internal bookkeeping [my translationg of "innere Buchhaltung"]

We try to figure out, were we help to step climate change, and we think, if we do enough of this, we are entitled for a "sin" here and there.

J'ai modifié la question que je trouvais difficile lire. C'est okay?

Je mettrais bien cette phrase dans la partie faire face à la volatilité. T'en penses quoi?

J'ai fait une modif histoire qjue ce soir comme lees 2 autres textes.

Et donc, que proposes-tu ? (si j'étais dans le public c'est la question que je te poserais).

Idem, tu pointes vers quelque chose ?

Référence biblio ?

Je ne comprends pas la logique de l'argument, il manque un wagon (sans doute parce que je ne sais pas ce que c'est que le "model of West's catalogue" ?)

Hiện tại em đã sử dụng tốt các công cụ cơ bản và có trao quyền cho học sinh tương tác. Tuy nhiên, em chưa thấy rõ tính ứng dụng thực tiễn của các công cụ nâng cao, hoặc thậm chí chưa biết đến sự tồn tại của một số công cụ đó 😲. Em mong được hướng dẫn thêm các ví dụ cụ thể để hiểu cách áp dụng hiệu quả hơn.

Hiện tại em thường sử dụng lời khen ngắn gọn, đơn giản như “Good job!”, “Yes!”, “OK” nhằm giúp học sinh nhận biết rằng mình đang làm đúng hoặc sai. Em cho rằng:

✅ Mục tiêu chính của lời khen là phản hồi kịp thời, giúp học sinh điều chỉnh hành vi, không nhất thiết phải cụ thể hoặc phức tạp.

🧠 Em đề cao sự tự chủ của học sinh – em mong các em học cách tự đánh giá, tự nhận ra sự tiến bộ và cảm thấy vui vì chính mình, chứ không hoàn toàn phụ thuộc vào lời khen từ giáo viên.

🤝 Em hiểu rằng việc khen ngợi cụ thể, sát sao có thể giúp học sinh thấy được sự quan tâm và xây dựng mối quan hệ tốt hơn, nhưng điều này cũng đòi hỏi thời gian, quan sát kỹ và sự đầu tư cảm xúc lớn từ giáo viên.

🎯 Em đồng ý rằng mình nên cố gắng quan tâm hơn, đặc biệt với những học sinh còn rụt rè, nhút nhát, chưa có khả năng tự đánh giá bản thân. Tuy nhiên, em mong có sự linh hoạt trong cách tiếp cận – để giáo viên có thể lựa chọn giữa khen ngợi cụ thể hay đơn giản, tùy theo hoàn cảnh và phong cách dạy học của mình.

Hiện tại, em đang ở mức điểm 3 – học sinh thường hoàn thành các nhiệm vụ được giao và có sự tập trung trong quá trình học. Tuy nhiên, việc học sinh chủ động đặt câu hỏi để hiểu thêm bài là điều không xảy ra thường xuyên.

🧠 Chỉ một số học sinh nổi bật, thường là những bạn thông minh, hiếu kỳ và mạnh dạn, mới đặt câu hỏi một cách tự nhiên.

📉 Phần lớn các em còn lại thiếu vốn từ hoặc sự tự tin, nên dù có tò mò thì cũng ít khi thể hiện bằng lời.

⏳ Việc chủ động hỏi cũng phụ thuộc vào thời điểm – khi chủ đề thực sự hấp dẫn hoặc được gợi mở đúng cách, học sinh mới bắt đầu có phản ứng rõ rệt.

Hiện tại, em đang thực hiện tổng kết bài học một cách đơn giản: nhắc lại các điểm chính và đặt một vài câu hỏi kiểm tra kiến thức để học sinh trả lời ☺️

Em quan niệm rằng sau khi đã hoàn thành quá trình giảng dạy, phần tổng kết chỉ cần nhẹ nhàng, giúp học sinh nhìn lại nhanh kiến thức đã học, không nhất thiết phải tổ chức quá cầu kỳ.

Hiện tại em đang ở mức điểm 2 – em có sử dụng hình ảnh và đồ vật để hỗ trợ học sinh giao tiếp. Tuy nhiên, em gặp một số khó khăn để tiến xa hơn:

🔍 Kết nối sâu sắc là điều không dễ: Ngay cả với giáo viên là người Việt như em, việc thiết lập những kết nối sáng tạo và thực sự sâu sắc với học sinh là điều rất khó, vì bị giới hạn bởi cả ngôn ngữ, văn hóa lẫn bối cảnh lớp học.

🙁 Câu hỏi mở thường không có “mở”: Những dạng câu hỏi như “Do you like...?” hoặc “What do you do after school?” về lý thuyết là "câu hỏi mở", nhưng trên thực tế chỉ dẫn đến những câu trả lời ngắn, không tạo được đà tương tác.

🔤 Năng lực ngôn ngữ là rào cản đôi chiều: * Học sinh có vốn tiếng Anh còn hạn chế, nên dù có động lực chia sẻ, các em cũng khó diễn đạt. * Giáo viên cũng không thể “vượt ngôn ngữ” để dẫn dắt sâu, trừ khi có kỹ thuật hỗ trợ cực kỳ cụ thể và phù hợp với trình độ.

🧠 Khái niệm “hỗ trợ phát triển ngôn ngữ” rất mơ hồ nếu không được làm rõ: Việc kỳ vọng giáo viên "phản hồi và mở rộng trải nghiệm học sinh" cần có mô hình, ví dụ minh họa cụ thể. Nếu không, giáo viên rất dễ rơi vào tình trạng “biết nên làm gì, nhưng không biết làm sao”.

📌 Em nghĩ rằng ngay cả đội học liệu cũng sẽ gặp khó khăn trong việc clarify (làm rõ) yêu cầu này nếu không tiếp cận một cách hệ thống:

🎯 Kỳ vọng của em: Em không mong hướng dẫn hoàn hảo, nhưng rất cần những chỉ dẫn đủ cụ thể – đơn giản – hiệu quả để: * Vượt qua sự mơ hồ * Làm được điều nhỏ trước, rồi mới đến sáng tạo sâu

Một lần nữa là cần đầu tư nghiên cứu 🙃. Hiện tại em chỉ đáp ứng được mức 1 — nhận biết được trình độ học sinh để chọn phần học liệu phù hợp. Các mức cao hơn cần chuẩn bị kỹ và học hỏi thêm ạ.

Em nghĩ cách dạy hiện tại của mình đang nghiêng về mức 1, vì em thường giới thiệu từ vựng kèm hình ảnh để học sinh phát âm, sau đó cho học sinh ghép từ với hình ảnh. Với phần ngữ pháp, em cung cấp trực tiếp quy tắc và ví dụ, rồi cho học sinh làm bài tập áp dụng.

Cách tiếp cận của em mang tính suy diễn (deductive reasoning) – nghĩa là học sinh được tiếp cận kiến thức rõ ràng ngay từ đầu trước khi luyện tập – thay vì khám phá ngữ pháp qua ví dụ (inductive). Tuy về hình thức có vẻ thụ động, nhưng em cho rằng đây là cách đơn giản, hiệu quả và phù hợp với nhiều học sinh trình độ cơ bản.

🧠 Lập luận so sánh: Deductive vs. Inductive reasoning (trong dạy ngữ pháp cho EFL learners) Tiêu chí Deductive reasoning Inductive reasoning Cách tiếp cận Cung cấp quy tắc rõ ràng → học sinh luyện tập áp dụng. Đưa ví dụ → học sinh tự khám phá quy tắc ngữ pháp. Thời gian & sự rõ ràng Hiểu nhanh, tiết kiệm thời gian – đặc biệt hữu ích trong lớp học có thời lượng hạn chế. Tốn thời gian hơn, học sinh dễ rơi vào suy đoán sai nếu thiếu vốn từ/ngữ cảnh. Phù hợp với trình độ nào Rất phù hợp với học sinh trình độ thấp – vốn từ và ngữ cảm chưa đủ để tự rút quy tắc. Thường chỉ phù hợp với học sinh có vốn tiếng Anh phong phú, thường xuyên tiếp xúc với ngôn ngữ (như người bản ngữ). Tính khả thi trong lớp học EFL Cao – giáo viên chủ động điều tiết nội dung, học sinh dễ theo dõi và luyện tập. Thấp – yêu cầu thời gian, kỹ năng ngôn ngữ nền tốt, khó áp dụng đồng đều cho cả lớp.

📌 Lý do chính: Học sinh EFL không được "expose" (tiếp xúc tự nhiên) với ngôn ngữ như người bản ngữ, nên việc tự suy luận ngữ pháp từ ví dụ thường gây nhiễu hoặc hiểu sai.

Trong khi đó, phương pháp deductive cung cấp khung ngữ pháp rõ ràng, giúp học sinh dễ dàng kết nối với bài tập, kiểm tra và củng cố kiến thức ngay trong buổi học.

Em nghĩ mình hiện đang ở mức 0 hoặc mức 1. Trong giờ học, nếu học liệu có phần đóng vai hoặc hỏi – đáp, học sinh sẽ có cơ hội tương tác với nhau. Tuy nhiên, mức độ tương tác này vẫn còn đơn giản và khá hạn chế, chủ yếu do rào cản về năng lực ngôn ngữ. Học sinh cần có đủ vốn từ vựng và cấu trúc câu thì mới có thể thực sự tham gia giao tiếp hiệu quả. Với các lớp nhỏ tuổi hoặc trình độ thấp, các em thường chỉ dừng lại ở việc lặp lại mẫu câu.

Tuy vậy, trong thời gian tới, em sẽ đầu tư thêm vào việc nghiên cứu và ứng dụng các hình thức tương tác đơn giản nhưng hiệu quả, cụ thể theo hai hướng sau:

1. Tăng cường tương tác hỏi – đáp giữa học sinh thông qua các trò chơi hoặc hoạt động đóng vai ngắn + fill in the blank

2. Tổ chức linh hoạt các hoạt động cặp/nhóm từ học liệu để học sinh có cơ hội lắng nghe và phản hồi lẫn nhau nhiều hơn.

VD: 🎯 Gợi ý trò chơi: Find Someone Who...

✅ Mục tiêu: Giúp học sinh luyện mẫu câu hỏi và trả lời, đồng thời khuyến khích di chuyển và tương tác trong lớp học.

🧩 Cách triển khai phù hợp với học sinh trình độ thấp: Ví dụ: Luyện mẫu câu “Do you like...?”

Giáo viên chuẩn bị bảng câu hỏi:

Find someone who... Tên ...likes cats.<br /> ...likes apples. <br /> ...likes dancing. <br /> ...likes ice cream.

Học sinh sẽ đi hỏi bạn bè trong lớp: → “Do you like cats?” → Nếu bạn trả lời “Yes, I do.” thì ghi tên bạn đó vào ô tương ứng.

⏱ Sau 5 phút: Cả lớp ngồi lại và chia sẻ: → “I found Linh. She likes cats!”

✏️ Mẫu câu cần luyện trước khi chơi:

Hỏi: “Do you like ___?”

Trả lời: “Yes, I do.” / “No, I don’t.”

👉 Em hy vọng trong tương lai, bên học liệu cũng sẽ thiết kế thêm nhiều hoạt động tương tác như vậy để em được “nhàn hơn” mà lớp vẫn vui ạ!

Em thường Freestyle nên hay bị điểm 0. 😖

Em có xu hướng dạy linh hoạt vì em coi mỗi giờ học là một trải nghiệm mới, và em tin rằng học sinh có học được gì đi chăng nữa đều là điều đáng quý. Vì vậy, em không quá rập khuôn về thời gian phân bổ cho từng phần, mà điều chỉnh theo tình hình thực tế.

Tuy nhiên, em vẫn luôn đảm bảo dạy đầy đủ và theo đúng trình tự các phần trong học liệu. Em chỉ linh hoạt trong cách triển khai để mỗi buổi học diễn ra tự nhiên, phù hợp với học sinh và tạo được sự thoải mái, hứng thú khi học tập.

Em đang ở mức điểm 2. Em đã sử dụng ngôn ngữ phù hợp với trình độ học sinh, kết hợp linh hoạt tiếng Anh – tiếng Việt giúp các em hiểu bài tốt hơn.

Em hy vọng tình hình sẽ cải thiện khi em làm tốt ở Tiêu chí 3 – Môi trường tương tác giữa các học sinh, vì nếu học sinh có thể tương tác với nhau bằng tiếng Anh nhiều hơn thì khả năng sử dụng ngôn ngữ của cả lớp sẽ được nâng cao một cách tự nhiên.

Em đang ở mức điểm 1. Trong giờ học, em thường đưa ra hướng dẫn bằng lời một cách rõ ràng, sau đó kiểm tra sự hiểu của học sinh bằng cách quan sát hành vi thực tế đúng/sai (true/false behaviour checking) – ví dụ như học sinh có làm đúng yêu cầu không – thay vì sử dụng các câu hỏi kiểm tra chỉ dẫn (ICQs). Em thấy đây là cách nhanh và hiệu quả trong bối cảnh lớp học hiện tại.

Ngoài ra, em rất ấn tượng với cách sử dụng ngôn ngữ cơ thể của một số giáo viên để tăng sự rõ ràng và sinh động. Tuy nhiên, em chưa dành thời gian luyện tập kỹ năng này, nên vẫn chưa áp dụng được nhiều. Em mong muốn sẽ cải thiện điều này trong thời gian tới.

Các phần đề mục trong buổi học thường theo một khung cố định và được lặp lại qua nhiều buổi, nên em ưu tiên chuyển hoạt động một cách ngắn gọn, rõ ràng, chủ yếu mang tính thông báo nhằm tiết kiệm thời gian và đảm bảo sự liền mạch của tiết học. Vì vậy, em hiện chưa áp dụng các kỹ thuật chuyển hoạt động mang tính sáng tạo như bài hát, vỗ tay hay trò chơi nhỏ.

Trừ khi hoạt động tiếp theo là phần nội dung trọng tâm, em mới dành thời gian dẫn dắt kỹ hơn để học sinh hiểu rõ mục tiêu và chuẩn bị tinh thần.

Tóm lại, em tin rằng sự nhất quán và đơn giản trong cách chuyển hoạt động, nếu được sử dụng phù hợp với đặc điểm lớp học, vẫn có thể giúp học sinh duy trì được nhịp lớp và tập trung vào nội dung chính một cách hiệu quả.

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So much value hiding in da Vinci's notebooks because he failed to publish and share his knowledge

Like many, da Vinci didn't publish much from his copious notebooks. He had huge volumes of material, but really not much to show for it in the end.

Da Vinci apprenticed to Andrea del Verrocchio who sculpted for the Medici. Verrocchio also ran a book-making workshop out of his house and manufactured zibaldoni to order.

Author response:

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

Reviewer #1 (Public review):

Summary:

Phytophathogens including fungal pathogens such as F. graminearum remain a major threat to agriculture and food security. Several agriculturally relevant fungicides including the potent Quinofumelin have been discovered to date, yet the mechanisms of their action and specific targets within the cell remain unclear. This paper sets out to contribute to addressing these outstanding questions.

We appreciate the reviewer's accurate summary of our manuscript.

Strengths:

The paper is generally well-written and provides convincing data to support their claims for the impact of Quinofumelin on fungal growth, the target of the drug, and the potential mechanism. Critically the authors identify an important pyrimidine pathway dihydroorotate dehydrogenase (DHODH) gene FgDHODHII in the pathway or mechanism of the drug from the prominent plant pathogen F. graminearum, confirming it as the target for Quinofumelin. The evidence is supported by transcriptomic, metabolomic as well as MST, SPR, molecular docking/structural biology analyses.

We appreciate the reviewer's recognition of the strengths of our manuscript.

Weaknesses:

Whilst the study adds to our knowledge about this drug, it is, however, worth stating that previous reports (although in different organisms) by Higashimura et al., 2022 https://pmc.ncbi.nlm.nih.gov/articles/PMC9716045/ had already identified DHODH as the target for Quinofumelin and hence this knowledge is not new and hence the authors may want to tone down the claim that they discovered this mechanism and also give sufficient credit to the previous authors work at the start of the write-up in the introduction section rather than in passing as they did with reference 25? other specific recommendations to improve the text are provided in the recommendations for authors section below.

We appreciate the reviewer's suggestion. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of previous work on quinofumelin by Higashimura et al., 2022 in the discussion section to more effectively contextualize their contributions. Moreover, we have made revisions and provided responses in accordance with the recommendations.

Reviewer #2 (Public review):

Summary:

In the current study, the authors aim to identify the mode of action/molecular mechanism of characterized a fungicide, quinofumelin, and its biological impact on transcriptomics and metabolomics in Fusarium graminearum and other Fusarium species. Two sets of data were generated between quinofumelin and no treatment group, and differentially abundant transcripts and metabolites were identified. The authors further focused on uridine/uracil biosynthesis pathway, considering the significant up- and down-regulation observed in final metabolites and some of the genes in the pathways. Using a deletion mutant of one of the genes and in vitro biochemical assays, the authors concluded that quinofumelin binds to the dihydroorotate dehydrogenase.

We appreciate the reviewer's accurate summary of our manuscript.

Strengths:

Omics datasets were leveraged to understand the physiological impact of quinofumelin, showing the intracellular impact of the fungicide. The characterization of FgDHODHII deletion strains with supplemented metabolites clearly showed the impact of the enzyme on fungal growth.

We appreciate the reviewer's recognition of the strengths of our manuscript.

Weaknesses:

Some interpretation of results is not accurate and some experiments lack controls. The comparison between quinofumelin-treated deletion strains, in the presence of different metabolites didn't suggest the fungicide is FgDHODHII specific. A wild type is required in this experiment.

Potential Impact: Confirming the target of quinofumelin may help understand its resistance mehchanism, and further development of other inhibitory molecules against the target.

The manuscript would benefit more in explaining the study rationale if more background on previous characterization of this fungicide on Fusarium is given.

We appreciate the reviewer's suggestion. Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil. In our previous study, quinofumelin not only exhibited excellent antifungal activity against the mycelial growth and spore germination of F. graminearum, but also inhibited the biosynthesis of deoxynivalenol (DON). We have added this part to the introduction section.

Reviewer #3 (Public review):

Summary:

The manuscript shows the mechanism of action of quinofumelin, a novel fungicide, against the fungus Fusarium graminearum. Through omics analysis, phenotypic analysis, and in silico approaches, the role of quinofumelin in targeting DHODH is uncovered.

We appreciate the reviewer's accurate summary of our manuscript.

Strengths:

The phenotypic analysis and mutant generation are nice data and add to the role of metabolites in bypassing pyrimidine biosynthesis.

We appreciate the reviewer's recognition of the strengths of our manuscript.

Weaknesses:

The role of DHODH in this class of fungicides has been known and this data does not add any further significance to the field. The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

There is no mention of the other fungicide within this class ipflufenoquin, as there is ample data on this molecule.

We appreciate the reviewer's suggestion. We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions. The information regarding action mechanism of ipflufenoquin against filamentous fungi was added in discussion section.

Reviewer #1 (Recommendations for the authors):

(1) Given that the DHODH gene had been identified as a target earlier, could the authors perform blast experiments with this gene instead and let us know the percentage similarity between the FgDHODHII gene and the Pyricularia oryzae class II DHODH gene in the report by Higashimura et al., 2022.

BLAST experiment revealed that the percentage similarity between the FgDHODHII gene and the class II DHODH gene of P. oryzae was 55.41%. We have added the description ‘Additionally, the amino acid sequence of the FgDHODHII exhibits 55.41% similarity to that of DHODHII from Pyricularia oryzae, as previously reported (Higashimura et al., 2022)’ in section Results.

(2) Abstract:

The authors started abbreviating new terms e.g. DEG, DMP, etc but then all of a sudden stopped and introduced UMP with no full meaning of the abbreviation. Please give the full meaning of all abbreviations in the text, UMP, STC, RM, etc.

We have provided the full meaning for all abbreviations as requested.

(3) Introduction section:

The introduction talks very little about the work of other groups on quinofumelin. Perhaps add this information in and reference them including the work of Higashimura et al., 2022 which has done quite significant work on this topic but is not even mentioned in the background

We have added the work of other groups on quinofumelin in section introduction.

(4) General statements:

Please show a model of the pyrimidine pathway that quinofumelin attacks to make it easier for the reader to understand the context. They could just copy this from KEGG

We have added the model (Fig. 7).

(5) Line 186:

The authors did a great job of demonstrating interactions with the Quinofumelin and went to lengths to perform MST, SPR, molecular docking, and structural biology analyses yet in the end provide no details about the specific amino acid residues involved in the interaction. I would suggest that site-directed mutagenesis studies be performed on FgDHODHII to identify specific amino acid residues that interact with Quinofumelin and show that their disruption weakens Quinofumelin interaction with FgDHODHII.

Thank you for this insightful suggestion. We fully agree with the importance of elucidating the interaction mechanism. At present, we are conducting site-directed mutagenesis studies based on interaction sites from docking results and the mutation sites of FgDHODHII from the resistant mutants; however, due to the limitations in the accuracy of existing predictive models, this work remains ongoing. Additionally, we are undertaking co-crystallization experiments of FgDHODHII with quinofumelin to directly and precisely reveal their interaction pattern

(6) Line 76:

What is the reference or evidence for the statement 'In addition, quinofumelin exhibits no cross-resistance to currently extensively used fungicides, indicating its unique action target against phytopathogenic fungi.

If two fungicides share the same mechanism of action, they will exhibit cross resistance. Previous studies have demonstrated that quinofumelin retains effective antifungal activity against fungal strains resistant to commercial fungicides, indicating that quinofumelin does not exhibit cross-resistance with other commercially available fungicides and possesses a novel mechanism of action. Additionally, we have added the relevant inference.

(7) Line 80-82:

Again, considering the work of previous authors, this target is not newly discovered. Please consider toning down this statement 'This newly discovered selective target for antimicrobial agents provides a valuable resource for the design and development of targeted pesticides.'

We have rewritten the description of this sentence.

(8) Line 138: If the authors have identified DHODH in experimental groups (I assume in F. graminearum), what was the exact locus tag or gene name in F. graminearum, and why not just continue with this gene you identified or what is the point of doing a blast again to find the gene if the DHODH gene if it already came up in your transcriptomic or metabolic studies? This unfortunately doesn't make sense but could be explained better.

The information of FgDHODHII (gene ID: FGSG_09678) has been added. We have revised this part.

Reviewer #2 (Recommendations for the authors):

(1) Line 40:

Please add a reference.

We have added the reference

(2) Line 47:

Please add a reference.

We have added the reference.

(3) Line 50:

The lack of target diversity in existing fungicides doesn't necessarily serve as a reason for discovering new targets being more challenging than identifying new fungicides within existing categories, please consider adjusting the argument here. Instead, the authors can consider reasons for the lack of new targets in the field.

We have revised the description.

(4) Line 63:

Please cite your source with the new technology.

We have added the reference.

(5) Line 68:

What are you referring to for "targeted medicine", do you have a reference?

We have revised the description and the reference.

(6) Line 74:

One of the papers referred to "quinoxyfen", what are the similarities and differences between the two? Please elaborate for the readership.

Quinoxyfen, similar to quinofumelin, contains a quinoline ring structure. It inhibits mycelial growth by disrupting the MAP kinase signaling pathway in fungi (https://www.frac.info). In addition, quinoxyfen still exhibits excellent antifungal activity against the quinofumelin-resistant mutants (the findings from our group), indicating that action mechanism for quinofumelin and quinoxyfen differ.

(7) Line 84:

Please introduce why RNA-Seq was designed in the study first. What were the groups compared? How was the experiment set up? Without this background, it is hard to know why and how you did the experiment.

According to your suggestions, we have added the description in Section Results. In addition, the experimental process was described in Section Materials and methods as follows: A total of 20 mL of YEPD medium containing 1 mL of conidia suspension (1×105 conidia/mL) was incubated with shaking (175 rpm/min) at 25°C. After 24 h, the medium was added with quinofumelin at a concentration of 1 μg/mL, while an equal amount of dimethyl sulfoxide was added as the control (CK). The incubation continued for another 48 h, followed by filtration and collection of hyphae. Carry out quantitative expression of genes, and then analyze the differences between groups based on the results of DESeq2 for quantitative expression.

(8) Figures:

The figure labeling is missing (Figures 1,2,3 etc). Please re-order your figure to match the text

The figures have been inserted.

(9) Line. 97:

"Volcano plot" is a common plot to visualize DEGs, you can directly refer to the name.

We have revised the description.

(10) Figure 1d, 1e:

Can you separate down- and up-regulated genes here? Does the count refer to gene number?

The expression information for down- and up-regulated genes is presented in Figure 1a and 1b. However, these bubble plots do not distinguish down- and up-regulated genes. Instead, they only display the significant enrichment of differentially expressed genes in specific metabolic pathways. To more clearly represent the data, we have added the detailed counts of down- and up-regulated genes for each metabolic pathway in Supplementary Table S1 and S2. Here, the term "count" refers to differentially expressed genes that fall within a certain pathway.

(11) Line 111:

Again, no reasoning or description of why and how the experiment was done here.

Based on the results of KEGG enrichment analysis, DEMs are associated with pathways such as thiamine metabolism, tryptophan metabolism, nitrogen metabolism, amino acid sugar and nucleotide sugar metabolism, pantothenic acid and CoA biosynthesis, and nucleotide sugar production compounds synthesis. To specifically investigate the metabolic pathways involved action mechanism of quinofumelin, we performed further metabolomic experiments. Therefore, we have added this description according the reviewer’s suggestions.

(12) Figure 2a:

It seems many more metabolites were reduced than increased. Is this expected? Due to the antifungal activity of this compound, how sick is the fungus upon treatment? A physiological study on F. graminearum (in a dose-dependent manner) should be done prior to the omics study. Why do you think there's a stark difference between positive and negative modes in terms of number of metabolites down- and up-regulated?

Quinofumelin demonstrates exceptional antifungal activity against Fusarium graminearum. The results indicate that the number of reduced metabolites significantly exceeds the number of increased metabolites upon quinofumelin treatment. Mycelial growth is markedly inhibited under quinofumelin exposure. Prior to conducting omics studies, we performed a series of physiological and biochemical experiments (refer to Qian Xiu's dissertation https://paper.njau.edu.cn/openfile?dbid=72&objid=50_49_57_56_49_49&flag=free). Upon quinofumelin treatment, the number of down-regulated metabolites notably surpasses that of up-regulated metabolites compared to the control group. Based on the findings from the down-regulated metabolites, we conducted experiments by exogenously supplementing these metabolites under quinofumelin treatment to investigate whether mycelial growth could be restored. The results revealed that only the exogenous addition of uracil can restore mycelial growth impaired by quinofumelin.

Quinofumelin exhibits an excellent antifungal activity against F. graminearum. At a concentration of 1 μg/mL, quinofumelin inhibits mycelial growth by up to 90%. This inhibitory effect indicates that life activities of F. graminearum are significantly disrupted by quinofumelin. Consequently, there is a marked difference in down- and up-regulated metabolites between quinofumelin-treated group and untreated control group. The detailed results were presented in Figures 1 and 2.

(13) Figure 2e:

This is a good analysis. To help represent the data more clearly, the authors can consider representing the expression using fold change with a p-value for each gene.

To more clearly represent the data, we have incorporated the information on significant differences in metabolites in the de novo pyrimidine biosynthesis pathway, as affected by quinofumelin, in accordance with the reviewer’s suggestions.

(14) Line 142:

Please indicate fold change and p-value for statistical significance. Did you validate this by RT-qPCR?

We validated the expression level of the DHODH gene under quinofumelin treatment using RT-qPCR. The results indicated that, upon treatment with the EC50 and EC90 concentrations of quinofumelin, the expression of the DHODH gene was significantly reduced by 11.91% and 33.77%, respectively (P<0.05). The corresponding results have been shown in Figure S4.

(15) Line 145:

It looks like uracil is the only metabolite differentially abundant in the samples - how did you conclude this whole pathway was impacted by the treatment?

The experiments involving the exogenous supplementation of uracil revealed that the addition of uracil could restore mycelial growth inhibited by quinofumelin. Consequently, we infer that quinofumelin disrupts the de novo pyrimidine biosynthesis pathway. In addition, as uracil is the end product of the de novo pyrimidine biosynthesis pathway, the disruption of this pathway results in a reduction in uracil levels.

(16) Figure 3:

What sequence was used as the root of the tree? Why were the species chosen? Since the BLAST query was Homo sapiens sequence, would it be good to use that as the root?

FgDHODHII sequence was used as the root of the tree. These selected fungal species represent significant plant-pathogenic fungi in agriculture production. According to your suggestion, we have removed the BLAST query of Homo sapiens in Figure 3.

(17) Figure 4:

How were the concentrations used to test chosen?

Prior to this experiment, we carried out concentration-dependent exogenous supplementation experiments. The results indicated that 50 μg/mL of uracil can fully restore mycelial growth inhibited by quinofumelin. Consequently, we chose 50 μg/mL as the testing concentration.

(18) Line 164:

Why do you hypothesize supplementing dihydroorotate would restore resistance? The metabolite seemed accumulated in the treatment condition, whereas downstream metabolites were comparable or even depleted. The DHODH gene expression was suppressed. Would accumulation of dihydroorotate be associated with growth inhibition by quinofumelin? Please include the hypothesis and rationale for the experimental setup.

DHODH regulates the conversion of dihydroorotate to orotate in the de novo pyrimidine biosynthesis pathway. The inhibition of DHODH by quinofumelin results in the accumulation of dihydroorotate and the depletion of the downstream metabolites, including UMP, uridine and uracil. Consequently, downstream metabolites were considered as positive controls, while upstream metabolite dihydroorotate served as a negative control. This design further demonstrates DHODH as action target of quinofumelin against F. graminearum. In addition, the accumulation of dihydroorotate is not associated with growth inhibition by quinofumelin; however, but the depletion of downstream metabolites in the de novo pyrimidine biosynthesis pathway is closely associated with growth inhibition by quinofumelin.

(19) Line 168:

I'm not sure if this conclusion is valid from your results in Figure 4 showing which metabolites restore growth.

o minimize the potential influence of strain-specific effects, five strains were tested in the experiments shown in Figure 4. For each strain, the first row (first column) corresponds to control condition, while second row (first column) represents treatment with 1 μg/mL of quinofumelin, which completely inhibits mycelial growth. The second row (second column) for each strain represents the supplementation with 50 μg/mL of dihydroorotate fails to restore mycelial growth inhibited by quinofumelin. In contrast, the second row (third column, fourth column, fifth colomns) for each strain demonstrated that the supplementation of 50 μg/mL of UMP, uridine and uracil, respectively, can effectively restore mycelial growth inhibited by quinofumelin.

(20) Figure 5a:

The fact you saw growth of the deletion mutant means it's not lethal. However, the growth was severely inhibited.

Our experimental results indicate that the growth of the deletion mutant is lethal. The mycelial growth observed originates from mycelial plugs that were not exposed to quinofumelin, rather than from the plates amended with quinofumelin.

(21) Figure 5b:

Would you expect different restoration of growth in the presence of quinofumelin vs. no treatment? The wild type control is missing here. Any conclusions about the relationship between quinofumelin, FgDHODHII, and other metabolites in the pathway?

Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil.

(22) Figure 6b:

Lacking positive and negative controls (known binder and non-binder). What does the Kd (in comparison to other interactions) indicate in terms of binding strength?

We tested the antifungal activities of publicly reported DHODH inhibitors (such as leflunomide and teriflunomide) against F. graminearum. The results showed that these inhibitors exhibited no significant inhibitory effects against the strain PH-1. Therefore, we lacked an effective chemical for use as a positive control in subsequent experiments. Biacore experiments offers detailed insights into molecular interactions between quinofumelin and DHODHII. As shown in Figure 6b, the left panel illustrates the time-dependent kinetic curve of quinofumelin binding to DHODHII. Within the first 60 s after quinofumelin was introduced onto the DHODHII surface, it bound to the immobilized DHODHII on the chip surface, with the response value increasing proportionally to the quinofumelin concentration. Following cessation of the injection at 60 s, quinofumelin spontaneously dissociated from the DHODHII surface, leading to a corresponding decrease in the response value. The data fitting curve presented on the right panel indicates that the affinity constant KD of quinofumelin for DHODHII is 6.606×10-6 M, which falls within the typical range of KD values (10-3 ~ 10-6 M) for protein-small molecule interaction patterns. A lower KD value indicates a stronger affinity; thus, quinofumelin exhibits strong binding affinity towards DHODHII.

Reviewer #3 (Recommendations for the authors):

The authors should add information about the other molecule within this class, ipflufenoquin, and what is known about it. There are already published data on its mode of action on DHODH and the role of pyrimidine biosynthesis.

We have added the information regarding action mechanism of ipflufenoquin against filamentous fungi in discussion section.

The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions.

It is unclear how the protein model was established and this should be included. What species is the molecule from and how was it obtained? How are they different from Fusarium?

The three-dimensional structural model of F. graminearum DHODHII protein, as predicted by AlphaFold, was obtained from the UniProt database. Additionally, a detailed description along with appropriate citations has been incorporated in the ‘Manuscript’ file.

DOI: 10.1242/dmm.037259

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DOI: 10.1038/s41467-019-09262-2

Resource: RRID:BDSC_27551

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DOI: 10.1016/j.cell.2025.05.016

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Casi siempre se asocia creatividad con la producción artística, sin embargo es mucho mas importante por que se puede aplicar en varias áreas como la solución de problemas matemáticos.

COMETARIO 1

**comentario 1. **

CASO 1: CIRUGIA CORZON ABIERTO. EJE,P,OO CASOA RESOLVER.

formal definition

https://hyp.is/vxZMTmcJEfCr4kcVStC-nA/github.com/johnnydecimal/index-spec

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

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Reply to the reviewers

Manuscript number: RC-2023-02191

Corresponding author: Jan Rehwinkel

1. General Statements

The authors wish to thank all three reviewers and the Review Commons team for carefully evaluating our study. We have addressed all points raised as detailed below.

We have thoroughly revised our bulk RNAseq analysis, which is now performed at the transcript level using the latest GENCODE release. We have updated Figure 3 and associated supplementary figures and tables. This change from gene to transcript level was important for accurate motif analysis as requested by reviewer 2: matching promoters to individual IFN-regulated transcripts – rather than aggregating all promoters per gene – avoids significant signal dilution. This strategy yields higher-resolution expression data and is biologically preferable. Indeed, several well characterised IFN-regulated RNAs (e.g., the ADAR1-202 transcript encoding the p150 isoform) originate from promoters located far from the constitutive promoters of their host genes. In our revised manuscript, we now provide in the new supplementary figure 13 the requested promoter motif analysis. Using two computational approaches – de novo motif search and analysis of a curated motif database – we find strong enrichment of interferon-stimulated response elements (ISREs) in promoters of type I IFN regulated transcripts. No other motifs reached similarly high levels of enrichment, and our analysis did not reveal differences between different type I IFNs. These new data show that all type I IFNs engage a common regulatory pathway, supporting our overall conclusion that different type I IFNs do not induce qualitatively different responses in PBMCs.

Regrettably, in the process of analysing the bulk RNAseq data at transcript level, we noticed that our original lncRNA analysis contained numerous false positives. Closer inspection showed that many “differentially expressed” LNCipedia models were likely not full-length transcripts and commonly shared a single IFN-induced set of exons that artificially inflated expression estimates for every overlapping model. To correct this issue, we replaced LNCipedia with the latest high-quality non-coding RNA catalogue from GENCODE, most entries of which were defined by full-length RNA sequencing [1]. We also tightened our filtering criteria and now report only transcripts that are robustly expressed in our dataset and are either classified as high-confidence by GENCODE or robustly supported at every splice junction by our RNAseq.

We hope our manuscript is sufficiently improved and suitable for publication in PLoS Biology. New or revised text is highlighted in green in our revised manuscript.

2. Point-by-point description of the revisions

Reviewer #1

Evidence, reproducibility and clarity:

The study can be directly connected to a landmark paper in the field (Mostafavi et al. , Cell 2016). By comparison with this study, the authors use improved technologies to address the question if and how responses to type I IFN differ between human peripheral blood-derived cells types. In line with Mostafavi et al. the authors conclude that only a comparably low number of interferon-stimulated genes (ISG) is induced in all cell types and that considerable differences exist between cell types in the IFN-induced transcriptome. The authors address a second relevant aspect, whether and how the many different subtypes of type I IFN differ in the way they engage IFN signals to produce transcriptome changes. The data lead the authors to conclude that any differences are of quantitative rather than qualitative nature.

The authors' conclusions are based on a mass cytometry approach to phenotype STAT activation in different cell types, bulk RNA sequencing to study ISG expression in PBMC, and single cell sequencing to study ISG responses in individual cell types. The data are solid, clear and reproducible in biological replicates (eg different blood donors).

Significance: While some of the data can be considered confirmatory, the comprehensive analysis of cell-type specificity and IFN-I subtype specificity advances the field and provides a reference for future analyses. The study is complete and there is no obvious lack of a critical experiment. The number of scientists interested in the multitude of open questions around type I IFN is large, thus the study is likely to attract a broad readership.

We thank the reviewer for her/his positive assessment of our study.

The biggest limitation is to my opinion the low sequencing depth of scRNAseq which is clearly the downside of this technology. Using 11 hematopoietic cell types and bulk RNA sequencing the total number of ISG was determined to be 975 by Mostafavi et al. and the core ISG numbered 166. This is in stark contrast to this studies' 10 core ISG. The authors limitations paragraph should discuss the fact that scRNAseq reduces the overall ISG number that can be analyzed.

Thank you for this valid comment. We amended the limitations paragraph as requested. We agree that the Mostafavi et al. 2016 Cell paper [2] is important but note that there are many differences to our study: Mostafavi et al. use mice, a seemingly very high IFN dose (10,000 Units) and microarrays (not RNAseq).

A minor point concerns the 25 supplementary figures of the study. There must be a better way to support the conclusions with the necessary data.

We agree that our supplementary materials are extensive. However, this is not unusual for studies reporting multiple large datasets. We would be delighted to organise our supplementary information differently in due course according to journal guidelines.

