and transported or diffuse into the neurites).

Again, the two orthogonal approaches: split GFP and SNAPR reporter use different constructs and methods, yet converge on similar results. Perhaps, the incredulity of the reviewer might be more productively directed at the current data “demonstrating” the translocation of LC in the synaptic button?

(7) The authors could also use an essay on neurotransmitter release monitoring by electrophysiology measurements to check the functional consequences of the kinetic diffusion of LC activity along the axon. Can the authors exclude that some toxin molecules translocate from the endocytic vesicles and block neurotransmission within minutes or a few hours?

It is well established that inhibition of neurotransmission does not occur within minutes in vivo and in vitro, but rather within hours or even days. This kinetic delay is experienced by many patients and is one of the key argument against the current model of self-translocation at the synaptic vesicle level.

Minor remarks

Thank you for pointing out all these.

(1) Please check typos. There are many. Check space before the parenthesis, between numbers and h (hours), reference style etc.

Thank you. We have reviewed the text and try to eliminate all these instances.

(2) Line 90: The C of HC should be capitalized.

Fixed

(3) Line 107: add space between "neurons(Donato".

Fixed

(4) Line 109: space "72 h".

Fixed

(5) Line 115: a word is missing ? ...to show retro-axonal... ? Please clarify this sentence.

Fixed

(6) Figure 1E: does nm refer to nM (nanomolar)? Please correct. No mention of panel F.

Fixed

(7) Line 161: do you mean ~16 µm/h? Please correct.

Fixed

(8) Line 168, words are missing.

Fixed, thank you

We verified that Cyt-mNG1-10 was expressed using the HA tag, the expression was homogeneously distributed in differentiated neurons and we observed no GFP signal (Figure2C).

(9) Line 171: Isn't mNG 11 the eleventh beta strand of the neon green fluorescent protein, not alpha helix? Otherwise, can the authors confirm it acquires the shape of an alpha helix? Same at line 326.

We have corrected the mistake; thanks for pointing it out.

(10) Figure 2 is doubled. The legend of Fig 2 refers to Figure 3. There is no legend for Figure 2. Then, some figures are shifted in their numbering.

Fixed

(11) The fluorescence in the cell body must appear before the fluorescence in the axon due to higher volume. Please discuss.

The fluorescence progresses in the neurites extensions in a centripetal fashion. The volume of the neurite near the cell body is not significantly different from the end of the neurite. Thus the fluorescence data is consistent with translocation in soma and not with an effect due to higher volume in the soma.

(12) Figure 2D, right: the term intoxication is improper for this experiment. Rather, it is the presence of the BoNT/A-mNG11 that is detected. I believe the authors should be particularly careful about the use of terms: intoxication means blockade of neurosecretion, SNAPR cleavage means activity etc.

While the reviewer is correct that it is the presence of BoNT/A-mNG11 that is detected, it remains that it is an active toxin, so the neurons are effectively intoxicated; as they are when we use the wild type toxin. We do not imply that we are measuring intoxication, but simply that the neurons are put into contact with a toxin.

(13) Line 196: Should we read TXNRD1 is required for BoNT/A LC translocation? TXNRD1 in the current model of translocation is located in the cytoplasm and is supposed to play a role in the cleavage of the disulfide bond linking LC to HC. In the model proposed by this study, LC is translocated through the Sec61 translocon. In this case, I would assume that the protein disulfide isomerase (PDI) in the endoplasmic reticulum would reduce the LC-HC disulfide bond. In that case, TXNRD1 would not be required anymore. Please discuss.

Why should we assume that a PDI is involved in the reduction of the LC-HC disulfide bond? In our previous studies on A-B toxins (PE and Ricin), different reduction systems seemed to be at play. There is no conceptual imperative to assume reduction in the ER because the Sec61 translocon is implicated. Reduction might occur on the cytosolic side by TXNRD1 or the effect of this reductase could be indirect.

(14) The legend of Figure 4 (in principle Figure 5?) is not matching with the panels and panel entries are missing (Figure 4F in particular).

Fixed

(15) Figure 6 panels E and H, please match colors with legend (grey and another color).

Not clear

(16) Please indicate BoNT/A construct concentrations in all Figure legends.

Done

(17) Line 416: isn't SV2 also involved in epilepsy?

Yes it is.

(18) Line 433: as above, shouldn't the disulfide bond linking LC to HC be cleaved by PDI in the ER in this model (as for other translocating bacterial toxins) rather than by thioredoxin reductases in the cytoplasm? Please discuss.

See above

(19) Identification of vATPase in the screen could be consistent with the endocytic vesicle acidification model of translocation.

Yes

(20) Did the authors add KCl in screening controls without toxins? This should be detailed in the Materials and Methods. Could there be a KCl effect on the cells? KCl exposure for 48 hours may be highly stressful for cells. The KCl exposure should last only several minutes for toxin entry.

We did not observe significant cell detah with the cell culture conditions used. Cell viability was controlled at multiple stages using nuclei number for instance

Reviewer #3 (Recommendations For The Authors):

Main comments: (1) In Figure 1B: could you devise a means to prevent proteosomal degradation of the tGFP cleaved part to assess whether this is formed?

We have also used a FRET assay after tintoxication and obtained similar results

(2) Line 152: Where it reads "was not surprising", maybe I missed something, but to me, this is indeed surprising. If the toxin is rapidly internalized and translocated (therefore, it is able to cleave SNAP25), the fact that tGFP requires 48 hours to be degraded seems surprising to me. Or does it mean that the toxin also slows down the degradation of the tGFP fragment? So, how can you differentiate between the effect being on cleavage of the fragment or in tGFP degradation?

The reviewer is correct, the “not” was a typo due to re-writting; the long delay between adding the toxin and observing cleavage was suprising indeed. Our interpretation is that it is trafficking that takes time, indeed, the split-GFP data kinetics indicates that the toxin takes about 48h to fill up the entire cytosol (Fig. 2D).

(3) Regarding the effect of Sec61G knockdown, is it possible that the observed effects are indirect and not due to the translocon being directly responsible for translocating the protein?

As discussed in the last part of the results,Sec61 knock-down results in block of intoxication, but does not prevent BonT from reaching the lumen of the ER (Figure 6G,H). Thus, Sec61 is “is instrumental to the translocation of BoNT/A LC into the neuronal cytosol at the soma.”

Minor comments:

(1) Fig. 3E: in the legend I think one of the NT3+ should be NT3-.

Yes, thanks for spotting it

(2) Would you consider adding Figure S4 as a main figure?

Thanks for the suggestion

(3) Please, check that all microscopy image panels have scale bars.

Done

(4) Figure 6B (bottom panes): why does it seem that there is a lot of mNeonGreen positive signal in regions that are not positive for HA? Shouldn't complementation keep HA in the complemented protein.

Our assumption i sthat there is an excess of receptor protein (HA tag) over reconstituted protein (GFP protein) given the relatively low concentration of toxin being internalized and translocated Refs: (1) Pirazzini M, Azarnia Tehran D, Leka O, Zanetti G, Rossetto O, Montecucco C. On the translocation of botulinum and tetanus neurotoxins across the membrane of acidic intracellular compartments. Biochim Biophys Acta. 2016 Mar;1858(3):467–474. PMID: 26307528

(2) Pirazzini M, Rossetto O, Eleopra R, Montecucco C. Botulinum Neurotoxins: Biology, Pharmacology, and Toxicology. Pharmacol Rev. 2017 Apr;69(2):200–235. PMCID: PMC5394922

(3) Dong M, Masuyer G, Stenmark P. Botulinum and Tetanus Neurotoxins. Annu Rev Biochem. Annual Reviews; 2019 Jun 20;88(1):811–837.

(4) Rossetto O, Pirazzini M, Fabris F, Montecucco C. Botulinum Neurotoxins: Mechanism of Action. Handb Exp Pharmacol. 2021;263:35–47. PMCID: 6671090

(5) Williams JM, Tsai B. Intracellular trafficking of bacterial toxins. Curr Opin Cell Biol. 2016 Aug;41:51–56. PMCID: PMC4983527

(6) Mesquita FS, van der Goot FG, Sergeeva OA. Mammalian membrane trafficking as seen through the lens of bacterial toxins. Cell Microbiol. 2020 Apr;22(4):e13167. PMCID: PMC7154709

(7) Hoch DH, Romero-Mira M, Ehrlich BE, Finkelstein A, DasGupta BR, Simpson LL. Channels formed by botulinum, tetanus, and diphtheria toxins in planar lipid bilayers: relevance to translocation of proteins across membranes. Proc Natl Acad Sci U S A. 1985 Mar;82(6):1692–1696. PMCID: PMC397338

(8) Donovan JJ, Middlebrook JL. Ion-conducting channels produced by botulinum toxin in planar lipid membranes. Biochemistry. 1986 May 20;25(10):2872–2876. PMID: 2424493

(9) Fischer A, Montal M. Single molecule detection of intermediates during botulinum neurotoxin translocation across membranes. Proc Natl Acad Sci U S A. 2007 Jun 19;104(25):10447–10452. PMCID: PMC1965533

(10) Fischer A, Nakai Y, Eubanks LM, Clancy CM, Tepp WH, Pellett S, Dickerson TJ, Johnson EA, Janda KD, Montal M. Bimodal modulation of the botulinum neurotoxin protein-conducting channel. Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1330–1335. PMCID: PMC2635780

(11) Fischer A, Montal M. Crucial role of the disulfide bridge between botulinum neurotoxin light and heavy chains in protease translocation across membranes. J Biol Chem. 2007Oct 5;282(40):29604–29611. PMID: 17666397

(12) Koriazova LK, Montal M. Translocation of botulinum neurotoxin light chain protease through the heavy chain channel. Nature structural biology. 2003. p. 13–18. PMID: 12459720

(13) Moreau D, Kumar P, Wang SC, Chaumet A, Chew SY, Chevalley H, Bard F.Genome-wide RNAi screens identify genes required for Ricin and PE intoxications. Dev Cell. 2011 Aug 16;21(2):231–244. PMID: 21782526

(14) Bassik MC, Kampmann M, Lebbink RJ, Wang S, Hein MY, Poser I, Weibezahn J, Horlbeck MA, Chen S, Mann M, Hyman AA, Leproust EM, McManus MT, Weissman JS. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell. 2013 Feb 14;152(4):909–922. PMCID: PMC3652613

(15) Tian S, Muneeruddin K, Choi MY, Tao L, Bhuiyan RH, Ohmi Y, Furukawa K, Furukawa K, Boland S, Shaffer SA, Adam RM, Dong M. Genome-wide CRISPR screens for Shiga toxins and ricin reveal Golgi proteins critical for glycosylation. PLoS Biol. 2018 Nov;16(11):e2006951. PMCID: PMC6258472

eLife assessment

In this valuable manuscript, Yeo et al. describe new methods for assessing the intracellular itinerary of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. The current manuscript challenges previously held views on how the catalytic portion of the toxin makes its way from the endocytic compartment to the cytosol, to meet its substrates. The approach taken is deemed innovative and the experiments are carefully performed, presenting solid evidence for some of the drawn conclusion; however, the conclusions one may draw from the experimental results are somewhat limited, as it is possible that the scope of their findings could be restricted to the specific neuron model and molecular tools that were used. This paper could be of interest to both cell biologists and physicians.

