Some stuff goes up and some goes down but overall civic participation does not change much

Not much going on to talk about, or time to discuss it

proxy for other things

Less going on, but is democratic participation in a place without strife actually needed?

More time in the car, less time hanging out at the bar

Varies

Same as above, more invested in the area, the thing is that is probably true for people who work there too

But I mean also if you're not seeing people in the office or at the corner store...

Certainly feels like at least one requirement

But is this connected to land, maybe the lack of fight actually makes them less engaged

People are moving into places that are just homes, this is where you might consider the east side very different

Sprawl

Talked about this in class

Urbanizing the suburbs

Would you expect the Tconv to influence the Treg compartment by a later time such as PND14?

Le lien renvoie vers un projet nommé The Self Organised Learning Environment (SOLE), partagé sur blog, pas une publication académique. Résumer cette approche à « supprimer les enseignants » est une simplification : le projet SOLE porte sur l'autonomie des apprenants, pas sur l'élimination de l'enseignant. La position citée est donc légèrement déformée.

The Conversation est une plateforme à part : les articles sont écrits par des chercheurs, mais destinés au grand public. Ce n'est pas une revue scientifique, et on le retrouve dans le slogan lui-même « rigueur académique, flair journalistique ».

Cela signifie que l'argumentation sera plus accessible mais potentiellement moins rigoureuse qu'un article scientifique.

this aged poorly huh

youy need an account, which takes only a moment

should be May

We are tempted to pose our language games onto other people's language games, even though they don't need it to understand it - the analysis is the "real" analysis according to the onlooker - imposing their outlook onto other people who are fine without it

They always mean the composite

but isn't it true, too, that something is lost in the analysed sentence? - what exactly is lost is not totally clear (simplicity?)

point of the criticism of analysis

the person will do the same thing but via different pictures - it will be done in two different ways - "point of the two orders is the same" -> this response forces an answer to fit a theory - if the point is the same, is the meaning not the same? - do we really want to reduce the meaning to the point of an order while erasing the differences between the two?

Am I saying the same thing when I ask for the broom vs. when I ask for the stick with the brush attached to it? this is not clear -- who thinks about the composition of the broom? - puts pressure on the idea that the analysed sentenced is better than the non-analysed sentence - logical formation seen in Russell's analysis of "the King of France is Bald" which Russell thinks solves a problem: but was there even a problem to begin with?

The stuff about the brush and the stick is contained within the notion of the broom.

The analysis of the sentence offers a more "complete" expression

e.g. "The stick that has the brush attached to it.."

we don't get the picture of atomism because we've seen it or that science has told us it's there; but it's something we are working with as framework/picture that eventually, there is an end to the cutting things into parts - even with a chair, there is an end to how far it can be broken down - atom: Greek for unbreakable --> Democritus (pre-Socrates)

It feels like there is something natural/intuitive to accept atomism.

object in the real world

After re-reading the Introduction, I'm finding it fragmented and hard to follow. You start with definition, then quickly jump to multiple algorithms, introducing representations (grids, NavMesh, probabilistic maps) mid-flow. “Issues with pathfinding” comes late, even though it should frame the need for PathMaker. And details about PathMaker come after a long technical buildup. I suggest you organize this chapter as follows:

1 Introduction

1.1 Pathfinding: Context and Challenges

1.1.1 Common Pathfinding Approaches

1.1.2 Challenges in Pathfinding

1.2 PathMaker Overview and Contributions

1.3 Design Choices (Brief Overview)

1.4 Limitations of Existing Tools

1.5 Contributions Summary

1.6 Ethical Implications

**What is Pathfinding? ** Keep: Define pathfinding Enhance: Give 2–3 application examples (GPS, games, robotics) Briefly introduce grid-based focus (move this up earlier)

Move out / reduce: Detailed algorithm explanations (A*, Dijkstra) - shorten or defer emphasis Long discussion of representations - keep minimal here

A*, Dijkstra, Weighted Grids - combine & condense to provide minimal technical grounding

Keep very concise summaries (2–4 sentences each) Emphasize differences (optimality, cost handling, use cases) Avoid deep mechanics (no step-by-step descriptions)

Issues with Pathfinding - Move earlier

This is the main motivation Briefly connect to representations (grids vs NavMesh vs probabilistic) Keep probabilistic maps/NavMesh as examples, not deep dives

Project Overview - Move earlier

Refocus this section to explicitly answer:

What is PathMaker? What problem does it solve? Why is it different from existing tools?