Reviewer #2

Evidence, reproducibility and clarity:

The manuscript entitled “Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

We thank the reviewer for evaluating our study and the suggestions made.

*Major Comments: *

• Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in the promoters of ISGs make them more responsive to IFN subtypes? (PMID: 32847859) We thank the reviewer for the suggestion to study the promoters of genes regulated by type I IFNs. The analyses outlined below were performed by A. Fedorov, who is now a new co-author of our study. To investigate promoter features that might underlie the observed transcriptional responses across type I IFNs, we first performed a de novo*motif search using STREME [3] on our bulk RNAseq dataset (Figure 3). Specifically, we compared the promoters of transcripts that were up- or down-regulated by each IFN subtype (e.g., IFN-β-induced) either with one another or with promoters of robustly expressed RNAs that remained unresponsive to any treatment. No significant motifs emerged from these comparisons, except when we compared promoters of IFN-induced transcripts to the background set of unresponsive RNAs. This comparison consistently yielded strong enrichment of interferon-stimulated response element (ISRE)-like motifs in the promoters of up-regulated RNAs (new Figure S13a).

Next, we conducted a complementary analysis using known transcription factor (TF) motifs from the JASPAR database [4]. We screened all promoters of annotated RNAs using clustered JASPAR motifs and Z-standardised motif scores relative to all high-confidence GENCODE RNAs, including those not expressed in PBMCs. We reasoned that TFs actively mediating IFN responses would likely bind promoters with high motif scores (Z ≥ 2), while promoters with low scores (Z ≤ -1) would represent an unregulated background. This approach produced two sets of RNAs per TF cluster: putatively regulated and unregulated. We then restricted each set to RNAs expressed in our dataset and associated each transcript with its estimated fold change in response to each type I IFN, regardless of statistical significance. Next, we compared median fold changes between the likely regulated and unregulated sets across all TF clusters and IFN subtypes (Figure S13b). Among all tested TF motifs, only the ISRE-like cluster showed strong and consistent associations with transcriptional changes across all IFN subtypes. We also observed statistically significant but much weaker associations for other TFs, including a known negative regulator of innate antiviral signaling, NRF1 [5]. However, effect sizes for these motifs were dwarfed by those of ISRE-like motifs, suggesting that no JASPAR TFs other than those within the ISRE-like cluster play a major role in PBMCs under our conditions. Overall, these findings support the idea that all type I IFNs engage a common regulatory pathway, differing primarily in the magnitude rather than the nature of their transcriptional effects.

How do they relate to the activation of kinases by IFN subtypes?

We did not analyse the activation of the canonical kinases (i.e., TYK2 and JAK1) downstream of IFNAR. This would be interesting and may be possible using phospho-specific antibodies to these kinases in our CyTOF setup. However, this would require a very large investment of time and resources to identify specific antibodies, optimise a new CyTOF staining panel and to acquire and analyse new datasets. We therefore believe this should be pursued as a separate future study.

*Are there distinct features that dictate differential responses in monocytes and lymphocytes? *

Following the computational approach described above, we applied STREME to identify DNA motifs that could distinguish promoters associated with monocyte- and lymphocyte-specific ISGs. Regrettably, this analysis did not yield any significant motifs, likely due in part to the limited number of genes in each category.

• Figure 2a, d-h - Consider using the same scale for all heatmaps. This will allow for comparison of pSTATs median expression. Consider increasing the range in the color scale as some of the subtle changes in STAT phosphorylation across subtypes are not well appreciated. This also applies to Supplementary figures related to Figure 2.*

Thank you for this suggestion. We tried using the same scale for all heatmaps. However, given that the values for pSTAT1 are higher than those for other pSTATs, the resulting heatmaps did not show differences for the other pSTATs well. We therefore decided to leave these panels unchanged. Please also note that Figures 2b and S3b provide comparison between pSTATs (and other markers) using the same scale.

Minor Comments:

• The title of subsections are a bit generic (e.g "Analysis of the signalling response to type I IFNs using mass cytometry". Consider updating them to reflect some of the findings from each analysis.* Thank you for this suggestion. We have amended sub-headers accordingly.

• Figure 3 and S3 - Increase the heatmap scale to better appreciate changes in gene expression.*

The scales have been enlarged for better visibility as requested.

• Consider combining panel a and b in figure S7 for better contrasts of the response to IFNa1 or IFNb. *

Thank you for the suggestion. We combined these panels.

• Figure 4 - The authors could visualize ISGs that are unique across IFN types or cell types. *

Figure 5 and several accompanying supplementary figures already depict ISGs unique to IFN subtypes or cell types. Whilst we appreciate the suggestion, we prefer not to add additional figures to avoid redundancies.

• The gene ontology analysis should be performed with higher statistical stringency to capture the most significant IFN responsive processes. *

Thank you for this comment. We changed the presentation of the GO analysis in Fig S11 by sorting on p-value (instead of % of hits in category). We hope this shows more clearly that GO category enrichment amongst genes encoding IFN-induced transcripts had high statistical significance (log10 p-values of about -5 or lower for many categories).

Significance:* ** The authors provide an extensive compendium of cell type specific changes in response to type I IFN stimulation. They have created a public repository which extends the value of this dataset. *

Audience: *** This is a valuable resource for immunologists, virologists, and bioinformaticians.*

Thank you for these encouraging comments.

Reviewer #3

Evidence, reproducibility and clarity:

*Summary *

Rigby and collaborators analyzed the signaling responses and changes in gene expression of human PBMCs stimulated with different IFN type I subtypes, using mass cytometry, bulk and single-cell RNA sequencing. Their study represents the first single-cell atlas of human PBMCs stimulated with five type I IFN subtypes. The generated datasets are useful resources for anyone interested in innate immunity. The data and the methods are well presented. We thus recommend publication.

Thank you for your positive assessment of our work and for recommending publication.

*Major comments: *

• *

*Two of the key conclusions are not very convincing. *

• *

First, the authors claim that the magnitude of the responses varied between the 5 types of IFNs, however, as they point out in the 'limitation' paragraph, doses of the different IFNs were normalized using bioactivity. Knowing that this bioactivity is based on assays performed on A549 lung cells, this normalization likely induces a bias. How do the authors explain similar antiviral bioactivity but differing magnitudes of modulation of ISG expression? Would the authors expect the same differences of expression between the several IFNs tested in A549 cells? We thus recommend being very cautious when comparing magnitude of the response between the 5 types of IFNs.

We thank the reviewer for this important point and included the following reasoning in our discussion:

“An important technical consideration for our study was the normalisation of type I IFN doses used to treat cells (see also ‘Limitations of the study’ below). We relied on bioactivity (U/ml) that is measured by the manufacturer of recombinant type I IFNs using a cytopathic effect (CPE) inhibition assay. In brief, the lung cancer cell line A549 is treated with type I IFN and is infected with the cytopathic encephalomyocarditis virus (EMCV). Control cells not treated with IFN are killed by EMCV, whereas cells treated with sufficient IFN survive. How, then, is it possible that different type I IFNs induce differing magnitudes of STAT phosphorylation and ISG expression despite being used at the same bioactivity? Cell survival in the CPE inhibition assay may be due to one or a few ISGs. Indeed, single ISGs can mediate powerful antiviral defence. For example, MX1 is crucial for host defence against influenza A virus [6]. Thus, similar bioactivity of different IFNs in A549 cells against EMCV-triggered cell death may not reflect the breadth of effects on many ISGs. Moreover, IFN-induced survival of A549 cells following EMCV infection is a binary readout. Induction of the relevant ISG(s) mediating protection beyond a threshold required for cell survival is unlikely to register in this assay. Thus, similar antiviral bioactivity (in the CPE inhibition assay) and differing magnitudes of modulation of ISG expression (at transcriptome level) are compatible.”

We believe inclusion of this paragraph demonstrates an appropriate level of caution in our data interpretation. Further, we would expect to make similar observations if we were to apply transcriptomic analysis to A549 cells treated with different type I IFNs. However, given our focus in this study on primary, normal cells, we decided not to pursue work with the transformed and lab adapted A549 cell line.

Second, the qualitatively different responses to type I IFN subtypes claimed by the authors were not apparent. This seems true at the level of the bulk population (Fig. S10) but not at cell-type level (Fig. S15/S16).

We believe there may be a misunderstanding here. In relation to Figure S10, we do not claim “qualitatively different responses to type I IFN subtypes”. Instead, we conclude that “differences in expression between the different type I IFNs were quantitative” (page 8; lines 229-230, now: 238-239). Moreover, Figures S15/S16 (now: S16/S17) do not refer to analyses of responses to different type I IFN subtypes.

The authors state (line 311-312) that 'Consistent with our bulk RNAseq data, differences were again quantitative rather than qualitative' at the cell-type level. The response between cell types seems very different to us since a core set of only 10 ISGs are shared by all cell types and all 5 type I IFNs. Knowing that the expression of hundreds, sometimes thousands of genes, are induced by IFN, this seems like a rather small overlap (and thus qualitatively different responses). Fig S15 and S16 nicely illustrate that the responses are qualitatively different between cell-type. Please modify this conclusion accordingly.

Thank you for highlighting this. The statement in lines 311-312 does not refer to differences between cell types but to differences between type I IFN subtypes. We are sorry this was not clear and changed this sentence (now lines 357-358). Furthermore, we have made it clearer in the revised text that qualitative differences were observed between cell types (e.g. lines 329 and 350-352).

*No additional experiments are needed to support the claims. However, we believe that two additional analyses could provide useful information. *

• *

The levels of IFNAR1 and IFNAR2 expressed at the plasma membrane probably vary between cell types and may thus influence the magnitude of the IFN response. While it would be difficult to measure these levels by flow cytometric analysis on the different cell types, could the authors extract information from their scRNAseq analysis on the expression level of IFNAR1/2 in all cell types? This would give a hint about potential differences in expression (and thus in magnitude).

We analysed IFNAR1/2 transcript levels in our scRNAseq dataset (Figure R1 below). Unfortunately, for many cells, IFNAR1 and IFNAR2 transcripts were not detected (see width of violin plots at zero), probably due to low sequencing depth inherent to scRNAseq analysis. We therefore prefer not to draw conclusions from these data.

Could the authors investigate further the expression of lncRNAs at the single-cell levels? It would be useful to also define a core set of lncRNAs that are shared between cell types and IFN subtypes. If such a core set does not exist (since lncRNAs are less conserved than coding genes), it would be nice to mention it.

Thank you for this suggestion. The expression of lncRNAs is generally lower than protein-coding genes, resulting in high drop-out rates in 10X datasets. Indeed, Zhao et al. comment that “current development of single-cell technologies may not yet be optimized for lncRNA detection and quantification” [7]. We only detected a small number of lncRNAs in our scRNAseq analysis, and only four lncRNAs were significantly differentially expressed between cell types. We thus could not perform a meaningful analysis of lncRNAs in our scRNAseq dataset. This is now mentioned in the limitations paragraph at the end of the manuscript.

Minor comments:

There is a typo in line 355 Fig.4C =>6C.

Thank you for spotting this.

***Referees cross-commenting** *

We agree with Reviewer 1 that the low sequencing depth of scRNAseq restricts the analysis and must be discussed in the 'limitation' paragraph. This would explain why the authors identified only 10 ISGs that are common to all cell types and all 5 IFN subtypes. Of note, as a comparison, Shaw et al (10.1371/journal.pbio.2004086) identified a core set of 90 ISGs that are upregulated upon IFN treatment in cells isolated mainly from kidney and skin of nine mammalian species ("core mammalian ISGs"). It is thus expected that stimulated blood cells isolated from a single mammalian species share more than 10 ISGs.

We amended the limitations section as requested. Shaw et al. [8] used a single type I IFN (universal or IFNα, depending on species) at a very high dose (1000 U/ml). Taken together with the use of bulk RNAseq in this study, it is unsurprising that our work identified fewer core ISGs. We believe our small list of core ISGs is nonetheless both a high confidence and a high utility set of ISGs: these genes are induced by multiple type I IFNs, in all major cell types in blood and their regulation can be measured even when sequencing depth is low.

Significance (Required)

*Multiple single-cell RNAseq analysis of PBMCs, stimulated or not, have been previously performed in multiple contexts (for instance with PBMCs isolated from the blood of patients infected with influenza virus or SARS-CoV-2). The technical advance is thus limited. *

• *

*However, the work represents a conceptual advance for the field since it provides the first single-cell atlas of PBMCs stimulated with five type-I IFN subtypes. The generated datasets represent a great resource for anyone interested in innate immunity (virologists, immunologists and cancerologists). *

• *

Of note, we are studying innate immunity in the context of RNA virus infection but we have no expertise on scRNA sequencing. We may thus have missed a flaw in the analyses.

We thank the reviewer for their positive assessment of the advances of our study and the value of our IFN resource.

A

B

C

D

Figure R1. IFNAR1/2 expression in scRNAseq data.

Violin plots showing expression of IFNAR1 (A,C) or IFNAR2 (B,D) in different cell types. In (A,B), data were pooled across conditions. In (C,D), data are shown separately for unstimulated control cells and cells stimulated with different type I IFNs.

References

Kaur G, Perteghella T, Carbonell-Sala S, Gonzalez-Martinez J, Hunt T, Madry T, et al. GENCODE: massively expanding the lncRNA catalog through capture long-read RNA sequencing. bioRxiv. 2024. Epub 20241031. doi: 10.1101/2024.10.29.620654. PubMed PMID: 39554180; PubMed Central PMCID: PMCPMC11565817. Mostafavi S, Yoshida H, Moodley D, LeBoite H, Rothamel K, Raj T, et al. Parsing the Interferon Transcriptional Network and Its Disease Associations. Cell. 2016;164(3):564-78. Epub 2016/01/30. doi: 10.1016/j.cell.2015.12.032. PubMed PMID: 26824662; PubMed Central PMCID: PMCPMC4743492. Bailey TL. STREME: accurate and versatile sequence motif discovery. Bioinformatics. 2021;37(18):2834-40. doi: 10.1093/bioinformatics/btab203. PubMed PMID: 33760053; PubMed Central PMCID: PMCPMC8479671. Rauluseviciute I, Riudavets-Puig R, Blanc-Mathieu R, Castro-Mondragon JA, Ferenc K, Kumar V, et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic acids research. 2024;52(D1):D174-D82. doi: 10.1093/nar/gkad1059. PubMed PMID: 37962376; PubMed Central PMCID: PMCPMC10767809. Zhao T, Zhang J, Lei H, Meng Y, Cheng H, Zhao Y, et al. NRF1-mediated mitochondrial biogenesis antagonizes innate antiviral immunity. The EMBO journal. 2023;42(16):e113258. Epub 20230706. doi: 10.15252/embj.2022113258. PubMed PMID: 37409632; PubMed Central PMCID: PMCPMC10425878. Grimm D, Staeheli P, Hufbauer M, Koerner I, Martinez-Sobrido L, Solorzano A, et al. Replication fitness determines high virulence of influenza A virus in mice carrying functional Mx1 resistance gene. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(16):6806-11. Epub 20070410. doi: 10.1073/pnas.0701849104. PubMed PMID: 17426143; PubMed Central PMCID: PMCPMC1871866. Zhao X, Lan Y, Chen D. Exploring long non-coding RNA networks from single cell omics data. Comput Struct Biotechnol J. 2022;20:4381-9. Epub 20220804. doi: 10.1016/j.csbj.2022.08.003. PubMed PMID: 36051880; PubMed Central PMCID: PMCPMC9403499. Shaw AE, Hughes J, Gu Q, Behdenna A, Singer JB, Dennis T, et al. Fundamental properties of the mammalian innate immune system revealed by multispecies comparison of type I interferon responses. PLoS Biol. 2017;15(12):e2004086. Epub 2017/12/19. doi: 10.1371/journal.pbio.2004086. PubMed PMID: 29253856.

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Referee #2

Evidence, reproducibility and clarity

The manuscript entitled "Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

Major Comments:

1. Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in the promoters of ISGs make them more responsive to IFN subtypes? (PMID: 32847859) How do they relate to the activation of kinases by IFN subtypes? Are there distinct features that dictate differential responses in monocytes and lymphocytes?
2. Figure 2a, d-h - Consider using the same scale for all heatmaps. This will allow for comparison of pSTATs median expression. Consider increasing the range in the color scale as some of the subtle changes in STAT phosphorylation across subtypes are not well appreciated. This also applies to Supplementary figures related to Figure 2.

Minor Comments:

1. The title of subsections are a bit generic (e.g "Analysis of the signalling response to type I IFNs using mass cytometry". Consider updating them to reflect some of the findings from each analysis.
2. Figure 3 and S3 - Increase the heatmap scale to better appreciate changes in gene expression.
3. Consider combining panel a and b in figure S7 for better contrasts of the response to IFNa1 or IFNb.
4. Figure 4 - The authors could visualize ISGs that are unique across IFN types or cell types.
5. The gene ontology analysis should be performed with higher statistical stringency to capture the most significant IFN responsive processes.

Significance

Significance:

The authors provide an extensive compendium of cell type specific changes in response to type I IFN stimulation. They have created a public repository which extends the value of this dataset.

Audience:

This is a valuable resource for immunologists, virologists, and bioinformaticians.

Reviewer #2 (Public review):

The manuscript by Ma et al. reports the identification of three unrelated people who are heterozygous for de novo missense variants in PLCG1, which encodes phospholipase C-gamma 1, a key signaling protein. These individuals present with partially overlapping phenotypes, including hearing loss, ocular pathology, cardiac defects, abnormal brain imaging results, and immune defects. None of the patients present with all of the above phenotypes. PLCG1 has also been implicated as a possible driver for cell proliferation in cancer.

The three missense variants found in the patients result in the following amino acid substitutions: His380Arg, Asp1019Gly, and Asp1165Gly. PLCG1 (and the closely related PLCG2) have a single Drosophila ortholog called small wing (sl). sl-null flies are viable but have small wings with ectopic wing veins and supernumerary photoreceptors in the eye. As all three amino acids affected in the patients are conserved in the fly protein, in this work Ma et al. tested whether they are pathogenic by expressing either reference or patient variant fly or human genes in Drosophila and determining the phenotypes produced by doing so.

Expression in Drosophila of the variant forms of PLCG1 found in these three patients is toxic; highly so for Asp1019Gly and Asp1165Gly, much more modestly for His380Arg. Another variant, Asp1165His which was identified in lymphoma samples and shown by others to be hyperactive, was also found to be toxic in the Drosophila assays. However, a final variant, Ser1021Phe, identified by others in an individual with severe immune dysregulation, produced no phenotype upon expression in flies.

Based on these results, the authors conclude that the PLCG1 variants found in patients are pathogenic, producing gain-of-function phenotypes through hyperactivity. In my view, the data supporting this conclusion are robust, despite the lack of a detectable phenotype with Ser1021Phe, and I have no concerns about the core experiments that comprise the paper.

Fig. 6, the last in the paper, provides information about PLCG1 structure and how the different variants would affect it. It shows that His380, Asp1019 and Asp1165 all lie within catalytic domains or intramolecular interfaces, and that variants in the latter two affect residues essential for autoinhibition. It also shows that Ser1021 falls outside the key interface occupied by Asp1019, but more could have been said about the potential effects of Ser1021Phe.

Overall, I believe the authors fully achieved the aims of their study. The work will have a substantial impact because it reports the identification of novel disease-linked genes, and because it further demonstrates the high value of the Drosophila model for finding and understanding gene-disease linkages.

Comments on revisions:

The single recommendation I made on the original version, which was to further examine H380 mutants, has been satisfactorily addressed in the revised version.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This manuscript provides an initial characterization of three new missense variants of the PLCG1 gene associated with diverse disease phenotypes, utilizing a Drosophila model to investigate their molecular effects in vivo. Through the meticulous creation of genetic tools, the study assesses the small wing (sl) phenotype - the fly's ortholog of PLCG1 - across an array of phenotypes from longevity to behavior in both sl null mutants and variants. The findings indicate that the Drosophila PLCG1 ortholog displays aberrant functions. Notably, it is demonstrated that overexpression of both human and Drosophila PLCG1 variants in fly tissue leads to toxicity, underscoring their pathogenic potential in vivo.

Strengths:

The research effectively highlights the physiological significance of sl in Drosophila. In addition, the study establishes the in vivo toxicity of disease-associated variants of both human PLCG1 and Drosophila sl.

Weaknesses:

The study's limitations include the human PLCG1 transgene's inability to compensate for the Drosophila sl null mutant phenotype, suggesting potential functional divergence between the species. This discrepancy signals the need for additional exploration into the mechanistic nuances of PLCG1 variant pathogenesis, especially regarding their gain-of-function effects in vivo.

Overall:

The study offers compelling evidence for the pathogenicity of newly discovered disease-related PLCG1 variants, manifesting as toxicity in a Drosophila in vivo model, which substantiates the main claim by the authors. Nevertheless, a deeper inquiry into the specific in vivo mechanisms driving the toxicity caused by these variants in Drosophila could significantly enhance the study's impact.

Reviewer #2 (Public Review):

The manuscript by Ma et al. reports the identification of three unrelated people who are heterozygous for de novo missense variants in PLCG1, which encodes phospholipase C-gamma 1, a key signaling protein. These individuals present with partially overlapping phenotypes including hearing loss, ocular pathology, cardiac defects, abnormal brain imaging results, and immune defects. None of the patients present with all of the above phenotypes. PLCG1 has also been implicated as a possible driver for cell proliferation in cancer.

The three missense variants found in the patients result in the following amino acid substitutions: His380Arg, Asp1019Gly, and Asp1165Gly. PLCG1 (and the closely related PLCG2) have a single Drosophila ortholog called small wing (sl). sl-null flies are viable but have small wings with ectopic wing veins and supernumerary photoreceptors in the eye. As all three amino acids affected in the patients are conserved in the fly protein, in this work Ma et al. tested whether they are pathogenic by expressing either reference or patient variant fly or human genes in Drosophila and determining the phenotypes produced by doing so.

Expression in Drosophila of the variant forms of PLCG1 found in these three patients is toxic; highly so for Asp1019Gly and Asp1165Gly, much more modestly for His380Arg. Another variant, Asp1165His which was identified in lymphoma samples and shown by others to be hyperactive, was also found to be toxic in the Drosophila assays. However, a final variant, Ser1021Phe, identified by others in an individual with severe immune dysregulation, produced no phenotype upon expression in flies.

Based on these results, the authors conclude that the PLCG1 variants found in patients are pathogenic, producing gain-of-function phenotypes through hyperactivity. In my view, the data supporting this conclusion are robust, despite the lack of a detectable phenotype with Ser1021Phe, and I have no concerns about the core experiments that comprise the paper.

Figure 6, the last in the paper, provides information about PLCG1 structure and how the different variants would affect it. It shows that the His380, Asp1019, and Asp1165 all lie within catalytic domains or intramolecular interfaces and that variants in the latter two affect residues essential for autoinhibition. It also shows that Ser1021 falls outside the key interface occupied by Asp1019, but more could have been said about the potential effects of Ser1021Phe.

Overall, I believe the authors fully achieved the aims of their study. The work will have a substantial impact because it reports the identification of novel disease-linked genes, and because it further demonstrates the high value of the Drosophila model for finding and understanding gene-disease linkages.

Reviewer #3 (Public Review):

Summary:

The paper attempts to model the functional significance of variants of PLCG2 in a set of patients with variable clinical manifestations.

Strengths:

A study attempting to use the Drosophila system to test the function of variants reported from human patients.

Weaknesses:

Additional experiments are needed to shore up the claims in the paper. These are listed below.

Major Comments:

(1) Does the pLI/ missense constraint Z score prediction algorithm take into consideration whether the gene exhibits monoallelic or biallelic expression?

To our knowledge, pLI and missense Z don't consider monoallelic or biallelic expression. Instead, they reflect sequence constraint and are calculated based on the observed versus expected variant frequencies in population databases.

(2) Figure 1B: Include human PLCG2 in the alignment that displays the species-wide conserved variant residues.

We have updated Figure 1B and incorporated the alignment of PLCG2.

(3) Figure 4A:

Given that

(i) sl is predicted to be the fly ortholog for both mammalian PLCγ isozymes: PLCG1 and PLCG2 [Line 62]

(ii) they are shown to have non-redundant roles in mammals [Line 71]

(iii) reconstituting PLCG1 is highly toxic in flies, leading to increased lethality.

This raises questions about whether sl mutant phenotypes are specifically caused by the absence of PLCG1 or PLCG2 functions in flies. Can hPLCG2 reconstitution in sl mutants be used as a negative control to rule out the possibility of the same?

The studies about the non-redundant roles of PLCG1 and PLCG2 mainly concern the immune system.

We have assessed the phenotypes in the sl^T2A/Y; UAS-hPLCG2 flies. Expression of human PLCG2 in flies is also toxic and leads to severely reduced eclosion rate.

We have updated the manuscript with these results, and included the eclosion rate of sl^T2A/Y; UAS-hPLCG2 flies in the new Figure 4B.

(4) Do slT2A/Y; UAS-PLCG1Reference flies survive when grown at 22{degree sign}C? Since transgenic fly expressing PLCG1 cDNA when driven under ubiquitous gal4s, Tubulin and Da, can result in viable progeny at 22{degree sign}C, the survival of slT2A/Y; UAS-PLCG1Reference should be possible.

The eclosion rate of sl^T2A/Y >PLCG1^Reference flies at 22°C is slightly higher than at 25°C, but remains severely reduced compared to the UAS-Empty control. We have presented these results in the updated Figure S3.

and similarly

Does slT2A flies exhibit the phenotypes of (i) reduced eclosion rate (ii) reduced wing size and ectopic wing veins and (iii) extra R7 photoreceptor in the fly eye at 22{degree sign}C?

The mutant phenotypes are still observed at 22 °C.

If so, will it be possible to get a complete rescue of the slT2A mutant phenotypes with the hPLCG1 cDNA at 22{degree sign}C? This dataset is essential to establish Drosophila as an ideal model to study the PLCG1 de novo variants.

Thank you for the suggestion. It is difficult to directly assess the rescue ability of the PLCG1 cDNAs due to the toxicity. However, our ectopic expression assays show that the variants are more toxic than the reference with variable severities, suggesting that the variants are deleterious.

The ectopic expression strategy has been used to evaluate the consequence of genetic variants and has significantly contributed to the interpretation of their pathogenicity in many cases (reviewed in Her et al., Genome, 2024, PMID: 38412472).

(5) Localisation and western blot assays to check if the introduction of the de novo mutations can have an impact on the sub-cellular targeting of the protein or protein stability respectively.

Thank you for the suggestion.

We expressed PLCG1 cDNAs in the larval salivary glands and performed antibody staining (rabbit anti-Human PLCG1; 1:100, Cell Signaling Technology, #5690). The larval salivary gland are composed of large columnar epithelia cells that are ideal for analyzing subcellular localization of proteins. The PLCG1 proteins are cytoplasmic and localize near the cell surface, with some enrichment in the plasma membrane region. The variant proteins are detected, and did not show significant difference in expression level or subcellular distribution compared to the reference. We did not include this data.

(6) Analysing the nature of the reported gain of function (experimental proof for the same is missing in the manuscript) variants:

Instead of directly showing the effect of introducing the de novo variant transgenes in the Drosophila model especially when the full-length PLCG1 is not able to completely rescue the slT2A phenotype;

(i) Show that the gain-of-function variants can have an impact on the protein function or signalling via one of the three signalling outputs in the mammalian cell culture system: (i) inositol-1,4,5-trisphosphate production, (ii) intracellular Ca2+ release or (iii) increased phosphorylation of extracellular signal-related kinase, p65, and p38.

We appreciate the reviewer’s suggestion. We utilized the CaLexA (calcium-dependent nuclear import of LexA) system (Masuyama et al., J Neurogenet, 2012, PMID: 22236090) to assess the intracellular Ca²⁺ change associated with the expression of PLCG1 cDNAs in fly wing discs. The results show that, compared to the reference, expression of the D1019G or D1165G variants leads to elevated intracellular Ca²⁺ levels, similar to the hyperactive S1021F and D1165H variants. However, the H380R or L597F variants did not show a detectable phenotype in this assay. These results suggest that D1019G and D1165G are hyperactive variants, whereas H380R and L597F variant are not, or their effect is too mild to be detected in this assay. We have updated the related sections in the manuscript and Figures 5A and S5.

OR

(ii) Run a molecular simulation to demonstrate how the protein's auto-inhibited state can be disrupted and basal lipase activity increased by introducing D1019G and D1165G, which destabilise the association between the C2 and cSH2 domains. The H380R variant may also exhibit characteristics similar to the previously documented H335A mutation which leaves the protein catalytically inactive as the residue is important to coordinate the incoming water molecule required for PIP2 hydrolysis.

We utilized the DDMut platform, which predicts changes in the Gibbs Free Energy (ΔΔG) upon single and multiple point mutations (Zhou et al., Nucleic Acid Res, 2023, PMID: 37283042), to gain insight into the molecular dynamics changes of variants. The results are now presented in Figure S7.

Additionally, we performed Molecular dynamics (MD) simulations. The results show that, similar to the hyperactive D1165H variant, the D1019G and D11656G variants exhibit increased disorganization, with a higher root mean square deviations (RMSD) compared to the reference PLCG1.The data are also presented in the updated Figure S7.

(7) Clarify the reason for carrying out the wing-specific and eye-specific experiments using nub-gal4 and eyless-gal4 at 29˚C despite the high gal4 toxicity at this temperature.

We used high temperature and high expression level to see if the mild H380R and L597F variants could show phenotypes in this condition.

The toxicity of the two strong variants (D1019G and D1165G) has been consistently confirmed in multiple assays at different temperatures.

(8) For the sake of completeness the authors should also report other variants identified in the genomes of these patients that could also contribute to the clinical features.

Thank you!

The additional variants and their potential contributions to the clinical features are listed and discussed in Table 1 and its legend.

Reviewer #1 (Recommendations For The Authors):

The manuscript's significant contribution is tempered by a lack of comprehensive analysis using the generated genetic reagents in Drosophila. To enhance our understanding of the PLCG1 orthologs, I suggest the following:

(1) A more detailed molecular analysis to distinguish the actions of sl variants from the wild-type could be very informative. For example, utilizing the HA-epitope tag within the current UAS-transgenes could reveal more about the cellular dynamics and abundance of these variants, potentially elucidating mechanisms beyond gain-of-function.

We appreciate the reviewer’s suggestion. The UAS-sl cDNA constructs contain stop codon and do not express an HA-epitope tag. Alternatively, we utilized commercially available antibodies against human PLCG1 antibodies to assess the subcellular localization and protein stability by expressing the reference and variant PLCG1 cDNAs in Drosophila larval salivary glands. The reference proteins are cytoplasmic with some enrichment along the plasma membrane. However, we did not observe significant differences between the reference and variant proteins in this assay. We did not include this data.

(2) I suggest further investigating the relative contributions of developmental processes and acute (Adult) effects on the sl-variant phenotypes observed. For example, employing systems that allow for precise temporal control of gene expression, such as the temperature-sensitive Gal80, could differentiate between these effects, shedding light on the mechanisms that affect longevity and locomotion. This knowledge would be vital for a deeper understanding of the corresponding human disorders and for developing therapeutic interventions.

We appreciate the reviewer’s suggestion. We utilized Tub-GAL4, Tub-GAL80^ts to drive the expression of sl wild-type or variant cDNAs, and performed temperature shifts after eclosion to induce expression of the cDNAs only in adult flies. The sl^D1184G variant (corresponding to PLCG1^D1165G) caused severely reduced lifespan and the flies mostly die within 10 days. The sl^D1041G variant (corresponding to PLCG1^D1019G) led to reduced longevity and locomotion. The sl^H384R variant (corresponding to PLCG1^H380R) showed only a mild effect on longevity and no significant effect on climbing ability. These results suggest that the two strong variants (sl<sup>D1041G<sup> and sl^D1184G) contribute to both developmental and acute effects while the H384R variant mainly contributes to developmental stages.

I also suggest a more refined analysis of overexpression toxicity. Rather than solely focusing on ubiquitous transgene expression, overexpressing transgene in endogenous pattern using sl-t2a-Gal4 may yield a more nuanced understanding of the pathogenic mechanisms of gain-of-function mutations, particularly in the pathogenesis associated with these variants exclusively located in the coding regions.

We appreciate the reviewer’s suggestion. We therefore performed the experiments using sl^T2A to drive overexpression ofPLCG1cDNAs in heterozygous female progeny with one copy of wild-type sl+ (sl^T2A/ yw > UAS-cDNAs). In this context, expression of PLCG1<sup>Reference<sup>, PLCG1^H380RorPLCG1^L597F is viable whereas expression of PLCG1^D1019G or PLCG1^D1165G is lethal, suggesting that the PLCG1^D1019G and PLCG1^D1165G variants exert a strong dominant toxic effect while the PLCG1^H380Rand PLCG1<sup>L597F<sup> are comparatively milder. Similar patterns have been consistently observed in other ectopic expression assays with varying degrees of severity. These results are updated in the manuscript and figures.

Reviewer #2 (Recommendations For The Authors):

The work in the paper could be usefully extended by determining the effects of expressing His380Phe and His380Ala in flies. These variants suppress PLCG1 activity, so their phenotype, if any, would be predicted not to be the same as His380Arg. Determining this would add further strength to the conclusions of the paper.

We thank the reviewer for the constructive suggestions! We have tested the enzymatic-dead H380A variant, which still exhibits toxicity when expressed in sl^T2A/Y hemizygous flies, but it is not toxic in heterozygous females suggesting that the reduced eclosion rate is likely not directly associated with enzymatic activity. We have updated the manuscript and figures accordingly.

Mais pas du tout. ;)) Et je te confirme que présentiel ou distanciel sont des mots récemment utilisés lors de la crise COVID de 2020-2021, mais ils sont parfaitement français et usités. C'est l'animatrice qui a trop bu de thé. :)))

Tu as bien fait de mixer termes anglosaxons et français/québecois pour expliquer ton rôle ou tes postes, c'est la meilleure méthode pour se faire comprendre.

Reviewer #3 (Public review):

Summary:

The paper studies learning rules in a simple sigmoidal recurrent neural network setting. The recurrent network has a single layer of 10 to 40 units. It is first confirmed that feedback alignment (FA) can learn a value function in this setting. Then so-called bio-plausible constraints are added: (1) when value weights (readout) is non-negative, (2) when the activity is non-negative (normal sigmoid rather than downscaled between -0.5 and 0.5), (3) when the feedback weights are non-negative, (4) when the learning rule is revised to be monotic: the weights are not downregulated. In the simple task considered all four biological features do not appear to impair totally the learning.

Strengths:

(1) The learning rules are implemented in a low-level fashion of the form: (pre-synaptic-activity) x (post-synaptic-activity) x feedback x RPE. Which is therefore interpretable in terms of measurable quantities in the wet-lab.

(2) I find that non-negative FA (FA with non negative c and w) is the most valuable theoretical insight of this paper: I understand why the alignment between w and c is automatically better at initialization.

(3) The task choice is relevant, since it connects with experimental settings of reward conditioning with possible plasticity measurements.

indiquer la date

Reviewer #1 (Public review):

Summary:

Chao et al. produced an updated version of the SpliceAI package using modern deep learning frameworks. This includes data preprocessing, model training, direct prediction, and variant effect prediction scripts. They also added functionality for model fine-tuning and model calibration. They convincingly evaluate their newly trained models against those from the original SpliceAI package and investigate how to extend SpliceAI to make predictions in new species. While their comparisons to the original SpliceAI models are convincing on the grounds of model performance, their evaluation of how well the new models match the original's understanding of non-local mutation effects is incomplete. Further, their evaluation of the new calibration functionality would benefit from a more nuanced discussion of what set of splice sites their calibration is expected to hold for, and tests in a context for which calibration is needed.

Strengths:

(1) They provide convincing evidence that their new implementation of SpliceAI matches the performance of the original model on a similar dataset while benefiting from improved computational efficiencies. This will enable faster prediction and retraining of splicing models for new species as well as easier integration with other modern deep learning tools.

(2) They produce models with strong performance on non-human model species and a simple, well-documented pipeline for producing models tuned for any species of interest. This will be a boon for researchers working on splicing in these species and make it easy for researchers working on new species to generate their own models.

(3) Their documentation is clear and abundant. This will greatly aid the ability of others to work with their code base.

Weaknesses:

(1) The authors' assessment of how much their model retains SpliceAI's understanding of "non-local effects of genomic mutations on splice site location and strength" (Figure 6) is not sufficiently supported. Demonstrating this would require showing that for a large number of (non-local) mutations, their model shows the same change in predictions as SpliceAI or that attribution maps for their model and SpliceAI are concordant even at distances from the splice site. Figure 6A comes close to demonstrating this, but only provides anecdotal evidence as it is limited to 2 loci. This could be overcome by summarizing the concordance between ISM maps for the two models and then comparing across many loci. Figure 6B also comes close, but falls short because instead of comparing splicing prediction differences between the models as a function of variants, it compares the average prediction difference as a function of the distance from the splice site. This limits it to only detecting differences in the model's understanding of the local splice site motif sequences. This could be overcome by looking at comparisons between differences in predictions with mutants directly and considering non-local mutants that cause differences in splicing predictions.

(2) The utility of the calibration method described is unclear. When thinking about a calibrated model for splicing, the expectation would be that the models' predicted splicing probabilities would match the true probabilities that positions with that level of prediction confidence are splice sites. However, the actual calibration that they perform only considers positions as splice sites if they are splice sites in the longest isoform of the gene included in the MANE annotation. In other words, they calibrate the model such that the model's predicted splicing probabilities match the probability that a position with that level of confidence is a splice site in one particular isoform for each gene, not the probability that it is a splice site more broadly. Their level of calibration on this set of splice sites may very well not hold to broader sets of splice sites, such as sites from all annotated isoforms, sites that are commonly used in cryptic splicing, or poised sites that can be activated by a variant. This is a particularly important point as much of the utility of SpliceAI comes from its ability to issue variant effect predictions, and they have not demonstrated that this calibration holds in the context of variants. This section could be improved by expanding and clarifying the discussion of what set of splice sites they have demonstrated calibration on, what it means to calibrate against this set of splice sites, and how this calibration is expected to hold or not for other interesting sets of splice sites. Alternatively, or in addition, they could demonstrate how well their calibration holds on different sets of splice sites or show the effect of calibrating their models against different potentially interesting sets of splice sites and discuss how the results do or do not differ.