Reviewer #1 (Public Review):

As outlined in my previous public review, Yeo et al. revised the current neuronal intoxication model, common to all serotypes of botulinum neurotoxins. Using a combination of genetic and imaging approaches, they demonstrate that upon internalization, BoNT/A-containing endosomes undergo retro-axonally trafficking to the neuronal soma. Within the soma, this particular serotype then traffics to the endoplasmic reticulum (ER) via the Golgi apparatus. At the ER, the SEC61 translocon complex facilitates the translocation of BoNT/A's metalloprotease domain (light chain, LC) from the ER lumen into the cytosol, where the thioredoxin reductase/thioredoxin system and HSP complexes release and refold the catalytic LC. Subsequently, the LC diffuses and cleaves SNAP25 first in the soma before reaching neurites and synapses.

Although I still acknowledge the well-executed and thoroughly analyzed genome-wide RNAi screen, I must once again highlight significant pitfalls and weaknesses in the paper due to the lack of essential controls and validations. Consequently, I suggest readers to approach the authors' findings with caution, as they may be limited to the combination of one specific cellular model and genetic engineering tools. During the revision process, authors declined to conduct additional experiments that could have strengthened their main conclusions. These include, but are not limited to:

(1) Investigating weather in the newly generated cell line Red-SNAPR, the GFP fragment produced upon toxin cleavage degrades more rapidly in the soma compared to axon terminals, possibly due to differences in proteasome activity in these two compartments.

(2) Validating toxin cleavage activity in the soma before reaching synapses by conducting an additional and more physiological approach, a time course experiment using native BoNT/A and staining BoNT/A-cleaved SNAP25 with specific antibodies.

(3) Assessing whether the addition of mNG1-11 to the LC affects the translocation process itself and quantifying the mean fluorescence intensity (MFI) per cell, taking into consideration the amount of HA-tagged Cyt-mG1-10, which appears predominantly expressed in the cytosol and less detected in neurites. This raises the question of potential bias toward the cell soma in this assay.

(4) Validating major hits (e.g., VPS34 and Sec61) by performing WB or IF analysis to test the cleavage of endogenous SNAP25.

Additionally, during the revision process, the authors raised concerns about the level of scrutiny applied by this reviewer, particularly in comparison to the seminal study of Lilia K. Koriazova & Mauricio Montal published in Nature Structural Biology (PMID: 12459720). In this 2003 paper, Montal's lab pioneered the use of single-channel recordings and substrate proteolysis analysis to reconstitute the translocation of BoNT/A light chain protease across an artificial lipid bilayer via the channel formed by its heavy chain. The authors highlighted that, when converting the experimental conditions from the aforementioned paper into molarity, it appears that the cis compartment was loaded with 10−8 M BoNT/A, and the reported translocated protease activity (measured by substrate cleavage) is equivalent to 10−17 M. This implies that only about 1 LC molecule in 100 million has crossed the membrane. The calculation performed by authors is indeed accurate. However, readers should be informed about another piece of information present in the same paper that might help them to clarify this important point. Koriazova & Montal, by discussing this experiment, have pointed out that this value (10−17 M) corresponds to ≈3600 LC molecules, a number closed to the maximum number of channels that can be formed under the used experimental conditions. Indeed, from the same paper, quotation: 'This number is in close agreement with the maximum number of channels inserted in the bilayer under the assay condition, ≈2000 (Fig. 3a), as estimated from macroscopic membrane conductance ∼1 × 105 pS and γ = 50 pS measured in 0.1 M KCl'. Another aspect that Yeo et al. forgot to mention in their rebuttal letter is that the system used by Koriazova & Montal lacks any chaperones in the trans compartment. Nowadays, we know that upon translocation, the refolding of the L chain is aided by Hsp90 (Azarnia Tehran et al., Cellular microbiology, 2017). Keeping this in mind, is not unrealistic to hypothesize that the number of LC molecules calculated more than 22 years ago by Koriazova & Montal (in an indirect way by checking SNAP25 cleavage using an ELISA-based assay) might be an underestimation. Indeed, the addition of Hsp90 in their system might aid in the refolding of LC molecules that, even if they have successfully be translocated, might not cleave the substrate due to their unfolded state.

As active scientist, I understand the challenges of peer review and publication, which can often be slow and frustrating involving seemingly endless rounds of review. Therefore, I am in favor of the new eLife publishing model. Indeed, this paper has already been published as Reviewed Preprints and will soon be declared as the final Version of Record, accompanied by this public review. Having said that, I hope that the readers of this journal and future scientists will prove me wrong. I hope they will engage with this paper, providing comments, validations (which are currently missing), and citations as frequently as they did for the seminal works of Koriazova & Montal.

Reviewer #2 (Public Review):

Summary:

The study by Yeo and co-authors addresses a long-lasting issue about botulinum neurotoxin (BoNT) intoxication. The current view is that the toxin binds to its receptors at the axon terminus by its HCc domain and is internalized in recycled neuromediator vesicles just after release of the neuromediators. Then, the HCn domain assists the translocation of the catalytic light chain (LC) of the toxin through the membrane of these endocytic vesicles into the cytosol of the axon terminus. There, the LC cleaves its SNARE substrate and blocks neurosecretion. However, other views involving kinetic aspects of intoxication suggest that the toxin follows the retrograde axonal transport up to the nerve cell body and then back to the nerve terminus before cleaving its substrate.

In the current study, the authors claim that the BoNT/A (isotype A of BoNT) not only progresses to the cell body but once there, follows the retrograde transport trafficking pathway in a retromer-dependent fashion, through the Golgi apparatus, until reaching the endoplasmic reticulum. Next, the LC dissociates from the HC (a process not studied here) and uses the translocon Sec61 machinery to retro-translocate into the cytosol. Only then, the LC traffics back to the nerve terminus following the anterograde axonal transport. Once there, LC cleaves its SNARE substrate (SNAP25 in the case of BoTN/A) and blocks neurosecretion.

To reach their conclusion, Yeo and co-authors use a combination of engineered tools: a cell line able to differentiate into neurons (ReNcell VN), a reporter dual fluorescent protein derived from SNAP25, the substrate of BoNT/A (called SNAPR), the use of either native BoNT/A or a toxin to which three fragment 11 of the reporter fluorescent protein Neon Green (mNG) are fused to the N-terminus of the LC (BoNT/A-mNG11x3), and finally ReNcell VN transfected with mNG1-10 (a protein consisting of the first 10 beta strands of the mNG).

SNAPR is stably expressed all over in the ReNcell VN. SNAPR is yellow (red and green) when intact and becomes red only when cleaved by BoNT/A LC, the green tip being degraded by the cell. When the LC of BoNT/A-mNG11x3 reaches the cytosol in ReNcell VN transfected by mNG1-10, the complete mNG is reconstituted and emits a green fluorescence.

In the first experiment, the authors show that the catalytic activity of the LC appears first in the cell body of neurons where SNAPR is cleaved first. This phenomenon starts 24 h after intoxication and progresses along the axon towards the nerve terminus during an additional 24 h. In a second experiment, the authors intoxicate the ReNcell VN transfected by mNG1-10 using the BoNT/A-mNG11x3. The fluorescence appears also first in the soma of neurons, then diffuses in the neurites in 48 h. The conclusion of these two experiments is that translocation occurs first in the cell body and that the LC diffuses in the cytosol of the axon in an anterograde fashion.

In the second part of the study, the authors perform a siRNA screen to identify regulators of BoNT/A intoxication. Their aim is to identify genes involved in intracellular trafficking of the toxin and translocation of the LC. Interestingly, they found positive and negative regulators of intoxication. Regulators could be regrouped according to the sequential events of intoxication. Genes affecting binding to the cell-surface receptor (SV2) and internalization. Genes involved in intracellular trafficking. Genes involved in translocation such as reduction of the disulfide bond linking the LC to the HC and refolding in the cytosol. Genes involved in signaling such as tyrosine kinases and phosphatases. All these groups of genes may be consistent with the current view of BoNT intoxication within the nerve terminus. However, two sets of genes were particularly significant to reach the main conclusion of the work and definitely constitute an original finding important to the field. One set of genes consists in those of the retromer, the other relates to the Sec61 translocon. This should indicate that once endocytosed, the BoNT traffics from the endosomes to Golgi apparatus, then to the ER. Ultimately, the LC should translocate from the ER lumen to the cytosol using the Sec61 translocon. The authors further control that the SV2 receptor for the BoNT/A traffics along the axon in a retromer-dependent fashion and that BoNT/A-mNG11x3 traverses the Golgi apparatus by fusing the mNG1-10 to a Golgi resident protein.

Strengths:

The findings in this work are convincing. The experiments are carefully done and are properly controlled. In the first part of the study, both the activity of the LC is monitored together with the physical presence of the toxin. In the second part of the work, the most relevant genes that came out of the siRNA screen are checked individually in the ReNcell VN / BoNT/A reporter system to confirm their role in BoNT/A trafficking and retro-translocation.<br /> These findings are important to the fields of toxinology and medical treatment of neuromuscular diseases by BoNTs. They may explain some aspects of intoxication such as slow symptom onset, aggravation and appearance of central effects.

Weaknesses:

The findings antagonize the current view of the intoxication pathway that is sustained by a vast amount of observations. The findings are certainly valid, but their generalization as the sole mechanism of BoNT intoxication should be tempered. These observations are restricted to one particular neuronal model and engineered protein tools. Other models such as isolated nerve/muscle preparations display nerve terminus paralysis within minutes rather than days. Also, the tetanus neurotoxin (TeNT), which mechanism of action involving axonal transport to the posterior ganglia in the spinal cord is well described, takes between 5 and 15 days. It is thus possible that different intoxication mechanisms co-exist for BoNTs or even vary depending on the type of neurons.

Although the siRNA experiments are convincing, it would be nice to reach the same observations with drugs affecting the endocytic to Golgi to ER transport (such as Retro-2, golgicide or brefeldin A) and the Sec61 retrotranslocation (such as mycolactone). Then, it would be nice to check other neuronal systems for the same observations.

Reviewer #3 (Public Review):

Summary:

The manuscript by Yeo et al. investigates the intracellular trafficking of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. Contrary to the prevailing understanding of BoNT/A translocation into the cytosol, the study suggests a retrograde migration from the synapse to the soma-localized Golgi in neurons. Using a genome-wide siRNA screen in genetically engineered neurons, the researchers identify over three hundred genes involved in this process. The study employs organelle-specific split-mNG complementation, revealing that BoNT/A traffics through the Golgi in a retromer-dependent manner before moving to the endoplasmic reticulum (ER). The Sec61 complex is implicated in the retro-translocation of BoNT/A from the ER to the cytosol. Overall, the research challenges the conventional model of BoNT/A translocation, uncovering a complex route from synapse to cytosol for efficient intoxication. The findings are based on a comprehensive approach, including the introduction of a fluorescent reporter for BoNT/A catalytic activity and genetic manipulations in neuronal cell lines. The conclusions highlight the importance of retrograde trafficking and the involvement of specific genes and cellular processes in BoNT/A intoxication.

Strengths:

The major part of the experiments are convincing. They are well-controlled and the interpretation of their results is balanced and sensitive.

Weaknesses:

To my opinion, the main weakness of the paper is that all experiments are performed using a single cellular system (RenVM neurons), as stated in the title. It is therefore unclear at the moment to what extent the findings in this paper can be generalized to other neuronal cell models / in vivo situation.