Tighten: Avoid repeating motivation language Clearly list capabilities: map creation algorithm execution benchmarking metrics

Implementation Details - Keep (But de-emphasize in intro)

Keep short explanations of Rust + SDL2 Frame as: “lightweight, cross-platform, low-overhead” Avoid deep technical detail (belongs in methods)

Current State of the Art - need to connect to your project to show gaps Structure it as: Visualizers - lack benchmarking Game engines - too complex APIs - too low-level Benchmark libraries - lack usability/integration

End this section with a clear gap statement

Motivation - Connect gap -> need for your tool

Reduce repetition Focus on: - difficulty of evaluating algorithms in practice - need for controlled experimentation

Goals of the Project Convert into a clean list of features: Custom map creation Algorithm implementation support Automated benchmarking Visualization + analysis

Avoid repeating earlier explanations ** Ethical Implications**

The tool is already made

This sentence is too long

What about these?

Figure 2 ...

Figure 1, where ...

I have been a fan of the Onion for a long time and I love reading their satirical articles. It looks like couple of the other annotations on this article haven't actually read it or don't understand that it's satirical. This is obviously satire for performative people online who like to show off on their social media accounts about what they did whether its helping others or accomplishing something. In this case, a woman has gone to a third world country for a short 6 day stay and has taken pictures of herself for clout instead of actually helping the kids in need.

This quote from the article: "These images are so ingrained in our imagination, most people that haven’t been to the continent believe that all of Africa looks like this, adding to the colonialist generalisation of Africa as “one country” was really interesting. Why? Because this view of Africa as this one homogenous place is one that is passivley accepted, even though it is inaccurate. Just because we see this voluntourism (a term, I have not heard of before, but I find extremley interesting), constantly through charities etc. It paints a picture, one that isn't quite reality. What is even more interesting is that the people behind these photos (for the voluntourism aspect) aren't necessarily doing it in bad intention either, in some way they are being a bit ignorant, although that is subjective. Overall, I just found this article to be really insightful as the topic isn't one that me an American wouldn't think about too often, but has an impact on how we see the world.

need to be change for aajshakjdhsj

other than aggregation of info, not the social capital

What a boring chapter to give us

No black people

white affluent flight

Sort of old for the suburbs

This is some examples of the kind of thing that might change in the suburbs

not the avenues

More worried about what is happening internally

A place where none of this happens is hard to envision as a community

Ok so we need them all

More caught up in their own lives

More important for mobilization

Very political, very formal

provide the most options and hope that the best one wins out

B/c of the different resources available

Or race, economic mobility

There isn't a pluralistic community to represent in the suburbs

rubber...road...etc

suburbs will be mid-sized, wealthy, white, newer, and with council-manager systems

And again probably the homogeneity of land use

What is the variation in wealth

And happen in urban areas as well

Population size is the only common denominator

Crazy variation

Maybe zoning reform to diversify areas

Gift of apathy

Because it takes issues out of the equation?

Citizens have to pick up the inevitable slack

Does not solve the problem

Suburb sort of get's equated to the American dream

Just good for mobilization

East side is probably a suburb, although technically in the jusidiction

No diversity

• Concept of "Surveillance Wages": Employers are increasingly using "surveillance wages," where algorithms analyze personal data to determine the minimum pay a candidate will accept, moving away from traditional experience-based pay.
• Data Sources: Companies may utilize signals of financial vulnerability, such as credit card balances, payday loan history, ZIP codes, and even social media activity to gauge a worker's leverage.
• Information Asymmetry: The practice creates a significant power imbalance, as employers may have access to a candidate's past compensation and financial health via background and credit checks, while the candidate has little to no insight into the company's internal data.
• Algorithmic Influence: Tools originally designed for "surveillance pricing" in consumer markets (e.g., airline tickets or retail) are being adapted for the labor market to predict behavior and optimize cost-cutting.
• Risks and Bias: Experts warn that these practices can lead to systemic discrimination, targeting those in desperate financial situations or predicting life events like pregnancy or union interest.
• Industry Adoption: An audit found that major companies in healthcare, logistics, customer service, and retail are already utilizing vendors that offer these data-driven algorithmic tools.

Hacker News Discussion

• Verification of Past Pay: Several users noted that while many states now ban asking for salary history, employers often bypass this by using third-party background check services (like The Work Number or credit reports) that provide precise historical income data.
• The Role of AI/Algorithms: Commenters discussed how companies like Salesforce and Intuit are being linked to these practices, using "behavioral science" to predict the lowest "reservation price" for a new hire.
• Data Privacy Concerns: There is significant skepticism regarding the "anonymization" of this data; many believe that even "aggregate" data is easily deanonymized when paired with specific resumes.
• Strategic Advice: Users suggest being extremely cautious about what is shared on social media and checking your own reports from major data brokers (like Equifax or LexisNexis) to see what information is being sold to potential employers.
• Legal and Ethical Debate: Some argue that this is a logical extension of market efficiency, while most view it as a predatory practice that exploits vulnerable workers and erodes the bargaining power of the labor force.