(3) It is difficult to assess how well their calibration method works in general because their original models are already well calibrated, so their calibration method finds temperatures very close to 1 and only produces very small and hard to assess changes in calibration metrics. This makes it very hard to distinguish if the calibration method works, as it doesn't really produce any changes. It would be helpful to demonstrate the calibration method on a model that requires calibration or on a dataset for which the current model is not well calibrated, so that the impact of the calibration method could be observed.

Author response:

Reviewer #1 (Public review):

Summary:

Chao et al. produced an updated version of the SpliceAI package using modern deep learning frameworks. This includes data preprocessing, model training, direct prediction, and variant effect prediction scripts. They also added functionality for model fine-tuning and model calibration. They convincingly evaluate their newly trained models against those from the original SpliceAI package and investigate how to extend SpliceAI to make predictions in new species. While their comparisons to the original SpliceAI models are convincing on the grounds of model performance, their evaluation of how well the new models match the original's understanding of non-local mutation effects is incomplete. Further, their evaluation of the new calibration functionality would benefit from a more nuanced discussion of what set of splice sites their calibration is expected to hold for, and tests in a context for which calibration is needed.

Strengths:

(1) They provide convincing evidence that their new implementation of SpliceAI matches the performance of the original model on a similar dataset while benefiting from improved computational efficiencies. This will enable faster prediction and retraining of splicing models for new species as well as easier integration with other modern deep learning tools.

(2) They produce models with strong performance on non-human model species and a simple, well-documented pipeline for producing models tuned for any species of interest. This will be a boon for researchers working on splicing in these species and make it easy for researchers working on new species to generate their own models.

(3) Their documentation is clear and abundant. This will greatly aid the ability of others to work with their code base.

We thank the reviewer for these positive comments.  

Weaknesses:

(1) The authors' assessment of how much their model retains SpliceAI's understanding of "nonlocal effects of genomic mutations on splice site location and strength" (Figure 6) is not sufficiently supported. Demonstrating this would require showing that for a large number of (non-local) mutations, their model shows the same change in predictions as SpliceAI or that attribution maps for their model and SpliceAI are concordant even at distances from the splice site. Figure 6A comes close to demonstrating this, but only provides anecdotal evidence as it is limited to 2 loci. This could be overcome by summarizing the concordance between ISM maps for the two models and then comparing across many loci. Figure 6B also comes close, but falls short because instead of comparing splicing prediction differences between the models as a function of variants, it compares the average prediction difference as a function of the distance from the splice site. This limits it to only detecting differences in the model's understanding of the local splice site motif sequences. This could be overcome by looking at comparisons between differences in predictions with mutants directly and considering non-local mutants that cause differences in splicing predictions.

We agree that two loci are insufficient to demonstrate preservation of non-local effects. To address this, we have extended our analysis to a larger set of sites: we randomly sampled 100 donor and 100 acceptor sites, applied our ISM procedure over a 5,001 nt window centered at each site for both models, and computed the ISM map as before. We then calculated the Pearson correlation between the collection of OSAI_MANE and SpliceAI ISM importance scores. We also created 10 additional ISM maps similar to those in Figure 6A, which are now provided in Figure S23.

Follow is the revised paragraph in the manuscript’s Results section:

First, we recreated the experiment from Jaganathan et al. in which they mutated every base in a window around exon 9 of the U2SURP gene and calculated its impact on the predicted probability of the acceptor site. We repeated this experiment on exon 2 of the DST gene, again using both SpliceAI and OSAI_MANE . In both cases, we found a strong similarity between the resultant patterns between SpliceAI and OSAI_MANE , as shown in Figure 6A. To evaluate concordance more broadly, we randomly selected 100 donor and 100 acceptor sites and performed the same ISM experiment on each site. The Pearson correlation between SpliceAI and OSAI_MANE yielded an overall median correlation of 0.857 (see Methods; additional DNA logos in Figure S23). 

To characterize the local sequence features that both models focus on, we computed the average decrease in predicted splice-site probability resulting from each of the three possible singlenucleotide substitutions at every position within 80bp for 100 donor and 100 acceptor sites randomly sampled from the test set (Chromosomes 1, 3, 5, 7, and 9). Figure 6B shows the average decrease in splice site strength for each mutation in the format of a DNA logo, for both tools.

We added the following text to the Methods section:

Concordance evaluation of ISM importance scores between OSAI_MANE and SpliceAI

To assess agreement between OSAI_MANE and SpliceAI across a broad set of splice sites, we applied our ISM procedure to 100 randomly chosen donor sites and 100 randomly chosen acceptor sites. For each site, we extracted a 5,001 nt window centered on the annotated splice junction and, at every coordinate within that window, substituted the reference base with each of the three alternative nucleotides. We recorded the change in predicted splice-site probability for each mutation and then averaged these Δ-scores at each position to produce a 5,001-score ISM importance profile per site.

Next, for each splice site we computed the Pearson correlation coefficient between the paired importance profiles from ensembled OSAI_MANE and ensembled SpliceAI. The median correlation was 0.857 for all splice sites. Ten additional zoom-in representative splice site DNA logo comparisons are provided in Supplementary Figure S23.

(2) The utility of the calibration method described is unclear. When thinking about a calibrated model for splicing, the expectation would be that the models' predicted splicing probabilities would match the true probabilities that positions with that level of prediction confidence are splice sites. However, the actual calibration that they perform only considers positions as splice sites if they are splice sites in the longest isoform of the gene included in the MANE annotation. In other words, they calibrate the model such that the model's predicted splicing probabilities match the probability that a position with that level of confidence is a splice site in one particular isoform for each gene, not the probability that it is a splice site more broadly. Their level of calibration on this set of splice sites may very well not hold to broader sets of splice sites, such as sites from all annotated isoforms, sites that are commonly used in cryptic splicing, or poised sites that can be activated by a variant. This is a particularly important point as much of the utility of SpliceAI comes from its ability to issue variant effect predictions, and they have not demonstrated that this calibration holds in the context of variants. This section could be improved by expanding and clarifying the discussion of what set of splice sites they have demonstrated calibration on, what it means to calibrate against this set of splice sites, and how this calibration is expected to hold or not for other interesting sets of splice sites. Alternatively, or in addition, they could demonstrate how well their calibration holds on different sets of splice sites or show the effect of calibrating their models against different potentially interesting sets of splice sites and discuss how the results do or do not differ.

We thank the reviewer for highlighting the need to clarify our calibration procedure. Both SpliceAI and OpenSpliceAI are trained on a single “canonical” transcript per gene: SpliceAI on the hg 19 Ensembl/Gencode canonical set and OpenSpliceAI on the MANE transcript set. To calibrate each model, we applied post-hoc temperature scaling, i.e. a single learnable parameter that rescales the logits before the softmax. This adjustment does not alter the model’s ranking or discrimination (AUC/precision–recall) but simply aligns the predicted probabilities for donor, acceptor, and non-splice classes with their observed frequencies. As shown in our reliability diagrams (Fig. S16-S22), temperature scaling yields negligible changes in performance, confirming that both SpliceAI and OpenSpliceAI were already well-calibrated. However, we acknowledge that we didn’t measure how calibration might affect predictions on non-canonical splice sites or on cryptic splicing. It is possible that calibration might have a detrimental effect on those, but because this is not a key claim of our paper, we decided not to do further experiments. We have updated the manuscript to acknowledge this potential shortcoming; please see the revised paragraph in our next response.

(3) It is difficult to assess how well their calibration method works in general because their original models are already well calibrated, so their calibration method finds temperatures very close to 1 and only produces very small and hard to assess changes in calibration metrics. This makes it very hard to distinguish if the calibration method works, as it doesn't really produce any changes. It would be helpful to demonstrate the calibration method on a model that requires calibration or on a dataset for which the current model is not well calibrated, so that the impact of the calibration method could be observed.

It’s true that the models we calibrated didn’t need many changes. It is possible that the calibration methods we used (which were not ours, but which were described in earlier publications) can’t improve the models much. We toned down our comments about this procedure, as follows.

Original:

“Collectively, these results demonstrate that OSAIs were already well-calibrated, and this consistency across species underscores the robustness of OpenSpliceAI’s training approach in diverse genomic contexts.” Revised:

“We observed very small changes after calibration across phylogenetically diverse species, suggesting that OpenSpliceAI’s training regimen yielded well‐calibrated models, although it is possible that a different calibration algorithm might produce further improvements in performance.”

Reviewer #2 (Public review):

Summary:

The paper by Chao et al offers a reimplementation of the SpliceAI algorithm in PyTorch so that the model can more easily/efficiently be retrained. They apply their new implementation of the SpliceAI algorithm, which they call OpenSpliceAI, to several species and compare it against the original model, showing that the results are very similar and that in some small species, pretraining on other species helps improve performance.

Strengths:

On the upside, the code runs fine, and it is well documented.

Weaknesses:

The paper itself does not offer much beyond reimplementing SpliceAI. There is no new algorithm, new analysis, new data, or new insights into RNA splicing. There is no comparison to many of the alternative methods that have since been published to surpass SpliceAI. Given that some of the authors are well-known with a long history of important contributions, our expectations were admittedly different. Still, we hope some readers will find the new implementation useful.

We thank the reviewer for the feedback. We have clarified that OpenSpliceAI is an open-source PyTorch reimplementation optimized for efficient retraining and transfer learning, designed to analyze cross-species performance gains, and supported by a thorough benchmark and the release of several pretrained models to clearly position our contribution.

Reviewer #3 (Public review):

Summary:

The authors present OpenSpliceAI, a PyTorch-based reimplementation of the well-known SpliceAI deep learning model for splicing prediction. The core architecture remains unchanged, but the reimplementation demonstrates convincing improvements in usability, runtime performance, and potential for cross-species application.

Strengths:

The improvements are well-supported by comparative benchmarks, and the work is valuable given its strong potential to broaden the adoption of splicing prediction tools across computational and experimental biology communities.

Major comments:

Can fine-tuning also be used to improve prediction for human splicing? Specifically, are models trained on other species and then fine-tuned with human data able to perform better on human splicing prediction? This would enhance the model's utility for more users, and ideally, such fine-tuned models should be made available.

We evaluated transfer learning by fine-tuning models pretrained on mouse (OSAI_Mouse), honeybee (OSAI_Honeybee), Arabidopsis (OSAI_Arabidopsis), and zebrafish (OSAI_Zebrafish) on human data. While transfer learning accelerated convergence compared to training from scratch, the final human splicing prediction accuracy was comparable between fine-tuned and scratch-trained models, suggesting that performance on our current human dataset is nearing saturation under this architecture.

We added the following paragraph to the Discussion section:

We also evaluated pretraining on mouse (OSAI_Mouse), honeybee (OSAI_Honeybee), zebrafish (OSAI_Zebrafish), and Arabidopsis (OSAI_Arabidopsis) followed by fine-tuning on the human MANE dataset. While cross-species pretraining substantially accelerated convergence during fine-tuning, the final human splicing-prediction accuracy was comparable to that of a model trained from scratch on human data. This result indicates that our architecture seems to capture all relevant splicing features from human training data alone, and thus gains little or no benefit from crossspecies transfer learning in this context (see Figure S24).

Reviewer #1 (Recommendations for the authors):

We thank the editor for summarizing the points raised by each reviewer. Below is our point-bypoint response to each comment:

(1) In Figure 3 (and generally in the other figures) OpenSpliceAI should be replaced with OSAI_{Training dataset} because otherwise it is hard to tell which precise model is being compared. And in Figure 3 it is especially important to emphasize that you are comparing a SpliceAI model trained on Human data to an OSAI model trained and evaluated on a different species.

We have updated the labels in Figures 3, replacing “OpenSpliceAI” with “OSAI_{training dataset}” to more clearly specify which model is being compared.

(2) Are genes paralogous to training set genes removed from the validation set as well as the test set? If you are worried about data leakage in the test set, it makes sense to also consider validation set leakage.

Thank you for this helpful suggestion. We fully agree, and to avoid any data leakage we implemented the identical filtering pipeline for both validation and test sets: we excluded all sequences paralogous or homologous to sequences in the training set, and further removed any sequence sharing > 80 % length overlap and > 80 % sequence identity with training sequences. The effect of this filtering on the validation set is summarized in Supplementary Figure S7C.

Figure S7. (C) Scatter plots of DNA sequence alignments between validation and training sets for Human-MANE, mouse, honeybee, zebrafish, and Arabidopsis. Each dot represents an alignment, with the x-axis showing alignment identity and the y-axis showing alignment coverage. Alignments exceeding 80% for both identity and coverage are highlighted in the redshaded region and were excluded from the test sets.

Reviewer #3 (Recommendations for the authors):

(1) The legend in Figure 3 is somewhat confusing. The labels like "SpliceAI-Keras (species name)" may imply that the model was retrained using data from that species, but that's not the case, correct?

Yes, “SpliceAI-Keras (species name)” was not retrained; it refers to the released SpliceAI model evaluated on the specified species dataset. We have revised the Figure 3 legends, changing “SpliceAI-Keras (species name)” to “SpliceAI-Keras” to clarify this.

(2) Please address the minor issues with the code, including ensuring the conda install works across various systems.

We have addressed the issues you mentioned. OpenSpliceAI is now available on Conda and can be installed with:  conda install openspliceai. 

The conda package homepage is at: https://anaconda.org/khchao/openspliceai We’ve also corrected all broken links in the documentation.

(3) Utility:

I followed all the steps in the Quick Start Guide, and aside from the issues mentioned below, everything worked as expected.

I attempted installation using conda as described in the instructions, but it was unsuccessful. I assume this method is not yet supported.

In Quick Start Guide: predict, the link labeled "GitHub (models/spliceai-mane/10000nt/)" appears to be incorrect. The correct path is likely "GitHub (models/openspliceaimane/10000nt/)".

In Quick Start Guide: variant (https://ccb.jhu.edu/openspliceai/content/quick_start_guide/quickstart_variant.html#quick-startvariant), some of the download links for input files were broken. While I was able to find some files in the GitHub repository, I think the -A option should point to data/grch37.txt, not examples/data/input.vcf, and the -I option should be examples/data/input.vcf, not data/vcf/input.vcf.

Thank you for catching these issues. We’ve now addressed all issues concerning Conda installation and file links. We thank the editor for thoroughly testing our code and reviewing the documentation.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This fundamental work employed multidisciplinary approaches and conducted rigorous experiments to study how a specific subset of neurons in the dorsal striatum (i.e., "patchy" striatal neurons) modulates locomotion speed depending on the valence of the naturalistic context.

Strengths:

The scientific findings are novel and original and significantly advance our understanding of how the striatal circuit regulates spontaneous movement in various contexts.

We appreciate the reviewer’s positive evaluation.

Weaknesses:

This is extensive research involving various circuit manipulation approaches. Some of these circuit manipulations are not physiological. A balanced discussion of the technical strengths and limitations of the present work would be helpful and beneficial to the field. Minor issues in data presentation were also noted.

We have incorporated the recommended discussion of technical limitations and addressed the physiological plausibility of our manipulations on Page 33 of the revised Discussion section. Specifically, we wrote:

“Judicious interpretation of the present data must consider the technical limitations of the various methods and circuit-level manipulations applied. Patchy neurons are distributed unevenly across the extensive structure of the striatum, and their targeted manipulation is constrained by viral spread in the dorsal striatum. Somatic calcium imaging using single-photon microscopy captures activity from only a subset of patchy neurons within a narrow focal plane beneath each implanted GRIN lens. Similarly, limitations in light diffusion from optical fibers may reduce the effective population of targeted fibers in both photometry and optogenetic experiments. For example, the more modest locomotor slowing observed with optogenetic activation of striatonigral fibers in the SNr compared to the stronger effects seen with Gq-DREADD activation across the dorsal striatum could reflect limited fiber optic coverage in the SNr. Alternatively, it may suggest that non-striatonigral mechanisms also contribute to generalized slowing. Our photometry data does not support a role for striatopallidal projections from patchy neurons in movement suppression. The potential contribution of intrastriatal mechanisms, discussed earlier, remains to be empirically tested. Although the behavioral assays used were naturalistic, many of the circuit-level interventions were not. Broad ablation or widespread activation of patchy neurons and their efferent projections represent non-physiological manipulations. Nonetheless, these perturbation results are interpreted alongside more naturalistic observations, such as in vivo imaging of patchy neuron somata and axon terminals, to form a coherent understanding of their functional role”.

Reviewer #2 (Public review):

Hawes et al. investigated the role of striatal neurons in the patch compartment of the dorsal striatum. Using Sepw1-Cre line, the authors combined a modified version of the light/dark transition box test that allows them to examine locomotor activity in different environmental valence with a variety of approaches, including cell-type-specific ablation, miniscope calcium imaging, fiber photometry, and opto-/chemogenetics. First, they found ablation of patchy striatal neurons resulted in an increase in movement vigor when mice stayed in a safe area or when they moved back from more anxiogenic to safe environments. The following miniscope imaging experiment revealed that a larger fraction of striatal patchy neurons was negatively correlated with movement speed, particularly in an anxiogenic area. Next, the authors investigated differential activity patterns of patchy neurons' axon terminals, focusing on those in GPe, GPi, and SNr, showing that the patchy axons in SNr reflect movement speed/vigor. Chemogenetic and optogenetic activation of these patchy striatal neurons suppressed the locomotor vigor, thus demonstrating their causal role in the modulation of locomotor vigor when exposed to valence differentials. Unlike the activation of striatal patches, such a suppressive effect on locomotion was absent when optogenetically activating matrix neurons by using the Calb1-Cre line, indicating distinctive roles in the control of locomotor vigor by striatal patch and matrix neurons. Together, they have concluded that nigrostriatal neurons within striatal patches negatively regulate movement vigor, dependent on behavioral contexts where motivational valence differs.

We are grateful for the reviewer’s thorough summary of our main findings.

In my view, this study will add to the important literature by demonstrating how patch (striosomal) neurons in the striatum control movement vigor. This study has applied multiple approaches to investigate their functionality in locomotor behavior, and the obtained data largely support their conclusions. Nevertheless, I have some suggestions for improvements in the manuscript and figures regarding their data interpretation, accuracy, and efficacy of data presentation.

We appreciate the reviewer’s overall positive assessment and have made substantial improvements to the revised manuscript in response to reviewers’ constructive suggestions. 

(1) The authors found that the activation of the striatonigral pathway in the patch compartment suppresses locomotor speed, which contradicts with canonical roles of the direct pathway. It would be great if the authors could provide mechanistic explanations in the Discussion section. One possibility is that striatal D1R patch neurons directly inhibit dopaminergic cells that regulate movement vigor (Nadal et al., Sci. Rep., 2021; Okunomiya et al., J Neurosci., 2025). Providing plausible explanations will help readers infer possible physiological processes and give them ideas for future follow-up studies.

We have added the recommended data interpretation and future perspectives on Page 30 of the revised Discussion section. Specifically, we wrote:

“Potential mechanisms by which striatal patchy neurons reduce locomotion involve the suppression of dopamine availability within the striatum. Dopamine, primarily supplied by neurons in the SNc and VTA, broadly facilitates locomotion (Gerfen and Surmeier 2011, Dudman and Krakauer 2016). Recent studies have shown that direct activation of patchy neurons leads to a reduction in striatal dopamine levels, accompanied by decreased walking speed (Nadel, Pawelko et al. 2021, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Patchy neuron projections terminate in structures known as “dendron bouquets”, which enwrap SNc dendrites within the SNr and can pause tonic dopamine neuron firing (Crittenden, Tillberg et al. 2016, Evans, Twedell et al. 2020). The present work highlights a role for patchy striatonigral inputs within the SN in decelerating movement, potentially through GABAergic dendron bouquets that limit dopamine release back to the striatum (Dong, Wang et al. 2025). Additionally, intrastriatal collaterals of patch spiny projection neurons (SPNs) have been shown to suppress dopamine release and associated synaptic plasticity via dynorphin-mediated activation of kappa opioid receptors on dopamine terminals (Hawes, Salinas et al. 2017). This intrastriatal mechanism may further contribute to the reduction in striatal dopamine levels and the observed decrease in locomotor speed, representing a compelling avenue for future investigation.”

(2) On page 14, Line 301, the authors stated that "Cre-dependent mCheery signals were colocalized with the patch marker (MOR1) in the dorsal striatum (Fig. 1B)". But I could not find any mCherry on that panel, so please modify it.

We have included representative images of mCherry and MOR1 staining in Supplementary Fig. S1 of the revised manuscript.

(3) From data shown in Figure 1, I've got the impression that mice ablated with striatal patch neurons were generally hyperactive, but this is probably not the case, as two separate experiments using LLbox and DDbox showed no difference in locomotor vigor between control and ablated mice. For the sake of better interpretation, it may be good to add a statement in Lines 365-366 that these experiments suggest the absence of hyperactive locomotion in general by ablating these specific neurons.

As suggested by the reviewer, we have added the following statement on Page 17 of the revised manuscript: “These data also indicate that PA elevates valence-specific speed without inducing general hyperactivity”.

(4) In Line 536, where Figure 5A was cited, the author mentioned that they used inhibitory DREADDs (AAV-DIO-hM4Di-mCherrry), but I could not find associated data on Figure 5. Please cite Figure S3, accordingly.

We have added the citation for the now Fig. S4 on Page 25 of the revised manuscript.

(5) Personally, the Figure panel labels of "Hi" and "ii" were confusing at first glance. It would be better to have alternatives.

As suggested by the reviewer, we have now labeled each figure panel with a distinct single alphabetical letter.

(6) There is a typo on Figure 4A: tdTomata → tdTomato

We have made the correction on the figure.

Reviewer #3 (Public review):

Hawes et al. combined behavioral, optical imaging, and activity manipulation techniques to investigate the role of striatal patch SPNs in locomotion regulation. Using Sepw1-Cre transgenic mice, they found that patch SPNs encode locomotion deceleration in a light-dark box procedure through optical imaging techniques. Moreover, genetic ablation of patch SPNs increased locomotion speed, while chemogenetic activation of these neurons decreased it. The authors concluded that a subtype of patch striatonigral neurons modulates locomotion speed based on external environmental cues. Below are some major concerns:

The study concludes that patch striatonigral neurons regulate locomotion speed. However, unless I missed something, very little evidence is presented to support the idea that it is specifically striatonigral neurons, rather than striatopallidal neurons, that mediate these effects. In fact, the optogenetic experiments shown in Fig. 6 suggest otherwise. What about the behavioral effects of optogenetic stimulation of striatonigral versus striatopallidal neuron somas in Sepw1-Cre mice?

Our photometry data implicate striatonigral neurons in locomotor slowing, as evidenced by a negative cross-correlation with acceleration and a negative lag, indicating that their activity reliably precedes—and may therefore contribute to—deceleration. In contrast, photometry results from striatopallidal neurons showed no clear correlation with speed or acceleration.

Figure 6 demonstrates that optogenetic manipulation within the SNr of Sepw1-Cre⁺ striatonigral axons recapitulated context-dependent locomotor changes seen with Gq-DREADD activation of both striatonigral and striatopallidal Sepw1-Cre⁺ cells in the dorsal striatum but failed to produce the broader locomotor speed change observed when targeting all Sepw1-Cre⁺ cells in the dorsal striatum using either ablation or Gq-DREADD activation. The more subtle speed-restrictive phenotype resulting from ChR activation in the SNr could, as the reviewer suggests, implicate striatopallidal neurons in broad locomotor speed regulation. However, our photometry data indicate that this scenario is unlikely, as activity of striatopallidal Sepw1-Cre⁺ fibers is not correlated with locomotor speed. Another plausible explanation is that the optogenetic approach may have affected fewer striatonigral fibers, potentially due to the limited spatial spread of light from the optical fiber within the SNr. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with optogenetics. We have added discussion of these technical limitations to the revised manuscript. Additionally, we now discuss the possibility that intrastriatal collaterals may contribute to reduced local dopamine levels by releasing dynorphin, which acts on kappa opioid receptors located on dopamine fibers (Hawes, Salinas et al. 2017), thereby suppressing dopamine release.

The reviewer also suggests an interesting experiment involving optogenetic stimulation of striatonigral versus striatopallidal somata in Sepw1-Cre mice. While we agree that this approach would yield valuable insights, we have thus far been unable to achieve reliable results using retroviral vectors. Moreover, selectively targeting striatopallidal terminals optogenetically remains technically challenging, as striatonigral fibers also traverse the pallidum, and the broad anatomical distribution of the pallidum complicates precise targeting. This proposed work will need to be pursued in a future study, either with improved retrograde viral tools or the development of additional mouse lines that offer more selective access to these neuronal populations as we documented recently (Dong, Wang et al. 2025).

In the abstract, the authors state that patch SPNs control speed without affecting valence. This claim seems to lack sufficient data to support it. Additionally, speed, velocity, and acceleration are very distinct qualities. It is necessary to clarify precisely what patch neurons encode and control in the current study.

We believe the reviewer’s interpretation pertains to a statement in the Introduction rather than the Abstract: “Our findings reveal that patchy SPNs control the speed at which mice navigate the valence differential between high- and low-anxiety zones, without affecting valence perception itself.” Throughout our study, mice consistently preferred the dark zone in the Light/Dark box, indicating intact perception of the valence differential between illuminated areas. While our manipulations altered locomotor speed, they did not affect time spent in the dark zone, supporting the conclusion that valence perception remained unaltered. We appreciate the reviewer’s insight and agree it is an intriguing possibility that locomotor responses could, over time, influence internal states such as anxiety. We addressed this in the Discussion, noting that while dark preference was robust to our manipulations, future studies are warranted to explore the relationship between anxious locomotor vigor and anxiety itself.

We report changes in scalar measures of animal speed across Light/Dark box conditions and under various experimental manipulations. Separately, we show that activity in both patchy neuron somata and striatonigral fibers is negatively correlated with acceleration—indicating a positive correlation with deceleration. Notably, the direction of the cross-correlational lag between striatonigral fiber activity and acceleration suggests that this activity precedes and may causally contribute to mouse deceleration, thereby influencing reductions in speed. To clarify this, we revised a sentence in the Results section: “Moreover, patchy neuron efferent activity at the SNr may causally contribute to deceleration, as indicated by the negative cross-correlational lag, thereby reducing animal speed.”. We also updated the Discussion to read: “Together, these data specifically implicate patchy striatonigral neurons in slowing locomotion by acting within the SNr to drive deceleration.”

One of the major results relies on chemogenetic manipulation (Figure 5). It would be helpful to demonstrate through slice electrophysiology that hM3Dq and hM4Di indeed cause changes in the activity of dorsal striatal SPNs, as intended by the DREADD system. This would support both the positive (Gq) and negative (Gi) findings, where no effects on behavior were observed.

We were unable to perform this experiment; however, hM3Dq has previously been shown to be effective in striatal neurons (Alcacer, Andreoli et al. 2017). The lack of effect observed in Gi-DREADD mice serves as an unintended but valuable control, helping to rule out off-target effects of the DREADD agonist JHU37160 and thereby reinforcing the specificity of hM3Dq-mediated activation in our study. We have now included an important caveat regarding the Gi-DREADD results, acknowledging the possibility that they may not have worked effectively in our target cells: “Potential explanations for the negative results in Gi-DREADD mice include inherently low basal activity among patchy neurons or insufficient expression of GIRK channels in striatal neurons, which may limit the effectiveness of Gi-coupling in suppressing neuronal activity (Shan, Fang et al. 2022).

Finally, could the behavioral effects observed in the current study, resulting from various manipulations of patch SPNs, be due to alterations in nigrostriatal dopamine release within the dorsal striatum?

We agree that this is an important potential implication of our work, especially given that we and others have shown that patchy striatonigral neurons provide strong inhibitory input to dopaminergic neurons involved in locomotor control (Nadel, Pawelko et al. 2021, Lazaridis, Crittenden et al. 2024, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Accordingly, we have expanded the discussion section to include potential mechanistic explanations that support and contextualize our main findings.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Here are some minor issues for the authors' reference:

(1) This work supports the motor-suppressing effect of patchy SPNs, and >80% of them are direct pathway SPNs. This conclusion is not expected from the traditional basal ganglia direct/indirect pathway model. Most experiments were performed using nonphysiological approaches to suppress (i.e., ablation) or activate (i.e., continuous chemo-optogenetic stimulation). It remains uncertain if the reported observations are relevant to the normal biological function of patchy SPNs under physiological conditions. Particularly, under what circumstances an imbalanced patch/matrix activity may be induced, as proposed in the sections related to the data presented in Figure 6. A thorough discussion and clarification remain needed. Or it should be discussed as a limitation of the present work.

We have added discussion and clarification of physiological limitations in response to reviewer feedback. Additionally, we revised the opening sentence of an original paragraph in the discussion section to emphasize that it interprets our findings in the context of more physiological studies reporting natural shifts in patchy SPN activity due to cognitive conflict, stress, or training. The revised opening sentence now reads: “Together with previous studies of naturally occurring shifts in patchy neuron activation, these data illustrate ethologically relevant roles for a subgroup of genetically defined patchy neurons in behavior.”

(2) Lines 499-500: How striato-nigral cells encode speed and deceleration deserves a thorough discussion and clarification. These striatonigral cells can target both SNr GABAergic neurons and dendrites of the dopaminergic neurons. A discussion of microcircuits formed by the patchy SPNs axons in the SNr GABAergic and SNC DAergic neurons should be presented.

We have added this point at lines 499–500, including a reference to a relevant review of microcircuitry. Additionally, we expanded the discussion section to address microcircuit mechanisms that may underlie our main findings.

(3) Line 70: "BNST" should be spelled out at the first time it is mentioned.

This has been done.

(4) Line 133: only GCaMP6 was listed in the method, but GCaMP8 was also used (Figure 4). Clarification or details are needed.

Thank you for your careful attention to detail. We have corrected the typographical errors in the Methods section. Specifically, in the Stereotaxic Injections section, we corrected “GCaMP83” to “GCaMP8s.” In the Fiber Implant section, we removed the incorrect reference to “GCaMP6s” and clarified that GCaMP8s was used for photometry, and hChR2 was used for optogenetics.

(5) Line 183: Can the authors describe more precisely what "a moment" means in terms of seconds or minutes?

This has been done.

(6) Line 288: typo: missing / in ΔF.

Thank you this has been fixed.

(7) Line 301-302: the statement of "mCherry and MOR1 colocalization" does not match the images in Figure 1B.

This has been corrected by proving a new Supplementary Figure S1.

(8) Related to the statement between Lines 303-304: Figure 1c data may reflect changes in MOR1 protein or cell loss. Quantification of NeuN+ neurons within the MOR1 area would strengthen the conclusion of 60% of patchy cell loss in Figure 1C.

Since the efficacy of AAV-FLEX-taCasp3 in cell ablation has been well established in our previous publications and those of others (Yang, Chiang et al. 2013, Wu, Kung et al. 2019), we do not believe the observed loss of MOR1 staining in Fig. 1C merely reflects reduced MOR1 expression. Moreover, a general neuronal marker such as NeuN may not reliably detect the specific loss of patchy neurons in our ablation model, given the technical limitations of conventional cell-counting methods like MBF’s StereoInvestigator, which typically exhibit a variability margin of 15–20%.

(9) Lines 313-314: "Similarly, PA mice demonstrated greater stay-time in the dark zone (Figure 1E)." Revision is needed to better reflect what is shown in Figure 1E and avoid misunderstandings.

Thank you this has been addressed.

(10) The color code in Figure 2Gi seems inconsistent with the others? Clarifications are needed.

Color coding in Figure 2Gi differs from that in 2Eii out of necessity. For example, the "Light" cells depicted in light blue in 2Eii are represented by both light gray and light red dots in 2Gi. Importantly, Figure 2G does not encode specific speed relationships; instead, any association with speed is indicated by a red hue.

(11) Lines 538-539: the statement of "Over half of the patch was covered" was not supported by Figure 5C. Clarification is needed.

Thank you. For clarity, we updated the x-axis labels in Figures 1C and 5C from “% area covered” to “% DS area covered,” and defined “DS” as “dorsal striatal” in the corresponding figure legends. Additionally, we revised the sentence in question to read: “As with ablation, histological examination indicated that a substantial fraction of dorsal patch territories, identified through MOR1 staining, were impacted (Fig. 5C).”

(12) Figure 3: statistical significance in Figure 3 should be labeled in various panels.

We believe the reviewer's concern pertains to the scatter plot in panel F—specifically, whether the data points are significantly different from zero. In panel 3F, the 95% confidence interval clearly overlaps with zero, indicating that the results are not statistically significant.

(13) Figures 6D-E: no difference in the speed of control mice and ChR2 mice under continuous optical stimulation was not expected. It was different from Gq-DRADDS study in Figure 5E-F. Clarifications are needed.

For mice undergoing constant ChR2 activation of Sepw1-Cre⁺ SNr efferents, overall locomotor speed does not differ from controls. However, the BIL (bright-to-illuminated) effect on zone transitions is disrupted: activating Sepw1-Cre⁺ fibers in the SNr blunts the typical increase in speed observed when mice flee from the light zone toward the dark zone. This impaired BIL-related speed increase upon exiting the light was similarly observed in the Gq-DREADD cohort. The reviewer is correct that this optogenetic manipulation within the SNr did not produce the more generalized speed reductions seen with broader Gq-DREADD activation of all Sepw1-Cre⁺ cells in the dorsal striatum. A likely explanation is the difference in targeting—ChR2 specifically activates SNr-bound terminals, whereas Gq-DREADD broadly activates entire Sepw1-Cre⁺ cells. Notably, many of the generalized speed profile changes observed with chemogenetic activation are opposite to those resulting from broad ablation of Sepw1-Cre⁺ cells.

The more subtle speed-restrictive phenotype observed with ChR2 activation targeted to the SNr may suggest that fewer striatonigral fibers were affected by this technique, possibly due to the limited spread of light from the fiber optic. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with an optogenetic approach. Alternatively, it could indicate that non-striatonigral Sepw1-Cre+ projections—such as striatopallidal or intrastriatal pathways—play a role in more generalized slowing. If striatopallidal fibers contributed to locomotor slowing, we would expect to see non-zero cross-correlations between neural activity and speed or acceleration, along with negative lag indicating that neural activity precedes the behavioral change. However, our fiber photometry data do not support such a role for Sepw1-Cre+ striatopallidal fibers.

We have also referenced the possibility that intrastriatal collaterals could suppress striatal dopamine levels, potentially explaining the stronger slowing phenotype observed when the entire striatal population is affected, as opposed to selectively targeting striatonigral terminals.

These technical considerations and interpretive nuances have been incorporated and clarified in the revised discussion section.

(14) Lines 632: "compliment": a typo?

Yes, it should be “complement”.

(15) Figure 4 legend: descriptions of panels A and B were swapped.

Thank you. This has been corrected.

6) Friedman (2020) was listed twice in the bibliography (Lines 920-929).

Thank you. This has been corrected.

Reviewer #3 (Recommendations for the authors):

It will be helpful to label and add figure legends below each figure.

Thank you for the suggestion.

Editor's note:

Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript. We noted some instances where only p values are reported.

Readers would also benefit from coding individual data points by sex and noting N/sex.

We have included detailed statistical information in the revised manuscript. Both male and female mice were used in all experiments in approximately equal numbers. Since no sex-related differences were observed, we did not report the number of animals by sex.

References

Alcacer, C., L. Andreoli, I. Sebastianutto, J. Jakobsson, T. Fieblinger and M. A. Cenci (2017). "Chemogenetic stimulation of striatal projection neurons modulates responses to Parkinson's disease therapy." J Clin Invest 127(2): 720-734.

Crittenden, J. R., P. W. Tillberg, M. H. Riad, Y. Shima, C. R. Gerfen, J. Curry, D. E. Housman, S. B. Nelson, E. S. Boyden and A. M. Graybiel (2016). "Striosome-dendron bouquets highlight a unique striatonigral circuit targeting dopamine-containing neurons." Proc Natl Acad Sci U S A 113(40): 11318-11323.

Dong, J., L. Wang, B. T. Sullivan, L. Sun, V. M. Martinez Smith, L. Chang, J. Ding, W. Le, C. R. Gerfen and H. Cai (2025). "Molecularly distinct striatonigral neuron subtypes differentially regulate locomotion." Nat Commun 16(1): 2710.

Dudman, J. T. and J. W. Krakauer (2016). "The basal ganglia: from motor commands to the control of vigor." Curr Opin Neurobiol 37: 158-166.

Evans, R. C., E. L. Twedell, M. Zhu, J. Ascencio, R. Zhang and Z. M. Khaliq (2020). "Functional Dissection of Basal Ganglia Inhibitory Inputs onto Substantia Nigra Dopaminergic Neurons." Cell Rep 32(11): 108156.

Gerfen, C. R. and D. J. Surmeier (2011). "Modulation of striatal projection systems by dopamine." Annual review of neuroscience 34: 441-466.

Hawes, S. L., A. G. Salinas, D. M. Lovinger and K. T. Blackwell (2017). "Long-term plasticity of corticostriatal synapses is modulated by pathway-specific co-release of opioids through kappa-opioid receptors." J Physiol 595(16): 5637-5652.

Lazaridis, I., J. R. Crittenden, G. Ahn, K. Hirokane, T. Yoshida, A. Mahar, V. Skara, K. Meletis, K. Parvataneni, J. T. Ting, E. Hueske, A. Matsushima and A. M. Graybiel (2024). "Striosomes Target Nigral Dopamine-Containing Neurons via Direct-D1 and Indirect-D2 Pathways Paralleling Classic Direct-Indirect Basal Ganglia Systems." bioRxiv.