"bunch crossings (events)"是指在高能物理实验，特别是加速器实验中，一束粒子被分成多个“束团”（bunches），这些束团在加速器中以接近光速的速度相对飞行。当两个束团在空间中的某个特定点“交叉”或对撞时，就会发生大量的粒子相互作用，产生大量的新粒子和辐射。这些粒子的相互作用和产生的事件就被称作“事件”（events）。在描述实验数据处理时，"bunch crossings (events)"指的是这些对撞产生的一个个独立的事件，每个事件都需要被单独记录和分析。

https://www.gbif.org/fr/species/4738

https://www.gbif.org/fr/species/8482956

1. Hello everyone. Who can help me with this Laurent series espansione? I don't understand the part I circled, which is how the summation is transformed. Thank you so much!

At Skinic, we pride ourselves on being at the forefront of cellulite treatment, providing our clients with the most effective and cutting-edge technologies available. With a track record of successful treatments, we are constantly seeking innovations to enhance our services.

2K + 2H2O ---->2KOH +H2

1. effervescence
2. lilac flame

Extraction with carbon isn't effective as you don't get pure tungsten; you only get tungsten carbide. Extraction with hydrogen in the most effective, as you get pure tungsten - no need for further extraction. Extraction by iron is also effective; you get 2 solids - pure tungsten and iron oxide.

atom economy formula: Total Mr of desired products / Total Mr of all reactants Mr of Tin:119 (desired product) Mr of SnO2 + C = 119+32+12 = 163 (all reactants) 119/163 = 0.73 As a percentage:73%

ruthenium

2+48+x=150 50+x=150 x=100

reason one: Gallium fit into the gaps that Mendeleev left in the periodic table. reason two: It fit into its group with similar properties.

Ga3+

Electrons: 31 Neutrons: 38

(6960)+(7140), all divided by 100, which equals: 69.8.

When two atoms of the same element have different numbers of neutrons.

Each carbon atom in graphite only makes 3 covalent bonds, meaning that it has delocalised electrons available to carry the charge throughout the structure. It is soft and slippery due to the layers sliding over eachother due to weak intermolecular bonds.

The covalent bonds are weak

C3H6O

Transport drugs around the body accurately

A sphere

一个有序的、自动化的工作流程，它定义了一系列步骤，这些步骤按照特定的顺序执行

正直，诚实，诚恳

Reviewer #1 (Public Review):

In this manuscript, the authors described a computational method catELMo for embedding TCR CDR3 sequences into numeric vectors using a deep-learning-based approach, ELMo. The authors applied catELMo to two applications: supervised TCR-epitope binding affinity prediction and unsupervised epitope-specific TCR clustering. In both applications, the authors showed that catELMo generated significantly better binding prediction and clustering performance than other established TCR embedding methods.

The authors have addressed all of my concerns except for one as following:

5. GIANA's result is like

## TIME:2020-12-14 14:45:14|cmd: GIANA4.py|COVID_test/rawData/hc10s10.txt|IsometricDistance_Thr=7.0|thr_v=3.7|thr_s=3.3|exact=True|Vgene=True|ST=3

## Column Info: CDR3 aa sequence, cluster id, other information in the input file<br /> CAISDGTAASSTDTQYF 1 TRBV10-3*01 6.00384245917387e-05 0.930103216755186 COVID19:BS-EQ-0002-T1-replacement_TCRB.tsv<br /> CAISDGTAASSTDTQYF 1 TRBV10-3*01 4.34559031223066e-05 0.918135389545364 COVID19:BS-EQ-0002-T2-replacement_TCRB.tsv<br /> CANATLLQVLSTDTQYF 2 TRBV21-1*01 3.00192122958694e-05 0.878695260046097 COVID19:BS-EQ-0002-T1-replacement_TCRB.tsv<br /> CANATLLQVLSTDTQYF 2 TRBV21-1*01 1.44853010407689e-05 0.768125375525736 COVID19:BS-EQ-0002-T2-replacement_TCRB.ts<br /> ...

as in its example file at: https://raw.githubusercontent.com/s175573/GIANA/master/data/hc10s10--RotationEncodingBL62.txt

The results directly give the clustering results in the second column, and there is no direct distance metric for hierarchical clustering. Therefore, it is still not clear how the authors conducted the hierarchical clustering on GIANA's results. Did the hierarchical clustering apply to each of the original clusters on the CDR3 distances within the same original cluster?

@misc{smith_2022, title={Built without wheels, this infinity bike looks to start a revolution}, url={https://mashable.com/video/infinity-bike-stephen-henrich}, journal={Mashable}, publisher={Mashable}, author={Smith, Emmett}, year={2022}, month={Apr} }

https://www.profil.at/oesterreich/auf-diese-10-personen-hoert-die-regierung/402655766

eLife assessment

This important study suggests that capsaicin nanoparticle administration in rats activates the transcription factor Nrf2 by directly binding to its repressor KEAP1, leading to cytoprotective gene induction, and preventing alcohol-induced gastric damage, an avenue to treat alcoholism-related gastric disorders. The evidence is currently incomplete as there is no experimental proof that capsaicin exerts its cytoprotective effects via Nrf2, and not via any of its multiple known pharmacological effects. In particular, Nrf2-deficient mice should be used to show that Nrf2 is causal to the cytoprotective effect, and better controls should be provided for the direct KEAP2-capsaicin interaction, given the high concentrations used.

Reviewer #1 (Public Review):

Summary:

This paper by Gao et al. describes the effect of capsaicin on the NRF2/KEAP1 pathway. The authors carried out a set of in vitro experiments that addressed the mechanisms of the protective effect of capsaicin on ethanol-induced cytotoxicity. They also conducted in vivo studies in rats focusing on ethanol-induced gastric mucosal oxidative damage. The authors conclude that capsaicin activates NRF2, which leads to the induction of cytoprotective genes, preventing oxidative damage. This effect has already been shown, and it is well established that capsaicin activates NRF2, but what can be novel in the paper is the demonstration that capsaicin may directly bind to KEAP1 and that it is a noncovalent modification of the Kelch domain. The authors also designed new albumin-coated capsaicin nanoparticles, which were tested for the therapeutic effect in vivo. Apart from novelty concerns, the manuscript may be potentially interesting, but in my opinion, it is not fully technically sound, which weakens the strength of the evidence.

Major concerns:

For studies investigating capsaicin binding to KEAP1, the authors used capsaicin concentrations that are toxic to cells (Figures S1D and 4F, G). In vivo studies were performed only in 3 rats per group. The T-test was used for the comparison of more than two groups. Given the well-known issues with the specificity of the NRF2 antibody, the authors should provide appropriate controls, especially for IF and IHC staining.

Reviewer #2 (Public Review):

Summary:

In this paper, the authors wanted to show that capsaicin can disrupt the interaction between Keap1 and Nrf2 by directly binding to Keap1 at an allosteric site. The resulting stabilization of Nrf2 would protect CAP-treated gastric cells from alcohol-induced redox stress and damage as well as inflammation (both in vitro and in vivo).

Strengths:

One major strength of the study is the use of multiple methods (CoIP, SPR, BLI, deuterium exchange MS, CETSA, MS simulations, target gene expression) that consistently show for the first time that capsaicin can disrupt the Nrf2/Keap1 interaction at an allosteric site and lead to stabilization and nuclear translocation of Nrf2.

Weaknesses:

One major weakness of the study is that plausibility is taken as proof for causality. The finding that capsaicin directly binds to Keap1 and releases Nrf2 from its fate of degradation (in vitro) is taken for granted as the sole explanation for the observed improved gastric health upon alcohol exposure (in vivo). There is no consideration or exclusion of any potential unrelated off-target effect of capsaicin, or proteins other than Nrf2 that are also controlled by Keap1.

Another point that hampers full appreciation of the capsaicin effect in cells is that capsaicin is not investigated alone, but mostly in combination with alcohol only.

Bottom Line:

Overall, the authors are convincing that capsaicin (although weakly) binds to Keap1 and releases Nrf2 from degradation. With this, the authors provide a significant finding with marked relevance for the redox/Nrf2 as well as natural products /hit discovery communities. Moreover, the employed toolbox of different complementary methodologies can set the bar for future PPI inhibitor studies. The translation of this novel finding in a biological setting (alcohol-stressed gastric cells) still leaves some open questions and concerns. These concerns mainly arise from lacking control experiments and/or somewhat biased conclusions from the obtained data sets.

Reviewer #3 (Public Review):

Summary:

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

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

Strengths:

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

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

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

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

Weaknesses:

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

Furthermore, the study primarily focuses on the interaction between CAP and the KEAP1-NRF2 axis in the context of ethanol-induced gastric mucosa damage. It may be beneficial to explore the broader effects of CAP on other pathways or conditions related to oxidative stress. CAP has been known for its interaction with the Transient Receptor Potential Vanilloid type 1 (TRPV1) channel and subsequent NRF2 signaling pathway activation. Those receptors are also expressed within the gastric mucosa and could potentially cross-react with CAP leading to the observed outcome. Including experiments to investigate this route of activation could strengthen the present study.

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

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

https://www.diepresse.com/6284413/die-umwege-einer-omv-expertise

eLife assessment

This study presents useful findings about daily rhythm changes of the Drosophila melanogaster adult gut metabolome, which is shown to be dependent on the fly microbiota, diet, and genotype. The phenomena observed are supported by solid experimental evidence, however, there are limitations regarding the analysis and a deeper interpretation of results would improve the manuscript. An absence of mechanistic functional investigation limits the power of the proposed conclusions. The experiments are currently incomplete as the effect of food intake timing was not directly addressed by measuring the quantity and timing of food consumption. The authors should strongly consider including the model organism used in the study in the title of the manuscript to reflect the work. This study will be of interest to physiologists of circadian biology and nutrition.

Reviewer #1 (Public Review):

The authors build on their previous study that showed the midgut microbiome does not oscillate in Drosophila. Here, they focus on metabolites and find that these rhythms are in fact microbiome-dependent. Tests of time-restricted feeding, a clock gene mutant, and diet reveal additional regulatory roles for factors that dictate the timing and rhythmicity of metabolites. The study is well-written and straightforward, adding to a growing body of literature that shows the time of food consumption affects microbial metabolism which in turn could affect the host.

Some additional questions and considerations remain:

(1) The main finding that the microbiome promotes metabolite rhythms is very interesting. Which microbiota are likely to be responsible for these effects? The author's previous work in this area may shed light on this question. Are specific microbiota linked to some of the metabolic pathways investigated in Figure 5?

(2) TF increases the number of rhythmic metabolites in both microbiome-containing and abiotic flies in Figure 1. This is somewhat surprising given that flies typically eat during the daytime rather than at night, very similar to TF conditions. I would have assumed that in a clock-functioning animal, the effect of restricting food availability should not make a huge difference in the time of food consumption, and thus downstream impacts on metabolism and microbiome. Can the authors measure food intake directly to compare the ad-lib vs TF flies to see if there are changes in food intake? Would restricting feeding to other times of day shift the timing of metabolites accordingly?

(3) In Figure 2, Per loss of function reveals a change in the phase of rhythmic metabolites. In addition, the effect of the microbiome on these is very different = The per mutants show increased numbers of rhythmic metabolites when the microbiome is absent, unlike the controls. Is it possible that these changes are due to altered daily feeding rhythms in per mutants? Testing the time and amount of food consumed by the per mutant flies would address this question. Would TF in the per mutants rescue their metabolite rhythms and make them resemble clock-functioning controls?

(4) The calorie content of each diet - normal vs high protein vs high-sugar are different. The possibility of a calorie effect rather than a difference in nutrition (protein/carbohydrate) should be discussed. Another issue worth considering is the effect of high protein/sugar on the microbiome itself. While the microbiome doesn't seem to affect rhythms in the high-protein diet, the high-sugar diet seems highly microbiome-dependent in Supplementary Fig 8C vs D. Does the diet impact the microbiome and thus metabolite rhythmicity downstream?