I think it is ironic how Elon Musk threatened to not buy twitter because the number of spam bots was too high and after he purchased Twitter, the problem only worsened. However, this isn't an issue with just Twitter/X. Bot traffic has surpassed human traffic online for the first time in 2024. Now that it's been two years, I can only assume that this has worsened. People already talk about "dead internet theory" where the internet is just bots reacting to bots. With a ton of AI content being pumped out daily and comments being filled with bots, a lot of online interaction is really just bots talking to bots.

Can you add a fourth experiment where the user prompts, LLM gives a response, the user does something incorrectly, and LLM corrects?

Give an example of how you applied this formula or where it was used in your tool

What does this look like? Text file? How is it then passed on?

Delete

Is this up to date screenshot?

Add an abstract - 250 word summary of the project, including results.

Does this explain why two or more different mythologies/religions can use the same symbol? For example, both pagan, wiccan, and Christian practices use eggs as a symbol of rebirth (especially during the spring seasons).

In other words, mythology is the literary portion of belief; it can be used in conjunction with religion but is not the same thing. A few months ago, I learned from a friend that religion is the system, and myths are the literature/stories.

This particular quote leads me to believe that myth is something of a creative concept. Like we as humans take our emotions, thoughts, and personalities and use them to create myths as a form of expression. Not to discredit anyone or say that certain religions and spiritualities associated with mythology are “just fantasies”, what I am saying is that the stories from mythologies heavily rely on pathos (known as the rhetoric/credibility of emotion). In simpler terms, myths are powerful because they are human.

fragmentation of life due to modernity

Reviewer #1 (Public review):

Summary:

Using electron microscopy, the authors report discontinuities in the plasma membrane of C. elegans embryos. They associate these discontinuities with cell division and speculate that membrane rupture and subsequent resealing contribute to cytokinesis. They further discuss the proximity of these sites to vesicles and propose a role for vesicle-mediated membrane extension.

Weaknesses:

(1) The possibility that the membrane discontinuity is an artifact

Although the authors focus on discontinuities in the plasma membrane, similar discontinuities are also observed in mitochondria, the nuclear envelope, and yolk granules. This raises concerns about whether the electron micrographs presented are suitable for assessing membrane continuity.

Electron micrographs result from a lengthy sample preparation process, including high-pressure freezing, freeze substitution in acetone containing OsO4, gradual warming, uranyl acetate staining, resin embedding, and ultrathin sectioning. In general, lipids are soluble in acetone at temperatures above −30 {degree sign}C, and preservation of membrane structures relies heavily on efficient OsO4 fixation. Insufficient OsO4 treatment would be expected to reduce membrane contrast.

C. elegans embryos are encapsulated by an eggshell that forms at fertilization and gradually develops during the first few cell divisions. It is unclear how efficiently OsO4 in acetone penetrates the eggshell during freeze substitution, raising further concern about plasma membrane preservation under the conditions used.

(2) Lack of evidence linking membrane discontinuity to cell division

The reported plasma membrane discontinuities are not specific to mitotic cells. If this were a physiological process playing an important role in cytokinesis, it should occur in a temporally and spatially coordinated manner with nuclear division. However, it remains unclear at what stage of the cell cycle the membrane rupture occurs and where it is located relative to chromosomes and the mitotic spindle.

(3) Lack of evidence for extension of the separated membrane

Although the authors speculate that resealing of the ruptured membrane occurs via extension of the separated membrane, no direct evidence supporting this mechanism is presented. Proximity to vesicles alone does not demonstrate that membrane extension occurs through vesicle fusion. More direct evidence is required to support this claim.

(4) Inconsistency with published work

Numerous studies have examined cell division in developing C. elegans embryos using the GFP::PH(PLC1δ1) marker expressed from the ltIs38 transgene [pAA1; pie-1::GFP::PH(PLC1δ1) + unc-119(+)], generated by the Oegema lab (https://wormbase.org/species/c_elegans/transgene/WBTransgene00000911#01--10 ). To date, no study has reported membrane ruptures of the magnitude described here. The complexity of cell surface morphology from the 8- to 12-cell stages onward has been well documented, for example, by Fu et al. (2016) using light-sheet microscopy and 3D reconstruction (doi:10.1038/ncomms11088).

Supplementary Movies 5, 6, and 10 of this paper illustrate how single-plane images can easily produce apparent membrane discontinuities, for example, due to membrane orientations nearly parallel to the imaging plane.