Nadel, J. A., S. S. Pawelko, J. R. Scott, R. McLaughlin, M. Fox, M. Ghanem, R. van der Merwe, N. G. Hollon, E. S. Ramsson and C. D. Howard (2021). "Optogenetic stimulation of striatal patches modifies habit formation and inhibits dopamine release." Sci Rep 11(1): 19847.

Okunomiya, T., D. Watanabe, H. Banno, T. Kondo, K. Imamura, R. Takahashi and H. Inoue (2025). "Striosome Circuitry Stimulation Inhibits Striatal Dopamine Release and Locomotion." J Neurosci 45(4).

Shan, Q., Q. Fang and Y. Tian (2022). "Evidence that GIRK Channels Mediate the DREADD-hM4Di Receptor Activation-Induced Reduction in Membrane Excitability of Striatal Medium Spiny Neurons." ACS Chem Neurosci 13(14): 2084-2091.

Wu, J., J. Kung, J. Dong, L. Chang, C. Xie, A. Habib, S. Hawes, N. Yang, V. Chen, Z. Liu, R. Evans, B. Liang, L. Sun, J. Ding, J. Yu, S. Saez-Atienzar, B. Tang, Z. Khaliq, D. T. Lin, W. Le and H. Cai (2019). "Distinct Connectivity and Functionality of Aldehyde Dehydrogenase 1a1-Positive Nigrostriatal Dopaminergic Neurons in Motor Learning." Cell Rep 28(5): 1167-1181 e1167.

Yang, C. F., M. C. Chiang, D. C. Gray, M. Prabhakaran, M. Alvarado, S. A. Juntti, E. K. Unger, J. A. Wells and N. M. Shah (2013). "Sexually dimorphic neurons in the ventromedial hypothalamus govern mating in both sexes and aggression in males." Cell 153(4): 896-909.

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

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

Na promoção do trabalho colaborativo é muito importante que os alunos se conheçam, ainda que de uma forma virtual, e sintam que fazem parte de um grupo. Assim, julgo extremamente importante uma sessão sincrona em que todos se apresentem e manifestem quais sao os objectivos individuais de participação na formação. Penso que a primeira atividade da Microcredencial de Educação à distância: Docência digital em rede, muito teria ganho com uma sessão síncrona de apresentação dos diversos elementos do curso.

la seconde notion

Reviewer #3 (Public review):

Summary:

This work aims to understand the role of Echinoderm Microtubule-associated Protein-like 3 (EML3) in embryogenesis and neocortical development. Importantly, this work shows that depletion of EML3 causes focal neuronal ectopias by disrupting the structural integrity of the pial basement membrane, describing a new model of cobblestone brain malformation. Another member of the EML family, EML1, has already been shown to trigger neuronal migration disorders, particularly subcortical band heterotopia, by affecting cell polarity. The results presented here point to a different mechanism of action. The authors show that EML3 is expressed in radial glia cells and mesenchymal cells in the pial region, and upon EML3 depletion (i.e., Eml3 mutant mice), the pial basement membrane is structurally damaged, allowing migrating neuroblasts to ectopically migrate through. Answering, in this case, that the weakening of the pial basement membrane is a prerequisite for focal neuronal ectopias. The authors provide a meticulous characterization of the Eml3 mutant mice, strengthening the conclusions of the results.

Strengths:

The authors provide a very detailed analysis of the defects observed in Eml3 mutant mice, by providing not only results by inferred day of conception but also by classifying embryos by their number of somite pairs.

Weaknesses:

(1) Besides the data provided in the figures, the authors report a significant amount of experiments/results as "Data not shown". Negative data is still important data to report, and the authors may want to choose some crucial "not shown data" to report in the manuscript.

(2) Results in Figure 3A apparently contradict results in 3B. A better explanation of the results should improve understanding of the data. Even though the conclusion that the "onset and progression of neurogenesis is normal in Eml3 null mice" seems logical based on the data, the final numbers are not (Figure 3A) and this should be acknowledged, as well.

(3) The authors should define which cell types are identified by SOX1 and PAX6.

Author response:

Reviewer #1 (Public Review):

The study would benefit from clearer evidence and additional experiments that would help to establish the molecular and cellular mechanisms underlying the brain phenotype, the central topic of the work.

We agree that additional experiments are necessary to elucidate the mechanism(s) by which EML3 deficiency causes the observed developmental phenotypes. However, as no further experimentation is possible due to the closure of our laboratory, we are committed to sharing available materials—including custom antibodies and cryopreserved sperm from our mouse lines. We will include previously generated experimental data not presented in the original submission. While these additional data do not reveal the mechanisms, we believe that sharing hypotheses that were experimentally ruled out will benefit the scientific community.

Reviewer #2 (Public Review):

While the manuscript presents valuable data, there are also several weaknesses that limit the overall impact of the study. Most notably, there is no clear mechanistic link established between the loss of Eml3 function and the observed phenotype, leaving the biological significance of the findings somewhat speculative, as it is not straightforward how a microtubule-associated protein can have an impact on the stability of the pial basement membrane. In this respect, but also in general for the whole manuscript, there seems to be a considerable amount of experimental work that has been conducted but is not presented, possibly due to the negative nature of the results. At least some of those results could be shown, particularly (but not only) the stainings for the composition of the ECM components.

We agree that additional experiments are necessary to elucidate the mechanisms at play. While we cannot conduct further experiments, we will include additional existing data, including supplemental ECM component staining, in a new figure or panel. As this reviewer rightly anticipated, these results might not clarify the mechanism but sharing the hypotheses that were already experimentally tested will be helpful.

Additionally, the phenotype reported appears to be dependent on the genetic background, as it is absent in the CD1 strain. This observation raises concerns as to how robust the results are and how much they can be generalized to other mouse strains, but, more importantly, to humans.

Indeed, we have determined that genetic background greatly influences the manifestation of developmental defects caused by absence or mutation of the EML3 protein in mice. Modifier genes appear to play a significant role in phenotypic expression. In humans, the presence or absence of such modifiers may result in a broad spectrum of outcomes—from no clinical relevance, as seen in CD1 mice, to potential intrauterine mortality. We agree that this underscores the challenge of translating mouse model findings to human implications. Future studies could include a search for EML3 non-coding regulatory mutations and expanded analysis of neuronal development defects, such as COB, as well as cases of intrauterine growth restriction (IUGR).

There is no data included in the manuscript about the generation and analysis of the Eml3AAA/AAA mouse line. This is an important omission, especially as no details on the validation or phenotypic characterization of this additional mouse line are provided. Including these elements would greatly strengthen the rigor and interpretability of the work, especially if that mouse line is to be shared with the scientific community.

We acknowledge this oversight and will add a Materials and Methods section describing the generation of Eml3 TQT86AAA mice as well as validation and phenotypic characterizations that were done for that mouse line.

Reviewer #3 (Public Review):

Besides the data provided in the figures, the authors report a significant amount of experiments/results as "Data not shown". Negative data is still important data to report, and the authors may want to choose some crucial "not shown data" to report in the manuscript.

We will incorporate key datasets previously omitted, with priority given to those requested by Reviewer #2.

Results in Figure 3A apparently contradict results in 3B. A better explanation of the results should improve understanding of the data. Even though the conclusion that the "onset and progression of neurogenesis is normal in Eml3 null mice" seems logical based on the data, the final numbers are not (Figure 3A) and this should be acknowledged, as well.

We will provide further explanations for the data presented in figures 3A and 3B to better convey the fact that the two datasets are not contradicting. In essence, since Eml3 null mice are developmentally delayed (as determined by the number of somites at a specific age, Fig. 1C), the milestones in neurogenesis are reached at a later age in Eml3 null mice (Fig. 3A). However, Eml3 null mice have reached the same neurogenesis milestones as their WT counterparts when they have the same number of somites (Fig. 3B).

The authors should define which cell types are identified by SOX1 and PAX6.

We will expand our manuscript to define the expression timing and cell identity marked by SOX1 and PAX6 in neural progenitors during cortical development.

Mettre au passé

Préciser fin de la cotutelle d'ici la fin de l'année

remplacer par : dans la vie de l'université Paris 8, l'EUR ArTeC et l'université Européenne ERUA https://www.univ-paris8.fr/-Universite-europeenne-ERUA-

DOI: 10.1186/s12933-025-02815-4

Resource: (R and D Systems Cat# AF-278-NA, RRID:AB_354440)

Curator: @scibot

SciCrunch record: RRID:AB_354440

What is this?

DOI: 10.1016/j.neuron.2025.06.015

Resource: (Thermo Fisher Scientific Cat# A-11039, RRID:AB_2534096)

Curator: @scibot

SciCrunch record: RRID:AB_2534096

What is this?

DOI: 10.1016/j.celrep.2025.115927

Resource: Human Islet Research Network (HIRN) (RRID:SCR_014393)

Curator: @scibot

SciCrunch record: RRID:SCR_014393

What is this?

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

Teachers aware of such a thing are now shocked and wondering how they would be able to handle such a serious and planned act and psychological support is now offered.

The student confessed his plans to attack the teacher along with the help of two other classmates. The three students along with their parents met with the school administration. The teachers may pursue legal action, but nothing has been filed yet.

The student's fellow classmate reported the threat to a teaching assistant which led to the staff searching the student's locked and finding the knives.

Label ba in ast

The previous long-term effects of the Earth's reduction of rotation doesn't affect it from being able to increase its rotation in the future, as seen recently.

When dinosaurs were alive, the days were 23.5 hours long, but because of the tidal friction against the earth, the earth has been slowly losing energy, resulting in longer days

But when the moon is closer to the Earth’s equator, it can cause the rotation of the Earth to speed up.

This usually effects the tides and the elevation of the moon, but it’s speculated if atmospheric factors are strong enough to have an effect on the rotation of the Earth.

There are several possibilities that can explain this but one of the could be atmospheric factors, or wind effects.

Last year, the shortest day was 1.66 milliseconds shorter than normal.

This shortening of day happens consistently every year, and the shortest day we’ve had this year was 1.5 milliseconds shorter than the regular 24-hour day.

The Earth's rotational speed has changed three times this year and is expected to happen 2 more times in the near future.

Summary: The Earth's rotational speed hasn't been consistent this year, and while there are multiple possibilities that can explain why this is, scientist have yet to find the causation of this effect.

Translation: The Earth

Title

"que embora tenha autopublicação como objeto, confirma que os estudos sobre Library Publishing em outras regiões na América Latina são quase inexistentes."

Author Response:

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

Reviewer #1 (Public review):

Summary: 

During early Drosophila pupal development, a subset of larval abdominal muscles (DIOMs) is remodelled using an autophagy-dependent mechanism. 

To better understand this not very well studied process, the authors have generated a transcriptomics time course using dissected abdominal muscles of various stages from wild-type and autophagy-deficient mutants. The authors have further identified a function for BNIP3 in muscle mitophagy using this system. 

Strengths: 

(1) The paper does provide a detailed mRNA time course resource for DIOM remodeling. 

(2) The paper does find an interesting BNIP3 loss of function phenotype, a block of mitophagy during muscle remodeling, and hence identifies a specific linker between mitochondria and the core autophagy machinery. This adds to the mechanism of how mitochondria are degraded. 

(3) Sophisticated fly genetics demonstrates that the larval muscle mitochondria are, to a large extent, degraded by autophagy during DIOM remodeling. 

Weaknesses: 

(1) Mitophagy during DIOM remodeling is not novel (earlier papers from Fujita et al.). 

(2) The transcriptomics time course data are not well connected to the autophagy part. Both could be separated into 2 independent manuscripts. 

(3) The muscle phenotypes need better quantifications, both for the EM and light microscopy data in various figures. 

(4) The transcriptomics data are hard to browse in the provided PDF format. 

Thank you for reviewing our manuscript and for your feedback. While we understand and appreciate the suggestion to divide the manuscript into two separate studies, we believe that presenting the work as a single manuscript is more appropriate. This is because the time-course RNA-seq of DIOMs provides critical insight into BNIP3-mediated mitophagy during DIOM remodeling, which ties together the two components of our study. In response to Reviewer #1’s recommendations, we have quantified data from both EM and confocal images, and we have revised the RNA counts table in Supplementary File 1 accordingly. Please see our detailed responses and revisions on the following pages.

Reviewer #2 (Public review): 

Summary: 

Autophagy (macroautophagy) is known to be essential for muscle function in flies and mammals. To date, many mitophagy (selective mitochondrial autophagy) receptors have been identified in mammals and other species. While the loss of mitophagy receptors has been shown to impair mitochondrial degradation (e.g., OPTN and NDP52 in Parkin-mediated mitophagy and NIX and BNIP3 in hypoxia-induced mitophagy) at the level of cultured cells, it remains unclear, especially under physiological conditions in vivo. In this study, the authors revealed that one of the receptors BNIP3 plays a critical role in mitochondrial degradation during muscle remodeling in vivo. 

Overall, the manuscript provides solid evidence that BNIP3 is involved in mitophagy during muscle remodeling with in vivo analyses performed. In particular, all experiments in this study are well-designed. The text is well written and the figures are very clear. 

Strengths: 

(1) In each experiment, appropriate positive and negative controls are used to indicate what is responsible for the phenomenon observed by the authors: e.g. FIP200, Atg18, Stx17 siRNAs during DIOM remodeling in Figure 2 and Full, del-LIR, del-MER in Figure 5. 

(2) Although the transcriptional dynamics of DIOM remodeling during metamorphosis is autophagy-independent, the transcriptome data obtained by the authors would be valuable for future studies. 

(3) In addition to the simple observation that loss of BNIP3 causes mitochondrial accumulation, the authors further observed that, by combining siRNA against STX17, which is required for fusion of autophagosomes with lysosomes, BNIP3 KO abolishes mitophagosome formation, which will provide solid evidence for BNIP3-mediated mitophagy. Furthermore, using a Gal80 temperature-sensitive approach, the authors showed that mitochondria derived from larval muscle, but not those synthesized during hypertrophy, remain in BNIP3 KO fly muscles. 

Weaknesses: 

(1) Because BNIP3 KO causes mitochondrial accumulation, it is expected that adult flies will have some physiological defects, but this has not been fully analyzed or sufficiently mentioned in the manuscript. 

(2) In Figure 5, the authors showed that BNIP3 binds to Atg18a by co-IP, but no data are provided on whether MER-mut or del-MER attenuates the affinity for Atg18a. 

Thank you for pointing out the critical issues in the previous version of our manuscript. In this revision, we have conducted several physiological assays using BNIP3 KO flies, as well as co-IP experiments to confirm that the DMER weakens the interaction with Atg18a. We have also addressed all the recommendations provided. Please see our detailed point-by-point responses below.

Reviewer #3 (Public review): 

Summary: 

Fujita et al build on their earlier, 2017 eLife paper that showed the role of autophagy in the developmental remodeling of a group of muscles (DIOM) in the abdomen of Drosophila. Most larval muscles undergo histolysis during metamorphosis, while DIOMs are programmed to regrow after initial atrophy to give rise to temporary adult muscles, which survive for only 1 day after eclosion of the adult flies (J Neurosci. 1990;10:403-1. and BMC Dev Biol 16, 12, 2016). The authors carry out transcriptomics profiling of these muscles during metamorphosis, which is in agreement with the atrophy and regrowth phases of these muscles. Expression of the known mitophagy receptor BNIP3/NIX is high during atrophy, so the authors have started to delve more into the role of this protein/mitophagy in their model. BNIP3 KO indeed impairs mitophagy and muscle atrophy, which they convincingly demonstrate via nice microscopy images. They also show that the already known Atg8a-binding LIR and Atg18a-binding MER motifs of human NIX are conserved in the Drosophila protein, although the LIR turned out to be less critical for in vivo protein function than the MER motif. 

Strengths: 

Established methodology, convincing data, in vivo model. 

Weaknesses: 

The significance for Drosophila physiology and for human muscles remains to be established. 

Thank you for reviewing our manuscript. In response to the comment, we have performed lifespan, adult locomotion, and eclosion assays in BNIP3 KO flies. Although we observed substantial mitochondrial accumulation in the DIOMs of BNIP3 KO flies, no significant differences were detected in these physiological assays under our experimental conditions. We plan to further investigate the physiological role of BNIP3 in flies and extend our studies to human muscle in future work. Please see our detailed responses below.

Reviewer #1 (Recommendations for the authors): 

Major points: 

(1) Unfortunately, the RNA counts file table in Supplementary file 1 is a PDF and not an Excel sheet. The labelling makes it unclear from which time points and genotype the listed values on the 650-page files are. 

We have now corrected the labelling of time points and genotypes in Supplementary File 1 to improve clarity and have provided the updated Excel file.

Looking at these counts it seems that sarcomere genes (Mhc, bt, sls, wupA, TpnC ) are 10x to 100x lower in sample "ctrl_1" compared to the three other control samples. Which time point is that? It is essential to have access to the full dataset, wild type and autophagy-deficient, to be able to assess the quality of the RNA SEQ data. These need to be deposited in a public database or to be provided in a useful format. 

Thank you for pointing that out. In the previous version, “Ctrl_1” referred to the Control sample at 1 day APF, when atrophy occurs. We have corrected the labeling in Supplementary File 1 accordingly and have deposited the RNA-seq data to GEO, where it is now publicly available (GSE293359).

(2) Which statistical test was used to assess the differences in muscle volumes in Figure 2E? I was not able to find a table with the measured data.

In Figure 2E, we used the Mann-Whitney test for statistical analysis. The raw data used for quantification have also been provided (Supplementary File 2).

The shown volumes do not correlate with the scheme shown in Figure 2A, in particular at the larval stage the muscle seems much larger.

We have revised the schematic models of muscle cells in Figures 1C and 2A in accordance with the reviewer’s suggestion.

(3) It is important to remember that adult Drosophila muscles are not homogenous, at least not the adult leg and abdominal muscles, as they are organised as tubes with myofibrils closer to the surface, and nuclei as well as mitochondria largely in the centre (see PMID 33828099). Hence, only showing a single plane in the muscle images can be very misleading. The authors should at least provide virtual XZ-cross section views in Figure 3G to ensure that similar muscle planes are compared. This applies to the interpretation of both, the mitochondria and the myofibril phenotypes in wildtype vs BNIP3-KO. 

Thank you for your comment. As suggested, we have added XZ-cross-sectional views in Figure 3G. The XY plane corresponds to a central section of the Z-stack, as indicated in the figure.

(4) The EM images are nice, however only 2 of the 4 conditions shown were quantified. As the section plane can be misleading, at least several planes should be analysed also for wild type and BNIP3-KO, and not only for stx17 RNAi and the double mutant. 

In response to the comment, we quantified the TEM images of wild-type and BNIP3-KO DIOMs and added the resulting graph to Figure 4C. The corresponding raw data have also been provided (Supplementary File 2).

(5) How was Figure 5D, 5D' quantified? What corresponds to "regular", "medium", "high"? A statistical test is missing. I would rather conclude that MIR and LIR are redundant as double mutant appears to be stronger than both singles. This is also concluded in some sections of the text, so the authors seem to contradict themselves. Why not measure the mitochondria areas as done in Figure 6A' instead? 

In the previous version, we manually categorized pooled, blinded images from different genotypes. However, as the reviewer pointed out, this approach was not quantitative. In the revised version, we analyzed the images using ImageJ to quantify the mitochondrial area per cell. Statistical significance was assessed using the Kruskal-Wallis test. Accordingly, we have revised Figure 5D, the method section, and the figure legend.

(6) Figure 6B data seem to come from a single image per genotype only. At least 3 or 4 animals should be measured and the values reported. 

We analyzed Pearson’s correlation coefficients (R values) from at least five images per genotype and performed statistical analysis. The resulting quantification is presented in Figure 6B’, and the corresponding text has been revised accordingly.

(7) As BNIP3 mutants are viable, it would be interesting to report if they can fly and how long they live. 

Additional data on adult lifespan, climbing ability, and elapsed time for eclosion in BNIP3 KO flies have been included as supplemental information (Figure 3-figure supplement 2). No significant differences were observed in those assays under our experimental conditions.

(8) The transcriptomics data are not well linked to the autophagy mechanism. In particular, the mutant transcriptomics data are confusing, as the abstract seems to suggest that blocking autophagy impacts transcriptomics, which is not (strongly) the case. I would at least re-write this part, as it is currently misleading and sparks wrong expectations to the reader. Also throughout the text, the authors need to make clear if there are transcriptomic changes or not and if there are, how these are linked to autophagy. 

In the abstract, we described the findings as “transcriptional dynamics independent of autophagy” (line 49) because the loss of autophagy had only a minimal effect on transcriptional changes. This conclusion is supported by the data presented in our manuscript. In the result section, we state: “In contrast to our prediction, the knockdown of Atg18a, FIP200, or Stx17 only had a slight impact on transcriptomic dynamics in DIOM remodeling (Fig. 2C), with only minor changes detected (Fig. 2-figure supplement 2G)” (lines 199-201). In the Discussion section, we further note: “The transcriptional dynamics associated with DIOM remodeling are largely independent of autophagy (Fig.2). Instead, our RNA-seq data suggest that it is regulated primarily by ecdysone signaling, with minimal influence from autophagy inhibition” (lines 326-328).

(9) No table with the measured data is provided. 

We have provided the raw data files corresponding to all quantified results as Supplementary File 2.

Minor points: 

(1) To my knowledge, it is standard to indicate the time after puparium formation in hours, instead of days, (e.g. 24h, 48h etc.). 

Thank you for the comments. In our previous publications on DIOM remodeling during metamorphosis (PMID: 28063257 and 33077556), we used days rather than hours to indicate developmental time points. To maintain consistency across our studies, we have chosen to continue using days in the present manuscript.

(2) "Myofibrils typically form beneath the sarcolemma (Mao et al., 2022; Sanger et al., 2010); therefore, when mitochondria accumulate, myofibrils are restricted to the cell periphery." This is quite a general statement that does not always hold, in particular not in Drosophila flight muscles and likely also not in abdominal muscles (see PMIDs 29846170, 28174246). 

Thank you for pointing that out. We rewrote the sentence as follows: In the absence of BNIP3, mitochondria derived from the larval muscle accumulate and cluster in the cell center, physically obstructing myofibril formation during hypertrophy and restricting myofibrils to the cell periphery (Fig. 6E) (lines 392-394).

Reviewer #2 (Recommendations for the authors): 

Suggestions for improved or additional experiments, data or analyses. 

The authors should test, by a co-IP experiment, whether BNIP3 mutants lose the interaction with HA-Atg18a. 

As requested, we tested the effect of MER deletion on the interaction between BNIP3 and Atg18a in co-IP experiment. As shown in the new Fig. 5C, the deletion of MER weakened the interaction. This result was confirmed in three independent experiments. Its corresponding text has also been revised as follows: “We confirmed that HA-tagged Drosophila Atg18a co-immunoprecipitated with GFP-tagged full-length Drosophila BNIP3, and that this interaction was attenuated by the deletion of the MER (residues 42-53) (Fig. 5C)” (lines 270-273).

Minor corrections to the text and figures 

(1) In the list of authors, Kawaguchi Kohei could be Kohei Kawaguchi_._ 

Thank you very much. It has been corrected.

(2) In Fig3D, other receptors (Zonda, CG12511, Key, Ref2P) should be mentioned briefly. 

Thank you for the suggestion. We have revised the sentences as follows: “The time course RNA-seq data (Fig. 1 and 2) indicated that, among the known mitophagy regulators, only BNIP3 was robustly expressed in 1 d APF DIOMs. In contrast, Zonda, CG12511, Pink1, Park, Key, Ref(2)P, and IKKe—the Drosophila orthologs of FKBP8, FUNDC1, PINK1, Parkin, Optineurin, p62, and TBK1, respectively—showed little or undetectable expression at this stage (Fig. 3D).” (lines 230-234).

Reviewer #3 (Recommendations for the authors): 

Remarks: 

(1) What is the consequence of impaired muscle remodeling on the organismal level? Is the eclosion of adult flies impaired? One could think of assays for this, such as quantifying failed eclosions and/or video microscopy of the eclosion process. Is muscle function impaired? One could measure the contractile force of isolated fibers during electrical stimulation as well, etc. I believe that showing the physiological importance of muscle remodeling would be the biggest advantage that could arise from using a complete animal model.

We appreciate the comments. We have added data on adult lifespan, climbing ability, and the elapsed time for eclosion in BNIP3 KO flies as supplemental information (Figures 3-figure supplement 2). In BNIP3 KO DIOMs, despite the massive accumulation of mitochondria, an organized peripheral myofibril layer with contractile function is retained. However, we have not measured the contractile force of isolated muscle cells due to technical limitations. We plan to address this in future studies.

A related note is that I missed the proper discussion of the function and fate of these short-lived adult muscles (please see references in my summary). 

We have added a sentence regarding the function and fate of DIOMs in the introduction (lines 80-82) as follows: “The remodeled adult DIOMs function during eclosion, persist for approximately 12 hours, and are subsequently eliminated via programmed cell death (Kimura and Truman, 1990; J Neurosci. 1990;10:403-1)”.

(2) I don't think that "data not shown" should be used these days, when supplemental data allow the inclusion of not-so-critical results. 

We have added the data as Figure 5-figure supplement 2. As shown in the figure, overexpression of GFP-BNIP3 in 3IL BWMs did not induce the formation of tdTomato-positive autolysosomes, which are abundantly accumulated in DIOMs at 1 and 2 d APF.

(3) The term "naked mitochondria" does not sound scientific enough to this reviewer. I suggest "cytosolic mitochondria" or "unengulfed mitochondria". 

In accordance with the reviewer’s suggestion, we have replaced “naked mitochondria” with “unengulfed mitochondria” (lines 251 and 670).

Author Response:

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

Reviewer #1 (Public Review):

Summary:

Two important factors in visual performance are the resolving power of the lens and the signal-to-noise ratio of the photoreceptors. These both compete for space: a larger lens has improved resolving power over a smaller one, and longer photoreceptors capture more photons and hence generate responses with lower noise. The current paper explores the tradeoff of these two factors, asking how space should be allocated to maximize eye performance (measured as encoded information).

Your summary is clear, concise and elegant. The competition is not just for space, it is for space, materials and energy. We  now emphasise that we are considering these three costs in our rewrites of the Abstract and the first paragraph of the Discussion.  

Strengths:

The topic of the paper is interesting and not well studied. The approach is clearly described and seems appropriate (with a few exceptions - see weaknesses below). In most cases, the parameter space of the models are well explored and tradeoffs are clear.  

Weaknesses:

Light level

The calculations in the paper assume high light levels (which reduces the number of parameters that need to be considered). The impact of this assumption is not clear. A concern is that the optimization may be quite different at lower light levels. Such a dependence on light level could explain why the model predictions and experiment are not in particularly good agreement. The paper would benefit from exploring this issue.

Thank you for raising this point. We briefly explained in our original Discussion, under Understanding the adaptive radiation of eyes (Version 1, Iines 756 – 762), how our method can be modified to investigate eyes adapted for lower light levels. We have some thoughts on how eyes might be adapted. In general, transduction rates are increased by increasing D, reducing f, increasing d_rh and increasing L . In addition, d_rh is increased to allow for a larger D within the constraint of eye radius/corneal surface area, and to avoid wasteful oversampling (the changes in D, f and d_rh increase acceptance angle ∆ρ). We suspect that in eyes optimised for the efficient use of space, materials and energy the increases in L will be relatively small, first because  increasing D, reducing f and increasing d_rh are much more effective at increasing transduction rate than increasing L. Second, increasing sensitivity by reducing f decreases the cost Vo whereas increasing sensitivity by increasing L increases the cost V_ph. This disadvantage, together with exponential absorption, might explain why L is only 10% - 20% longer in the apposition eyes of nocturnal bees (Somanathan et al, J. comp. Physiol. A195, 571583, 2009). Because this line of argument is speculative and enters new territory, we have not included it in our revised version. We already present a lot of new material for readers to digest, and we agree with referee 2 that “It is possible to extend the theory to other types of eyes, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory”. Nonetheless, we take your point that some of the eyes in our data set might be adapted for lower light levels, and we have rewritten the Discussion section, How efficiently do insects allocate resources within their apposition eyes accordingly. On line 827 – 843 we address the assumption that eyes are adapted for full daylight,  and also take the opportunity  to mention two more reasons for increasing the eye parameter p: namely increasing image velocity (Snyder, 1979), and constructing  bright zones that increase the detectability of small targets (van Hateren et al., 1989; Straw et al., 2006).

Discontinuities

The discontinuities and non-monotonicity of the optimal parameters plotted in Figure 4 are concerning. Are these a numerical artifact? Some discussion of their origin would be quite helpful.

Good points, we now address the discontinuities in the Results, where they are first observed (lines 311 - 319) 

Discrepancies between predictions and experiment

As the authors clearly describe, experimental measurements of eye parameters differ systematically from those predicted. This makes it difficult to know what to take away from the paper. The qualitative arguments about how resources should be allocated are pretty general, and the full model seems a complex way to arrive at those arguments. Could this reflect a failure of one of the assumptions that the model rests on - e.g. high light levels, or that the cost of space for photoreceptors and optics is similar? Given these discrepancies between model and experiment, it is also hard to evaluate conclusions about the competition between optics and photoreceptors (e.g. at the end of the abstract) and about the importance for evolution (end of introduction).

Your misgivings boil down to two issues: what use is a model that fails to fit the data, and do we need a complicated model to show something that seems to be intuitively obvious?  Our study is useful because it introduces new approaches, methods, factors and explanations which advance our analysis and understanding of eye design and evolution. Your comments make it clear that we failed to get this message across and we have revised the manuscript accordingly. We have rewritten the Abstract and the first paragraph of the Discussion to emphasise the value of our new measure of cost, specific volume, by including more of its practical advantages. In particular, our use of specific volume 1) opens the door to the morphospace of all eyes of given type and cost. 2) This allows one to construct performance surfaces across morphospace that not only identify optima, but by evaluating the sub-optimal cast light on efficiency and adaptability. 3) Shows that photoreceptor energy costs have a major impact on design and efficiency, and 4) allows us to calculate and compare the capacities and efficiencies of compound eyes and simple eyes using a superior measure of cost. It is also possible that your dissatisfaction was deepened by disappointment. The first sentence of our original Abstract said that the goal of design is to maximize performance, so you might have expected to see that eyes are optimised.  Given that optimization provides cast iron proof that a system is designed to be efficient, and previous studies of coding by fly LMCs (Laughlin, 1981; Srinivasan et al., 1982 & van Hateren 1992) validated Barlow’s Efficient Coding Hypothesis by showing that coding is optimised, your expectation is reasonable. However, our investigation of how the allocation of resources to optics and photoreceptors affects an eye’s performance, efficiency and design does not depend a priori  on finding optima, therefore we have removed the “maximized”. Our revised Abstract now says, “to improve performance”.  

In short, our study illustrates an old adage in statistics “All models fail to fit, but some are useful”. As is often the case, the way in which our model fails is useful. In the original version of the Results and Discussion, we argued that the allocation of resources is efficient, and identified factors that can, in principle, explain the scattering of data points. Indeed, our modelling identifies two of these deficiencies; a lack of data on species-specific energy usage, and the need for models that quantify the relationship between the quality of the captured image and the behavioural tasks for which an eye might be specialised. Thus, by examining the model’s failings we identify critical factors and pose new questions for future research.  We have rewritten the Discussion section How efficiently do insects allocate resources…. to make these points. We hope that these revisions will convince you that we have established a starting point for definitive studies, invented a vehicle that has travelled far enough to discover new territory, and shown that it can be modified to cope with difficult terrain.

Turning to the need for a complicated model, because the costs and benefits depend on elementary optics and geometry, we too thought that there ought to be a simple model. However, when we tried to formulate a simple set of equations that approximate the definitive findings of our more complicated model we discovered that this is not as straightforward as we thought.  Many of the parameters in our model interact to determine costs and benefits, and many of these interactions are non-linear (e.g. the volumes of shells in spheres involve quadratic and cubic terms, and information depends on the log of a square root). So, rather than hold back publication of our complicated model, we decided to explain how it works as clearly as we can and demonstrate its value.

In response to your final comment, “it is hard to evaluate conclusions about the competition between optics and photoreceptors (e.g. at the end of the abstract) and about the importance for evolution (end of introduction)”, we stand by our original argument. There must be competition in an eye of fixed cost, and because competition favours a heavy investment in photoreceptors, both in theory and in practice, it  is a significant factor in eye design. A match between investments in optics and photoreceptors is predicted by theory and observed in fly NS eyes, therefore this is a design principle. As for evolution, no one would deny that it is important to view the adaptive radiation of eyes through a cost-benefit lens. Our lens is the first to view the whole eye, optics and photoreceptor array, and the first to treat the costs of space, materials and energy. Although the view through our lens is a bit fuzzy, it reveals that costs, benefits and trade-offs are important. Thus we have established a promising starting point for a new and more comprehensive cost-benefit approach to understanding eye design and evolution.  As for the involvement of genes, when there are heritable changes in phenotype genes must be involved and if, as we suggest, efficient resource allocation is beneficial, the developmental mechanisms responsible for allocating resources to optics and photoreceptor array will be playing a formative role in eye evolution.

Reviewer #2 (Public Review):

Summary:

In short, the paper presents a theoretical framework that predicts how resources should be optimally distributed between receptors and optics in eyes.

Strengths:

The authors build on the principle of resource allocation within an organism and develop a formal theory for optimal distribution of resources within an eye between the receptor array and the optics. Because the two parts of eyes, receptor arrays and optics, share the same role of providing visual information to the animal it is possible to isolate these from resource allocation in the rest of the animal. This allows for a novel and powerful way of exploring the principles that govern eye design. By clever and thoughtful assumptions/constraints, the authors have built a formal theory of resource allocation between the receptor array and the optics for two major types of compound eye as well as for camera-type eyes. The theory is formalized with variables that are well characterized in a number of different animal eyes, resulting in testable predictions.

The authors use the theory to explain a number of design features that depend on different optimal distribution of resources between the receptor array and the optics in different types of eyes. As an example, they successfully explain why eye regions with different spatial resolution should be built in different ways. They also explain differences between different types of eyes, such as long photoreceptors in apposition compound eyes and much shorter receptors in camera type eyes. The predictive power in the theory is impressive.

To keep the number of parameters at a minimum, the theory was developed for two types of compound eye (neural superposition, and apposition) and for camera-type eyes. It is possible to extend the theory to other types of eyes, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory.

The paper extends a previous theory, developed by the senior author, that develops performance surfaces for optimal cost/benefit design of eyes. By combining this with resource allocation between receptors and optics, the theoretical understanding of eye design takes a major leap and provides entirely new sets of predictions and explanations for why eyes are built the way they are.

The paper is well written and even though the theory development in the Results may be difficult to take in for many biologists, the Discussion very nicely lists all the major predictions under separate headings, and here the text is more tuned for readers that are not entirely comfortable with the formalism of the Results section. I must point out though that the Results section is kept exemplary concise. The figures are excellent and help explain concepts that otherwise may go above the head of many biologists.

We are heartened by your appreciation of our manuscript - it persuaded us not to undertake extensive revisions – thank you.

Reviewer #3 (Public Review):

Summary:

This is a proposal for a new theory for the geometry of insect eyes. The novel costbenefit function combines the cost of the optical portion with the photoreceptor portion of the eye. These quantities are put on the same footing using a specific (normalized) volume measure, plus an energy factor for the photoreceptor compartment. An optimal information transmission rate then specifies each parameter and resource allocation ratio for a variable total cost. The elegant treatment allows for comparison across a wide range of species and eye types. Simple eyes are found to be several times more efficient across a range of eye parameters than neural superposition eyes. Some trends in eye parameters can be explained by optimal allocation of resources between the optics and photoreceptors compartments of the eye.

Strengths:

Data from a variety of species roughly align with rough trends in the cost analysis, e.g. as a function of expanding the length of the photoreceptor compartment.

New data could be added to the framework once collected, and many species can be compared.

Eyes of different shapes are compared.

Weaknesses:

Detailed quantitative conclusions are not possible given the approximations and simplifying assumptions in the models and poor accounting for trends in the data across eye types.

Reviewer #1 (Recommendations For The Authors):

Figure 1: Panel E defines the parameters described in panel d. Consider swapping the order of those panels (or defining D and Delta Phi in the figure legend for d). Order follows narrative, eye types then match 

We think that you are referring to Figure 1. We modified the legend.

Lines 143-145: How does a different relative cost impact your results?

Thank you for raising this question. Because our assumption that relative costs are the same is our starting point, and for optics it is not an obvious mistake, we do not raise your question here. We address your question where you next raise it because, for photoreceptors the assumption is obviously wrong.  We now emphasise that our method for accounting for photoreceptor energy costs can be applied to other costs. 

Lines 187-190: Same as above - how do your results change if this assumption is not accurate?

We have revised our manuscript to emphasise that we are dealing with the situation in which our initial assumption (costs per unit volume are equal) breaks down. On (lines 203 - 208) we write “ However, this assumption breaks down when we consider specific metabolic rates. To enable and power phototransduction, photoreceptors have an exceptionally high specific metabolic rate (energy consumed per gram, and hence unit volume, per second) (Laughlin et al., 1998; Niven et al., 2007; Pangršič et al., 2005). We account for this extra cost by applying an energy surcharge, S_E. To equate…. 

We also revised part of the Discussion section, Specific volume is a useful measure of cost to make it clear that we are able take account for situations in which the costs per unit volume are not equal, and we give our treatment of photoreceptor energy costs as an example of how this is done. On lines 626 - 640 we say  

Cost estimates can be adjusted for situations in which costs per unit volume are not equal, as illustratedby our treatment of photoreceptor energy consumption.  To support transduction the photoreceptor array has an exceptionally high metabolic rate (Laughlin et al., 1998; Niven et al., 2007; Pangršič et al., 2005). We account forthis higher energy cost by using the animal’s specific metabolic rate (power per unit mass and hence power per unit volume) to convert an array’s power consumption into an equivalent volume (Methods). Photoreceptor ion pumps are the major consumers of energy and the smaller contribution of pigmented glia (Coles, 1989) is included in our calculation of the energy tariff K_E. (Methods) The higher costs of materials and their turnover in the photoreceptor array can be added the energy tariff K_E but given the magnitude of the light-gated current (Laughlin et al., 1998) the relative increase will be very small. Thus for our intents and purposes the effects of these additional costs are covered by our models. For want of sufficient data…”.