(5) It would be good if a supplementary table was provided outlining the specific metabolites that are shown in the radial plots. It is not clear if the rhythms shown in the figures refer to the same metabolites peaking at the same time, or rather the overall abundance of completely different metabolites. This information would be useful for future research in this area.

Reviewer #2 (Public Review):

Summary:

The paper addresses several factors that influence daily changes in concentration of metabolites in the Drosophila melanogaster gut. The authors describe metabolomes extracted from fly guts at four time-points during a 24-hour period, comparing profiles of primary metabolites, lipids, and biogenic amines. The study elucidates that the percentage of metabolites that exhibit a circadian cycle, peak phases of their increased appearance, and the cycling amplitude depends on the combination of factors (microbiome status, composition or timing of the diet, circadian clock genotype). Multiple general conclusions based on the data obtained with modern metabolomics techniques are provided in each part of the article. Descriptive analysis of the data supports the finding that microbiome increases the number of metabolites for which concentration oscillates during the day period. Results of the experiments show that timed feeding significantly enhanced metabolite cycling and changed its phase regardless of the presence of a microbiome. The authors suggest that the host circadian rhythm modifies both metabolite cycling period and the number of such metabolites.

Strengths:

The obvious strength of the study is the data on circadian cycling of the detected 843, 4510, and 4330 total primary metabolites, lipids, and biogenic amines respectively in iso31 flies and 623, 2245, and 2791 respective metabolites in per01 mutants. The comparison of the abundance of these metabolites, their cycling phase, and the ratio of cycling/non-cycling metabolites is well described and illustrated. The conditions tested represent significant experimental interest for contemporary chronobiology: with/without microbiota, wild-type/mutant period gene, ad libitum/time-restricted feeding, and high-sugar/high-protein diet. The authors conclude that the complex interaction between these factors exists, and some metabolic implications of combinations of these factors can be perceived as reminiscent of metabolic implications of another combination ("...the microbiome and time-restricted feeding paradigms can compensate for each other, suggesting that different strategies can be leveraged to serve organismal health"). Enrichment analysis of cycling metabolites leads to an interesting suggestion that oscillation of metabolites related to amino acids is promoted by the absence of microbiota, alteration of circadian clock, and time-restricted feeding. In contrast, association with microbiota induces oscillation of alpha-linolenic acid-related metabolites. These results provide the initial step for hypothesising about functional explanations of the uncovered observations.

Weaknesses:

Among the weaknesses of the study, one might point out too generalist interpretations of the results, which propose hypothetical conclusions without their mechanistic proof. The quantitative indices analysed are obviously of particular interest, however are not self-explaining and exhaustive. More specific biological examples would add valuable insights into the results of this study, making conclusions clearer. More specific comments on the weaknesses are listed below:

(1) The criterion of the percentage of cycling metabolites used for comparisons has its own limitations. It is not clear, whether the cycling metabolites are the same in the guts with/without microbiota, or whether there are totally different groups of metabolites that cycle in each condition. GO enrichment analysis gives only a partial assessment, but is still not quantitative enough.

(2) The period of cycling data is based on only 4 time points during 24 hours in 4 replicates (>200 guts per replicate) on the fifth day of the experiment (10-12-day-old adults). It does not convincingly prove that these metabolites cycle the following days or more finely within the day. Moreover, it is not clear how peaks in polar histogram plots were detected in between the timepoints of ZT0, ZT6, ZT12, and ZT18.

(3) Average expression and amplitude are analysed for groups of many metabolites, whereas the data on distinct metabolites is hidden behind these general comparisons. This kind of loss of information can be misleading, making interpretation of the mentioned parameters quite complicated for authors and their readers. Probably more particular datasets can be extracted to be discussed more thoroughly, rather than those general descriptions.

(4) The metabolites' preservation is crucial for this type of analysis, and both proper sampling plus normalisation require more attention. More details about measures taken to avoid different degradation rates, different sizes of intestines, and different amounts of microbes inside them will be beneficial for data interpretation.

(5) The data in the article describes formal phenomena, not directly connected with organism physiology. The parameters discussed obviously depend on the behaviour of flies. Food consumption, sleep, and locomotor activity could be additionally taken into account.

(6) Division of metabolites into three classes limits functional discussion of found differences. Since the enrichment analysis provided insights into groups of metabolites of particular interest (for example, amino acid metabolism), a comparison of their cycling characteristics can be shown separately and discussed.

Reviewer #3 (Public Review):

Summary:

The authors. sought to quantify the influence of the gut microbiome on metabolite cycling in a Drosophila model with extensive metabolomic profiling over a 24-hour period. The major strength of the work is the production of a large dataset of metabolites that can be the basis for hypothesis generation for more specific experiments. There are several weaknesses that make the conclusions difficult to evaluate. Additional experiments to quantify food intake over time will be required to determine the direct role of the microbiome in metabolite cycling.

Strengths:

An extensive metabolomic dataset was collected with treatments designed to determine the influence of the gut microbiome on metabolite circadian cycling.

Weaknesses:

(1) The major strength of this study is the extensive metabolomic data, but as far as I can tell, the raw data is not made publicly available to the community. The presentation of highly processed data in the figures further underscores the need to provide the raw data (see comment 3).

(2) Feeding times heavily influence the metabolome. The authors use timed feeding to constrain when flies can eat, but there is no measurement of how much they ate and when. That needs to be addressed.

Since food is the major source of metabolites, the timing of feeding needs to be measured for each of the treatment groups. In the previous paper (Zhang et al 2023 PNAS), the feeding activity of groups of 4 male flies was measured for the wildtype flies. That is not sufficient to determine to what extent feeding controls the metabolic profile of the flies. Additionally, timed feeding opportunities do not equate to the precise time of feeding. They may also result in dietary restriction, leading to the loss of stress resistance in the TF flies. The authors need to measure food consumption over time in the exact conditions under which transcriptomic and metabolomic cycling are measured. I suggest using the EX-Q assay as it is much less effort than the CAFE assay and can be more easily adapted to the rearing conditions of the experiments.

(3) The data on the cycling of metabolites is presented in a heavily analyzed form, making it difficult to evaluate the validity of the findings, particularly when a lack of cycling is detected. The normalization to calculate the change in cycling due to particular treatments is particularly unclear and makes me question whether it is affecting the conclusions. More presentation of the raw data to show when cycling is occurring versus not would help address this concern, as would a more thorough explanation of how the normalization is calculated - the brief description in the methods is not sufficient.

For instance, the authors state that "timed feeding had less effect on flies containing a microbiome relative to sterile flies." One alternative interpretation of that result is that both treatments are cycling but that the normalization of one treatment to the other removes the apparent effect. This concern should be straightforward to address by showing the raw data for individual metabolites rather than the group.

eLife assessment

This is a valuable paper that uses super-resolution microscopy to show the nanoclustering of the Nipah virus fusion protein on cell and viral membranes. Some of the conclusions regarding the clustering of viral fusion proteins is supported by solid biochemical and super-resolution imaging data while other conclusions such as significance for viral fusion mechanisms is not fully supported by the data provided.

Reviewer #1 (Public Review):

Summary:

In this work by Wang et al., the authors use single-molecule super-resolution microscopy together with biochemical assays to quantify the organization of Nipah virus fusion protein F (NiV-F) on cell and viral membranes. They find that these proteins form nanoscale clusters which favors membrane fusion activation, and that the physical parameters of these clusters are unaffected by protein expression level and endosomal cleavage. Furthermore, they find that the cluster organization is affected by mutations in the trimer interface on the NiV-F ectodomain and the putative oligomerization motif on the transmembrane domain, and that the clusters are stabilized by interactions among NiV-F, the AP2-complex, and the clathrin coat assembly. This work improves our understanding of the NiV fusion machinery, which may have implications also for our understanding of the function of other viruses.

Strengths:

The conclusions of this paper are well-supported by the presented data. This study sheds light on the activation mechanisms underlying the NiV fusion machinery.

Weaknesses:

The authors provide limited details of the convolutional neural network they developed in this work. Even though custom-codes are made available, a description of the network and specifications of how it was used in this work would aid the readers in assessing its performance and applicability. The same holds for the custom-written OPTICS algorithm. Furthermore, limited details are provided for the imaging setup, oxygen scavenging buffer, and analysis for the single-molecule data, which limits reproducibility in other laboratories. The claim of 10 nm resolution is not backed up by data and seems low given the imaging conditions and fluorophores used. Fourier Ring Correlation analysis would have validated this claim. If the authors refer to localization precision rather than resolution, then this should be specified and appropriate data provided to support this claim.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Wang and co-workets employ single molecule light microscopy (SMLM) to detect Nipah virus Fusion protein (NiV-F) in the surface of cells. They corroborate that these glycoproteins form microclusters (previously seen and characterized together with the NiV-G and Nipah Matrix protein by Liu and co-workers (2018) also with super-resolution light microscopy). Also seen by Liu and coworkers the authors show that the level of expression of NiV-F does not alter the identity of these microclusters nor endosomal cleavage. Moreover, mutations and the transmembrane domain or the hexamer-of-trimer interface seem to have a mild effect on the size of the clusters that the authors quantified. Importantly, it has also been shown that these particles tend to cluster in Nipah VLPs.

Strengths:

The authors have tried to perform SMLM in single VLPs and have shown partially the importance of NiV-F clustering.

Weaknesses:

The labelling strategy for the NiV-F is not sufficiently explained. The use of a FLAG tag in the extracellular domain should be validated and compared with the unlabelled WT NiV-F when expressed in functional pseudoviruses (for example HIV-1 based particles decorated with NiV-F). This experiment should also be carried out for both infection and fusion (including BlaM-Vpr as a readout for fusion). I would also suggest to run a time-of-addition BlaM experiment to understand how this particular labelling strategy affects single virion fusion as compared to the the WT. It would also be very important to compare the FLAG labelling approach with recent advances in the field (for instance incorporating noncanonical amino acids (ncAAs) into NiV-F by amber stop-codon suppression, followed by click chemistry).

The correlation between the existence of microclusters of a particular size and their functionality is missing. Only cell-cell fusion assays are shown in supplementary figures and clearly, single virus entry and fusion cannot be compared with the biophysics of cell-cell fusion. Not only the environment is completely different, membrane curvature and the number of NiV-F drastically varies also. Therefore, specific fusion assays (either single virus tracking and/or time-of-addition BlaM kinetics with functional pseudoviruses) are needed to substantiate this claim.

The authors also claim they could not characterize the number of NiV-F particles per cluster. Another technique such as number and brightness (Digman et al., 2008) could support current SMLM data and identify the number of single molecules per cluster. Also, this technology does not require complex microscopy apparatus. I suggest they perform either confocal fluorescence fluctuation spectroscopy or TIRF-based nandb to validate the clusters and identify how many molecule are present in these clusters. Also, it is not clear how many cells the authors employ for their statistics (at least 30-50 cells should be employed and not consider the number of events blinking events). I hope the authors are not considering only a single cell to run their stats... The differences between the mutants and the NiV-F is minor even if their statistical analyses give a difference (they should average the number and size of the clusters per cell for a total of 30-50 cells with experiments performed at least in three different cells following the same protocol). They should also compare the level of expression (with the number of molecules per cell provided by number and brightness) with the total number of clusters. Overall, it seems that the authors have only evaluated a very low number of cells.