Reviewer #2 (Recommendations For The Authors):

A few comments for consideration by the authors:

(1) In the abstract, Maybe give another example explaining why other eyes should be different to those of fast diurnal insects.

This worthwhile extrapolation is best kept to the Discussion.

(2) Would it be worthwhile mentioning that the photopigment density is low in rhabdoms compared to vertebrate outer segments? This will have major effects on the relative size of retina and optics.

Thank you, we now make this good point in the Discussion (lines 698-702).

(3) It took me a while to understand what you mean by an energy tariff. For the less initiated reader many other variables may be difficult to comprehend. A possible remedy would be to make a table with all variables explained first very briefly in a formal way and then explained again with a few more words for readers less fluent in the formalism.

A very useful suggestion. We have taken your advice (p.4).

(4) The "easy explanation" on lines 356-357 need a few more words to be understandable.

We have expanded this argument, and corrected a mistake, the width of the head front to back is not 250 μm, it is 600 μm (lines 402-407)

(5) Maybe devote a short paragraph in the Discussion to other types of eye, such as optical superposition eyes and pinhole eyes. This could be done very shortly and without formalism. I'm sure the authors already have a good idea of the optimal ratio of receptor arrays and optics in these eye types.

We do not discuss this because we have not found a full account of the trade-offs and their  effects on costs and benefits. We hope that our analysis of apposition and simple eyes will encourage people to analyse the relationships between costs and benefits in other eye types. To this end we pointed out in the Discussion that recent advances in imaging and modelling could be helpful.

(6)  Could the sentence on lines 668-671 be made a little clearer?

“Efficiency is also depressed by increasing the photoreceptor energy tariff K_E, and in line with the greater impact of photoreceptor energy costs in simple eyes, the reduction in efficiency is much greater in simple eyes (Figure 8b).0.

We replaced this sentence with “In both simple and apposition eyes efficiency is reduced by increasing the photoreceptor energy tariff K_E. This effect is much greater in simple eyes, thus as found for reductions in photoreceptor length (Figure 7b),K_E has more impact on the design of simple eyes” (lines749 – 752).

(7)  I have some reservations about the text on lines 789-796. The problem is that optics can do very little to improve the performance of a directional photoreceptor where delrho should optimally be very wide. Here, membrane folding is the only efficient way to improve performance (SNR). The option to reduce delrho for better performance comes later when simultaneous spatial resolution (multiple pixels) is introduced.

Yes, we have been careless. We have rewritten this paragraph to say (lines 920-931)

“Two key steps in the evolution of eyes were the stacking of photoreceptive membranes to absorb more photons, and the formation of optics to intercept more photons and concentrate them according to angle of incidence to form an image (Nilsson, 2013, 2021). Our modelling of well-developed image forming eyes shows that to improve performance stacked membranes (rhabdomeres) compete with optics for the resources invested in an eye, and this competition profoundly influences both form and function. It is likely that competition between optics and photoreceptors was shaping eyes as lenses evolved to support low resolution spatial vision. Thus the developmental mechanisms that allocate resources within modern high resolution eyes (Casares & MacGregor, 2021), by controlling cell size and shape, and as our study emphasises, gradients in size and shape across an eye, will have analogues or homologues in more ancient eyes. Their discovery….” (lines 920-931

Reviewer #3 (Recommendations For The Authors):

Suggestions for major revisions:

While the approach is novel and elegant, the results from the analysis of insect morphology do not broadly support the optimization argument and hardly constrain parameters, like the energy tariff value, at all. The most striking result of the paper is the flat plateau in information across a broad range of shape parameters and the length, and resolution trend in Figure 5.

At no point in the Results and Discussion do we argue that resource allocation is optimized. Indeed, we frequently observe that it is not. Our mistake was to start the Abstract by observing that animals evolve to minimise costs. We have rewritten the Abstract accordingly.

The information peaks are quite shallow. This might actually be a very important and interesting result in the paper - the fact that the information plateaus could give the insect eye quite a wide range of parameters to slide between while achieving relatively efficient sensing of the environment. Instead of attempting to use a rather ad hoc and poorly supported measure of energetics in PR cost, perhaps the pitch could focus on this flexibility. K_E does not seem to constrain eye parameters and does not add much to the paper.

We agree, being able to construct performance surfaces across morphospace is an important advance in the field of eye design and evolution, and the performance surface’s flat top has interesting implications for the evolution of adaptations. Encouraged by your remarks, we have rewritten the Abstract and the introductory paragraph of the Discussion to draw attention to these points. 

We are disappointed that we failed to convince you that our energy tariff, K_E , is no better than a poorly supported ad hoc parameter that does not add much to the paper. In our opinion a resource allocation model that ignores photoreceptor energy consumption is obviously inadequate because the high energy cost of phototransduction is both wellknown and considered to be a formative factor in eye evolution (Niven and Laughlin, 2008). One of the advantages of modelling is that one can assess the impact of factors that are known to be present, are thought to be important, but have not been quantified. We followed standard modelling practice by introducing a cost that has the same units as the other costs and, for good physiological reasons, increases linearly with the number of microvilli, according to K_E. We then vary this unknown cost parameter to discover when and why it is significant. We were pleased to discover that we could combine data on photoreceptor energy demands and whole animal metabolic rates to establish the likely range of K_E. This procedure enabled us to unify the cost-benefit analyses of optics and photoreceptors, and to discover that realistic values of K_E have a profound impact on the structure and performance of an efficient eye. We hope that this advance will encourage people to collect the data needed to evaluate K_E.To emphasise the importance of K_E and dispel doubts associated with the failure of the model to fit the data, we have revised two sections:  Flies invest efficiently in costly photoreceptor arrays in the Results, and How efficiently do insects allocate resources within their apposition eyes?  in the Discussion. These rewrites also explain why it is impossible for us to infer K_E by adjusting its value so that the model’s predictions fit the data.

The graphics after Figure 3 are quite dense and hard to follow. None of the plateau extent shown in Fig 3 is carried through to the subsequent plots, which makes the conclusions drawn from these figures very hard to parse. If the peak information occurs on a flat plateau, it would be more helpful to see those ranges of parameters displayed in the figures.

Ideally one should do as you suggest and plot the extent of the plateau, but in our situation this is not very helpful. In the best data set, flies, optimised models predict D well, get close to ∆φ in larger eyes, and demonstrate that these optimum values are not very sensitive to K_E L is a different matter, it is very sensitive to K_E L which, as we show (and frequently remind) is poorly constrained by experimental data. The best we can do is estimate the envelope of L vs C_tot  curves, as defined by a plausible range of K_EL . Because most of the plateau boundaries you ask for will fall within this envelope, plotting them does little to clear the fog of uncertainty. We note that all three referees agree that our model can account for two robust trends, i) in apposition eyes L increase with optical resolving power and acuity, both within individual eyes and among eyes of different sizes, and ii) L is much longer is apposition eyes than in simple eyes. Nonetheless, the scatter of data points and their failure to fit creates a bad impression. We gave a number of reasons why the model does not fit the data points, but these were scattered throughout the Results and Discussion and, as referees 1 and 3 point out, this makes it difficult to draw convincing conclusions. To rectify this failing, we have rewritten two sections, in the Results Flies invest efficiently in costly photoreceptor arrays and in the Discussion, How efficiently do insects allocate resources within their apposition eyes?, to discuss these reasons en bloc, draw conclusions and suggest how better data and refinements to modelling could resolve these issues.  

Throughout the figures, the discontinuities in the optimal cuts through parameter space are not sufficiently explained.

We added a couple of sentences that address the “jumps” (lines 313 – 318)

None of the data seems to hug any of the optimal lines and only weakly follow the trends shown in the plots. This makes interpretation difficult for the reader and should be better explained. The text can be a little telegraphic in the Results after roughly page 10, and requires several readings to glean insight into the manuscript's conclusions.

We revised the Results section in which we compare the best data set, flies’  NS eyes with theoretical predictions, Flies invest efficiently in costly photoreceptor arrays,  to expand our interpretation of the data and clarify our arguments. The remaining sections have not been expanded. In the next section, which is on fused rhabdom apposition eyes, our interpretation of the scattering of data points follows the same line of argument. The remaining Results sections are entirely theoretical.  

Overall, the rough conclusions outlined in the Results seem moderately supported by the matches of the data to the optimal information transmission cuts through parameter space, but only weakly.

We agree, more data is required to test and refine our theoretical predictions.

The Discussion is long and well-argued, and contains the most cogent writing in the manuscript.

Thank you: this is most pleasing. We submitted our study to eLife because it allows longer Discussions, but we worried that ours was too long. However, we felt that our extensive Discussion was necessary for two reasons. First, we are introducing a new approach to understanding of eye design and evolution. Second, because the data on eye morphology and costs are limited, we had to make a number of assumptions and by discussing these, warts and all, we hoped to encourage experimentalists to gather more data and focus their efforts on the most revealing material.  

Minor comments:

We have acted upon most of your minor comments and we confine our remarks to our disagreements. We are grateful for your attention to details that we \textshould have picked up on.  

It's a more standard convention to say "cost-benefit" rather than with a colon. 

"equation" should be abbreviated "eq" or "eqn", never with a "t"

when referring to the work of van Hateren, quote the paper and the database using "van Hateren" not just "Hateren"

small latex note: use "\textit{SNR}" to get the proper formatting for those letters when in the math environment

Line 100-110: "f" is introduced, but only f' is referenced in the figure. This should be explained in order. d_rh is not included in the figure. Also in this section, d_rh/f is also referenced before \Delta \rho_rf, which is the same quantity, without explanation.  

Figure 1 shows eye structure and geometry. f’ is a lineal dimension of the eye but f is not, so f is not shown in Fig 1e. We eliminated the confusion surrounding ∆ρ_rh  by deleting “and changing the acceptance angle of the photoreceptive waveguide ∆ρ_rh (Snyder, 1979)”.  

Fig 1 caption: this says "From dorsal to ventral," then describes trends that run ventral to dorsal, which is a confusing typo.

Fig 3 - adding some data points to these plots might help the reader understand how (or if) K_E is constrained by the data.

It is not possible to add data points because to total cost, Ctot ,is unknown.

Fig 4c (and in other subplots): the jumps in L with C_tot could be explained better in the text - it wasn't clear to this reviewer why there are these discontinuities.

Dealt with in the revised text (lines  310-318).

Fig 4d: The caption for this subplot could be more clearly written.

We have rewritten the subscript for subplot 4d.

Fig 5 and other plots with data: please indicate which symbols are samples from the same species. This info is hard to reconstruct from the tables.

We have revised Figure 5 accordingly. Species were already indicated in Figure 6.

Line 328: missing equation number

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On July 20, violent thunderstorms caused significant damage in eastern France, resulting in one death and six injuries.

Eastern France experienced violent thunderstorms, strong winds, hail, and significant damage, causing one death and six injuries. Météo-France lifted the warning on Monday morning.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The objective of this research is to understand how the expression of key selector transcription factors, Tal1, Gata2, Gata3, involved in GABAergic vs glutamatergic neuron fate from a single anterior hindbrain progenitor domain is transcriptionally controlled. With suitable scRNAseq, scATAC-seq, CUT&TAG, and footprinting datasets, the authors use an extensive set of computational approaches to identify putative regulatory elements and upstream transcription factors that may control selector TF expression. This data-rich study will be a valuable resource for future hypothesis testing, through perturbation approaches, of the many putative regulators identified in the study. The data are displayed in some of the main and supplemental figures in a way that makes it difficult to appreciate and understand the authors' presentation and interpretation of the data in the Results narrative. Primary images used for studying the timing and coexpression of putative upstream regulators, Insm1, E2f1, Ebf1, and Tead2 with Tal1 are difficult to interpret and do not convincingly support the authors' conclusions. There appears to be little overlap in the fluorescent labeling, and it is not clear whether the signals are located in the cell soma nucleus.

Strengths:

The main strength is that it is a data-rich compilation of putative upstream regulators of selector TFs that control GABAergic vs glutamatergic neuron fates in the brainstem. This resource now enables future perturbation-based hypothesis testing of the gene regulatory networks that help to build brain circuitry.

We thank Reviewer #1 for the thoughtful assessment and recognition of the extensive datasets and computational approaches employed in our study. We appreciate the acknowledgment that our efforts in compiling data-rich resources for identifying putative regulators of key selector transcription factors (TFs)—Tal1, Gata2, and Gata3—are valuable for future hypothesis-driven research.

Weaknesses:

Some of the findings could be better displayed and discussed.

We acknowledge the concerns raised regarding the clarity and interpretability of certain figures, particularly those related to expression analyses of candidate upstream regulators such as Insm1, E2f1, Ebf1, and Tead2 in relation to Tal1. We agree that clearer visualization and improved annotation of fluorescence signals are crucial to accurately support our conclusions. In our revised manuscript, we will enhance image clarity and clearly indicate sites of co-expression for Tal1 and its putative regulators, ensuring the results are more readily interpretable. Additionally, we will expand explanatory narratives within the figure legends to better align the figures with the results section.

Reviewer #2 (Public review):

Summary:

In the manuscript, the authors seek to discover putative gene regulatory interactions underlying the lineage bifurcation process of neural progenitor cells in the embryonic mouse anterior brainstem into GABAergic and glutamatergic neuronal subtypes. The authors analyze single-cell RNA-seq and single-cell ATAC-seq datasets derived from the ventral rhombomere 1 of embryonic mouse brainstems to annotate cell types and make predictions or where TFs bind upstream and downstream of the effector TFs using computational methods. They add data on the genomic distributions of some of the key transcription factors and layer these onto the single-cell data to get a sense of the transcriptional dynamics.

Strengths:

The authors use a well-defined fate decision point from brainstem progenitors that can make two very different kinds of neurons. They already know the key TFs for selecting the neuronal type from genetic studies, so they focus their gene regulatory analysis squarely on the mechanisms that are immediately upstream and downstream of these key factors. The authors use a combination of single-cell and bulk sequencing data, prediction and validation, and computation.

We also appreciate the thoughtful comments from Reviewer #2, highlighting the strengths of our approach in elucidating gene regulatory interactions that govern neuronal fate decisions in the embryonic mouse brainstem. We are pleased that our focus on a critical cell-fate decision point and the integration of diverse data modalities, combined with computational analyses, has been recognized as a key strength.

Weaknesses:

The study generates a lot of data about transcription factor binding sites, both predicted and validated, but the data are substantially descriptive. It remains challenging to understand how the integration of all these different TFs works together to switch terminal programs on and off.

Reviewer #2 correctly points out that while our study provides extensive data on predicted and validated transcription factor binding sites, clearly illustrating how these factors collectively interact to regulate terminal neuronal differentiation programs remains challenging. We acknowledge the inherently descriptive nature of the current interpretation of our combined datasets.

In our revision, we will clarify how the different data types support and corroborate one another, highlighting what we consider the most reliable observations of TF activity. Additionally, we will revise the discussion to address the challenges associated with interpreting the highly complex networks of interactions within the gene regulatory landscape.

We sincerely thank both reviewers for their constructive feedback, which we believe will significantly enhance the quality and accessibility of our manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The results in Figure 3 and several associated supplements are mainly a description/inventory of putative CREs some of which are backed to some extent by previous transgenic studies. But given the way the authors chose to display the transgenic data in the Supplements, it is difficult to fully appreciate how well the transgenic data provide functional support. Take, for example, the Tal +40kb feature that maps to a midbrain enhancer: where exactly does +40kb map to the enhancer region? Is Tal +40kb really about 1kb long? The legend in Supplemental Figure 6 makes it difficult to interpret the bar charts; what is the meaning of: features not linked to gene -Enh? Some of the authors' claims are not readily evident or are inscrutable. For example, Tal locus features accessible in all cell groups are not evident (Fig 2A,B). Other cCREs are said to closely correlate with selector expression for example, Tal +.7kb and +40kb. However, inspection of the data seems to indicate that the two cCREs have very different dynamics and only +40kb seems to correlate with the expression track above it. Some features are described redundantly such as the Gata2 +22 kb, +25.3 kb, and +32.8 kb cCREs above and below the Gata3 cCRE. What is meant by: The feature is accessible at 3' position early, and gains accessibility at 5' positions ... Detailed feature analysis later indicated the binding of Nkx6-1 and Ascl1 that are expressed in the rV2 neuronal progenitors, at 3' positions, and binding of Insm1 and Tal1 TFs that are activated in early precursors, at 5' positions (Figure 3C).

To allow easier assessment of the overlap of the features described in this study in reference to the transgenic studies, we have added further information about the scATAC features, cCREs and previously published enhancers, as well as visual schematics of the feature-enhancer overlaps in the Supplementary table 4. The Supplementary Table 4 column contents are also now explained in detail in the table legend (under the table). We hope those changes make the feature descriptions clearer. To answer the reviewer's question about the Tal1+40kb enhancer, the length of the published enhancer element is 685 bp and the overlapping scATAC feature length is 2067 bp (Supplementary Table 3, sheet Tal1, row 103).

The legend and the chart labelling in the Supplementary Figure 5 (formerly Supplementary figure 6) have been elaborated, and the shown categories explained more clearly.

Regarding the features at the Tal1 locus, the text has been revised and the references to the features accessible in all cell groups were removed. These features showed differences in the intensity of signal but were accessible in all cell groups. As the accessibility of these features does not correlate with Tal1 expression, they are of less interest in the context of this paper.

The gain in accessibility of the +0.7kb and +40 kb features correlates with the onset of Tal1 RNA expression. This is now more clearly stated in the text, as " For example, the gain in the accessibility of Tal1 cCREs at +0.7 and +40 kb correlated temporally with the expression of Tal1 mRNA (Figure 2B), strongly increasing in the earliest GABAergic precursors (GA1) and maintained at a lower level in the more mature GABAergic precursor groups (GA2-GA6), " (Results, page 4). The reviewer is right that the later dynamics of the +0.7 and +40 cCREs differ and this is now stated more clearly in the text (Results, page 5, last chapter).

The repetition in the description of the Gata2 +22 kb, +25.3 kb, and +32.8 kb cCREs has been removed.

The Tal1 +23 kb cCRE showed within-feature differences in accessibility signal. This is explained in the text on page 5, referring to the relevant figure 2A, showing the accessibility or scATAC signal in cell groups and the features labelled below, and 3C, showing the location of the Nkx6-1 and Ascl1 binding sites in this feature: "The Tal1 +23 kb cCRE contained two scATAC-seq peaks, having temporally different patterns of accessibility. The feature is accessible at 3' position early, and gains accessibility at 5' positions concomitant with GABAergic differentiation (Figure 2A, accessibility). Detailed feature analysis later indicated that the 3' end of this feature contains binding sites of Nkx6-1 and Ascl1 that are expressed in the rV2 neuronal progenitors, while the 5' end contains TF binding sites of Insm1 and Tal1 TFs that are activated in early precursors (described below, see Figure 3C)."

(2) Supplementary Figure 3 is not presented in the Results.

Essential parts of previous Supplementary Figure 3 have been incorporated into the Figure 4 and the previous Supplementary Figure omitted.

(3) The significance of Figure 3 and the many related supplements is difficult to understand. A large number of footprints with wide-ranging scores, many very weak or unbound, are displayed in the various temporal cell groups in different epigenomic regions of Tal1 and Vsx2. The footprints for GA1 and Ga2 are combined despite Tal1 showing stronger expression in GA1 and stronger accessibility (Figure 2). Many possibilities are outlined in the Results for how the many different kinds of motifs in the cCREs might bind particular TFs to control downstream TF expression, but no experiments are performed to test any of the possibilities. How well do the TOBIAS footprints align with C&T peaks? How was C&T used to validate footprints? Are Gata2, 3, and Vsx2 known to control Tal1 expression from perturbation experiments?

Figure 3 and related supplements present examples of the primary data and summarise the results of comprehensive analysis. The methods of identifying the selector TF regulatory features and the regulators are described in the Methods (Materials and Methods page 16). Briefly, the correlation between feature accessibility and selector TF RNA expression (assessed by the LinkPeaks score and p-value) were used to select features shown in the Figure 3.

We are aware of differences in Tal1 expression and accessibility between GA1 and GA2. However, number of cells in GA2 was not high enough for reliable footprint calculations and therefore we opted for combining related groups throughout the rV2 lineage for footprinting.

As suggested, CUT&Tag could be used to validate the footprinting results with some restrictions. In the revised manuscript, we included analysis of CUT&Tag peak location and footprints similarly to an earlier study (Eastman et al. 2025). In summary, we analysed whether CUT&Tag peaks overlap locations in which footprinting was also recognized and vice versa. Per each TF with CUT&Tag data we calculated a) Total number of CUT&Tag consensus peaks b) Total number of bound TFBS (footprints) c) Percentage of CUT&Tag overlapping bound TFBS d) Percentage of bound TFBS overlapping CUT&Tag. These results are shown in Supplementary Table 6 and in Supplementary figure 11 with analysis described in Methods (Materials and Methods, page 19). There is considerable overlap between CUT&Tag peaks and bound footprints, comparable to one shown in Eastman et al. 2025. However, these two methods are not assumed to be completely matching for several reasons: binding by related/redundant TFs, antigen masking in the TF complex, chromatin association without DNA binding, etc. In addition, some CUT&Tag peaks with unbound footprints could arise from non-rV2 cells that were part of the bulk CUT&Tag analysis but not of the scATAC footprint analysis.

The evidence for cross-regulation of selector genes and the regulation of Tal1 by Gata2, Gata3 and Vsx2 is now discussed (Discussion, chapter Selector TFs directly autoregulate themselves and cross-regulate each other, page 12-13). The regulation of Tal1 expression by Vsx2 has, to our knowledge, not been earlier studied.

(4) Figure 4 findings are problematic as the primary images seem uninterpretable and unconvincing in supporting the authors' claims. There is a lack of clear evidence in support of TF coexpression and that their expression precedes Tal1.

Figure 4 has been entirely redrawn with higher resolution images and a more logical layout. In the revised Figure 4, only the most relevant ISH images are shown and arrowheads are added showing the colocalization of the mRNA in the cell cytoplasm. Next to the plots of RNA expression along the apical-basal axis of r1, an explanatory image of the quantification process is added (Figure 4D).

(5) What was gained from also performing ChromVAR other than finding more potential regulators and do the results of the two kinds of analyses corroborate one another? What is a dual GATA:TAL BS?

Our motivation for ChromVAR analysis is now more clearly stated in the text (Results, page 9): “In addition to the regulatory elements of GABAergic fate selectors, we wanted to understand the genome-wide TF activity during rV2 neuron differentiation. To this aim we applied ChromVAR (Schep et al., 2017)" Also, further explanation about the Tal1and Gata binding sites has been added in this chapter (Results, page 9).

The dual GATA:Tal BS (TAL1.H12CORE.0.P.B) is a 19-bp motif that consists of an E-box and GATA sequence, and is likely bound by heteromeric Gata2-Tal1 TF complex, but may also be bound by Gata2, Gata3 or Tal1 TFs separately. The other TFBSs of Tal1 contain a strong E-box motif and showed either a lower activity (TAL1.H12CORE.1.P.B) or an earlier peak of activity in common precursors with a decline after differentiation (TAL1.H12CORE.2.P.B) (Results, page 9).

(6) The way the data are displayed it is difficult to see how the C&T confirmed the binding of Ebf1 and Insm1, Tal1, Gata2, and Gata3 (Supplementary Figures 9-11). Are there strong footprints (scores) centered at these peaks? One can't assess this with the way the displays are organized in Figure 3. What is the importance of the H3K4me3 C&T? Replicate consistency, while very strong for some TFs, seems low for other TFs, e.g. Vsx2 C&T on Tal1 and Gata2. The overlaps do not appear very strong in Supplementary Figure 10. Panels are not letter labeled.

We have added an analysis of footprint locations within the CUT&Tag peaks (Supplementary Figure 11). The Figure shows that the footprints are enriched at the middle regions of the CUT&Tag peaks, which is expected if TF binding at the footprinted TFBS site was causative for the CUT&Tag peaks.

The aim of the Supplementary Figures 9-11 (Supplementary Figures 8-10 in the revised manuscript) was to show the quality and replicability of the CUT&Tag.

The anti-H3K4me3 antibody, as well as the anti-IgG antibody, was used in CUT&Tag as part of experiment technical controls. A strong CUT&Tag signal was detected in all our CUT&Tag experiments with H3K4me3. The H3K4me3 signal was not used in downstream analyses.

We have now labelled the H3K4me3 data more clearly as "positive controls" in the Supplementary Figure 8. The control samples are shown only on Supplementary Figure 8 and not in the revised Supplementary Figure 10, to avoid repetition. The corresponding figure legends have been modified accordingly.

To show replicate consistency, the genome view showing the Vsx2 CUT&Tag signal at Gata2 gene has been replaced by a more representative region (Supplementary Figure 8, Vsx2). The Vsx2 CUT&Tag signal at the Gata2 locus is weak, explaining why the replicability may have seemed low based on that example.

Panel labelling is added on Supplementary Figures S8, S9, S10.  

(7) It would be illuminating to present 1-2 detailed examples of specific target genes fulfilling the multiple criteria outlined in Methods and Figure 6A.

We now present examples of the supporting evidence used in the definition of selector gene target features and target genes. The new Supplementary Figure 12 shows an example gene Lmo1 that was identified as a target gene of Tal1, Gata2 and Gata3.

Reviewer #2 (Recommendations for the authors):

(1) The authors perform CUT&Tag to ask whether Tal1 and other TFs indeed bind putative CREs computed. However, it is unclear whether some of the antibodies (such as Gata3, Vsx2, Insm1, Tead2, Ebf1) used are knock-out validated for CUT&Tag or a similar type of assay such as ChIP-seq and therefore whether the peaks called are specific. The authors should either provide specificity data for these or a reference that has these data. The Vsx2 signal in Figure S9 looks particularly unconvincing.

Information about the target specificity of the antibodies can be found in previous studies or in the product information. The references to the studies have been now added in the Methods (Materials and Methods, CUT&Tag, pages 18-19). Some of the antibodies are indeed not yet validated for ChIP-seq, Cut-and-run or CUT&Tag. This is now clearly stated in the Materials and Methods (page 19): "The anti-Ebf1, anti-Tal1, anti-IgG and anti-H3K4me3 antibodies were tested on Cut-and-Run or ChIP-seq previously (Boller et al., 2016b; Courtial et al., 2012) and Cell Signalling product information). The anti-Gata2 and anti-Gata3 antibodies are ChIP-validated ((Ahluwalia et al., 2020a) and Abcam product information). There are no previous results on ChIP, ChIP-seq or CUT&Tag with the anti-Insm1, anti-Tead2 and anti-Vsx2 antibodies used here. The specificity and nuclear localization have been demonstrated in immunohistochemistry with anti-Vsx2 (Ahluwalia et al., 2020b) and anti-Tead2 (Biorbyt product information). We observed good correlation between replicates with anti-Insm1, similar to all antibodies used here, but its specificity to target was not specifically tested". We admit that specificity testing with knockout samples would increase confidence in our data. However, we have observed robust signals and good replicability in the CUT&Tag for the antibodies shown here.

Vsx2 CUT&Tag signal at the loci previously shown in Supplementary Figure S9 (now Supplementary Figure 8) is weak, explaining why the replicability may seem low based on those examples. The genome view showing the Vsx2 CUT&Tag signal at Gata2 gene locus in Supplementary Figure 8 (previously Supplementary figure 9) has now been replaced by a view of Vsx2 locus that is more representative of the signal.

(2) It is unclear why the authors chose to focus on the transcription factor genes described in line 626 as opposed to the many other putative TFs described in Figure 3/Supplementary Figure 8. This is the major challenge of the paper - the authors are trying to tell a very targeted story but they show a lot of different names of TFs and it is hard to follow which are most important.

We agree with the reviewer that the process of selection of the genes of interest is not always transparent. We are aware that interpretations of a paper are based on the known functions of the putative regulatory TFs, however additional aspects of regulation could be revealed even if the biological functions of all the TFs were known. This is now stated in the Discussion “Caveats of the study” chapter. It would be relevant to study all identified candidate genes, but as often is the case, our possibilities were limited by the availability of materials (probes, antibodies), time, and financial resources. In the revised manuscript, we now briefly describe the biological processes related to the selected candidate regulatory TFs of the Tal1 gene (Results, page 8, "Pattern of expression of the putative regulators of Tal1 in the r1"). We hope this justifies the focus on them in our RNA co-expression analysis. The TFs analysed by RNAscope ISH are examples, which demonstrate alignment of the tissue expression patterns with the scRNA-seq data, suggesting that the dynamics of gene expression detected by scRNA-seq generally reflects the pattern of expression in the developing brainstem.

(3) How is the RNA expression level in Figure 5B and 4D-L computed? These are the clusters defined by scATAC-seq. Is this an inferred RNA expression? This should be made more clear in the text.

The charts in Figures 5B and 4G,H,I show inferred RNA expression. The Y-axis labels have now been corrected and include the term inferred’. RNA expression in the scATAC-seq cell clusters is inferred from the scRNA-seq cells after the integration of the datasets.

(4) The convergence of the GABA TFs on a common set of target genes reminds me of a nice study from the Rubenstein lab PMID: 34921112 that looked at a set of TFs in cortical progenitors. This might be a good comparison study for the authors to use as a model to discuss the convergence data.

We thank the reviewer for bringing this article to our attention. The article is now discussed in the manuscript (Discussion, page 11).

(5) The data in Figure 4, the in-situ figure, needs significant work. First, the images especially B, F, and J appear to be of quite low resolution, so they are hard to see. It is unclear exactly what is being graphed in C, G, and K and it does not seem to match the text of the results section. Perhaps better labeling of the figure and a more thorough description will make it clear. It is not clear how D, H, and L were supposed to relate to the images - presumably, this is a case where cell type is spatially organized, but this was unclear in the text if this is known and it needs to be more clearly described. Overall, as currently presented this figure does not support the descriptions and conclusions in the text.

Figure 4 has been entirely redrawn with higher resolution images and more logical layout. In the revised Figure 4, the ISH data and the quantification plots are better presented; arrows showing the colocalization of the mRNA in the cell cytoplasm were added; and an explanatory image of the quantification process is added on (D).

Minor points

(1) Helpful if the authors include scATAC-seq coverage plots for neuronal subtype markers in Figure 1/S1.

We are unfortunately uncertain what is meant with this request. Subtype markers in Figure 1/S1 scATAC-seq based clusters are shown from inferred RNA expression, and therefore these marker expression plots do not have any coverage information available.

(2) The authors in line 429 mention the testing of features within TADs. They should make it clear in the main text (although tadmap is mentioned in the methods) that this is a prediction made by aggregating HiC datasets.

Good point and that this detail has been added to both page 3 and 16.

(3) The authors should include a table with the phastcons output described between lines 511 and 521 in the main or supplementary figures.

We have now clarified int the text that we did not recalculate any phastcons results, we merely used already published and available conservation score per nucleotide as provided by the original authors (Siepel et al. 2005). (Results, page 5: revised text is " To that aim, we used nucleotide conservation scores from UCSC (Siepel et al., 2005). We overlaid conservation information and scATAC-seq features to both validate feature definition as well as to provide corroborating evidence to recognize cCRE elements.")

(4) It is very difficult to read the names of the transcription factor genes described in Figure 3B-D and Supplementary Figure 8 - it would be helpful to resize the text.

The Figures 3B-D and Supplementary Figure 7 (former Supplementary figure 8) have been modified, removing unnecessary elements and increasing the size of text.

(5) It is unclear what strain of mouse is used in the study - this should be mentioned in the methods.

Outbred NMRI mouse strain was used in this study. Information about the mouse strain is added in Materials and Methods: scRNA-seq samples (page 14), scATAC-seq samples (page 15), RNAscope in situ hybridization (page 17) and CUT&Tag (page 18).

(6) Text size in Figure 6 should be larger. R-T could be moved to a Supplementary Figure.

The Figure 6 has been revised, making the charts clearer and the labels of charts larger. The Figure 6R-S have been replaced by Supplementary table 8 and the Figure 6T is now shown as a new Figure (Figure 7).

Additional corrections in figures

Figure 6 D,I,N had wrong y-axis scale. It has been corrected, though it does not have an effect on the interpretation of the data as Pos.link and Neg.link counts were compared to each other’s (ratio).

On Figure 2B, the heatmap labels were shifted making it difficult to identify the feature name per row. This is now corrected.

Reviewer #2 (Public review):

The mechanisms governing autophagic membrane expansion remain incompletely understood. ATG2 is known to function as a lipid transfer protein critical for this process; however, how ATG2 is coordinated with the broader autophagic machinery and endomembrane systems has remained elusive. In this study, the authors employ an elegant proximity labeling approach and identify two ER-Golgi intermediate compartment (ERGIC)-localized proteins-Rab1 and ARFGAP1-as novel regulators of ATG2 during autophagic membrane expansion.

Their findings support a model in which autophagosome formation occurs within a specialized subdomain of the ER that is enriched in both ER exit sites (ERES) and ERGIC, providing valuable mechanistic insight. The overall study is well-executed and offers an important contribution to our understanding of autophagy.

Specific Comments

(1) Integration with Prior Literature<br /> The data convincingly implicate the ERES-ERGIC interface in autophagosome biogenesis. It would strengthen the manuscript to discuss previous studies reporting ERES and ERGIC remodeling and formation of ERERS-ERGIC contact sites (PMID: 34561617; PMID: 28754694) in the context of the current findings.

(2) Experimental Conditions<br /> In Figures 2A-C and Figure 4, it is unclear how the cells were treated. Were they starved in EBSS? This information should be included in the corresponding figure legends.

(3) LC3 Lipidation vs. Cleavage<br /> In Figure 2A, ARFGAP1 knockdown appears to reduce LC3 lipidation without affecting Halo-LC3 cleavage. Clarifying this observation would help readers better understand the functional specificity of ARFGAP1 in the pathway.

(4) Use of HT-mGFP in Figure 2C<br /> It should be clarified whether the assay in Figure 2C was performed in the presence of HT-mGFP. Explaining the rationale would aid the interpretation of the results.

(5) COPII Inhibition Strategy<br /> The authors used the dominant-active SAR1(H79G) mutant to inhibit COPII function. While this is effective in in vitro budding assays, the GDP-locked mutant SAR1(T39N) has been shown to be more effective in blocking COPII-mediated trafficking in cells. Including SAR1(T39N) in the analysis would provide stronger support for the conclusions.

Reviewer #3 (Public review):

The manuscript by Fuller et al describes a crosstalk between ARTG2A with components of the early secretory pathway, namely RAB1A and ARFGAP1. They show that ATG2A is recruited to membranes positive for RAB1A, which they also show to interact with ATG2A. In agreement with earlier findings by other groups, silencing RAB1A negatively affects autophagy. While ARFGAP1 was also found on ATG2A-positive membranes, silencing ARFGAP1 had no impact on autophagy. Notably, these ARFGAP1-positive membranes are not Golgi membranes.

The findings are interesting, and in general, the data are of good quality; however, I have outstanding questions. An answer to any of these questions might strengthen the manuscript:

(1) Are the membranes to which ATG2A is recruited a form of ERGIC?

(2) Figure 3A/B: Is it possible to show a better example? The difference is barely detectable by eye. Since immunoblotting is not really a quantitative method, I think that such a weak effect is prone to be wrong. Is there another tool/assay to validate this result?

(3) Is the curvature-sensitive region of ARFGAP1 required for its co-localization with ATG2A?

(4) What does Rab1A do? What is its effector? Or does the GTPase itself remodel the membrane?

(5) What about Arf1? It appears that the role of ARFGAP1 is unrelated to Arf1 and COPI? Thus, one would predict that Arf1 does not localize to these structures and does not affect ATG2A function.

(6) Does ARFGAP1 promote fission of the membrane from its donor compartment?

(7) What are ARFGAP1 and Rab1A recruited to? What is the lipid composition or protein that recruits these two players to regulate autophagy?

Reviewer #3 (Public review):

Summary:

In their study McDermott et al. investigate the neurocomputational mechanism underlying sensory prediction errors. They contrast two accounts: representational sharpening and dampening. Representational sharpening suggests that predictions increase the fidelity of the neural representations of expected inputs, while representational dampening suggests the opposite (decreased fidelity for expected stimuli). The authors performed decoding analyses on EEG data, showing that first expected stimuli could be better decoded (sharpening), followed by a reversal during later response windows where unexpected inputs could be better decoded (dampening). These results are interpreted in the context of opposing process theory (OPT), which suggests that such a reversal would support perception to be both veridical (i.e., initial sharpening to increase the accuracy of perception) and informative (i.e., later dampening to highlight surprising, but informative inputs).

Strengths:

The topic of the present study is of significant relevance for the field of predictive processing. The experimental paradigm used by McDermott et al. is well designed, allowing the authors to avoid several common confounds in investigating predictions, such as stimulus familiarity and adaptation. The introduction of the manuscript provides a well written summery of the main arguments for the two accounts of interest (sharpening and dampening), as well as OPT. Overall, the manuscript serves as a good overview of the current state of the field.

Weaknesses:

In my opinion some details of the methods, results and manuscript raise some doubts about the reliability of the reported findings. Key concerns are:

(1) In the previous round of comments, I noted that: "I am not fully convinced that Figures 3A/B and the associated results support the idea that early learning stages result in dampening and later stages in sharpening. The inference made requires, in my opinion, not only a significant effect in one-time bin and the absence of an effect in other bins. Instead to reliably make this inference one would need a contrast showing a difference in decoding accuracy between bins, or ideally an analysis not contingent on seemingly arbitrary binning of data, but a decrease (or increase) in the slope of the decoding accuracy across trials. Moreover, the decoding analyses seem to be at the edge of SNR, hence making any interpretation that depends on the absence of an effect in some bins yet more problematic and implausible". The authors responded: "we fitted a logarithmic model to quantify the change of the decoding benefit over trials, then found the trial index for which the change of the logarithmic fit was < 0.1%. Given the results of this analysis and to ensure a sufficient number of trials, we focused our further analyses on bins 1-2". However, I do not see how this new analysis addresses the concern that the conclusion highlights differences in decoding performance between bins 1 and 2, yet no contrast between these bins are performed. While I appreciate the addition of the new model, in my current understanding it does not solve the problem I raised. I still believe that if the authors wish to conclude that an effect differs between two bins they must contrast these directly and/or use a different appropriate analysis approach.

Relatedly, the logarithmic model fitting and how it justifies the focus on analysis bin 1-2 needs to be explained better, especially the rationale of the analysis, the choice of parameters (e.g., why logarithmic, why change of logarithmic fit < 0.1% as criterion, etc), and why certain inferences follow from this analysis. Also, the reporting of the associated results seems rather sparse in the current iteration of the manuscript.

(2) A critical point the authors raise is that they investigate the buildup of expectations during training. They go on to show that the dampening effect disappears quickly, concluding: "the decoding benefit of invalid predictions [...] disappeared after approximately 15 minutes (or 50 trials per condition)". Maybe the authors can correct me, but my best understanding is as follows: Each bin has 50 trials per condition. The 2:1 condition has 4 leading images, this would mean ~12 trials per leading stimulus, 25% of which are unexpected, so ~9 expected trials per pair. Bin 1 represents the first time the participants see the associations. Therefore, the conclusion is that participants learn the associations so rapidly that ~9 expected trials per pair suffice to not only learn the expectations (in a probabilistic context) but learn them sufficiently well such that they result in a significant decoding difference in that same bin. If so, this would seem surprisingly fast, given that participants learn by means of incidental statistical learning (i.e. they were not informed about the statistical regularities). I acknowledge that we do not know how quickly the dampening/sharpening effects develop, however surprising results should be accompanied with a critical evaluation and exceptionally strong evidence (see point 1). Consider for example the following alternative account to explain these results. Category pairs were fixed across and within participants, i.e. the same leading image categories always predicted the same trailing image categories for all participants. Some category pairings will necessarily result in a larger representational overlap (i.e., visual similarity, etc.) and hence differences in decoding accuracy due to adaptation and related effects. For example, house  barn will result in a different decoding performance compared to coffee cup  barn, simply due to the larger visual and semantic similarity between house and barn compared to coffee cup and barn. These effects should occur upon first stimulus presentation, independent of statistical learning, and may attenuate over time e.g., due to increasing familiarity with the categories (i.e., an overall attenuation leading to smaller between condition differences) or pairs.

(3) In response to my previous comment, why the authors think their study may have found different results compared to multiple previous studies (e.g. Han et al., 2019; Kumar et al., 2017; Meyer and Olson, 2011), particularly the sharpening to dampening switch, the authors emphasize the use of non-repeated stimuli (no repetition suppression and no familiarity confound) in their design. However, I fail to see how familiarity or RS could account for the absence of sharpening/dampening inversion in previous studies.

First, if the authors argument is about stimulus novelty and familiarity as described by Feuerriegel et al., 2021, I believe this point does not apply to the cited studies. Feuerriegel et al., 2021 note: "Relative stimulus novelty can be an important confound in situations where expected stimulus identities are presented often within an experiment, but neutral or surprising stimuli are presented only rarely", which indeed is a critical confound. However, none of the studies (Han et al., 2019; Richter et al., 2018; Kumar et al., 2017; Meyer and Olson, 2011) contained this confound, because all stimuli served as expected and unexpected stimuli, with the expectation status solely determined by the preceding cue. Thus, participants were equally familiar with the images across expectation conditions.

Second, for a similar reason the authors argument for RS accounting for the different results does not hold either in my opinion. Again, as Feuerriegel et al. 2021 correctly point out: "Adaptation-related effects can mimic ES when the expected stimuli are a repetition of the last-seen stimulus or have been encountered more recently than stimuli in neutral expectation conditions." However, it is critical to consider the precise design of previous studies. Taking again the example of Han et al., 2019; Kumar et al., 2017; Meyer and Olson, 2011. To my knowledge none of these studies contained manipulations that would result in a more frequent or recent repetition of any specific stimulus in the expected compared to unexpected condition. The crucial manipulation in all these previous studies is not that a single stimulus or stimulus feature (which could be subject to familiarity or RS) determines the expectation status, but rather the transitional probability (i.e. cue-stimulus pairing) of a particular stimulus given the cue. Therefore, unless I am missing something critical, simple RS seems unlikely to differ between expectation condition in the previous studies and hence seems implausible to account for differences in results compared to the current study.

Moreover, studies cited by the authors (e.g. Todorovic & de Lange, 2012) showed that RS and ES are separable in time, again making me wonder how avoiding stimulus repetition should account for the difference in the present study compared to previous ones. I am happy to be corrected in my understanding, but with the currently provided arguments by the authors I do not see how RS and familiarity can account for the discrepancy in results.

I agree with the authors that stimulus familiarity is a clear difference compared to previous designs, but without a valid explanation why this should affect results I find this account rather unsatisfying. I see the key difference in that the authors manipulated category predictability, instead of exemplar prediction - i.e. searching for a car instead of your car. However, if results in support of OPT would indeed depend on using novel images (i.e. without stimulus repetition), would this not severely limit the scope of the account and hence also its relevance? Certainly, the account provided by the authors casts the net wider and tries to explain visual prediction. Relatedly, if OPT only applies during training, as the authors seem to argue, would this again not significantly narrow the scope of the theory? Combined these two caveats would seem to demote the account from a general account of prediction and perception to one about perception during very specific circumstances. In my understanding the appeal of OPT is that it accounts for multiple challenges faced by the perceptual system, elegantly integrating them into a cohesive framework. Most of this would be lost by claiming that OPT's primary prediction would only apply to specific circumstances - novel stimuli during learning of predictions. Moreover, in the original formulation of the account, as outlined by Press et al., I do not see any particular reason why it should be limited to these specific circumstances. This does of course not mean that the present results are incorrect, however it does require an adequate discussion and acknowledgement in the manuscript.

Impact:

McDermott et al. present an interesting study with potentially impactful results. However, given my concerns raised in this and the previous round of comments, I am not entirely convinced of the reliability of the results. Moreover, the difficulty of reconciling some of the present results with previous studies highlights the need for more convincing explanations of these discrepancies and a stronger discussion of the present results in the context of the literature.

Author response:

The following is the authors’ response to the original reviews

Public reviews:

Reviewer 1 (Public Review):

Many thanks for the positive and constructive feedback on the manuscript.

This study reveals a great deal about how certain neural representations are altered by expectation and learning on shorter and longer timescales, so I am loath to describe certain limitations as 'weaknesses'. But one limitation inherent in this experimental design is that, by focusing on implicit, task-irrelevant predictions, there is not much opportunity to connect the predictive influences seen at the neural level to the perceptual performance itself (e.g., how participants make perceptual decisions about expected or unexpected events, or how these events are detected or appear).

Thank you for the interesting comment. We now discuss the limitation of task-irrelevant prediction . In brief, some studies which showed sharpening found that task demands were relevant, while some studies which showed dampening were based on task-irrelevant predictions, but it is unlikely that task relevance - which was not manipulated in the current study - would explain the switch between sharpening and dampening that we observe within and across trials.

The behavioural data that is displayed (from a post-recording behavioural session) shows that these predictions do influence perceptual choice - leading to faster reaction times when expectations are valid. In broad strokes, we may think that such a result is broadly consistent with a 'sharpening' view of perceptual prediction, and the fact that sharpening effects are found in the study to be larger at the end of the task than at the beginning. But it strikes me that the strongest test of the relevance of these (very interesting) EEG findings would be some evidence that the neural effects relate to behavioural influences (e.g., are participants actually more behaviourally sensitive to invalid signals in earlier phases of the experiment, given that this is where the neural effects show the most 'dampening' a.k.a., prediction error advantage?)

Thank you for the suggestion. We calculated Pearson’s correlation coefficients for behavioural responses (difference in mean reaction times), neural responses during the sharpening effect (difference in decoding accuracy), and neural responses during the dampening effect for each participant, which resulted in null findings.

Reviewer 2 (Public Review):

Thank you for your helpful and constructive comments on the manuscript.

The strength in controlling for repetition effects by introducing a neutral (50% expectation) condition also adds a weakness to the current version of the manuscript, as this neutral condition is not integrated into the behavioral (reaction times) and EEG (ERP and decoding) analyses. This procedure remained unclear to me. The reported results would be strengthened by showing differences between the neutral and expected (valid) conditions on the behavioral and neural levels. This would also provide a more rigorous check that participants had implicitly learned the associations between the picture category pairings.

Following the reviewer's suggestion, we have included the neutral condition in the behavioural analysis and performed a repeated measures ANOVA on all three conditions.

It is not entirely clear to me what is actually decoded in the prediction condition and why the authors did not perform decoding over trial bins in prediction decoding as potential differences across time could be hidden by averaging the data. The manuscript would generally benefit from a more detailed description of the analysis rationale and methods.

In the original version of the manuscript, prediction decoding aimed at testing if the upcoming stimulus category can be decoded from the response to the preceding ( leading) stimulus. However, in response to the other Reviewers’ comments we have decided to remove the prediction decoding analysis from the revised manuscript as it is now apparent that prediction decoding cannot be separated from category decoding based on pixel information.

Finally, the scope of this study should be limited to expectation suppression in visual perception, as the generalization of these results to other sensory modalities or to the action domain remains open for future research.

We have clarified the scope of the study in the revised manuscipt .

Reviewer 3 (Public Review):

Thank you for the thought-provoking and interesting comments and suggestions.

(1) The results in Figure 2C seem to show that the leading image itself can only be decoded with ~33% accuracy (25% chance; i.e. ~8% above chance decoding). In contrast, Figure 2E suggests the prediction (surprisingly, valid or invalid) during the leading image presentation can be decoded with ~62% accuracy (50% chance; i.e. ~12% above chance decoding). Unless I am misinterpreting the analyses, it seems implausible to me that a prediction, but not actually shown image, can be better decoded using EEG than an image that is presented on-screen.

Following this and the remaining comments by the Reviewer (see below), we have decided to remove the prediction analysis from the manuscript. Specifically, we have focused on the Reviewer’s concern that it is implausible that image prediction would be better decoded that an image that is presented on-screen. This led us to perform a control analysis, in which we tried to decode the leading image category based on pixel values alone (rather than on EEG responses). Since this decoding was above chance, we could not rule out the possibility that EEG responses to leading images reflect physical differences between image categories. This issue does not extend to trailing images, as the results of the decoding analysis based on trailing images are based on accuracy comparisons between valid and invalid trials, and thus image features are counterbalanced. We would like to thank the Reviewer for raising this issue

(2) The "prediction decoding" analysis is described by the authors as "decoding the predictable trailing images based on the leading images". How this was done is however unclear to me. For each leading image decoding the predictable trailing images should be equivalent to decoding validity (as there were only 2 possible trailing image categories: 1 valid, 1 invalid). How is it then possible that the analysis is performed separately for valid and invalid trials? If the authors simply decode which leading image category was shown, but combine L1+L2 and L4+L5 into one class respectively, the resulting decoder would in my opinion not decode prediction, but instead dissociate the representation of L1+L2 from L4+L5, which may also explain why the time-course of the prediction peaks during the leading image stimulus-response, which is rather different compared to previous studies decoding predictions (e.g. Kok et al. 2017). Instead for the prediction analysis to be informative about the prediction, the decoder ought to decode the representation of the trailing image during the leading image and inter-stimulus interval. Therefore I am at present not convinced that the utilized analysis approach is informative about predictions.

In this analysis, we attempted to decode ( from the response to leading images) which trailing categories ought to be presented. The analysis was split between trials where the expected category was indeed presented (valid) vs. those in which it was not (invalid). The separation of valid vs invalid trials in the prediction decoding analysis served as a sanity check as no information about trial validity was yet available to participants. However, as mentioned above, we have decided to remove the “prediction decoding” analysis based on leading images as we cannot disentangle prediction decoding from category decoding.

(3) I may be misunderstanding the reported statistics or analyses, but it seems unlikely that >10  of the reported contrasts have the exact same statistic of Tmax= 2.76 . Similarly, it seems implausible, based on visual inspection of Figure 2, that the Tmax for the invalid condition decoding (reported as Tmax = 14.903) is substantially larger than for the valid condition decoding (reported as Tmax = 2.76), even though the valid condition appears to have superior peak decoding performance. Combined these details may raise concerns about the reliability of the reported statistics.

Thank you for bringing this to our attention. This copy error has now been rectified.

(4) The reported analyses and results do not seem to support the conclusion of early learning resulting in dampening and later stages in sharpening. Specifically, the authors appear to base this conclusion on the absence of a decoding effect in some time-bins, while in my opinion a contrast between time-bins, showing a difference in decoding accuracy, is required. Or better yet, a non-zero slope of decoding accuracy over time should be shown ( not contingent on post-hoc and seemingly arbitrary binning).

Thank you for the helpful suggestion. We have performed an additional analysis to address this issue, we calculated the trial-by-trial time-series of the decoding accuracy benefit for valid vs. invalid for each participant and averaged this benefit across time points for each of the two significant time windows. Based on this, we fitted a logarithmic model to quantify the change of this benefit over trials, then found the trial index for which the change of the logarithmic fit was < 0.1% (i.e., accuracy was stabilized). Given the results of this analysis and to ensure a sufficient number of trials, we focussed our further analyses on bins 1-2 to directly assess the effects of learning. This is explained in more detail in the revised manuscript .

(5) The present results both within and across trials are difficult to reconcile with previous studies using MEG (Kok et al., 2017; Han et al., 2019), single-unit and multi-unit recordings (Kumar et al., 2017; Meyer & Olson 2011), as well as fMRI (Richter et al., 2018), which investigated similar questions but yielded different results; i.e., no reversal within or across trials, as well as dampening effects with after more training. The authors do not provide a convincing explanation as to why their results should differ from previous studies, arguably further compounding doubts about the present results raised by the methods and results concerns noted above.

The discussion of these findings has been expanded in the revised manuscript . In short, the experimental design of the above studies did not allow for an assessment of these effects prior to learning. Several of them also used repeated stimuli (albeit some studies changed the pairings of stimuli between trials), potentially allowing for RS to confound their results.

Recommendations for the Authors:

Reviewer 1 (Recommendations for the authors):

(1) On a first read, I was initially very confused by the statement on p.7 that each stimulus was only presented once - as I couldn't then work out how expectations were supposed to be learned! It became clear after reading the Methods that expectations are formed at the level of stimulus category (so categories are repeated multiple times even if exemplars are not). I suspect other readers could have a similar confusion, so it would be helpful if the description of the task in the 'Results' section (e.g., around p.7) was more explicit about the way that expectations were generated, and the (very large) stimulus set that examples are being drawn from.

Following your suggestion, we have clarified the paradigm by adding details about the categories and the manner in which expectations are formed.

(2) p.23: the authors write that their 1D decoding images were "subjected to statistical inference amounting to a paired t-test between valid and invalid categories". What is meant by 'amounting to' here? Was it a paired t-test or something statistically equivalent? If so, I would just say 'subjected to a paired t-test' to avoid any confusion, or explaining explicitly which statistic inference was done over.

We have rephrased this as “subjected to (1) a one-sample t-test against chance-level, equivalent to a fixed-effects analysis, and (2) a paired t-test”.

Relatedly, this description of an analysis amounting to a 'paired t-test' only seems relevant for the sensory decoding and memory decoding analyses (where there are validity effects) rather than the prediction decoding analysis. As far as I can tell the important thing is that the expected image category can be decoded, not that it can be decoded better or worse on valid or invalid trials.

In the previous version of the manuscript, the comparison of prediction decoding between valid and invalid trials was meant as a sanity check. However, in response to the other Reviewers’ comments we have decided to remove the prediction decoding analysis from the revised manuscript due to confounds.

It would be helpful if authors could say a bit more about how the statistical inferences were done for the prediction decoding analyses and the 'condition against baseline' contrasts (e.g., when it is stated that decoding accuracy in valid trials *,in general,* is above 0 at some cluster-wise corrected value). My guess is that this amounts to something like a one-sample t-test - but it may be worth noting that one-sample t-tests on information measures like decoding accuracy cannot support population-level inference, because these measures cannot meaningfully be below 0 (see Allefeld et al, 2016).

When testing for decoding accuracy against baseline, we used one-sample t-tests against chance level (rather than against 0) throughout the manuscript. We now clarify in the manuscript that this corresponds to a fixed-effects analysis (Allefeld et al., 2016). In contrast, when testing for differences in decoding accuracy between valid and invalid conditions, we used paired-sample t-tests. As mentioned above, the prediction decoding analysis has been removed from the analysis.

(3) By design, the researchers focus on implicit predictive learning which means the expectations being formed are ( by definition) task-irrelevant. I thought it could be interesting if the authors might speculate in the discussion on how they think their results may or may not differ when predictions are deployed in task-relevant scenarios -  particularly given that some studies have found sharpening effects do not seem to depend on task demands ( e.g., Kok et al, 2012 ; Yon et al, 2018)  while other studies have found that some dampening effects do seem to depend on what the observer is attending to ( e.g., Richter et al, 2018) . Do these results hint at a possible explanation for why this might be? Even if the authors think they don't, it might be helpful to say so!

Thank you for the interesting comment. We have expanded on this in the revised manuscript.

Reviewer 2  (Recommendations for the authors):

Methods/results

(1) The goal of this study is the assessment of expectation effects during statistical learning while controlling for repetition effects, one of the common confounds in prediction suppression studies (see, Feuerriegel et al., 2021). I agree that this is an important aspect and I assume that this was the reason why the authors introduced the P=0.5 neutral condition (Figure 1B, L3). However, I completely missed the analyses of this condition in the manuscript. In the figure caption of Figure 1C, it is stated that the reaction times of the valid, invalid, and neutral conditions are shown, but only data from the valid and invalid conditions are depicted. To ensure that participants had built up expectations and had learned the pairing, one would not only expect a difference between the valid and invalid conditions but also between the valid and neutral conditions. Moreover, it would also be important to integrate the neutral condition in the multivariate EEG analysis to actually control for repetition effects. Instead, the authors constructed another control condition based on the arbitrary pairings. But why was the neutral condition not compared to the valid and invalid prediction decoding results? Besides this, I also suggest calculating the ERP for the neutral condition and adding it to Figure 2A to provide a more complete picture.

As mentioned above, we have included the neutral condition in the behavioural analysis, as outlined in the revised manuscript. We have also included a repeated measures ANOVA on all 3 conditions. The purpose of the neutral condition was not to avoid RS, but rather to provide a control condition. We avoided repetition by using individual, categorised stimuli. Figure 1C has been amended to include the neutral condition). In response to the remaining comments, we have decided to remove the prediction decoding analysis from the manuscript.

(2) One of the main results that is taken as evidence for the OPT is that there is higher decoding accuracy for valid trials (indicate sharpening) early in the trial and higher decoding accuracy for invalid trials (indicate dampening) later in the trial. I would have expected this result for prediction decoding that surprisingly showed none of the two effects. Instead, the result pattern occurred in sensory decoding only, and partly (early sharpening) in memory decoding. How do the authors explain these results? Additionally, I would have expected similar results in the ERP; however, only the early effect was observed. I missed a more thorough discussion of this rather complex result pattern. The lack of the opposing effect in prediction decoding limits the overall conclusion that needs to be revised accordingly.

Since sharpening vs. dampening rests on the comparison between valid and invalid trials, evidence for sharpening vs. dampening could only be obtained from decoding based on responses to trailing images. In prediction decoding (removed from the current version), information about the validity of the trial is not yet available. Thus, our original plan was to compare this analysis with the effects of validity on the decoding of trailing images (i.e. we expected valid trials to be decoded more accurately after the trailing image than before). The results of the memory decoding did mirror the sensory decoding of the trailing image in that we found significantly higher decoding accuracy of the valid trials from 123-180 ms. As with the sensory decoding, there was a tendency towards a later flip (280-296 ms) where decoding accuracy of invalid trials became nominally higher, but this effect did not reach statistical significance in the memory decoding.

(3) To increase the comprehensibility of the result pattern, it would be helpful for the reader to clearly state the hypotheses for the ERP and multivariate EEG analyses. What did you expect for the separate decoding analyses? How should the results of different decoding analyses differ and why? Which result pattern would (partly, or not) support the OPT?

Our hypotheses are now stated in the revised manuscript.

(4) I was wondering why the authors did not test for changes during learning for prediction decoding. Despite the fact that there were no significant differences between valid and invalid conditions within-trial, differences could still emerge when the data set is separated into bins. Please test and report the results.

As mentioned above, we have decided to remove the prediction decoding analysis from the current version of the manuscript.

(5) To assess the effect of learning the authors write: 'Given the apparent consistency of bins 2-4, we focused our analyses on bins 1-2.' Please explain what you mean by 'apparent consistency'. Did you test for consistency or is it based on descriptive results? Why do the authors not provide the complete picture and perform the analyses for all bins? This would allow for a better assessment of changes over time between valid and invalid conditions. In Figure 3, were valid and invalid trials different in any of the QT3 or QT4 bins in sensory or memory encoding?

We have performed an additional analysis to address this issue. The reasoning behind the decision to focus on bins 1-2 is now explained in the revised manuscript. In short, fitting a learning curve to trial-by-trial decoding estimates indicates that decoding stabilizes within <50% of the trials. To quantify changes in decoding occurring within these <50% of the trials while ensuring a sufficient number of trials for statistical comparisons, we decided to focus on bins 1-2 only.

(6) Please provide the effect size for all statistical tests.

Effect sizes have now been provided.

(7) Please provide exact p-values for non-significant results and significant results larger than 0.001.

Exact p-values have now been provided.

(8) Decoding analyses: I suppose there is a copy/paste error in the T-values as nearly all T-values on pages 11 and 12 are identical (2.76) leading to highly significant p-values (0.001) as well as non-significant effects (>0.05). Please check.

Thank you for bringing this to our attention. This error has now been corrected.

(9) Page 12:  There were some misleading phrases in the result section. To give one example: 'control analyses was slightly above change' - this sounds like a close to non-significant effect, but it was indeed a highly significant effect of p<0.001. Please revise.

This phrase was part of the prediction decoding analysis and has therefore been removed.

(10) Sample size: How was the sample size of the study be determined (N=31)? Why did only a subgroup of participants perform the behavioral categorization task after the EEG recording? With a larger sample, it would have been interesting to test if participants who showed better learning (larger difference in reaction times between valid and invalid conditions) also showed higher decoding accuracies.

This has been clarified in the revised manuscript. In short, the larger sample size of N=31 was based on previous research; ten participants were initially tested as part of a pilot which was then expanded to include the categorisation task.

(11) I assume catch trials were removed before data analyses?

We have clarified that catch trials were indeed removed prior to analyses.

(12) Page 23, 1st line: 'In each, the decoder...' Something is missing here.

Thank you for bringing this to our attention, this sentence has now been rephrased as “In both valid and invalid analyses” in the revised manuscript.

Discussion

(1) The analysis over multiple trials showed dampening within the first 15 min followed by sharpening. I found the discussion of this finding very lengthy and speculative (page 17). I recommend shortening this part and providing only the main arguments that could stimulate future research.

Thank you for the suggestion. Since Reviewer 3 has requested additional details in this part of the discussion, we have opted to keep this paragraph in the manuscript. However, we have also made it clearer that this section is relatively speculative and the arguments provided for the across trials dynamics are meant to stimulate further research.

(2) As this task is purely perceptual, the results support the OPT for the area of visual perception. For action, different results have been reported. Suppression within-trial has been shown to be larger for expected than unexpected features of action targets and suppression even starts before the start of the movement without showing any evidence for sharpening ( e.g., Fuehrer et al., 2022, PNAS). For suppression across trials, it has been found that suppression decreases over the course of learning to associate a sensory consequence to a specific action (e.g., Kilteni et al., 2019, ELife). Therefore, expectation suppression might function differently in perception and action (an area that still requires further research). Please clarify the scope of your study and results on perceptual expectations in the introduction, discussion, and abstract.

We have clarified the scope of the study in the revised manuscript.

Figures

(1) Figure 1A: Add 't' to the arrow to indicate time.

This has been rectified.

(2) Figure 3:  In the figure caption, sensory and memory decoding seem to be mixed up. Please correct. Please add what the dashed horizontal line indicates.

Thank you for bringing this to our attention, this has been rectified.

Reviewer 3  (Recommendations for the authors):

I applaud the authors for a well-written introduction and an excellent summary of a complicated topic, giving fair treatment to the different accounts proposed in the literature. However, I believe a few additional studies should be cited in the Introduction, particularly time-resolved studies such as Han et al., 2019; Kumar et al., 2017; Meyer and Olson, 2011. This would provide the reader with a broader picture of the current state of the literature, as well as point the reader to critical time-resolved studies that did not find evidence in support of OPT, which are important to consider in the interpretation of the present results.

The introduction has been expanded to include the aforementioned studies in the revised manuscript.

Given previous neuroimaging studies investigating the present phenomenon, including with time-resolved measures (e.g. Kok et al., 2017; Han et al., 2019; Kumar et al., 2017; Meyer & Olson 2011), why do the authors think that their data, design, or analysis allowed them to find support for OPT but not previous studies? I do not see obvious modifications to the paradigm, data quantity or quality, or the analyses that would suggest a superior ability to test OPT predictions compared to previous studies. Given concerns regarding the data analyses (see points below), I think it is essential to convincingly answer this question to convince the reader to trust the present results.

The most obvious alteration to the paradigm is the use of non-repeated stimuli. Each of the above time-resolved studies utilised repeated stimuli (either repeated, identical stimuli, or paired stimuli where pairings are changed but the pool of stimuli remains the same), allowing for RS to act as a confound as exemplars are still presented multiple times. By removing this confound, it is entirely plausible that we may find different time-resolved results given that it has been shown that RS and ES are separable in time (Todorovic & de Lange, 2012). We also test during learning rather than training participants on the task beforehand. By foregoing a training session, we are better equipped to assess OPT predictions as they emerge. In our across-trial results, learning appears to take place after approximately 15 minutes or 432 trials, at which point dampening reverses to sharpening. Had we trained the participants prior to testing, this effect would have been lost.

What is actually decoded in the "prediction decoding" analysis? The authors state that it is "decoding the predictable trailing images based on the leading images" (p.11). The associated chance level (Figure 2E) is indicated as 50%. This suggests that the classes separated by the SVM are T6 vs T7. How this was done is however unclear. For each leading image decoding the predictable trailing images should be equivalent to decoding validity (as there are only 2 possible trailing images, where one is the valid and the other the invalid image). How is it then possible that the analysis is performed separately for valid and invalid trials? Are the authors simply decoding which leading image was shown, but combine L1+L2 and L4+L5 into one class respectively? If so, this needs to be better explained in the manuscript. Moreover, the resulting decoder would in my opinion not decode the predicted image, but instead learn to dissociate the representation of L1+L2 from L4+L5, which may also explain why the time course of the prediction peaks during the leading image stimulus-response, which is rather different compared to previous studies decoding (prestimulus) predictions (e.g. Kok et al. 2017). If this is indeed the case, I find it doubtful that this analysis relates to prediction. Instead for the prediction analysis to be informative about the predicted image the authors should, in my opinion, train the decoder on the representation of trailing images and test it during the prestimulus interval.

As mentioned above, the prediction decoding analysis has been removed from the manuscript. The prediction decoding analysis was intended as a sanity check, as validity information was not yet available to participants.

Related to the point above, were the leading/trailing image categories and their mapping to L1, L2, etc. in Figure 1B fixed across subjects? I.e. "'beach' and 'barn' as 'Leading' categories would result in 'church' as a 'Trailing' category with 75% validity" (p.20) for all participants? If so, this poses additional problems for the interpretation of the analysis discussed in the point above, as it may invalidate the control analyses depicted in Figure 2E, as systematic differences and similarities in the leading image categories could account for the observed results.

Image categories and their mapping were indeed fixed across participants. While this may result in physical differences and similarities between images influencing results, counterbalancing categories across participants would not have addressed this issue. For example, had we swapped “beach” with “barn” in another participant, physical differences between images may still be reflected in the prediction decoding. On the other hand, counterbalancing categories across trials was not possible given our aim of examining the initial stages of learning over trials. Had we changed the mappings of categories throughout the experiment for each participant, we would have introduced reversal learning and nullified our ability to examine the initial stages of learning under flat priors. In any case, the prediction decoding analysis has been removed from the manuscript, as outlined above.

Why was the neutral condition L3 not used for prediction decoding? After all, if during prediction decoding both the valid and invalid image can be decoded, as suggested by the authors, we would also expect significant decoding of T8/T9 during the L3 presentation.

In the neutral condition, L3 was followed by T8 vs. T9 with 50% probability, precluding prediction decoding. While this could have served as an additional control analysis for EEG-based decoding, we have opted for removing prediction decoding from the analysis. However, in response to the other Reviewers’ comments, the neutral condition has now been included in the behavioral analysis.

The following concern may arise due to a misunderstanding of the analyses, but I found the results in Figures 2C and 2E concerning. If my interpretation is correct, then these results suggest that the leading image itself can only be decoded with ~33% accuracy (25% chance; i.e. ~8% above chance decoding). In contrast, the predicted (valid or invalid) image during the leading image presentation can be decoded with ~62% accuracy (50% chance; i.e. ~12% above chance decoding). Does this seem reasonable? Unless I am misinterpreting the analyses, it seems implausible to me that a prediction but not actually shown image can be better decoded than an on-screen image. Moreover, to my knowledge studies reporting decoding of predictions can (1) decode expectations just above chance level (e.g. Kok et al., 2017; which is expected given the nature of what is decoded) and (2) report these prestimulus effects shortly before the anticipated stimulus onset, and not coinciding with the leading image onset ~800ms before the predicted stimulus onset. For the above reasons, the key results reported in the present manuscript seem implausible to me and may suggest the possibility of problems in the training or interpretation of the decoding analysis. If I misunderstood the analyses, the analysis text needs to be refined. If I understood the analyses correctly, at the very least the authors would need to provide strong support and arguments to convince the reader that the effects are reliable (ruling out bias and explaining why predictions can be decoded better than on-screen stimuli) and sensible (in the context of previous studies showing different time-courses and results).

As explained above, we have addressed this concern by performing an additional analysis, implementing decoding based on image pixel values. Indeed we could not rule out the possibility that “prediction” decoding reflected stimulus differences between leading images.

Relatedly, the authors use the prestimulus interval (-200 ms to 0 ms before predicted stimulus onset) as the baseline period. Given that this period coincides with prestimulus expectation effects ( Kok et al., 2017) , would this not result in a bias during trailing image decoding? In other words, the baseline period would contain an anticipatory representation of the expected stimulus ( Kok et al., 2017) , which is then subtracted from the subsequent EEG signal, thereby allowing the decoder to pick up on this "negative representation" of the expected image. It seems to me that a cleaner contrast would be to use the 200ms before leading image onset as the baseline.

The analysis of trailing images aimed at testing specific hypotheses related to differences between decoding accuracy in valid vs. invalid trials. Since the baseline was by definition the same for both kinds of trials (since information about validity only appears at the onset of the trailing image), changing the baseline would not affect the results of the analysis. Valid and invalid trials would have the same prestimulus effect induced by the leading image.

Again, maybe I misunderstood the analyses, but what exactly are the statistics reported on p. 11 onward? Why is the reported Tmax identical for multiple conditions, including the difference between conditions? Without further information this seems highly unlikely, further casting doubts on the rigor of the applied methods/analyses. For example: "In the sensory decoding analysis based on leading images, decoding accuracy was above chance for both valid (Tmax= 2.76, pFWE < 0.001) and invalid trials (Tmax= 2.76, pFWE < 0.001) from 100 ms, with no significant difference between them (Tmax= 2.76, pFWE > 0.05) (Fig. 2C)" (p.11).

Thank you for bringing this to our attention. As previously mentioned, this copy error has been rectified in the revised manuscript.

Relatedly, the statistics reported below in the same paragraph also seem unusual. Specifically, the Tmax difference between valid and invalid conditions seems unexpectedly large given visual inspection of the associated figure: "The decoding accuracy of both valid (Tmax = 2.76, pFWE < 0.001) and invalid trials (Tmax = 14.903, pFWE < 0.001)" (p.12). In fact, visual inspection suggests that the largest difference should probably be observed for the valid not invalid trials (i.e. larger Tmax).

This copy error has also been rectified in the revised manuscript.

Moreover, multiple subsequent sections of the Results continue to report the exact same Tmax value. I will not list all appearances of "Tmax = 2.76" here but would recommend the authors carefully check the reported statistics and analysis code, as it seems highly unlikely that >10 contrasts have exactly the same Tmax. Alternatively, if I misunderstand the applied methods, it would be essential to better explain the utilized method to avoid similar confusion in prospective readers.

This error has also now been rectified. As mentioned above the prediction decoding analysis has been removed.

I am not fully convinced that Figures 3A/B and the associated results support the idea that early learning stages result in dampening and later stages in sharpening. The inference made requires, in my opinion, not only a significant effect in one-time bin and the absence of an effect in other bins. Instead to reliably make this inference one would need a contrast showing a difference in decoding accuracy between bins, or ideally an analysis not contingent on seemingly arbitrary binning of data, but a decrease ( or increase) in the slope of the decoding accuracy across trials. Moreover, the decoding analyses seem to be at the edge of SNR, hence making any interpretation that depends on the absence of an effect in some bins yet more problematic and implausible.

Thank you for the helpful suggestion. As previously mentioned we fitted a logarithmic model to quantify the change of the decoding benefit over trials, then found the trial index for which the change of the logarithmic fit was < 0.1 %. Given the results of this analysis and to ensure a sufficient number of trials, we focussed our further analyses on bins 1-2 . This is explained in more detail in the revised manuscript.

Relatedly, based on the literature there is no reason to assume that the dampening effect disappears with more training, thereby placing more burden of proof on the present results. Indeed, key studies supporting the dampening account (including human fMRI and MEG studies, as well as electrophysiology in non-human primates) usually seem to entail more learning than has occurred in bin 2 of the present study. How do the authors reconcile the observation that more training in previous studies results in significant dampening, while here the dampening effect is claimed to disappear with less training?

The discussion of these findings has been expanded on in the revised manuscript. As previously outlined, many of the studies supporting dampening did not explicitly test the effect of learning as they emerge, nor did they control for RS to the same extent.

The Methods section is quite bare bones. This makes an exact replication difficult or even impossible. For example, the sections elaborating on the GLM and cluster-based FWE correction do not specify enough detail to replicate the procedure. Similarly, how exactly the time points for significant decoding effects were determined is unclear (e.g., p. 11). Relatedly, the explanation of the decoding analysis, e.g. the choice to perform PCA before decoding, is not well explained in the present iteration of the manuscript. Additionally, it is not mentioned how many PCs the applied threshold on average resulted in.

Thank you for this suggestion, we have described our methods in more detail.

To me, it is unclear whether the PCA step, which to my knowledge is not the default procedure for most decoding analyses using EEG, is essential to obtain the present results. While PCA is certainly not unusual, to my knowledge decoding of EEG data is frequently performed on the sensor level as SVMs are usually capable of dealing with the (relatively low) dimensionality of EEG data. In isolation this decision may not be too concerning, however, in combination with other doubts concerning the methods and results, I would suggest the authors replicate their analyses using a conventional decoding approach on the sensory level as well.

Thank you for this suggestion, we have explained our decision to use PCA in the revised manuscript.

Several choices, like the binning and the focus on bins 1-2 seem rather post-hoc. Consequently, frequentist statistics may strictly speaking not be appropriate. This further compounds above mentioned concerns regarding the reliability of the results.

The reasoning behind our decision to focus on bins 1-2 is now explained in more detail in the revised manuscript.

A notable difference in the present study, compared to most studies cited in the introduction motivating the present experiment, is that categories instead of exemplars were predicted.

This seems like an important distinction to me, which surprisingly goes unaddressed in the Discussion section. This difference might be important, given that exemplar expectations allow for predictions across various feature levels (i.e., even at the pixel level), while category predictions only allow for rough (categorical) predictions.

The decision to use categorical predictions over exemplars lies in the issue of RS, as it is impossible to control for RS while repeating stimuli over many trials. This has been discussed in more detail in the revised manuscript.

While individually minor problems, I noticed multiple issues across several figures or associated figure texts. For example: Figure 1C only shows valid and invalid trials, but the figure text mentions the neutral condition. Why is the neutral condition not depicted but mentioned here? Additionally, the figure text lacks critical information, e.g. what the asterisk represents. The error shading in Figure 2 would benefit from transparency settings to not completely obscure the other time-courses. Increasing the figure content and font size within the figure (e.g. axis labels) would also help with legibility (e.g. consider compressing the time-course but therefore increasing the overall size of the figure). I would also recommend using more common methods to indicate statistical significance, such as a bar at the bottom of the time-course figure typically used for cluster permutation results instead of a box. Why is there no error shading in Figure 2A but all other panels? Fig 2C-F has the y-axis label "Decoding accuracy (%)" but certainly the y-axis, ranging roughly from 0.2 to 0.7, is not in %. The Figure 3 figure text gives no indication of what the error bars represent, making it impossible to interpret the depicted data. In general, I would recommend that the authors carefully revisit the figures and figure text to improve the quality and complete the information.

Thank you for the suggestions. Figure 1C now includes the neutral condition. Asterisks denote significant results. The font size in Figure 2C-E has been increased. The y-axis on Figure 2C-E has been amended to accurately reflect decoding accuracy in percentage. Figure 2A has error shading, however, the error is sufficiently small that the error shading is difficult to see. The error bars in Figure 3 have been clarified.

Given the choice of journal (eLife), which aims to support open science, I was surprised to find no indication of (planned) data or code sharing in the manuscript.