The same applies to the VLP assay. I assume the authors have only taken VLPs expressing both NiV-M and NiV-F (and NiV-G). But even if this is not clearly stated I would urge the authors to show how many viruses were compared per condition (normally I would expect 300 particles per condition coming from three independent experiments). As a negative control to evaluate the cluster effect I would mix the different conditions. Clearly you have clusters with all conditions and the differences in clustering depending on each condition are minimal. Therefore you need to increase the n for all experiments.

Reviewer #3 (Public Review):

Summary:

The manuscript by Wang and colleagues describes single molecule localization microscopy to quantify the distribution and organization of Nipah virus F expressed on cells and on virus-like particles. Notably the crystal structure of F indicated hexameric assemblies of F trimers. The authors propose that F clustering favors membrane fusion.

Strengths:

The manuscript provides solid data on imaging of F clustering with the main findings of:<br /> - F clusters are independent of expression levels<br /> - Proteolytic cleavage does not affect F clustering<br /> - Mutations that have been reported to affect the hexamer interface reduce clustering on cells and its distribution on VLPs<br /> - - F nanoclusters are stabilized by AP

Weaknesses:

The relationship between F clustering and fusion is per se interesting, but looking at F clusters on the plasma membrane does not exclude that F clustering occurs for budding. Many viral glycoproteins cluster at the plasma membrane to generate micro domains for budding. This does not exclude that these clusters include hexamer assemblies or clustering requires hexamer assemblies.<br /> Assuming that the clusters are important for entry, hexameric clusters are not unique to Nipah virus F. Similar hexameric clusters have been described for the HEF on influenza virus C particles (Halldorsson et al 2021) and env organization on Foamy virus particles (Effantin et al 2016), both with specific interactions between trimers. What is the organization of F on Nipah virus particles? If F requires to be hexameric for entry, this should be easily imaged by EM on infectious or inactivated virus particles.<br /> AP stabilization of the F clusters is curious if the clusters are solely required for entry? Virus entry does not recruit the clathrin machinery. Is it possible that F clusters are endocytosed in the absence of budding?

Other points:<br /> Fig. 3: Some of the V108D and L53D clusters look similar in size than wt clusters. It seems that the interaction is important but not absolutely essential? Would a double mutant abrogate clustering completely?<br /> Fig. 4: The distribution of F on VLPs should be confirmed by cryoEM analyses. This would also confirm the symmetry of the clusters.

The manuscript by Chernomordik et al. JBC 2004 showed that influenza HA outside the direct contact zone affects fusion, which could be further elaborated in the context of F clusters and the fusion mechanism.

Reviewer #1 (Public Review):

Summary:

The study "Endogenous oligomer formation underlies DVL2 condensates and promotes Wnt/β-catenin signaling" by Senem Ntourmas et al. contributes to the understanding of phase separation in Dishevelled (DVL) proteins, specifically focusing on DVL2. It builds upon existing research by investigating the endogenous complexes of DVL2 using ultracentrifugation and contrasting them with DVL1 and DVL3 behavior. The study identifies a DVL2-specific region involved in condensate formation and introduces the "two-step" concept of DVL2 condensate formation, enriching the field's knowledge.

Strengths:

A notable strength of this study is the validation of endogenous DVL2 complexes, providing insights into its behavior compared to DVL1 and DVL3. The functional validation of the DVL C-terminus (here termed conserved domain 2 (CD2) and the identification of DVL2-specific regions (here termed LCR4) involved in condensate formation are significant contributions that complement the current knowledge on the importance of DVL DIX domain, DEP domain and intrinsically disordered regions between DIX and PDZ domains. Additionally, the introduction of the concept where oligomerization (step 1) precedes condensate formation (step 2) is an interesting hypothesis, which can be further experimentally challenged in the future.

Weaknesses:

However, the applicability of the findings to full-length DVL2 protein, hence the physiological relevance, is limited. This is mostly due to the fact that the authors almost completely depend on the set of DVL2 mutants, which lack the (i) DEP domain and (ii) nuclear export signal (NES). These variants fail to establish DEP domain-mediated interactions, including those with FZD receptors. Of note, the DEP domain itself represents a dimerization/tetramerization interface, which could affect the protein condensate formation of these mutants. Possibly even more importantly, the used mutants localize into the nucleus, which has different biochemical & biophysical properties than a cytoplasm, where DVL typically reside, which in turn affects the condensate formation. On top, in the nucleus, most of the DVL binding partners, including relevant kinases, which were reported to affect protein condensate formation, are missing.

Second, the use of an overexpression system, while suitable for comparing DVL2 protein condensate features, falls short in functional assays. The study could benefit from employing established "rescue systems" using DVL1/2/3 knockout cells and re-expression of DVL variants for more robust functional assessments.

Furthermore, the discussion and introduction overlook some essential aspects of DVL biology. One such example is the importance of the open/close conformation of DVL and its effects on DVL phase separation and activity. In the context of this study, it is important to say that this conformational plasticity is mediated by DVL C-terminus (CD2 in this study). The second example is the reported roles of DVL1 and DVL3, which can both mediate the Wnt3a signal. How this can be interpreted when DVL1 and DVL3 lack LCR4 and still form condensates?

In order to increase the physiological relevance of the study, I would recommend analyzing several key mutants in the context of the full-length DVL2 protein using the rescue/complementation system. Further, a more thorough discussion and connections with the existing literature on DVL protein condensates/puncta/LLPS can improve the impact of the study.

eLife assessment

This valuable study contributes to the understanding of phase separation in Dishevelled (DVL) proteins, by investigating the endogenous complexes of DVL2 using ultracentrifugation and contrasting them with DVL1 and DVL3 behavior and the functional validation of the DVL2 intrinsically disordered regions mediating the protein condensate. The study is, however, incomplete due to the lack of several controls and its focus on overexpression and mutants lacking key domains.

Reviewer #2 (Public Review):

Summary:

The authors aimed to identify which regions of DVL2 contribute to its endogenous/basal clustering, as well as the relevance of such domains to condensate/phase separation and WNT activation.

Strengths:

A strength of the study is the focus on endogenous DVL2 to set up the research questions, as well as the incorporation of various techniques to tackle it. I found also quite interesting that DVL2-CFR addition to DVL1 increased its MW in density gradients.

Weaknesses:

I think that several of the approaches of the manuscript are subpar to achieve the goals and/or support several of the conclusions. For example:

(1) Although endogenous DVL2 indeed seems to form complexes (Figure 1A), neither the number of proteins involved nor whether those are homo-complexes can be determined with a density gradient. Super-resolution imaging or structural analyses are needed to support these claims.

(2) Follow-up analyses of the relevance of the DVL2 domains solely rely on overexpressed proteins. However, there were previous questions arising from o/e studies that prompted the focus on endogenous, physiologically relevant DVL interactions, clustering, and condensate formation. Although the title, conclusions, and relevance all point to the importance of this study for understanding endogenous complexes, only Figures 1A and B deal with endogenous DVL2.

(3) Mutants lacking activity/complex formation, e.g. DVL2_1-418, may need further validation. For instance, DVL2_1-506 (same mutant but with DEP) seems to form condensates and it is functional in WNT signalling (King et al., 20223). These differences could be caused by the lack of DEP domain in this particular construct and/or folding differences.

(4) The key mutants, DeltaCFR and VV/FF only show mild phenotypes. The authors' results suggest that these regions contribute but are not necessary for 1) complex formation (Density gradient Figures 7A and B), condensate formation (Figures 7C and D), and WNT activity (Figure 7E). Of note Figure 7C shows examples for the mutants with no condensates while the qualification indicates that 50% of the cells do have condensates.

(5) Most of the o/e analyses (including all reporter assays) should be performed in DVL1-3 KO cells in order to explore specifically the behaviour of the investigated mutants.

(6) How comparable are condensates found in the cytoplasm (usually for wt DVL) with those located in the nucleus (DEP mutants)?

Several studies in the last two decades have analysed the relevance of DVL homo - and hetero-clustering by relying on overexpressed proteins. Recent studies also explored the possibility of DVL undergoing liquid-liquid phase separation following similar principles. As highlighted by the authors in the introduction, there is a need to understand DVL dynamics under endogenous/physiological conditions. Recent super-resolution studies aimed at that question by characterising endogenously edited DVL2. The authors seemed to aim in the same direction with their initial findings (Figure 1A) but quickly moved to o/e proteins without going back to the initial question. This reviewer thinks that to support their conclusions and advance in this important question, the authors should introduce the relevant mutations in the endogenous locus (e.g. by Cas9+ donor template encoding the required 3' exons, as done by others before for WNT components, including DVL2) and determine their impact in the above-indicated processes.

Reviewer #1 (Public Review):

Summary:

In this study, a chromosome-level genome of the rose-grain aphid M. dirhodum was assembled with high quality, and A-to-I RNA-editing sites were systematically identified. The authors then demonstrated that: 1) Wing dimorphism induced by crowding in M. dirhodum is regulated by 20E (ecdysone signaling pathway); 2) an A-to-I RNA editing prevents the binding of miR-3036-5p to CYP18A1 (the enzyme required for 20E degradation), thus elevating CYP18A1 expression, decreasing 20E titer, and finally regulating the wing dimorphism of offspring.

Strengths:

The authors present both genome and A-to-I RNA editing data. An interesting finding is that a A-to-I RNA editing site in CYP18A1 ruin the miRNA binding site of miR-3036-5p. And loss of miR-3036-5p regulation lead to less 20E and winged offspring.

Weaknesses:

How crowding represses the miR-3036-5p is still unclear.

Reviewer #2 (Public Review):

Summary:

Environmental influences on development are ubiquitous, affecting many phenotypes in organisms. However molecular genetic and cellular mechanisms transducing environmental signals are still only barely understood. This study examines part of one such intracellular mechanism in a polyphenic (or dimorphic) aphid.

Strengths:

While other published reports have linked phenotypic plasticity to RNA editing before, this study reports such an interaction in insects. The study uses a wide array of molecular tools to identify connections upstream and downstream of the RNA editing to elucidate the regulatory mechanism, which is illuminating.

Weaknesses:

While this system is intriguing, this report does not foster confidence in its conclusions. Many of the analyses seem based on very small sample sizes. It is itself problematic that sample sizes are not obvious in most figures, although based on Methods section covering RNAseq, they seem to be either 3, 6 or 9, depending on whether stages were pooled, but that point is not made clear. With such small sample sizes, statistical tests of any kind are unreliable. Besides the ambiguity on sample sizes, it's unclear what error bars or whiskers show in plots throughout this study. When sample sizes are small estimates of variance are not reliable. Student's t-test is not appropriate for comparisons with such small sample sizes. Presently, it is not possible to replicate the tests shown in Figures 3, 4 and 6. (Besides the HT-seq reads, other data should also be made publicly available, following the journal's recommendations.) Regardless, effect sizes in some comparisons (Fig 3J, 4A-C, 6E,H) are clearly not large, making confidence in conclusions low. The authors should be cautious about over-interpreting these data.

eLife assessment

This study presents an important finding on the molecular mechanism for transduction of environmentally induced polyphenism. The evidence supporting the claims of the author is incomplete due to limited sample sizes and inadequate analysis. This paper would be of interest to those studying aphids wing dimorphism.

eLife assessment

This study presents important new insights linking obesity to kidney disease using a Drosophila model. A series of compelling experiments demonstrated that a high-fat diet induces the excretion of a leptin-like JAK-STAT ligand from the fat body, driving the adipose-nephrocyte axis through activated JAK-STAT signaling and subsequently causing a functional defect in nephrocytes. While the combination of genetic tools and pharmacological intervention provides solid data and confirms the mechanistic link, the phenotypic analysis is restricted to tracer endocytosis and would benefit from immunofluorescence studies and higher animal numbers.