Plans for sharing code/data are now outlined in the revised manuscript.

While it is explained in sufficient detail later in the Methods section, it was not entirely clear to me, based on the method summary at the beginning of the Results section, whether categories or individual exemplars were predicted. The manuscript may benefit from clarifying this at the start of the Results section.

Thank you for this suggestion, following this and suggestions from other reviewers, the experimental paradigm and the mappings between categories has been further explained in the revised manuscript, to make it clearer that predictions are made at the categorical level.

"Unexpected trials resulted in a significantly increased neural response 150 ms after image onset" (p.9). I assume the authors mean the more pronounced negative deflection here. Interpreting this, especially within the Results section as "increased neural response" without additional justification may stretch the inferences we can make from ERP data; i.e. to my knowledge more pronounced ERPs could also reflect increased synchrony. That said, I do agree with the authors that it is likely to reflect increased sensory responses, it would just be useful to be more cautious in the inference.

Thank you for the interesting comment, this has been rephrased as a “more pronounced negative deflection” in the revised manuscript.

Why was the ERP analysis focused exclusively on Oz? Why not a cluster around Oz? For object images, we may expect a rather wide dipole.

Feuerriegel et al (2021) have outlined issues questioning the robustness of univariate analyses for ES, as such we opted for a targeted ROI approach on the channel showing peak amplitude of the visually evoked response (Fig. 2B). More details on this are in the revised manuscript.           

How exactly did the authors perform FWE? The description in the Method section does not appear to provide sufficient detail to replicate the procedure.

FWE as implemented in SPM is a cluster-based method of correcting for multiple comparisons using random field theory. We have explained our thresholding methods in more detail in the revised manuscript.

If I misunderstand the authors and they did indeed perform standard cluster permutation analyses, then I believe the results of the timing of significant clusters cannot be so readily interpreted as done here (e.g. p.11-12); see: Maris & Oostenveld 2007; Sassenhagen & Dejan 2019.

All statistics were based on FWE under random field theory assumptions (as implemented in SPM) rather than on cluster permutation tests (as implemented in e.g.  Fieldtrip)

Why did the authors choose not to perform spatiotemporal cluster permutation for the ERP results?

As mentioned above, we opted to target our ERP analyses on Oz due to controversies in the literature regarding univariate effects of ES (Feuerriegel et al., 2021).

Some results, e.g. on p.12 are reported as T29 instead of Tmax. Why?

As mentioned above, prediction decoding analyses have been removed from the manuscript.

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

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Reply to the reviewers

1. General Statements

We would like to thank all reviewers assigned by Review Commons for their thoughtful and constructive feedback, which helped us to further improve the quality and clarity of our manuscript. In this study, we developed a novel fluorescence-based live-cell imaging platform for detecting mitochondria-endoplasmic reticulum contact sites (MERCS), which we named MERCdRED. This system enables quantitative analysis of MERCS dynamics in living cells by combining stable gene expression of dimerization-dependent fluorescent proteins with single-cell cloning. Using this tool, we uncovered a nutrient-dependent regulatory mechanism of MERCS formation mediated by the ER-localized tethering protein PDZD8. We appreciate that all the reviewers acknowledged the methodological robustness of this work. In response to reviewers' comments, we will significantly improve the manuscript by adding the live-cell imaging to assess the reversible propertyof MERCdRED, and investigating the physiological impacts of MERCS remodeling in regulating metabolism in response to nutrient starvation. We believe that both the methodological advance and the biological findings presented in this study will be of broad interest to the cell biology community.

1. Description of the planned revisions

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

Summary: In this study, the authors successfully established a stable cell line expressing MERCdRED, a dimerization-dependent fluorescent protein (ddFP)-based sensor for monitoring mitochondrial-ER contact sites (MERCS). Through light and electron microscopy analyses, they demonstrated that MERCS formation is regulated by nutrient availability and requires PDZD8. While the work is technically sound and well-presented, the biological implications of nutrient-dependent MERCS regulation remain underexplored.

Major Concerns: Although the manuscript is methodologically robust and suitable for a Methods-type article, its biological significance is limited. The findings primarily serve as proof-of-concept for the MERCdRED tool, without substantially advancing our understanding of MERCS regulation.

We appreciate the reviewer for acknowledging the methodological robustness of our study. We would like to respectfully emphasize that, using the MERCdRED cell system, we uncovered the distinct features of MERCS dynamics by comparing structures of various sizes (Figure 4A-D). Furthermore, we discovered an unexpected biological finding: nutrient starvation leads to a reduction in MERCS formation, which contrasts with previous reports using cell lines (former Figure 4E-H). Additionally, we revealed that PDZD8 mediates nutrient-dependent MERCS regulation (former Figure 4E-H).

To clarify these findings, we have now separated the former Figure 4 into two distinct figures (now Figure 4 and 5). Furthermore, to assess the functional relevance of PDZD8-mediated MERCS regulation upon nutritional change, we will perform rescue experiments by overexpressing PDZD8 in starved cells, along with a metabolomic analysis in these conditions. We will add these new data in Figure 6.

Taken together, we believe that our data provide novel mechanistic insights into how MERCS are modulated and utilized for the regulation of metabolism under physiological stress, thereby contributing to a deeper understanding of the roles and regulation of MERCS beyond the scope of a mere proof-of-concept study.

Reviewer #1 (Significance (Required)):

To enhance the impact of the study, the authors could use this sensor to investigate novel biological questions-such as the molecular pathways linking nutrient sensing to MERCS dynamics-or explore downstream activities of nutrient-dependent MERCS formation. Deeper mechanistic insights would significantly strengthen the work's contribution to the field.

We thank the reviewer for their constructive suggestions. We fully agree that the MERCdRED cell system has great potential for investigating upstream signaling pathways regulating MERCS dynamics, as well as the downstream consequences of nutrient-dependent MERCS modulation. As mentioned above, this study already presents important findings, including the discovery of PDZD8 as a key protein linking the nutrient starvation and MERCS remodeling, and a relationship between MERCS dynamics and contact site size.

To further assess the biological consequence of the MERCS remodeling, we will perform metabolomics analysis in PDZD8-overexpressing cells under starved conditions.

Additionally, to further reinforce the utility of MERCdRED and extend the findings presented in this study, we performed live-cell imaging experiments using MERCdRED. The preliminary results demonstrated dynamic and reversible changes in MERCS in response to nutrient starvation and subsequent recovery (Please see the response to Reviewer 3 below, Reviewer-only Figure 1).

These new data will significantly strengthen the contribution of this study to the field.

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

This manuscript entitled "Live-cell imaging reveals nutrient-dependent dynamics of ER-mitochondria contact formation via PDZD8" by Saeko Aoyama-Ishiwatari et al., describes a novel methodology for visualizing contacts between mitochondria and the endoplasmic reticulum (MERCs) by fluorescence microscopy. Inter-organelle contacts, defined as membrane proximities below ~30 nm, fall below the diffraction limit of conventional light microscopy. The method developed by Hirabayashi's laboratory leverages dimerization-dependent fluorescence complementation to create a reporter capable of both visualizing and quantifying ER-mitochondria contacts (MERCs).

Reviewer #2 (Significance (Required)):

This timely study provides a valuable and innovative approach to overcoming a longstanding technical limitation in the field, enabling dynamic analysis of ER-mitochondria contacts.

We appreciate the reviewer for recognizing the timeliness and innovation of our work.

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

In this manuscript, the authors develop a new system to study mitochondrial-ER contact sites in living mouse embryonic fibroblast cells and explore the impact that nutritent starvation has on these contact sites in real time. By stably expressing a bicistroic reporter construct of a dimerization-dependent fluorescent protein that will generate a signal once the two moities, one anchored in the ER by Sec61b, and the other anchored in the outer membrane of mitochondria, via TOM20, comme in close apposition. This cell model is validated using sofisticated CLEM experiments and via the ablation of known regulators of MERCs, such as PDZD8 and FKBP8.

Major comments

The authors claim to have developed a new for the study of MERCs. They indeed have benchmarked this system using very sophisticated CLEM approaches and through the ablation of known regulators of MERCs, all of which is very carefully performed and convincing.

We appreciate the reviewer for acknowledging our efforts in the development and validation of the MERCdRED system presented in this study.

They argue that the generation of a stable cell line via lentiviral delivery is an improvement over the transient transfection approaches that have been applied in the past (see cited references), which I would generally agree. However, they have not contrasted or compared their system to the widly-used SLPICs system from the Tito Cali group (Vallese, F. et al. An expanded palette of improved SPLICS reporters detects multiple organelle contacts in vitro and in vivo. Nat. Commun. 11, 6069 (2020)) which measures bi and tri-partite interactions with other membrane contacts, including mitochondria and ER at two specific distances, which in my opinion has been more extensivley used to study cell and tissue physiology. They accurately point out that the reversability of this and other systems is challenging and it would be important to define highlight whether the current system allows the study of reversible MERCs. It does not appear as though the reversability of MERCs has been explored in this study.

We thank the reviewer for these thoughtful suggestions and agree that further investigation into the reversibility of MERCS using the MERCdRED system would be valuable. Following the reviewer's suggestion, we performed a live-cell imaging experiment using MERCdRED to monitor dynamic changes in MERCS in response to nutrient starvation and subsequent recovery. The preliminary results were obtained as shown in Reviewer-only Figure 1, which strongly suggests the utility of MERCdRED for detecting reversible MERCS formation. The data will be added in Figure 5 if the reproducibility is confirmed. This new data set highlights the distinct utility of the MERCdRED system in studying MERCS dynamics.

We acknowledge that the SPLICS system has been widely adopted for studying membrane contact sites. In the revised manuscript, we will include a comparative discussion of MERCdRED, SPLICS, and other existing MERCS reporters, particularly with respect to their capabilities in capturing the reversible nature of these contacts.

The genetic (PDZD8, FKBP8) and nutritional (starvation) interventions are very helpful to benchmark the system. The description of the methods and data appear to be reproducible and the stastical analyses are acceptable.

We thank the reviewer for their positive evaluation of our data and analyses.

Minor comments

As mentioned above, it would be helpful to reference and compare the current study in the context of reversability, which the current MERCdRED system has the potential to provide beyond the state-of-the-art.

We thank the reviewer for this helpful suggestion. We will include additional discussion comparing the reversibility of the MERCdRED system with that of existing tools, highlighting the potential advantages of MERCdRED in capturing dynamic and reversible MERCS.

Reviewer #3 (Significance (Required)):

Significance

The major strength of this study is the development of a stable cell line that allows for the study of MERCs, which has the potential to study the reversible nature of these membrane contact sites. It is debatable as a stable cell line rather than a transient transfection offers a major advancement, even if it does make the study of the system more straightforward, especially if the phenomenon of reversibilty is to be explored.I believe that the CLEM study offers a very informative and precise way to benchmark the ddFP system. Defining how MERC formation and separation (once again the reversibility discovery) have impacts in cell physiology beyond the distances altered by starvation would improve the study. Examining the impact on calcium homeostasis, lipid metabolism, and other aspects of biology that are known to be influenced by MERCs would be interesting. As such, there are no new conceptual, mechanistic, or functional advances, simply minor technical advances in the creation of a stable cell line followed by very solid benchmarking experiments. More complex tri-partite interactions, studied elsewhere, which are conceptually very important for cell and organelle biology, have not been attempted here. Similarly, the notion of studied different types of MERCs, which have been proposed to be important for cell biology, has not been explored using this single reporter. The target audience for this study is one that is interested in membrane contact sites and quantitative biology. My expertise is in mitochondrial fluorescence imaging and biology. I am not an expert in CLEM.

We thank the reviewer for their thoughtful and detailed comments. We would like to respectfully emphasize that the establishment of a clonal cell line has enabled us to uncover a striking and unexpected biological finding-namely, that nutrient starvation leads to a reduction, rather than an increase, in MERCS formation, and that this change is regulated by PDZD8. This observation directly contradicts previous reports and highlights the value of our robust and quantitative system for re-evaluating previously held assumptions.

We agree that demonstrating the reversibility of MERCS formation using our system would further strengthen the utility and reliability of the MERCdRED platform. To address this, as mentioned above, we performed a live-cell imaging to assess the dynamic reversibility of MERCS formation (Reviewer-only Figure 1) and will add the results in the revised manuscript.

We agree that investigation of tri-partite interactions is conceptually important for understanding the broader landscape of organelle communication. However, assessing tri-partite organelle contacts is beyond the scope of this study. We recognize that this is one of the key directions for future studies and believe that the MERCdRED platform is a promising tool for exploring such complex interactions.

Regarding different types of MERCS, we would like to clarify that our study does address this point to some extent. We identified distinct features of MERCS behavior by comparing structures of different sizes-an aspect that, to our knowledge, has not been previously examined. These findings contribute conceptually to our understanding of the dynamic and heterogeneous nature of ER-mitochondria contacts.

We believe that our methodological development provides important mechanistic insights into MERCS dynamics, as described above. In line with the reviewer's suggestion, we will investigate the physiological impacts of MERCS remodeling in regulating metabolism in response to nutrient starvation. We hope these forthcoming data will further enhance the biological relevance of our findings.

Taken together, we believe our study provides both a solid technical advance and novel mechanistic insights into MERCS biology, which will be of interest to researchers working on membrane contact sites, organelle dynamics, and cell physiology.

We will revise the manuscript to more clearly convey the significance and implications of this study.

1. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #2

Major points:

In Figure 1E (and the rest of the manuscript), the meaning of the label "MERCdRED on Mito" is unclear. A portion of the MERCdRED signal does not co-localize with mitochondria. The authors should clearly define what "MERCdRED on Mito" represents which appears to be the intensity of the MERCdRED signal within the mitochondrial mask. How about the global MERCdRED signal intensity? When the authors knocked-out PDZD8, did the global fluorescence intensity of the MERCdRED signal decrease?

As the reviewer pointed out, some red signals appear outside of mitochondria in MERCdRED cells, which are presumably due to autofluorescence. While the global red channel fluorescence intensity also decreased upon PDZD8 conditional knockout (cKO), as shown in Reviewer-only Figure 2A, the reduction was less pronounced than the decrease observed when only the red signals on mitochondria were measured (Reviewer-only Figure 2B). We consider the mitochondrial red signals to represent MERCdRED signals, and we agree that the label "MERCdRED on Mito" may be misleading. To improve clarity, we revised the figure labels as follows: "MERCdRED" was changed to "Red channel," and "MERCdRED on Mito" was changed to "MERCdRED (Red signals on Mito)."

1. While the authors demonstrate that MERCdRED can quantify a reduction in MERCs (e.g., in PDZD8 knockout conditions), it would be valuable to assess its sensitivity to increases in MERCs as well. For example, previous work from the authors (Nakamura et al., 2025) showed that FKBP8 overexpression leads to an increase in MERCs.

We thank the reviewer for suggesting this valuable experiment. To assess whether the dynamic range of MERCdRED covers increased MERCS formation, we overexpressed PDZD8 in MERCdRED cells. Notably, PDZD8 overexpression resulted in a significant increase in MERCdRED signal intensity, demonstrating that the system is indeed capable of detecting enhanced MERCS formation. These new data were added in the revised manuscript as new Figure 3D-E.

Minor points: 1. Please revise the sentence "First, signals from MERCdRED fluorescence overlapped with the mitochondrial marker Tomm20-iRFP were detected by confocal microscopy in living cells."

We revised this sentence to "First, fluorescence from MERCdRED and the mitochondrial marker Tomm20-iRFP wasdetected by confocal microscopy in living cells."

1. Description of analyses that authors prefer not to carry out

Reviewer #2

Major points: 1. The authors claim that their construct enables balanced expression of the RA and GB moieties of the reporter. This should be substantiated by showing protein expression levels via Western blot analysis.

We thank the reviewer for pointing this out. In our system, Tomm20-GB and RA-Sec61β are expressed from a single plasmid using a self-cleaving P2A peptide sequence, which ensures that the two proteins are produced in equimolar amounts upon translation. Therefore, their expression levels are expected to be approximately equal. Given that comparing the expression levels of these two proteins by Western blotting would require extensive work, including obtaining reconstituted proteins to normalize band intensities, but remains inconclusive due to the semi-quantitative nature of the method, we have decided not to pursue this approach.

Minor points:

1. In Figure 2, the ER structures are not segmented in the EM images. It would enhance the manuscript to show the three-dimensional spatial relationship between mitochondria and the ER, rather than only highlighting the regions identified as contacts.

We agree that visualizing the entire ER structure would enhance the reader's understanding of the three-dimensional spatial relationship between mitochondria and the ER. However, complete segmentation of the ER in EM images is extremely labor-intensive. Given the scope and focus of this study, we have decided not to include full ER segmentation in this manuscript.

Author Response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant issues regarding the experimental design and potential misinterpretations of key findings. Consequently, the manuscript contributes little to our understanding of SynGap1 loss mechanisms.

Major issues in the second version of the manuscript:

In the review of the first version there were major issues and contradictions with the sEPSC and mEPSC data, and were not resolved after the revision, and the new control experiments rather confirmed the contradiction.

In the original review I stated: "One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity. The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar." Contradictions remained after the revision of the manuscript. On one hand, the authors claimed in the revised version that "We found no difference in mEPSC amplitude between the two genotypes (Fig. 1g), indicating that the observed difference in sEPSC amplitude (Figure 1b) could arise from decreased network excitability". On the other hand, later they show "no significative difference in either amplitude or inter-event intervals between sEPSC and mEPSC, suggesting that in acute slices from adult A1, most sEPSCs may actually be AP independent." The latter means that sEPSCs and mEPSCs are the same type of events, which should have the same sensitivity to manipulations.

We thank the reviewer for the detailed comments. Our results suggest a diverse population of PV+ cells, with varying reliance on action potential-dependent and -independent release. Several PV+ cells indeed show TTX sensitivity (reduced EPSC event amplitudes following TTX application: See new Supplementary Figure 2b-e), but their individual responses are diluted when all cells are pooled together. To account for this variability, we recorded sEPSC followed by mEPSC from more mice of both genotypes (new Figure 1f-j). Further, following the editors and reviewers’ suggestions, we removed speculations about the role of network activity changes.

In summary, our data confirmed that TTX blocked APs in PV+ cells and that recordings were stable as indicated by lack of changes in series resistance during the recording period in our experimental setup (new Suppl. Figure 2f-i). We found no difference in mEPSC amplitude between the two genotypes (Fig. 1g, right), indicating that the observed difference in sEPSC amplitude (Figure 1c, right) could be due to impaired AP-dependent release in cHet mice and the presence of large-amplitude sEPSCs that are preferentially affected by TTX in control mice (new Suppl. Figure 2b-e). Conversely, cHet mice showed longer inter-mEPSC time interval (cumulative distribution in Figure 1g, left), and significantly lower charge transfer and DQ*f (Figure 1j) compared to controls littermates, suggesting a decrease of glutamatergic presynaptic release sites onto PV+ cells. 

Concerns about the quality of the synapse counting experiments were addressed by showing additional images in a different and explaining quantification. However, the admitted restriction of the analysis of excitatory synapses to the somatic region represent a limitation, as they include only a small fraction of the total excitation - even if, the slightly larger amplitudes of their EPSPs are considered.

We agree with the reviewer that restricting the anatomical analysis of excitatory synapses to PV cell somatic region is a limitation, as highlighted it in the discussion of the revised manuscript. Recent studies, based on serial block-face scanning electron microscopy, suggest that cortical PV+ interneurons receive more robust excitatory inputs to their perisomatic region as compared to pyramidal neurons (see for example, Hwang et al. 2021, Cerebral Cortex, http://doi.org/10.1093/cercor/bhaa378). It is thus possible that putative glutamatergic synapses, analysed by vGlut1/PSD95 colocalisation around PV+ cell somata, may be representative of a substantially major excitatory input population. Since analysing putative excitatory synapses onto PV+ dendrites would be difficult and require a much longer time, we re-phrased the text to more clearly highlight the rationale and limitation of this approach.

New experiments using paired-pulse stimulation provided an answer to issues 3 and 4. Note that the numbering of the Figures in the responses and manuscript are not consistent.

We are glad that the reviewer found that the new paired-pulse experiments answered previously raised concerns. We corrected the discrepancy in figure numbers in the manuscript. Thank you for noticing.

I agree that low sampling rate of the APs does not change the observed large differences in AP threshold, however, the phase plots are still inconsistent in a sense that there appears to be an offset, as all values are shifted to more depolarized membrane potentials, including threshold, AP peak, AHP peak. This consistent shift may be due to a non-biological differences in the two sets of recordings, and, importantly, it may negate the interpretation of the I/f curves results (Fig. 5e).

We agree with the reviewers that higher sampling rate would allow to more accurately assess different parameters, such as AP peak, half-width, rise time, etc., while it would not affect the large differences in AP threshold we observed between control and mutant mice. Since the phase plots to not add to our result analysis, we removed them from the revised manuscript. 

Additional issues:

The first paragraph of the Results mentioned that the recorded cells were identified by immunolabelling and axonal localization. However, neither the Results nor the Methods mention the criteria and levels of measurements of axonal arborization.

Recorded MGE-derived interneurons were filled with biocytin, and their identity was confirmed by immunolabeling for neurochemical markers (PV or SST) and analysis of anatomical properties. In particular, whole biocytin-positive immunolabelled neurons were acquired using a Leica SP8-DLS confocal microscope (20x objective, NA 0.75; Z-step 1 1μm).  For each imaged neuron, which was the result of multiple merged confocal stacks, we visually determined the spatial distribution across cortical layers of the axonal arbor and whether its dendrites carried spines.  We added this information in the method section. Furthermore, to better represent our methodological approach, we added a new figure (Supplemental Figure 1) including 1) two examples of PV+ interneurons, showing dendrites devoid of spines and axons spreading from Layer II to Layer V (new Suppl. Figure 1a); and 2) two examples of SST+ interneurons showing dendritic with spines and axons projecting from Layer IV to Layer I where they gave rise to multiple collaterals (new Suppl. Figure 1b).  

The other issues of the first review were adequately addressed by the Authors and the manuscript improved by these changes.

We are happy the reviewer found that the other issues were well addressed.

Reviewer #3 (Public review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences between control and mutants in both interneuron populations, although they claim a predominance in PV+ cells. These results suggest that altered PVinterneuron functions in the auditory cortex may contribute to the network dysfunctions observed in Syngap1 haploinsufficiency-related intellectual disability.

The subject of the work is interesting, and most of the approach is rather direct and straightforward, which are strengths. There are also some methodological weaknesses and interpretative issues that reduce the impact of the paper.

(1) Supplementary Figure 3: recording and data analysis. The data of Supplementary Figure 3 show no differences either in the frequency or amplitude of synaptic events recorded from the same cell in control (sEPSCs) vs TTX (mEPSCs). This suggests that, under the experimental conditions of the paper, sEPSCs are AP-independent quantal events. However, I am concerned by the high variability of the individual results included in the Figure. Indeed, several datapoints show dramatically different frequencies in control vs TTX, which may be explained by unstable recording conditions. It would be important to present these data as time course plots, so that stability can be evaluated. Also, the claim of lack of effect of TTX should be corroborated by positive control experiments verifying that TTX is working (block of action potentials, for example). Lastly, it is not clear whether the application of TTX was consistent in time and duration in all the experiments and the paper does not clarify what time window was used for quantification.

We understand the reviewer’s concern about high variability. To account for this variability, we recorded sEPSC followed by mEPSC from more mice of both genotypes (see new Figure 1f-j). We confirmed that TTX worked as expected several times through the time course of this study, in different aliquots prepared from the same TTX vial that was used for all experiments. The results of the last test we performed, showing that TTX application blocks action potentials in a PV+ cell, are depicted in new Suppl. Figure 2a. Furthermore, new Suppl. Figure 2f-i shows series resistance (Rs) over time for 4 different PV+ interneurons, indicating recording stability. These results are representative of the entire population of recorded neurons, which we have meticulously analysed one by one. TTX was applied using the same protocol for all recorded neurons. In particular, sEPSCs were first sampled over a 2 min period. A TTX (1μM; Alomone Labs)-containing solution was then perfused into the recording chamber at a flow rate of 2 mL/min. We then waited for 5 min before sampling mEPSCs over a 2 min period. We added this information in the revised manuscript methods.

(2)  Figure 1 and Supplementary Figure 3: apparent inconsistency. If, as the authors claim, TTX does not affect sEPSCs (either in the control or mutant genotype, Supplementary Figure 3 and point 1 above), then comparing sEPSC and mEPSC in control vs mutants should yield identical results. In contrast, Figure 1 reports a _selective_ reduction of sEPSCs amplitude (not in mEPSCs) in mutants, which is difficult to understand. The proposed explanation relying on different pools of synaptic vesicles mediating sEPSCs and mEPSCs does not clarify things. If this was the case, wouldn't it also imply a decrease of event frequency following TTX addition? However, this is not observed in Supplementary Figure 3. My understanding is that, according to this explanation, recordings in control solution would reflect the impact of two separate pools of vesicles, whereas, in the presence of TTX, only one pool would be available for release. Therefore, TTX should cause a decrease in the frequency of the recorded events, which is not what is observed in Supplementary Figure 3.

To account for the large variability and clarify these results, we recorded sEPSCs followed by mEPSCs from more mice of both genotypes (new Figure 1f-j). We found no difference in mEPSC amplitude between the two genotypes (Fig. 1g, right), indicating that the observed difference in sEPSC amplitude (Figure 1c, right) could be due to impaired AP-dependent release in cHet mice and the presence of large-amplitude sEPSCs that are preferentially affected by TTX in control mice (new Suppl. Figure 2b-e). Conversely, cHet mice showed longer inter-mEPSC time interval (cumulative distribution in Figure 1g, left), and significantly lower charge transfer and DQ*f (Figure 1j) compared to controls littermates, suggesting a decrease of glutamatergic presynaptic release sites. We rephrased the text in the revised manuscript according to the updated data and, following the reviewer’s suggestions, we removed speculations relying on different pools of synaptic vesicles.

(3) Figure 1: statistical analysis. Although I do appreciate the efforts of the authors to illustrate both cumulative distributions and plunger plots with individual data, I am confused by how the cumulative distributions of Figure 1b (sEPSC amplitude) may support statistically significant differences between genotypes, but this is not the case for the cumulative distributions of Figure 1g (inter mEPSC interval), where the curves appear even more separated. A difference in mEPSC frequency would also be consistent with the data of Supplementary Fig 2b, which otherwise are difficult to reconciliate. I would encourage the authors to use the Kolmogorov-Smirnov rather than a t-test for the comparison of cumulative distributions.

We thank the reviewer for this thoughtful suggestion. We recorded more mice of both genotypes and the updated data now show a significant difference between the cumulative distributions of the inter mEPSC intervals recorded from the two genotypes (new Figure 1g). For statistical analysis, we based our conclusion on the statistical results generated by LMM, modelling animal as a random effect and genotype as fixed effect. We used this statistical analysis because we considered the number of mice as independent replicates and the number of cells in each mouse as repeated measures (Berryer et al. 2016; Heggland et al., 2019; Yu et al., 2022). For cumulative distributions, the same number of events was chosen randomly from each cell and analysed by LMM, modelling animal as a random effect and genotype as fixed effect. The reason we decided to use LMM for our statistical analyses is based on the growing concern over reproducibility in biomedical research and the ongoing discussion on how data are analysed (see for example, Yu et al (2022), Neuron 110:21-35 https://doi: 10.1016/j.neuron.2021.10.030; Aarts et al. (2014). Nat Neurosci 17, 491–496. https://doi.org/10.1038/nn.3648). We acknowledge that patch-clamp data has been historically analysed using t-test and analysis of variance (ANOVA), or equivalent nonparametric tests. However, these tests assume that individual observations (recorded neurons in this case) are independent of each other. Whether neurons from the same mouse are independent or correlated variables is an unresolved question, but does not appear to be likely from a biological point of view. Statisticians have developed effective methods to analyze correlated data, including LMM.

(4) Methods. I still maintain that a threshold at around -20/-15 mV for the first action potential of a train seems too depolarized (see some datapoints of Fig 5c and Fig7c) for a healthy spike. This suggest that some cells were either in precarious conditions or that the capacitance of the electrode was not compensated properly.

As suggested by the reviewer, in the revised figures we excluded the neurons with threshold at -20/-15 mV. In addition, we performed statistical analysis with and without these cells (data reported below) and found that whether these cells are included or excluded, the statistical significance of the results does not change.

Fig.5c: including the 2 outliers from cHet group with values of -16.5 and 20.6 mV: 42.6±1.01 mV in control, n=33 cells from 15 mice vs -35.3±1.2 mV in cHet, n=40 cells from 17 mice, ***p<0.001, LMM; excluding the 2 outliers from cHet group -42.6±1.01 mV in control, n=33 cells from 15 mice vs -36.2±1.1 mV in cHet, n=38 cells from 17 mice, ***p<0.001, LMM.

Fig.7c: including the 2 outliers from cHet group with values of -16.5 and 20.6 mV: 43.4±1.6 mV in control, n=12 cells from 9 mice vs -33.9±1.8 mV in cHet, n=24 cells from 13 mice, **p=0.002, LMM; excluding the 2 outliers from cHet group -43.4±1.6 mV in control, n=12 cells from 9 mice vs -35.4±1.7 mV in cHet, n=22 cells from 13 mice, *p=0.037, LMM.

(5) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties (Figure 8d,e); however, their evoked firing properties were affected with fewer AP generated in response to the same depolarizing current injection".

This sentence is intrinsically contradictory. Action potentials triggered by current injections are dependent on the integration of passive and active properties. If the curves of Figure 8f are different between genotypes, then some passive and/or active property MUST have changed. It is an unescapable conclusion. The general _blanket_ statement of the authors that there are no significant changes in active and passive properties is in direct contradiction with the current/#AP plot.

We agreed with the reviewer and rephrased the abstract, results and discussion according to better represent the data. As discussed in the previous revision, it's possible that other intrinsic factors, not assessed in this study, may have contributed to the effect shown in the current/#AP plot. 

(6) The phase plots of Figs 5c, 7c, and 7h suggest that the frequency of acquisition/filtering of current-clamp signals was not appropriate for fast waveforms such as spikes. The first two papers indicated by the authors in their rebuttal (Golomb et al., 2007; Stevens et al., 2021) did not perform a phase plot analysis (like those included in the manuscript). The last work quoted in the rebuttal (Zhang et al., 2023) did perform phase plot analysis, but data were digitized at a frequency of 20KHz (not 10KHz as incorrectly indicated by the authors) and filtered at 10 kHz (not 2-3 kHz as by the authors in the manuscript). To me, this remains a concern.

We agree with the reviewer that higher sampling rate would allow to more accurately assess different AP parameters, such as AP peak, half-width, rise time, etc. The papers were cited in context of determining AP threshold, not performing phase plot analysis. We apologize for the confusion and error. Finally, we removed the phase plots since they did not add relevant information. 

(7)  The general logical flow of the manuscript could be improved. For example, Fig 4 seems to indicate no morphological differences in the dendritic trees of control vs mutant PV cells, but this conclusion is then rejected by Fig 6. Maybe Fig 4 is not necessary. Regarding Fig 6, did the authors check the integrity of the entire dendritic structure of the cells analyzed (i.e. no dendrites were cut in the slice)? This is critical as the dendritic geometry may affect the firing properties of neurons (Mainen and Sejnowski, Nature, 1996).

As suggested by the reviewer, we removed Fig.4. All the reconstructions used for dendritic analysis contained intact cells with no evidently cut dendrites.

Author response:

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

We thank the editors at eLife and the one reviewer for engaging our revised manuscript. As we noted in our previous response to reviewers, which we wrote in October 2024 when we submitted our initial revision the majority of critique we received was targeted not so much at the argument of this manuscript but at the debate regarding the evidence in the two other manuscripts that this one accompanied; “ Evidence for deliberate burial of the dead by Homo naledi” and “241,000 to 335,000 Years Old Rock Engravings Made by Homo naledi in the Rising Star Cave system, South Africa.” Because of that critique we revised this manuscript to emphasize that the key element in constructing our argument is that H. naledi engaged in mortuary behavior (the movement of dead H. naledi by living H. naledi into the Rising Star cave system) and place that in context of a) the increasingly complex later Pleistocene record of meaning making activity and b) the assumed correlations between brain size and cognitive capacities in Pliocene and Pleistocene hominins. This framing, as noted in the eLife editorial comment, is the main thrust of our manuscript. There is a growing convergence of evidence that totality of the currently available data and analyses for H. naledi in the Rising Star cave system support mortuary behavior: that is, the agential and intentional action by H. naledi individuals in the transport of bodies to the Lesedi Chamber and Dinaledi Subsystem--see Berger et al. 2025 plus the 2nd round reviews and the eLife editorial comment associated with it, and also Van Rooyen et al. 2025. We acknowledge the serious debates around the assertion of funerary behavior (cultural burial) and seek to illustrate that while we believe the data support the funerary behavior hypothesis, it is not a necessary requirement for our main argument.

A few specific responses to the reviewer in this revised manuscript:

Reviewer states: “Claims for a positive correlation between absolute and/or relative brain size and cognitive ability are not common in discussions surrounding the evolution of Middle- and Late Pleistocene hominin behavior.” We are not making the argument that absolute brain size in the later Pleistocene is a point of focus, rather that there are many arguments and assertions about EQ and cognitive capacity that are central in the proposals for the evolution of hominins in general and genus Homo in particular across the Plio-pleistocene period. We offer a brief review of this in the text and suggest, as noted by this reviewer, that “exploration of the specific/potential socio-cultural, neuro-structural, ecological and other factors will be more informative than the emphasis on absolute/relative brain size”…this (in their words) is exactly our main point. However, we contend that such a framing should not be exclusive to later Pleistocene contexts, but rather that the examination of earlier hominins might also be better served by moving away from the traditional assumptions of cognitive complexity associated with absolute/relative brain size. The reviewer states: “The authors use, in a number of instances throughout the paper, secondary sources of information such as review papers (e.g., McBrearty & Brooks 2000; Scerri & Will 2023; Galway-Witham et al. 2019) instead of the original works that are the basis for making the desired case.” We do indeed use review papers in the main body of the text for clarity, brevity, and to acknowledge robust previous review work in these areas, however in the supplemental text and with the figures and table we offer substantive bibliographies of the original citations and studies. We encourage readers to please spend time with those materials as well. Finally, the reviewer states: “Given the inadequate analyses in the accompanying papers, and the lack of evidence for stone tools in the naledi sites, the present claims for the expression of culturally and symbolically mediated behaviors by this small-brained hominin must be adequately established.” We are quite specific in this manuscript, and in other publications, that we are not arguing for “symbolically mediated” behavior, but do stand by our non-controversial suggestions of meaning-making, and cultural behavior, as relevant in Pleistocene hominins (e.g. Kissel and Fuentes 2017, 2018). We do not argue that stone tools are necessary as mandatory indicators of such possibilities and lay out the H. naledi information in the context of the broader and increasing datasets and analyses for meaning-making behavior in Pleistocene hominins (see Figure 1 and table 1, and in the text).

Our point with this manuscript which we reiterate here is that “The increasing data for complex behavior and meaning-making across the Pleistocene should play a major element in structuring how we investigate, explain, and model the origins and patterns of hominin and human evolution” and we feel that the current evidence for H. naledi behavior contributes to the broader suites of data, hypotheses, analyses, and theory building in this endeavor.

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

Before laying out how we addressed the specific comments on this manuscript we want to clarify the goal and intent of this paper to maximize effective critical reading of its contents. We appreciate and look forward to continued critique and enhanced discussion of this topic and argument.

Our starting point for constructing the argument in this manuscript is that H. naledi engaged in mortuary behavior. This emerges from the totality of the currently available data and analyses for Homo naledi in the Rising Star cave system, which support agential and intentional action by Homo naledi individuals in the transport of bodies to the Lesedi Chamber and Dinaledi Subsystem. We do feel that the data support the cultural burial hypothesis as well as the likelihood that at least some of the markings reported as engravings are non-naturally occurring (see Martinón-Torres et al. 2024) and made by Homo naledi. But these two elements are not necessary for the validity of the argument we pursue in this manuscript.

Our second key point is that gross brain size does not necessarily correlate with particular patterns of complex behavior in Pleistocene hominins. On this there is wide agreement, yet both scholarly and public arguments for the success of the genus Homo and the success of Homo sapiens have incorporated an assumption of a Rubicon of cerebral size. From this we propose a third point: that smaller brained Pleistocene hominins, including Homo naledi, are part of a Pleistocene hominin niche that includes patterns of complex social and cognitive behavior. Such behavior was historically considered to be exclusive to Homo sapiens but is now documented to occur earlier, across a range of hominin taxa in the latter half of the Pleistocene. We offer the case of H. naledi behavior in the Rising Star system as an example of this. This case contributes to the development of a broader approach to the cognitive, physiological, and behavioral framings of, and explanations for, Pleistocene hominin behavior.

Responses to specific critiques in the eLife reviews centered on this manuscript:

Reviewer #1:

All inferences regarding hominin behaviour and biology of Homo naledi, discussed by Fuentes and colleagues, are wholly dependent on the evidence presented in the archaeology preprints being true.

Reviewer #2:

Fuentes et al. provide a detailed and thoughtful commentary on the evolutionary and behavioral implications of complex behaviors associated with a small-brained hominin, Homo naledi…..While the review by Fuentes et al. highlights important assumptions about the relationship between hominin brain size, cognition, and complex behaviors, the evidence presented by Berger et al. 2023a,b does not support the claim that Homo naledi engaged in burial practices or symbolic expression through wall engravings.

Reviewer #3:

This paper presents the cognitive implications of claims made in two accompanying papers (Berger et al. 2023a, 2023b) about the creation of rock engravings, the intentional disposal of the dead, and fire use by Homo naledi. The importance of the paper, therefore, relies on the validity of the claims for the presence of socio-culturally complex and cognitively demanding behaviors that are presented in the associated papers. Given the archaeological, hominin, and taphonomic analyses in the associated papers are not adequate to enable the exceptional claims for nalediassociated complex behaviors, the inferences made in this paper are currently inadequate and incomplete.