Reviewer #1 (Public Review):

Summary:

Zhao and colleagues employ Drosophila nephrocytes as a model to investigate the effects of a high-fat diet on these podocyte-like cells. Through a highly focused analysis, they initially confirm previous research in their hands demonstrating impaired nephrocyte function and move on to observe the mislocalization of a slit diaphragm-associated protein (pyd). Employing a reporter construct, they identify the activation of the JAK/STAT signaling pathway in nephrocytes. Subsequently, the authors demonstrate the involvement of this pathway in nephrocyte function from multiple angles, using a gain-of-function construct, silencing of an inhibitor, and ectopic overexpression of a ligand. Silencing the effector Stat92E via RNAi or inhibiting JAK/STAT with Methotrexate effectively restored impaired nephrocyte function induced by a high-fat diet, while showing no impact under normal dietary conditions.

Strengths:

The findings establish a link between JAK/STAT activity and the impact of a high-fat diet on nephrocytes. This nicely underscores the importance of organ crosstalk for nephrocytes and supports a potential role for JAK/STAT in diabetic nephropathy, as previously suggested by other models.

Weaknesses:

The analysis is overly reliant on tracer endocytosis and single lines. Immunofluorescence of slit diaphragm proteins would provide a more specific assessment of the phenotypes.

Reviewer #2 (Public Review):

Summary:

In their manuscript, Zhao et al. describe a link between JAK-STAT pathway activation in nephrocytes on a high-fat diet. Nephrocytes are the homologs to mammalian podocytes and it has been previously shown, that metabolic syndrome and obesity are associated with worse outcomes for chronic kidney disease. A study from 2021 (Lubojemska et al.) could already confirm a severe nephrocyte phenotype upon feeding Drosophila a high-fat diet and also linking lipid overflow by expressing adipose triglyceride lipase in the fat body to nephrocyte dysfunction. In this study, the authors identified a second pathway and mechanism, how lipid dysregulation impact on nephrocyte function. In detail, they show activation of JAK-STAT signaling in nephrocytes upon feeding them a high-fat diet, which was induced by Upd2 expression (a leptin-like hormone) in the fat body, and the adipose tissue in Drosophila. Further, they could show genetic and pharmacological interventions can reduce JAK-STAT activation and thereby prevent the nephrocyte phenotype in the high-fat diet model.

Strengths:

The strength of this study is the combination of genetic tools and pharmacological intervention to confirm a mechanistic link between the fat body/adipose tissue and nephrocytes. Inter-organ communication is crucial in the development of several diseases, but the underlying mechanisms are only poorly understood. Using Drosophila, it is possible to investigate several players of one pathway, here JAK-STAT. This was done, by investigating the functional role of Hop, Socs36E, and Stat92E in nephrocytes and has also been combined with feeding a high-fat diet, to assess restoration of nephrocyte function by inhibiting JAK-STAT signaling. Adding a translational approach was done by inhibiting JAK-STAT signaling with methotrexate, which also resulted in attenuated nephrocyte dysfunction. Expression of the leptin-like hormone upd2 in the fat body is a good approach to studying inter-organ communication and the impact of other organs/tissue on nephrocyte function and expands their findings from nephrocyte function towards whole animal physiology.

Weaknesses:

Although the general findings of this study are of great interest, there are some weaknesses in the study, which should be addressed. Overall, the number of flies investigated for the majority of the experiments is very low (6 flies) and it is not clear whether the flies used, are from independent experiments to exclude problems with food/diet. For the analysis, the mean values of flies should be calculated, as one fly can be considered a biological replicate, but not all individual cells. By increasing the number of flies investigated, statistical analysis will become more solid. In addition, the morphological assessment is rather preliminary, by only using a Pyd antibody. Duf or Sns should be visualized as well, also the investigation of the different transgenic fly strains studying the importance of JAK-STAT signaling in nephrocytes needs to include a morphological assessment. Moreover, the expected effect of feeding a high-fat diet on nephrocytes needs to be shown (e.g. by lipid droplet formation) and whether upd2 is actually increased here should also be assessed. The time points of assessment vary between 1, 3, and 7 days and should be consistent throughout the study or the authors should describe why they use different time points.

Reviewer #2 (Public Review):

This work deals with a very difficult physical problem: relating the assembly of building blocks on a molecular scale to the appearance of large, macroscopic assemblies. This problem is particularly difficult to treat, because of the large number of units involved, and of the complex way in which these units-monomers-interact with each other and with the solvent. In order to make the problem treatable, the authors recur to a number of approximations: Among these, there is the assumption that the system is spatially homogeneous, i.e., its features are the same in all regions of space. In particular, the homogeneity assumption may not hold in biologically relevant systems such as cells, where the behavior close to the cell membrane may strongly differ from the one in the bulk. As a result, this hypothesis calls for a cautious consideration and interpretation of the results of this work. Another notable simplification introduced by the authors is the assumption that the system can only follow two possible behaviors: In the first, each monomer interacts equally with the solvent; no matter the size of the cluster of which it is part. In the second case, monomers in the bulk of a cluster and monomers at the assembly boundary interact with the solvent in a different way. These two cases are considered not only because they simplify the problem, but also because they are inspired by biologically relevant proteins.

With these simplifications, the authors trace the phase diagram of the system, characterizing its phases for different fractions of the volume occupied by the monomers and solvent, and for different values of the temperature. The results qualitatively reproduce some features observed in recent experiments, such as an anomalous distribution of cluster sizes below the system saturation threshold, and the gelation of condensed phases above such threshold.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This paper provides a straightforward mechanism of how mycobacterial cAMP level is increased under stressful conditions and shows that the increase is important for the survival of the bacterium in animal hosts. The cAMP level is increased by decreasing the expression of an enzyme that degrades cAMP.

We thank the reviewer for these extremely encouraging comments.

Strengths:

The paper shows that under different stresses the response regulator PhoP represses a phosphodiesterase (PDE) that degrades cAMP specifically. Identification of PhoP as a regulator of cAMP is significant progress in understanding Mtb pathogenesis, as increase in cAMP apparently increases bacterial survival upon infection. On the practical side, reduction of cAMP by increasing PDE can be a means to attenuate the growth of the bacilli. The results have wider implications since PhoP is implicated in controlling diverse mycobacterial stress responses and many bacterial pathogens modulate host cell cAMP level. The results here are straightforward, internally consistent, and of both theoretical and applied interests. The results also open considerable future work, especially how increases in cAMP level help to increase survival of the pathogen.

Weaknesses:

It is not clear whether PhoP-PDE Rv0805 is the only pathway to regulate cAMP level under stress.

Reviewer 1 (Recommendations for the authors):

(1) L.1: "maintenance of" or 'regulating'- I thought change in cAMP level upon stress is the whole point of the paper. Also, can replace "intracellular survival" with 'survival in host macrophages' if you want to be more specific.

We agree with the reviewer, and therefore, we have now replaced “maintenance of” with “regulating cAMP level” in the title. However, we feel more comfortable with “intracellular survival” rather than being more specific with ‘survival in host macrophages’ as we have also shown animal experiments to demonstrate ‘in vivo’ effect in mice lung and spleen.

(2) L.26: ---requires the bacterial virulence regulator –

The suggested change has been made to the text.

(3) L.30: Replace "phoP locus since the" with 'PhoP since this'. (The product, not the locus, is the regulator). The same comment for l.113.

We agree with the reviewer. The suggested changes have been made to the text.

(4) L.31: Change represtsor to repressor.

We are sorry for the embarrassing spelling mistake. We have rectified the mistake in the revised version.

(5) L.32: "hydrolytically degrades" or hydrolyses? (lytic and degrade sound like tautology). Same comment for l.117.

We agree. The suggested change has been made to the text in both places of the revised manuscript.

(6) L.35: I would also suggest changing "intra-mycobacterial" to 'intra bacterial' because you are talking about one bacterium here. The same change is recommended in l.29.

Following reviewer’s recommendation, we have made the changes in the revised manuscript.

(7) L.37: bacillus unless use of the plural form is the norm in the field.

We agree. The suggested change has been made to the text.

(8) L.43: Delete "intracellular" and change "intracellular" to host in l.44.

The suggested changes have been made to the text.

(9) L.66: --that a burst--

We have corrected the mistake in the revised manuscript.

(10) L.76: Receptor or receptor?

We have corrected the mistake in the revised manuscript.

(11) L.86: -- mechanisms of regulation of mycobacterial cAMP level. (homeostasis needs to be introduced first, and not used in the concluding statement for the first time).

The suggested changes have been made to the text.

(12) L.96: "essential" or 'a requirement'. (reduction is not the same as elimination)

We understand the reviewer’s concern. However, several studies have independently established that phoPR remains an essential requirement for mycobacterial virulence.

(13) L.97: Moreover, a mutant

The suggested change has been made to the text.

(14) L.113: --locus since PhoP has been –

The suggested change has been made to the text.

(15) L.119: mechanism or manner? (you are stating a fact, not a mechanism)

We agree. We have now replaced ‘mechanism’ with ‘manner’ in the revised manuscript.

(16) L.130: --lacking copies of both phoP and phoR (I am assuming you don't have two copies of each gene)

We understand the reviewer’s concern. For better clarity, we have now clearly mentioned that the phoPR-KO mutant lacks both the single copies of phoP and phoR genes.

(17) L.156: Indicate why GroEL2? - cells as another cytoplasmic protein, GroEL2 was also undetectable

We have now mentioned it in the secretion experiments that mycobacterial cells did not undergo autolysis. To prove this point, we have used cytoplasmic GroEL2 as a marker protein. The absence of detectable GroEL2 in the culture filtrates (CFs) suggests absence of autolysis. To this end, we have modified the sentence in the revised manuscript (duplicated below):

“Fig. 1C confirms absence of autolysis of mycobacterial cells as GroEL2, a cytoplasmic protein, was undetectable in the culture filtrates (CF).”

(18) L.266: May delete "Together". Start with These data--, which would draw more attention to integrated view. In l.268-270, a reminder that intracellular pH is acidic in the normal course would enhance the physiological significance of the present results.

We agree. We have made the suggested changes to the text. In view of the second comment of the reviewer, we have modified the text (duplicated below):

“These data represent an integrated view of our results suggesting that PhoP-dependant repression of rv0805 regulates intra-mycobacterial cAMP level. In keeping with these results, activated PhoP under acidic pH conditions significantly represses rv0805, and intracellular mycobacteria most likely utilizes a higher level of cAMP to effectively mitigate stress for survival under hostile environment including acidic pH of the phagosome.”

(19) L.272: Delete "and intracellular survival" (?) (I am assuming the survival is due to stress tolerance; also the section talks about stress only). No period in l.273.

Following reviewer’s recommendations, the suggested changes have been made to the text.

(20) L.295: Start the sentence thus: It appears that at least one of ---. (This would put more emphasis on the inference)

We agree. We have now incorporated the recommended changes in the revised version.

(21) L.301: No parenthesis.

The parenthesis has been removed in the revised manuscript.

(22) L.306: Together already implies these. Either delete Together (which I would prefer) or say 'Together, the results suggest that strains expressing wild type and mutant----properties, and the results are

We agree. We have now deleted ‘Together’ in the revised manuscript.

(23) L.311: These results support our view that higher---- (to avoid repetition of l.266)

We agree. We have now incorporated the suggested change in the revised manuscript.

(24) L.316: Using or with?

We think “with” goes well with the statement.

(25) L.329: Rephrase thus: Effect of intra-bacterial cAMP level on in vivo--

The recommended change has been made to the text.

(26) L.333: I would use ~, if you want to indicate about.

We agree. We have now used ‘~’ in the revised version. Changes were incorporated in lines 328, 330 and 333 of the revised manuscript.

(27) L.350: Change "somewhat functionally" to phenotypically?

We thank the reviewer for this suggestion. We have changed “somewhat functionally” to “phenotypically” in the revised manuscript.

(28) L.361: Change "is connected to" to 'regulates'.

The suggested change has been made to the text.

(29) L.365: ACs (to be parallel with PDEs)

We agree. The suggested change has been made to the text.

(30) L.366: delete "very" (let the readers decide how recent from the reference date).

The suggested change has been made to the text.

(31) L.382: level remained unknown before the present study.

The recommended change has been made to the text.

(32) L.399: add at the end of the sentence 'under stress'. Also, represent, not represents.

The recommended changes have been made to the text.

(33) L.560 and 571: Section headings formatted differently from the rest. Similar problem in l.900.

We have rectified the issue and all of the section headings are now formatted in the same style.

Reviewer #2 (Public Review):

Summary:

In the manuscript, the authors have presented new mechanistic details to show how intracellular cAMP levels are maintained linked to the phosphodiesterase enzyme which in turn is controlled by PhoP. Later, they showed the physiological relevance linked to altered cAMP concentrations.

Strengths:

Well thought out experiments. The authors carefully planned the experiments well to uncover the molecular aspects of it diligently.

We thank the reviewer for these extremely encouraging comments.

Weaknesses:

Some fresh queries were made based on the author's previous responses and hope to get satisfactory answers this time.

We provide below a point-by-point response to the fresh queries.

(2) Line 134: please describe the complementation strain features as it is mentioned for the first time (plasmid, copy number, promoter etc.) in the manuscript. Especially under NO stress what could be the authors' justification regarding the high cAMP concentration in the complementation strain?

As recommended by the reviewer, the details of construction of the complemented strain have been incorporated in the 'Materials and Methods' section of the revised manuscript (duplicated below): "To complement phoPR expression, pSM607 containing a 3.6-kb DNA fragment of M. tuberculosis phoPR including 200-bp phoP promoter region, a hygromycin resistance cassette, attP site and the gene encoding phage L5 integrase, as detailed earlier (Walters et al., 2006) was used to transform phoPR mutant to integrate at the L5 attB site.

" To address the reviewer's other concern, we have now included the following sentence in the 'Results' section of the revised manuscript (duplicated below): "A higher cAMP level in the complemented strain under NO stress is possibly attributable to reproducibly higher phoP expression in the complemented mutant under specific stress condition (Khan et al., 2022)."

Reference: Khan et al. (2022) Convergence of two global regulators to coordinate expression of essential virulence determinants of Mycobacterium tuberculosis. eLife 2022, 11:e80965.

New query: The complemented gene (in pSM607 plasmid) becomes a single copy after chromosomal integration, so it should ideally behave like a WT strain. How could authors still justify the high cAMP concentration under NO stress?

We agree with the reviewer. We are unable to provide a cogent justification regarding this result. We speculate that PhoP is strikingly activated under NO stress by a non-canonical mechanism and strongly represses rv0805 expression. As a result, there is a significantly higher cAMP concentration in case of the complemented mutant under NO stress.

(13) Line 292: There is a difference between red and green bars. Authors should do statistical analysis and then comment on whether overexpression of WT and mutant pde are different or similar, to me they are different; also, explain why the WT-Rv0805 strain is different than the phoPR-KO strain in the context of cell wall metabolism.

As recommended by the reviewer, we have now included statistical significance of the data in the revised version, and modified the text accordingly in the manuscript.

New query: Authors are asked to put a statistical significance test between WT-Rv0805 and WT-Rv0805M.

We have included it in the modified figure. Also, to explain it we incorporated new text in the legend to Fig. 4C of the revised manuscript (duplicated below):

“Note that similar to phoPR-KO, WT-Rv0805 shows a comparably higher sensitivity to CHP relative to WT bacilli. However, WT-Rv0805M expressing a mutant Rv0805, shows a significantly lower sensitivity to CHP relative to WT-Rv0805, as measured by the corresponding CFU values.”

(14) Line 299-303: Authors should explain how the colocalization % are calculated. Also, in the figure 4D merge panel please highlight the difference.

As suggested by the reviewer, we have now explained the methodology used to calculate percent colocalization in greater details. Also, we have modified Figure 4D to highlight the difference between samples shown in merge panel. Please see our response to comment # 33 from the Reviewer 1.

New query: In the figure legend it should be mentioned that the white arrow indicates non-co-localization which is visibly higher in WT and WT Rvo805M.

We thank the reviewer for this very important suggestion. We have now included the following text in the legend to Fig. 4D of the revised manuscript.

“White arrowheads in the merge panels indicate non-colocalization, which remains higher in WT-H37Rv and WT-Rv0805M relative to phoPR-KO or WT-Rv0805.”

eLife assessment

This important study builds on a previous publication (with partially overlapping authors), demonstrating that T. brucei has a continuous endomembrane system, which probably facilitates high rates of endocytosis. Using a range of cutting-edge approaches, the authors present compelling evidence that an actomyosin system, with the myosin TbMyo1 as the molecular motor, is localized close to the endosomal system in the bloodstream form (BSF) of Trypanosoma brucei. It shows convincingly that actin is important for the organization and integrity of the endosomal system, and that the trypanosome Myo1is an active motor that interacts with actin and transiently associates with endosomes, but a role of Myo1 in endomembrane function in vivo was not directly demonstrated. This work should be of interest to cell biologists and microbiologists working on the cytoskeleton, and unicellular eukaryotes.

Reviewer #1 (Public Review):

Using a combination of cutting-edge high-resolution approaches (expansion microscopy, SIM, and CLEM) and biochemical approaches (in vitro translocation of actin filaments, cargo uptake assays, and drug treatment), the authors revisit previous results about TbMyo1 and TbACT in the bloodstream form (BSF) of Trypanosoma brucei. They show that a great part of the myosin motor is cytoplasmic but the fraction associated with organelles is in proximity to the endosomal system. In addition, they show that TbMyo1 can move actin filaments in vitro and visualize for the first time this actomyosin system using specific antibodies, a "classical" antibody for TbMyo1, and a chromobody for actin. Finally, using latrunculin A, which sequesters G-actin and prevents F-actin assembly, the authors show the delocalization and eventually the loss of the filamentous actin signal as well as the concomitant loss of the endosomal system integrity. However, they do not assess the localization of TbMyo1 in the same conditions.

Overall the work is well conducted and convincing. The conclusions are not over-interpreted and are supported by the experimental results.

Reviewer #2 (Public Review):

Summary:

The study by Link et al. advances our understanding of the actomyosin system in T. brucei, focusing on the role of TbMyo1, a class I myosin, within the parasite's endosomal system. Using a combination of biochemical fractionation, in vitro motility assays, and advanced imaging techniques such as correlative light and electron microscopy (CLEM), this paper demonstrates that TbMyo1 is dynamically distributed across early and late endosomes, the cytosol, is associated with the cytoskeleton, and a fraction has an unexpected association with glycosomes. Notably, the study shows that TbMyo1 can translocate actin filaments at velocities suggesting an active role in intracellular trafficking, potentially higher than those observed for similar myosins in other cell types. This work not only elucidates the spatial dynamics of TbMyo1 within T. brucei but also suggests its broader involvement in maintaining the complex architecture of the endosomal network, underscoring the critical role of the actomyosin system in a parasite that relies on high rates of endocytosis for immune evasion.

Strengths:

A key strength of the study is its exceptional rigor and successful integration of a wide array of sophisticated techniques, such as in vitro motility assays, and advanced imaging methods, including correlative light and electron microscopy (CLEM) and immuno-electron microscopy. This combination of approaches underscores the study's comprehensive approach to examining the ultrastructural organization of the trypanosome endomembrane system. The application of functional data using inhibitors, such as latrunculin A for actin depolymerization, further strengthens the study by providing insights into the dynamics and regulatory mechanisms of the endomembrane system. This demonstrates how the actomyosin system contributes to cellular morphology and trafficking processes. Furthermore, the discovery of TbMyo1 localization to glycosomes introduces a novel aspect to the potential roles of myosin I proteins within the cell, particularly in the context of organelles analogous to peroxisomes. This observation not only broadens our understanding of myosin I functionality but also opens up new avenues for research into the cellular biology of trypanosomatids, marking a significant contribution to the field.

Weaknesses:

Certain limitations inherent in the study's design and scope render the narrative incomplete and make it challenging to reach definitive conclusions. One significant limitation is the reliance on spatial association data, such as colocalization of TbMyo1 with various cellular components-or the absence thereof-to infer functional relationships. Although these data suggest potential interactions, the authors do not confirm functional or direct physical interactions.

While TbMyo1's localization is informative, the authors do not directly demonstrate its biochemical or mechanical activities in vivo, leaving its precise role in cellular processes speculative. Direct assays that manipulate TbMyo1 levels, activity, and/or function, coupled with observations of the outcomes on cellular processes, would provide more definitive evidence of the protein's specific roles in T. brucei. A multifaceted approach, including genetic manipulations, uptake assays, kinetic trafficking experiments, and imaging, would offer a more robust framework for understanding TbMyo1's roles. This comprehensive approach would elucidate not just the "what" and "where" of TbMyo1's function but also the "how" and "why," thereby deepening our mechanistic insights into T. brucei's biology.

Reviewer #3 (Public Review):

Summary:

In this work, Link and colleagues have investigated the localization and function of the actomyosin system in the parasite Trypanosoma brucei, which represents a highly divergent and streamlined version of this important cytoskeletal pathway. Using a variety of cutting-edge methods, the authors have shown that the T. brucei Myo1 homolog is a dynamic motor that can translocate actin, suggesting that it may not function as a more passive crosslinker. Using expansion microscopy, iEM, and CLEM, the authors show that MyoI localizes to the endosomal pathway, specifically the portion tasked with internalizing and targeting cargo for degradation, not the recycling endosomes. The glycosomes also appear to be associated with MyoI, which was previously not known. An actin chromobody was employed to determine the localization of filamentous actin in cells, which was correlated with the localization of Myo1. Interestingly, the pool of actomyosin was not always closely associated with the flagellar pocket region, suggesting that portions of the endolysomal system may remain at a distance from the sole site of parasite endocytosis. Lastly, the authors used actin-perturbing drugs to show that disrupting actin causes a collapse of the endosomal system in T. brucei, which they have shown recently does not comprise distinct compartments but instead a single continuous membrane system with subdomains containing distinct Rab markers.

Strengths:

Overall, the quality of the work is extremely high. It contains a wide variety of methods, including biochemistry, biophysics, and advanced microscopy that are all well-deployed to answer the central question. The data is also well-quantitated to provide additional rigor to the results. The main premise, that actomyosin is essential for the overall structure of the T. brucei endocytic system, is well supported and is of general interest, considering how uniquely configured this pathway is in this divergent eukaryote and how important it is to the elevated rates of endocytosis that are necessary for this parasite to inhabit its host.