We have clarified in the manuscript text and above why we argue that the inferences we are setting as core to our argument do not require cultural burial or engravings by H. naledi be demonstrated. However, we do clarify in the revision that the current evidence for the transport of dead conspecifics into difficult to reach areas deep into the cave system by naledi is well supported by the archeological and paleoanthropological data currently available (e.g. Berger et al. 2024, Elliott et al. 2021, Robbins et al. 2021, Hawks et al. 2017) and that this is the basis for our argument.

Reviewer #3:

The claimed behaviors are widely recognized as complex and even quintessential to Homo sapiens. The implications of their unequivocal association with such a small-brained Middle Pleistocene hominin are thus far reaching. Accordingly, the main thrust of the paper is to highlight that greater cognition and complex socio-cultural behaviors were not necessarily associated with a positively encephalized brain. This argument begs the obvious question of whether absolute brain size and/or encephalization quotient (i.e., the actual brain volume of a given species relative the expected brain size for a species of the same average body size) can measure cognitive capacity and the complexity of socio-cultural behaviors among late Middle Pleistocene hominins….Claims for a positive correlation between absolute and/or relative brain size and cognitive ability are not common in discussions surrounding the evolution of Middle- and Late Pleistocene hominin behavior.

We assert that claims for a positive correlation between absolute and/or relative brain size and cognitive ability are central—either explicitly or implicitly—in most arguments concerning cognitively complex behavior in the genus Homo. This is especially true for ideas about success of Pleistocene Homo relative to other hominins. We clarify this in the text offering various citations in support of this position (e.g. Meneganzin and Currie 2022, Galway-Witham, Cole, and Stringer 2019, DeCasien, Barton, and Higham 2022, Dunbar 2003, Kissel and Fuentes 2021, Muthukrishna et al. 2018, Püschelet al. 2021, Tattersall 2023).

Reviewer #3:

Currently, the bulk of the evidence for early complex technological and social behaviors derives from multiple sites across South Africa and postdates the emergence of H. sapiens by more than 100,000 years. Such lag in the expression of complex technologies and behaviors within our species renders the brain size-implies-cognitive capacity argument moot. Instead, a rich body of research over the past several decades has focused on aspects related to sociocultural, environmental, and even the wiring of the brain in order to understand factors underlying the expression of the capacity for greater behavioral variability. In this regard, even if the claimed evidence for complex behaviors among the small-brained naledi populations proves valid, the exploration of the specific/potential socio-cultural, neuro-structural, ecological and other factors will be more informative than the emphasis on absolute/relative brain size.”

While not at all denying the critically important and rich record of cultural complexity in the Late Pleistocene South African archeological record, we disagree that “the bulk of the evidence for early complex technological and social behaviors derives from multiple sites across South Africa and postdates the emergence of H. sapiens by more than 100,000 years”. We offer a range of examples and citations in support of our assertion in the text (esp. in pp12-14 and Table 1 and Figure 1)

We lay out the currently available data for such cultural complexity in Figure 1 with extensive documentation and citations for each case in the Supplementary material (both aa a table and a bibliography). We wholly agree with Reviewer 3 that “the exploration of the specific/potential socio-cultural, neuro-structural, ecological and other factors will be more informative than the emphasis on absolute/relative brain size” and are attempting to do just that in the manuscript.

Reviewer #3:

The paper presents as supporting evidence previous claims for the appearance of similar complex behaviors predating the emergence of our species, H. sapiens, although it does acknowledge their controversial nature. It then uses the current claims for the association of such behaviors with H. naledi as decisive. Given the inadequate analyses in the accompanying papers and the lack of evidence for stone tools in the naledi sites, the present claims for the expression of culturally and symbolically mediated behaviors by this small-brained hominin must be adequately established.

We respond to the first part of this critique above (regarding the other papers). But again, we emphasize that although we do feel that the argument for cultural burial is supported (see Berger et al. 2024 preprint) what we are arguing for in this paper is that the agential and intentional transportation of dead (mortuary behavior) is the sufficient factor undergirding our proposal. We do not agree that absence of recognizable stone tools at the site negates our proposal and assert that the context provided by Figure 1, and the data in the table for figure 1 in the SOM, in concert with the supported mortuary behavior (transport and emplacement of the dead) offer sufficient support for the argument we make in the text regarding brain size and the role of emotional cognition and complex behavior in the Pleistocene hominin niche and H. naledi’s participation in it.

https://freemason.org/discover-masonry/lodges/?search=CA

Aquí debería sacar la imagen con las estadísticas del año @roberto_admin

Me gustaría probar la segunda columna, pero aún veo que no esta desarrollada.@maria_desarrolladora 1. Debe ser un carrusel de fotos ¿Cuando podría probarlo?

Las instrucciones deben listarse de la más importante a la menos @roberto_admin

1. Carrusel de fotos, me gustaría la funcionalidad de un carrusel con fotos linkeadas. @roberto_admin

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Ce n'est plus le cas. Il faudrait supprimer cette phrase.

finer points

Les éléments ne se mettent pas plutôt les uns à la suite des autres de manière horizontale ?

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periode direct na een epileptische aanval. Tijdens deze fase kan iemand last hebben van verwardheid, vermoeidheid, geheugenstoornissen en prikkelbaarheid

creo que este parrafo con el que le sigue apuntan a lo mismo: al controlar por cohorte la incluimos como predictor para evaluar heterogenidad, lo ajustaría

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Reply to the reviewers

Reviewer #1

Major points

• *

• • The introduction describes the effects of different environmental cues and aging on fibroblast phenotype, but it would be good to note the developmental origins of dermal fibroblasts, which specifies their fate and function (Driskell et al, Nature 2013).* Our response:

In accordance with the reviewers' suggestions, we have incorporated a summary of prior research regarding the developmental origins of dermal fibroblasts into lines 53–56 of the Introduction.

• In Fig 2, how do TEWL measurements compare to constructs without an epidermal layer or human skin? It may seem obvious that barrier function would be negligible in these models, but it would be a helpful negative control for interpreting the relative effects of vasculature on barrier function.*

We appreciate your valuable comments regarding the accurate interpretation of TEWL measurements. Estimated TEWL values for human skin have been reported in a systematic review and meta-analysis by Kottner et al. Specifically, the estimated TEWL (95% CI) for individuals aged 18–64 years varies by anatomical site: 15.4 (13.9–17.0) g/m²h for the right cheek, 6.5 (6.2–6.8) g/m²h for the midvolar right forearm, and 36.3 (29.5–43.1) g/m²h for the right palm. In comparison, the TEWL of our EDV model was 9.68 g/m²h, a value relatively close to that of human skin.

We also considered measuring TEWL in artificial skin models lacking epidermis. However, we found that such models remain moist due to culture medium, and pressing the measurement probe against them risks water droplets adhering to the sensor and causing damage. Although we recognize the significance of this measurement as a negative control, we refrained from conducting it due to the limitations of the equipment.

This information has been added to the Results section, lines 178–182.

• *

• The mechanical measurements in Fig 2 are a nice idea, but it is a bit difficult to interpret without comparison to other conditions (e.g. human skin) or by reporting more universal mechanical parameters (e.g. Young's modulus).*

We greatly appreciate your insightful comments regarding the interpretation of skin viscoelasticity measurements using the Cutometer. The Cutometer is a device that applies negative pressure to the skin to elevate its surface, allowing for the calculation of biomechanical properties based on the temporal changes in skin displacement. Notably, the R7 parameter—defined as the ratio of immediate retraction after pressure release to the maximum deformation during suction—has been shown to correlate significantly with age.

In this study, we evaluated HSEs under the same measurement conditions as those used in previous human clinical studies. Accordingly, we have cited past Cutometer data for human skin and discussed the relationship between those findings and our HSE measurements. These revisions have been made to lines 205–215.

We determined that performing Cutometer measurements on human skin would be impractical due to the ethical committee procedures and associated costs. Although evaluating Young’s modulus using techniques such as AFM to assess the mechanical properties of collagen fibers is a fascinating and informative approach, we have opted not to pursue this analysis due to the substantial time and cost required for sample preparation.

• The induction of region-specific fibroblast markers is interesting and a bit unexpected since all the fibroblasts came from the same source before seeding into HSEs. The conclusions require additional support from quantification of the IF staining in Fig 3.*

Our response:

Thank you for your valuable advice on strengthening the conclusion of our manuscript. We are currently conducting quantitative analysis through manual counting across multiple fields for all mesenchymal cell markers and Vimentin immunostaining data presented in Fig. 3.

• *

• Likewise, could the authors clarify whether the cells were passaged before seeding into the HSE, and if so, what passage number. Could passaging affect the responses observed? Please add a discussion point about this.*

Our response:

For all cell types, passage 4 or 5 cells were utilized for the reconstitution of human skin equivalents (HSE). Indeed, Philippeos et al. demonstrated that while CD39, CD90, and CD36 are detectable in primary CD31⁻CD45⁻Ecad⁻ dermal cells, the expression of CD39 is lost after a single passage. In contrast, CD90 and CD36 remain detectable for up to four passages. These findings underscore the impact of in vitro culture on the depletion of fibroblast marker expression. Since we employed NHDFs that had undergone four to five passages for HSE reconstruction, it is reasonable to assume that these cells had already lost specific fibroblast subpopulations, including CD39⁺ cells. Consistent with this, our scRNA-seq analysis revealed that most fibroblasts cultured in 2D formed an artificial population comprising cells in the S and G2M phases, along with secretory-reticular fibroblasts. Additionally, immunohistochemical analysis confirmed a near-complete absence of CD39⁺, CD90⁺, FAP⁺, NG2⁺, and αSMA⁺ cells in the dermis of both D and DV models, further indicating that serial passaging significantly reduces the expression of markers associated with papillary fibroblasts, reticular fibroblasts, and pericytes. Interestingly, the introduction of vascular endothelial cells into the HSE appears to facilitate a partial restoration of fibroblast heterogeneity in cells passaged four to five times. However, whether this effect can be replicated in more extensively passaged fibroblasts remains to be verified. It is well established that excessive passaging induces cellular senescence, leading to reduced proliferative and differentiation capacities in mesenchymal stem cells. Therefore, it is conceivable that fibroblasts beyond a certain passage number may fail to recapitulate dermal mesenchymal cell heterogeneity, even in the presence of endothelial cells.

We have added this discussion to the revised manuscript on lines 372-385, 391–397, and 470-471. However, due to the prolonged culture period required, we regret that we are unable to perform the additional validation experiments at this time.

• The scRNA-seq suggests that the in vitro populations do not discriminate between secretory papillary and pro-inflammatory fibroblasts. Could the authors add some further analysis or discussion regarding this point?*

Our response:

We are currently conducting an additional enrichment analysis on fibroblast subpopulations #0, 1, 2, 6, 8, and 11, identified through UMAP analysis integrating HSE and human skin datasets. We believe that this analysis will elucidate the functional characteristics of each in vitro subpopulation and enable us to speculate on the underlying factors contributing to the observed differences from the human skin analysis results.

• In Fig 6, it will be important to add quantification of epidermal thickness and differentiation marker expression to support the conclusions.*

Our response:

Thank you for your valuable advice regarding quantitative analysis. We are currently measuring the thickness of the entire epidermal layer, the CK5-positive cell layer, and the CK10-positive cell layer based on HE-stained and IHC-stained images.

• A key question is how NP and AA conditions affect the fibroblast populations as this seems to be a key factor in HSE maturation and would then link back to the previous sections. It would be good to stain for fibroblast markers in these samples.*

Our response:

We are grateful for your insightful comments, which are crucial for a more precise understanding of the physiological relevance of the NP culture model. In response, we are currently undertaking additional analyses to investigate the expression patterns of dermal mesenchymal markers under both NP and AA conditions.

• As noted above, the ability of the vasculature to direct differentiation of a common fibroblast population into different phenotypes is one of the key findings of the study. To strengthen these observations, could additional analysis of the transcriptional data be possible. For example, would trajectory analysis potentially show how the different populations are evolving or related? In addition, could the CellChat analysis be performed between the vasculature and the different populations in Fig 5, which are mapped to in vivo populations? This might be a more relevant analysis than the populations in Fig 4.*

Our response:

As pointed out by reviewers, we acknowledge that elucidating the process and underlying mechanisms by which fibroblasts, whose heterogeneity is compromised in 2D culture, re-differentiate into distinct dermal mesenchymal subtypes constitutes a critical additional analysis to strengthen our findings. Accordingly, we are currently conducting trajectory analysis using Monocle3. This includes identifying branch points that regulate the differentiation of dermal mesenchymal clusters shown in Fig. 4b, as well as predicting transcription factors and cell signaling pathways playing pivotal roles at those branch points. Furthermore, we are planning a CellChat analysis between vascular endothelial cells and dermal mesenchymal cells. We anticipate that integrating the results of these two analyses will provide valuable insights into the differentiation processes of dermal mesenchymal cells, particularly the induction of perivascular cell differentiation.

• *

• *

Reviewer #1

Minor points

• *

• • The abstract states that enabling in vitro evaluation of drug efficacy using methodologies that are identical to those used in human clinical studies. This seems to be an over interpretation of the study and not well supported by the data. Please consider revising or removing.*

Our Response:

Upon thorough consideration, we have deleted the statements that may be regarded as exaggerated (line 26-28 and 346-348).

• Check referencing formatting in lines 118-121*

Our Response:

We appreciate your attention to the reference format error. The necessary revisions have been completed.

Reviewer #2 Major comments:

1. • Despite its strengths, the study has several limitations that warrant further investigation. The authors describe a "senescent-like" phenotype under nutrient-poor (NP) conditions, yet do not provide direct evidence of cellular senescence using canonical markers such as SA-β-gal staining, p16^INK4a or p21 expression, or SASP profiling-weakening their aging-related conclusions.*

Our Response

Thank you for your valuable advice, which has helped clarify the physiological phenomena modeled by the NP condition. We are planning additional experiments involving histological analysis, including SA-β-gal staining and the detection of p16^INK4a and/or p21.

• The 500 μM dose of ascorbic acid (AA), while within the reported range for skin models, is at the higher end compared to commonly used concentrations (100-300 μM) and lacks justification via dose/response data. Normal physiological levels and changes in aging dermis should be referenced in discussion. AA is also an additive in their standard HSE media, but this was not sufficiently emphasized to draw attention. Would its removal from the baseline media make a difference?*

Our Response

We sincerely appreciate the important comment regarding the rationale behind the ascorbic acid concentration used in the culture medium. As Reviewer 3 rightly pointed out, concentrations around 100–300 μM are commonly employed in general in vitro assays. In our artificial skin model, we opted for a concentration of 500 μM AA in the growth medium based on two considerations: (1) the model contains a high cell density of approximately 4 × 10⁶ cells immediately after reconstruction, which is expected to result in substantial AA consumption, and (2) AA is not sufficiently stable in culture medium. Given the relatively long medium exchange interval of 48–72 hours, we deemed it necessary to maintain a certain AA level throughout this period. While no rigorous dose–response validation has been conducted, we have confirmed that this concentration does not induce toxicity or abnormalities in skin morphogenesis.

As part of the revision, we considered revisiting the basal medium formulation; however, due to the significant time and resource demands, we have decided to forgo further optimization at this stage.

As described on lines 307–311, the NP medium was formulated to evaluate the potential impact of age-related declines in plasma component transport. We apologize for any confusion regarding the relationship between the HSE growth medium and the NP medium. In response to the reviewer’s suggestion, we have added clarifying explanations and cautionary notes regarding the composition and rationale of these two media in both the Results and Methods sections (line 307-311 and 634-636).

• Mechanistically, fibroblast heterogeneity is attributed to keratinocyte and vascular signals, but the signaling pathways involved (e.g., Wnt, TGF-β, VEGF) are not directly examined. Validating which paracrine factors (VEGF, PDGF, LAMA5, KGF) are mediating fibroblast transitions using inhibitors or RNA profiling could shed more light.*

Our response:

As pointed out by reviewers, we acknowledge that elucidating the process and underlying mechanisms by which fibroblasts, whose heterogeneity is compromised in 2D culture, re-differentiate into distinct dermal mesenchymal subtypes constitutes a critical additional analysis to strengthen our findings. Accordingly, we are currently conducting trajectory analysis using Monocle3. This includes identifying branch points that regulate the differentiation of dermal mesenchymal clusters shown in Fig. 4b, as well as predicting transcription factors and cell signaling pathways playing pivotal roles at those branch points. Furthermore, we are planning a CellChat analysis between vascular endothelial cells and dermal mesenchymal cells. We anticipate that integrating the results of these two analyses will provide valuable insights into the differentiation processes of dermal mesenchymal cells, particularly the induction of perivascular cell differentiation. We fully recognize that validation using specific inhibitors is crucial to substantiate the mechanisms suggested by the scRNA-seq analysis. However, given that the reconstruction and reanalysis of the artificial skin model requires more than three months, we have decided not to include these experiments in the current revision and instead consider them as important subjects for future investigation.

Minor comments: 1. The role of pericytes is also underexplored; while their presence is confirmed, functional assays or transcriptomic analyses to elucidate their contribution to ECM remodeling or vascular stability are not fully explored. The origin of pericyte-like cells remains uncertain without lineage tracing or barcoding to distinguish whether they derive from fibroblasts, endothelial cells, or culture artifacts. Since they observe induced differentiation of fibroblast-like cells in 3D culture, it would be compelling to reconstruct differentiation trajectories (pseudotime analysis) from progenitor states to papillary/reticular/pericyte-like states from their scRNAseq data.

Our respnse:

This point will be addressed and validated through our response to Major Comment 3 from Reviewer #2.

• Although AA enhanced collagen production and elasticity in the vascularized EDV model, the lack of response in the ED model is not addressed mechanistically.*

Our response

We have planned additional experiments to examine two hypotheses regarding the mechanism underlying the improved responsiveness of the EDV model to AA. The first hypothesis posits that the behavior of ascorbic acid uptake in the cells constituting the EDV model differs from that in the ED model. To investigate this, we plan to analyze the expression patterns of transporter genes potentially involved in the uptake and efflux of ascorbic acid, such as SVCT1 (SLC23A1), SVCT2 (SLC23A2), GLUT1 (SLC2A1), GLUT3 (SLC2A3), GLUT4 (SLC2A4), and MRP4, using scRNA-seq data. The second hypothesis suggests that the absence of bFGF signaling and low FBS treatment under NP conditions may affect subpopulations of dermal mesenchymal cells in the HSEs. To test this, we plan to analyze the expression patterns of dermal mesenchymal cell markers by IHC under NP and AA conditions, following the same approach as shown in Fig. 3.

• The omission of immune cells which are key players in skin aging and homeostasis could increase physiological relevance of the model.*

Our response:

As rightly noted by Reviewer 2, immune cells are integral to skin aging and the maintenance of tissue homeostasis, underscoring the necessity of incorporating them into future research models. Nonetheless, the primary aim of the present study is to elucidate the influence of vascular endothelial cells on dermal mesenchymal cell heterogeneity and to establish an in vitro research model specifically addressing this heterogeneity, with particular emphasis on perivascular cells. Accordingly, we would prefer to consider the analysis of immune cells as a subject for future investigation.

• The exclusive use of standard HUVECs may not fully capture the behavior of tissue-specific microvascular endothelial cells, potentially limiting the fidelity of the vascular niche.*

In this study, we opted to use HUVECs as vascular endothelial cells due to their relative ease of expansion in culture. Consequently, we acknowledge the potential limitation in fully recapitulating the functions of tissue-specific endothelial cells. To address this concern, we have revised and expanded the Discussion section on lines 352–356.

Reviewer #3 Major comments:

1. • Are the key conclusions convincing? The core claim-that tricellular interactions recapitulate dermal mesenchymal heterogeneity and enhance skin functionality-is well-supported by histology, immunohistochemistry, functional assays (TEWL, elasticity), and scRNA-seq.
2. Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The assertion that HSEs enable "identical" methodology to clinical studies (p. 2, line 29) is exaggerated. While elasticity was measured via Cutometer (used clinically), the model lacks immune/neural components and long-term stability for full translational equivalence.* Our Response:

Upon thorough consideration, we have deleted the statements that may be regarded as exaggerated (line 26-28 and 346-348).

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Adequacy of Experimental Evidence & Need for Additional Experiments: No essential control appears to be missing: the authors include conditions {plus minus}ascorbic acid and {plus minus}vascular cells to isolate those effects. One could suggest a few additional experiments to further bolster the conclusions, but they are not strictly required for the main message. For example, to pinpoint the contribution of each mesenchymal subset, the authors could engineer HSE variants lacking one component at a time (omit pericytes or use only papillary vs. only reticular fibroblasts) to see how each omission affects barrier or elasticity. This would directly confirm each cell type's role. However, such experiments may be technically involved (especially isolating pure papillary vs. reticular fibroblast populations and ensuring viability in 3D culture) and might be beyond the scope of a single study. Another possible extension could be mechanistic assays, such as examining specific molecular signals: e.g., testing if blocking known paracrine factors from pericytes or fibroblast subsets diminishes the observed improvements. Given that pericytes can secrete laminin-511 and other factors that promote keratinocyte growth, the authors might, in future work, explore whether such factors mediate the enhanced epidermal proliferation seen with the vascularized HSE. Overall, the current data are sufficiently convincing that additional experiments are not absolutely necessary for publication.
• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments-*

Our response

We are deeply grateful for the reviewer’s constructive feedback. As rightly pointed out, cell ablation and mechanistic assays utilizing signaling inhibitors to assess the contribution of individual mesenchymal subsets are indispensable for reinforcing our findings and claims. However, as the reviewer has also indicated, these experiments would require no less than four months to complete. Consequently, we have opted to forgo high-cost additional experiments such as the optimization of HSE construction protocols and inhibitor-based assays. Instead, we are proactively conducting mechanism-oriented analyses using our existing scRNA-seq and histological datasets. Specifically, we are currently implementing an integrated approach combining Monocle3 and CellChat to pinpoint critical branch points in dermal mesenchymal cell differentiation and to elucidate the signaling pathways orchestrating these bifurcations.

• Are the experiments adequately replicated and statistical analysis adequate? The manuscript's data are presented in a manner that generally supports reproducibility. The authors state that all data are presented as "mean {plus minus} SD" (Methods, p.36). This is acceptable and clearly reported. However, I suggest that the authors consider using mean {plus minus} SEM for specific datasets where the primary goal is to assess statistical significance between groups - for example, for the Ki67-positive cell proliferation data (Fig. 6c) - as SEM better reflects the precision of the group mean for inferential comparisons. In contrast, for functional measures that inherently exhibit biological variation across samples (e.g., TEWL, skin elasticity), using mean {plus minus} SD remains fully appropriate, as SD reflects true inter-sample variability. To improve clarity and reproducibility, I encourage the authors to briefly state in the Methods or figure legends why SD or SEM is used in each case, in line with best practice guidelines.*

Our Response:

We appreciate your guidance regarding appropriate statistical analysis and data presentation. We planned to revise the depiction of error margins in accordance with best practice guidelines.

Reviewer #3 Minor comments: 1. For Figure 4e, it would be helpful if the authors could clarify in the figure legend or Methods whether the heatmap shows log-normalized expression values (as derived from the Seurat object) or z-scored expression across cells or samples. This distinction affects the interpretation of relative versus absolute expression levels of the collagen and elastic fiber-related genes, which are central to the study's conclusions about ECM remodeling.

Our response:

Thank you for pointing out the inconsistency in data representation. We have revised the manuscript to clearly indicate that Fig. 4e presents the Z-score normalized average expression levels.

• Typos: "factr" → "factor" (p. 16, line 244); "severl" → "several" (p. 22, line 367).

*

Our response

Thanks for pointing out the typo, we have corrected it.

Reviewer #4

Minor Points:

1. • The human skin control in Fig. 1c seems thinner than normal and would suggest that the ED and EDV models are hyperproliferative. Replacing the control with one that shows normal thickness would prevent incorrect conclusions of the data.* Our response:

In accordance with the reviewer’s suggestion, the display area of the human skin image in Fig. 1c has been modified.

KI67 and TEWL readings for human skin as controls for Fig. 2b-c would help gauge how the organoids perform and whether they are abnormal. What is the elasticity index for facial sagging?

Thank you for your valuable advice, which has deepened our understanding of the evaluation results of HSEs. We are currently planning and conducting an additional analysis by including the quantification of Ki67-positive cells in human skin samples. Regarding the assessment of skin barrier and viscoelasticity using TEWL and Cutometer measurements, we have reffered data from previous clinical studies and added an explanation of the functional differences between HSEs and human skin.

• Ascorbic acid utilizes SLC23A1 and SLC23A2 to transport across cell membranes. Are their expression more pronounced in cluster 14 fibroblasts? This would help connect the scRNA-seq data to the ascorbic acid experiments.

*

Our response:

We appreciate the valuable suggestions provided to investigate the mechanisms underlying the altered VC responsiveness observed in the EDV model. We plan to analyze the expression patterns of transporter genes potentially involved in the uptake and efflux of ascorbic acid, such as SVCT1 (SLC23A1), SVCT2 (SLC23A2), GLUT1 (SLC2A1), GLUT3 (SLC2A3), GLUT4 (SLC2A4), and MRP4, using scRNA-seq data.

There seems to be quite a bit of variability between replicant immunostains, in particular, vimentin in Fig. 3. Can the authors discuss this variability and whether any of the HSE organoid combinations reduced this variability?

Our response:

Thank you for your comments regarding the immunostaining. A reanalysis of the data, including newly acquired immunostaining images during the revision process, is planned.

• Please provide number of replicates throughout figure legends.*

Our response:

Thank you for your valuable advice. We have added the number of replicates to all figure legends.

• Line 148 states "E and EV models were transparent and extremely soft", should read "E and ED models".*

Our response:

The photographic data for the EV and ED models in Fig. 1b was incorrect and has therefore been corrected. We sincerely apologize for our oversight. As it was actually the E and EV models that appeared transparent, the description in the text remains unchanged.

• Line 150-151 states "In the E and EV models, an abnormal epidermis lacking a basal cell layer formed". The Krt5 staining in Figure 2 clearly shows a basal cell layer in these models, albeit abnormal. Stating that this the abnormal epidermis displayed a disrupted basal cell layer or columnar shape of basal cells were disrupted is more appropriate. In addition, these results do not show "crosstalk between NHEKs and NHDFs is essential for epithelialization" as the E and EV organoid models show epithelial stratification.*

Our response:

We sincerely appreciate your insightful guidance regarding the accurate presentation of the histological analysis results. Accordingly, we have revised lines 154–156 in the Results section in line with your recommendations.

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Referee #3

Evidence, reproducibility and clarity

The study develops a tricellular human skin equivalent (HSE) model incorporating epidermal keratinocytes (NHEKs), dermal fibroblasts (NHDFs), and vascular endothelial cells (HUVECs). This model autonomously organizes pericytes, papillary fibroblasts, and reticular fibroblasts, mimicking in vivo dermal mesenchymal heterogeneity. The EDV model (all three cell types) demonstrates enhanced epidermal barrier function (reduced TEWL), dermal elasticity, collagen deposition, and vascular organization compared to simpler models. Single-cell RNA-seq confirms the emergence of pericyte-like and fibroblast subpopulations resembling in vivo counterparts. Nutrient-poor (NP) culture replicates aging phenotypes (reduced proliferation, barrier dysfunction, disordered collagen), rescued by ascorbic acid (AA), highlighting vascular cells' role in skin homeostasis. However, several key methodological clarifications (e.g., heatmap normalization, statistical reporting), more precise qualification of certain claims, and enhanced contextualization within the literature are needed before the work can be considered suitable for publication; I therefore recommend major revision.

Major comments:

1. Are the key conclusions convincing?<br /> The core claim-that tricellular interactions recapitulate dermal mesenchymal heterogeneity and enhance skin functionality-is well-supported by histology, immunohistochemistry, functional assays (TEWL, elasticity), and scRNA-seq.
2. Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The assertion that HSEs enable "identical" methodology to clinical studies (p. 2, line 29) is exaggerated. While elasticity was measured via Cutometer (used clinically), the model lacks immune/neural components and long-term stability for full translational equivalence.
3. Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Adequacy of Experimental Evidence & Need for Additional Experiments: No essential control appears to be missing: the authors include conditions {plus minus}ascorbic acid and {plus minus}vascular cells to isolate those effects. One could suggest a few additional experiments to further bolster the conclusions, but they are not strictly required for the main message. For example, to pinpoint the contribution of each mesenchymal subset, the authors could engineer HSE variants lacking one component at a time (omit pericytes or use only papillary vs. only reticular fibroblasts) to see how each omission affects barrier or elasticity. This would directly confirm each cell type's role. However, such experiments may be technically involved (especially isolating pure papillary vs. reticular fibroblast populations and ensuring viability in 3D culture) and might be beyond the scope of a single study. Another possible extension could be mechanistic assays, such as examining specific molecular signals: e.g., testing if blocking known paracrine factors from pericytes or fibroblast subsets diminishes the observed improvements. Given that pericytes can secrete laminin-511 and other factors that promote keratinocyte growth, the authors might, in future work, explore whether such factors mediate the enhanced epidermal proliferation seen with the vascularized HSE. Overall, the current data are sufficiently convincing that additional experiments are not absolutely necessary for publication.

4. Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments

5. Are the data and the methods presented in such a way that they can be reproduced? Yes
6. Are the experiments adequately replicated and statistical analysis adequate? The manuscript's data are presented in a manner that generally supports reproducibility. The authors state that all data are presented as "mean {plus minus} SD" (Methods, p.36). This is acceptable and clearly reported. However, I suggest that the authors consider using mean {plus minus} SEM for specific datasets where the primary goal is to assess statistical significance between groups - for example, for the Ki67-positive cell proliferation data (Fig. 6c) - as SEM better reflects the precision of the group mean for inferential comparisons. In contrast, for functional measures that inherently exhibit biological variation across samples (e.g., TEWL, skin elasticity), using mean {plus minus} SD remains fully appropriate, as SD reflects true inter-sample variability. To improve clarity and reproducibility, I encourage the authors to briefly state in the Methods or figure legends why SD or SEM is used in each case, in line with best practice guidelines.

Minor comments:

1. For Figure 4e, it would be helpful if the authors could clarify in the figure legend or Methods whether the heatmap shows log-normalized expression values (as derived from the Seurat object) or z-scored expression across cells or samples. This distinction affects the interpretation of relative versus absolute expression levels of the collagen and elastic fiber-related genes, which are central to the study's conclusions about ECM remodeling.
2. Typos: "factr" → "factor" (p. 16, line 244); "severl" → "several" (p. 22, line 367).

Significance

The study innovatively reconstructs dermal mesenchymal heterogeneity using commercially available cells and autonomous tricellular interactions, bypassing costly cell-sorting approaches. This democratizes complex HSE models for broader labs. This study demonstrates that vascularization is critical not only for nutrient supply but for instructing fibroblast/pericyte differentiation and ECM organization. The NP+AA paradigm (Fig. 6) offers a facile in vitro model for skin aging interventions, highlighting AA's efficacy via perivascular mechanisms.

Audience: Tissue engineers, dermatologists, cosmetic/pharma researchers (anti-aging screening), and developmental biologists studying mesenchymal niche regulation.

Placement in existing literature: Recent advances in skin tissue engineering have highlighted the importance of dermal fibroblast heterogeneity in skin homeostasis and regeneration. Single-cell transcriptomic studies (Tabib et al., J Invest Dermatol 2018; Solé-Boldo et al., Commun Biol 2020) have established that papillary and reticular fibroblasts exhibit distinct gene expression and functional roles. Prior engineered skin models incorporating fibroblast subtypes (Moreira et al., Biomater Sci 2023) or pericytes (Paquet-Fifield et al., J Clin Invest 2009) demonstrated improvements in vascularization or epidermal differentiation. However, a unified 3D human skin equivalent integrating vascular cells, pericytes, and spatially organized fibroblast subpopulations has not been systematically achieved. The present work by Kimura et al. advances the field by demonstrating that autonomous interaction among keratinocytes, endothelial cells, pericytes, and heterogeneous fibroblasts significantly enhances both barrier function and dermal elasticity, thus bringing engineered skin models closer to physiological skin. This addresses a key gap between prior single-cell descriptive studies and functional tissue engineering.

Define your field of expertise with a few keywords: experimental dermatology, skin cancer, tissue engineering and 3D skin models, cell biology, tumor microenvironment, and the skin microbiome and barrier function.

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Referee #2

Evidence, reproducibility and clarity

In this study, the authors present a novel and well-executed approach to reconstructing human skin equivalents (HSEs) that more faithfully replicate the functional complexity of native skin by incorporating the natural heterogeneity of dermal mesenchymal cells, including spatially organized pericytes, papillary fibroblasts, and reticular fibroblasts. Through autonomous interactions among keratinocytes, fibroblasts, and vascular endothelial cells, the fully tricellular EDV model emerged as the most functionally complete among seven engineered HSE variants, demonstrating enhanced epithelialization, barrier integrity, dermal elasticity, and angiogenic architecture. The study's strengths lie in its realistic aging induction via nutrient deprivation by mimicking aspects of vascular insufficiency in the papillary dermis, and its integration of diverse and rigorous evaluation methods, including histological and molecular analyses (Ki67, ECM markers), barrier function (TEWL), and mechanical testing. Notably, ascorbic acid treatment improved epidermal turnover and extracellular matrix organization, particularly through effects on perivascular niche cells, highlighting its translational relevance for anti-aging interventions. Although the EDV model showed superior elasticity via suction testing, more comprehensive mechanical characterization and longitudinal ECM analysis could further elucidate how mesenchymal heterogeneity supports biomechanical resilience. Overall, this work underscores the importance of multicellular crosstalk in skin physiology and positions the EDV model as a robust in vitro platform with high relevance for regenerative medicine, aging research, and therapeutic screening, offering the potential to eliminate animal models in skin biology.

Major comments:

Despite its strengths, the study has several limitations that warrant further investigation. The authors describe a "senescent-like" phenotype under nutrient-poor (NP) conditions, yet do not provide direct evidence of cellular senescence using canonical markers such as SA-β-gal staining, p16^INK4a or p21 expression, or SASP profiling-weakening their aging-related conclusions.

The 500 μM dose of ascorbic acid (AA), while within the reported range for skin models, is at the higher end compared to commonly used concentrations (100-300 μM) and lacks justification via dose/response data. Normal physiological levels and changes in aging dermis should be referenced in discussion. AA is also an additive in their standard HSE media, but this was not sufficiently emphasized to draw attention. Would its removal from the baseline media make a difference? Mechanistically, fibroblast heterogeneity is attributed to keratinocyte and vascular signals, but the signaling pathways involved (e.g., Wnt, TGF-β, VEGF) are not directly examined. Validating which paracrine factors (VEGF, PDGF, LAMA5, KGF) are mediating fibroblast transitions using inhibitors or RNA profiling could shed more light.

Minor comments:

The role of pericytes is also underexplored; while their presence is confirmed, functional assays or transcriptomic analyses to elucidate their contribution to ECM remodeling or vascular stability are not fully explored. The origin of pericyte-like cells remains uncertain without lineage tracing or barcoding to distinguish whether they derive from fibroblasts, endothelial cells, or culture artifacts. Since they observe induced differentiation of fibroblast-like cells in 3D culture, it would be compelling to reconstruct differentiation trajectories (pseudotime analysis) from progenitor states to papillary/reticular/pericyte-like states from their scRNAseq data. Although AA enhanced collagen production and elasticity in the vascularized EDV model, the lack of response in the ED model is not addressed mechanistically. The omission of immune cells which are key players in skin aging and homeostasis could increase physiological relevance of the model. The exclusive use of standard HUVECs may not fully capture the behavior of tissue-specific microvascular endothelial cells, potentially limiting the fidelity of the vascular niche.

Significance

This study presents a robust and innovative approach to human skin equivalent (HSE) reconstruction by integrating pericyte-like and endothelial cells with dermal fibroblast subtypes, using only commercially available cell types. A key strength lies in its ability to recapitulate aspects of in vivo fibroblast heterogeneity, including papillary, reticular, and perivascular populations, and to demonstrate functional consequences on tissue architecture, barrier integrity, ECM dynamics, and mechanical properties under aging-like, nutrient-poor conditions. The spontaneous emergence of a pericyte-like population without relying on freshly isolated primary pericytes or complex sorting protocols represents a methodological advance that increases the model's accessibility and scalability. Furthermore, the use of ascorbic acid to reverse aging-associated features in a vascular cell-dependent manner adds a compelling functional dimension, linking cell composition with therapeutic response.

Compared to existing models that either lack vascular cell compartments or do not account for dermal fibroblast heterogeneity, this study fills an important gap at the intersection of skin aging, vascular biology, and mesenchymal-epithelial interactions. The advance is both conceptual by elucidating the role of vascular and perivascular cells in shaping fibroblast identity and function and methodological, through the generation of a human skin model that approximates in vivo complexity without requiring animal models or ethically limited human tissue. The work will be of strong interest to basic science researchers in dermatology, tissue engineering, and aging, and has potential influence in regenerative medicine, cosmetic science, and drug screening, especially in the context of skin repair and anti-aging therapies. The audience is broad but most relevant to specialized communities in skin biology, mesenchymal cell biology, vascular biology, and organoid modeling, and may also attract attention from those developing non-animal testing platforms in applied and translational settings.

As a reviewer with expertise in inflammatory skin disease modeling using both animal systems and 3D organoid cultures, I bring a critical understanding of how cellular composition, microenvironmental cues, and co-culture conditions influence skin physiology and pathology. My interest in developing advanced co-culture systems to recapitulate human skin complexity positions me well to evaluate the relevance, innovation, and translational potential of this vascularized HSE model. I am especially qualified to assess the biological fidelity of the reconstructed skin architecture, the functional outcomes of introducing pericyte-like populations, and the implications of nutrient deprivation and ascorbic acid supplementation as aging-relevant perturbations.

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

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Referee #1

Evidence, reproducibility and clarity