Weaknesses:

(1) Did the authors observe any negative effects on parasite growth or phenotypes like BigEye upon expression of the actin chromobody?

(2) The Garcia-Salcedo EMBO paper cited included the production of anti-actin polyclonal antibodies that appeared to work quite well. The localization pattern produced by the anti-actin polyclonals looks similar to the chromobody, with perhaps a slightly larger labeling profile that could be due to differences in imaging conditions. I feel that the anti-actin antibody labeling should be expressly mentioned in this manuscript, and perhaps could reflect differences in the F-actin vs total actin pool within cells.

(3) The authors showed that disruption of F-actin with LatA leads to disruption of the endomembrane system, which suggests that the unique configuration of this compartment in T. brucei relies on actin dynamics. What happens under conditions where endocytosis and endocyctic traffic is blocked, such as 4 C? Are there changes to the localization of the actomyosin components?

(4) Along these lines, the authors suggest that their LatA treatments were able to disrupt the endosomal pathway without disrupting clathrin-mediated endocytosis at the flagellar pocket. Do they believe that actin is dispensable in this process? That seems like an important point that should be stated clearly or put in greater context.

4

eLife assessment

This study presents a useful characterization of 3D chromosome conformation changes in activated T lymphocytes, linking risk variants for autoimmune disease to putative target genes. The study employs solid methods and approaches and demonstrates the utility of using chromatin conformation to understand gene regulatory processes. However, the same data modality (chromatin conformation) was previously generated by another group in the same model system, and a more in-depth comparison of results would have improved the utility of this study.

Reviewer #1 (Public Review):

Summary:

The authors profile gene expression, chromatin accessibility, and chromosomal architecture (by Hi-C) in activated CD4 T cells and use this information to link non-coding variants associated with autoimmune diseases with putative target genes. They find over 1000 genes physically linked with autoimmune disease loci in these cells, many of which are upregulated upon T cell activation. Focusing on IL2, they dissect the regulatory architecture of this locus, including the allelic effects of GWAS variants. They also intersect their variant-to-gene lists with data from CRISPR screens for genes involved in CD4 T cell activation and expression of inflammatory genes, finding enrichments for regulators. Finally, they showed that pharmacological inhibition of some of these genes impacts T-cell activation.

This is a solid study that follows a well-established canvas for variant-to-gene prioritisation using 3D genomics, applying it to activated T cells. The authors go some way in validating the lists of candidate genes, as well as exploring the regulatory architecture of a candidate GWAS locus. Jointly with data from previous studies performing variant-to-gene assignment in activated CD4 T cells (and other immune cells), this work provides a useful additional resource for interpreting autoimmune disease-associated genetic variation.

Suggestions for improvement:

Autoimmune disease variants were already linked with genes in CD28-stimulated CD4 T cells using chromosome conformation capture, specifically Promoter CHi-C and the COGS pipeline (Javierre et al., Cell 2016; Burren et al., Genome Biol 2017; Yang et al., Nat Comms 2020). The authors cite these papers and present a comparative analysis of their variant-to-gene assignments (in addition to scRNA-seq eQTL-based assignments). Furthermore, they find that the Burren analysis yields a higher enrichment for gold standard genes.

The obvious question that the authors don't venture into is why the results are quite different. In principle, this could be due to the differences between:<br /> (a) the cell stimulation procedure<br /> (b) the GWAS datasets used<br /> (c) the types of assay (Hi-C vs Capture Hi-C)<br /> (d) approaches for defining gene-linked regions (loops vs neighbourhoods)<br /> (e) how the GWAS signals at gene-linked regions are aggregated (e.g., the flavours of COGS in Javierre and Burren vs the authors' approach).

Re (a), I'm not sure the authors make it explicitly clear in the main text that the Capture Hi-C-based studies also use *stimulated* CD4 T cells, particularly in the section "Comparative predictive power...". So the cells used are pretty much the same, and the differences likely arise from points (b) to (e).

It would be useful for the community to understand more clearly what is driving these differences, ideally with some added data. Could the authors, for example, take the PCHi-C data from Javierre/Burren and use their GWAS data and variant-to-gene assignment algorithms?

In addition, given that the authors use Hi-C, a popular method for V2G prioritisation for this type of data is currently ABC (Nasser et al, Nature 2021). Could the authors provide a comparative analysis with respect to the V2G assignments in the paper and, if they see it appropriate, also run ABC-based GWAS integration on their own Hi-C data?

Reviewer #2 (Public Review):

Summary:

There is significant interest in characterizing the mechanisms by which genetic mutations linked to autoimmunity perturb immune processes. Pahl et al. collect information on dynamic accessible regions, genes, and 3D contacts in primary CD4+ T cell samples that have been stimulated ex vivo. The study includes a variety of analyses characterizing these dynamic changes. With TF footprinting they propose factors linked to active regulatory elements. They compare the performance of their variant mapping pipeline that uses their data versus existing datasets. Most compelling there was a deep dive into additional study of regulatory elements nearby the IL2 gene. Finally, they perform a pharmacological screen targeting several genes they suggest are involved in T cell proliferation.

Strengths:

The work done characterizing elements at the IL2 locus is impressive.

Weaknesses:

- Missing critical context to evaluate claims. There are extensive studies performed on resting and activated immune cell states (CD4+ T cells and other cell types) and some at multiple time points or concentrations of stimuli that collect ATAC-seq and/or RNA-seq that have been ignored by this study. How do conclusions from previous studies compare to what the authors conclude here? It is impossible to evaluate the claims without this additional context. These are a few studies I am familiar with (the authors should perform a more comprehensive search to be sure they're not ignoring existing observations) that would be important to compare/contrast conclusions:<br /> o Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424-431 (2018).<br /> o Calderon, D., Nguyen, M.L.T., Mezger, A. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat Genet 51, 1494-1505 (2019).<br /> o Gate, R.E., Cheng, C.S., Aiden, A.P. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat Genet 50, 1140-1150 (2018).<br /> o Glinos, D.A., Soskic, B., Williams, C. et al. Genomic profiling of T-cell activation suggests increased sensitivity of memory T cells to CD28 costimulation. Genes Immun 21, 390-408 (2020).<br /> o Gutierrez-Arcelus, M., Baglaenko, Y., Arora, J. et al. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci. Nat Genet 52, 247-253 (2020).<br /> o Kim-Hellmuth, S. et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nat. Commun. 8, 266 (2017).<br /> o Ye, C. J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).

- As a general point, I appreciate it when each claim includes a corresponding effect size and p-value, which helps me evaluate the strength of significance of supporting evidence.

Reviewer #3 (Public Review):

Summary:

This paper used RNAseq, ATACseq, and Hi-C to assess gene expression, chromatin accessibility, and chromatin physical associations for native CD4+ T cells as they respond to stimulation through TCR and CD28. With these data in hand, the author identified 423 GWAS signals to their respective target genes, where most of these were not in the proximal promoter, but rather distal enhancers. The IL-2 gene was used as an example to identify new distal cis-regulatory regions required for optimal IL-2 gene transcription. These distal elements interact with the proximal IL2 promoter region. When the distal enhancer contained an autoimmune SNP, it affected IL-2 gene transcription. The authors also identified genetic risk variants that were associated with genes upon activation. Some of these regulate proliferation and cytokine production, but others are novel.

Strengths:

This paper provides a wealth of data related to gene expression after CD4 T cells are activated through the TCR and CD28. An important strength of this paper is that these data were intensively analyzed to uncover autoimmune disease SNPs in cis-acting regions. Many of these could be assigned to likely target genes even though they often are in distal enhancers. These findings help to provide a better understanding concerning the mechanism by which GWAS risk elements impact gene expression.

Another strength of this study was the proof-of-principle studies examining the IL-2 gene. Not only were new cis-acting enhancers discovered, but they were functionally shown to be important in regulating IL-2 expression, including susceptibility to colitis. Their importance was also established with respect to such distal enhancers harboring disease-relevant SNPs, which were shown to affect IL-2 transcription.

The data from this study were also mined against past CRISPR screens that identified genes that control aspects of CD4 T cell activation. From these comparisons, novel genes were identified that function during T cell activation.

Weaknesses:

A weakness of this study is that few individuals were analyzed, i.e., RNAseq and ATACseq (n=3) and HiC (n=2). Thus, the authors may have underestimated potentially relevant risk associations by their chromatin capture-based methodology. This might account for the low overlap of their data with the eQTL-based approach or the HIEI truth set.

Impact:

This study indicates that defining distal chromatin interacting regions helps to identify distal genetic elements, including relevant variants, that contribute to gene activation.

eLife assessment

This is a valuable manuscript describing the competitive binding between the RING2 and phosphorylated Ubl domains within Parkin involved in the regulation of Parkin activity. The evidence supporting this conclusion is incomplete, as it primarily relies on a single biochemical assay and does not utilize more stringent, quantitative biophysical approaches to probe this competitive binding. This work will be of interest to the research communities focused on the molecular basis of ubiquitin ligase regulation, PINK-PARKIN-regulated mitophagy, and mitochondrial quality control.

Reviewer #1 (Public Review):

Summary:

The authors used structural and biophysical methods to provide insight into Parkin regulation. The breadth of data supporting their findings was impressive and generally well-orchestrated. Still, the impact of their results builds on recent structural studies and the stated impact is based on these prior works.

Strengths:

(1) After reading through the paper, the major findings are:<br /> - RING2 and pUbl compete for binding to RING0.<br /> - Parkin can dimerize.<br /> - ACT plays an important role in enzyme kinetics.

(2) The use of molecular scissors in their construct represents a creative approach to examining inter-domain interactions.

(3) From my assessment, the experiments are well-conceived and executed.

Weaknesses:

(1) The manuscript, as written, is NOT for a general audience. Admittedly, I am not an expert on Parkin structure and function, but I had to do a lot of homework to try to understand the underlying rationale and impact. This reflects, I think, that the work generally represents an incremental advance on recent structural findings.

(2) To this point, it is hard to understand the impact of this work without more information highlighting the novelty. There are several structures of Parkin in various auto-inhibited states, and it was hard to delineate how this is different.

(3) As noted, I appreciated the use of protease sites in the fusion protein construct. It is unclear how the loop region might affect the protein structure and function. The authors worked to demonstrate that this did not introduce artifacts, but the biological context is missing.

(4) While it is likely that the binding is competitive between the Ubl and RING2 domains, the data is not quantitative. Is it known whether the folding of the distinct domains is independent? Or are there interactions that alter folding? It seems plausible that conformational rearrangements may invoke an orientation of domains that would be incompatible. The biological context for the importance of this interaction was not clear to me.

(5) What is the rationale for mutating Lys211 to Asn? Were other mutations tried? Glu? Ala? Just missing the rationale. I think this may have been identified previously in the field, but not clear what this mutation represents biologically.

(6) I was confused about how the phospho-proteins were generated. After looking through the methods, there appear to be phosphorylation experiments, but it is unclear what the efficiency was for each protein (i.e. what % gets modified). In the text, the authors refer to phospho-Parkin (T270R, C431A), but not clear how these mutations might influence this process. I gather that these are catalytically inactive, but it is unclear to me how this is catalyzing the ubiquitination in the assay.

(7) The authors note that "ACT can be complemented in trans; however, it is more efficient in cis", but it is unclear whether both would be important or if the favored interaction is dominant in a biological context.

(8) The authors repeatedly note that this study could aid in the development of small-molecule regulators against Parkin to treat PD, but this is a long way off. And it is not clear from their manuscript how this would be achieved. As stated, this is conjecture.