The three single-plane images from only three embryos presented in Figure 6 are insufficient to support the authors' strong conclusions. Raw 3D data should be provided.

Reviewer #2 (Public review):

Summary:

Liang et al. explore an unusual observation of membrane discontinuities in dividing C. elegans embryonic cells. This report is the first to demonstrate that, instead of the classical invagination of membranes during cytokinesis, cells in the early embryos of C. elegans exhibit separation of sister membranes that extend independently. TEM images of high-pressure-frozen samples provide strong evidence for the presence of Membrane Openings (MOs) in cells at various stages of the cell cycle, predominantly during mitosis. High-resolution images (x 30,000) clearly show the wrinkled plasma membrane and smooth MOs.<br /> The electron microscopy data are supported by the live cell imaging of strains with fluorescently tagged membrane markers. This study opens up the possibility of tracking MOs at other stages of C. elegans development, and also asks if it might be a common phenomenon in other species that exhibit rapid embryonic growth and divisions.

Strengths:

(1) Thorough verification of Membrane Openings (MO) by several methods:

(a) 4 independent sample batches.

(b) Examined historical collections.

(c) Analysed embryos at different stages of development. The absence of MOs in later stages (comma) serves as a negative control and gives confidence that MOs are genuine and not technical artifacts.

(2) Live cell imaging of strain with fluorescently labelled membranes provides real-time dynamics of membrane rupture.

(3) After observing the membrane rupture, the next obvious question is - what prevents the cytosol from leaking out? The EM images showing PBL and PEL - extracellular matrix serving as barriers for the cytosol are convincing.

Weakness:

(1) The association of membrane discontinuities with cell division is not convincing, as there are 159 cells out of 425 showing MOs, but it is not mentioned clearly how many of these are undergoing cell division. Also, it's not clear whether the 20 dividing cells analysed for MOs are a part of the 159 cells or a separate dataset. A graphical representation of the number of samples and observed frequencies would be helpful to understand the data collection workflow.

(2) In Figures 3A and 3B, the resolution of the images is not enough to verify 3A as classical membrane invagination and 3B as detached sister membranes.

(3) Figure 6 lacks controls. How does the classical invagination look in this strain? Also, adding nuclear dye would be informative, in order to correlate the nuclear division with membrane rupture, as claimed.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors challenge a dogma in cell biology, namely that cells are at any time point engulfed by a continuous plasma membrane. Liang et al. find that during C elegans embryogenesis, a high number of cells are not entirely surrounded by a plasma membrane but show membrane openings (MOs). These openings are enriched at the embryo's periphery, towards the eggshell. The authors propose that plasma membrane discontinuities emerge during metaphase of mitosis and that independent extension of "sister membranes" engulfs the daughter cells.

Strengths:

On the positive side, the authors find plasma membrane discontinuities not only by electron microscopy but also by fluorescence microscopy and provide information about the dynamics of membrane openings and their emergence. While this is assuring, the authors conclude that MOs emerge during metaphase. From what the authors show, this particular information cannot be deduced, as there is no dynamic capture of a membrane scission event together with a chromatin marker that would indicate mitosis. The authors could, however, attempt to find such events in live movies, given the high incidence of MOs reported from their EM data.

Weaknesses:

In order to convincingly demonstrate the absence of any plasma membrane in the respective regions of the embryonic periphery or between cells of the embryo, the authors would have to show consecutive serial TEM sections where MOs are detected over more z-planes, beyond the mere 3D reconstructions. Although the authors state in the methods section that continuous ultrathin sections were cut for the metaphase sample (page 21, line 472), consecutive sections are never shown in TEM. While we do see the 3D reconstructions, better documentation of the underlying TEM data is missing. It would be necessary to show a membrane opening in consecutive z sections. Alternatively, the authors could seek the possibility to convincingly back up their claims with volume imaging by focused ion beam scanning EM (FIBSEM), where cellular volumes can be sectioned in almost isotropic resolution.

Another critical issue concerns the detection of the membrane discontinuities in electron micrographs, which, in my opinion, is ambiguous. How do the authors reliably discriminate in their TEM images whether there is a plasma membrane or not? The absence - or weak appearance - of the stain of the electron dense material at membranes, which seems to be their criterion for MOs, is also apparent at other, intracellular membranes, like at the NE or at the ER (for example, see Figure 1C). Also, the plasma membrane itself appears unevenly stained in regions that the authors delineate as intact (for example, Figure 1C, 2B/1).

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Using electron microscopy, the authors report discontinuities in the plasma membrane of C. elegans embryos. They associate these discontinuities with cell division and speculate that membrane rupture and subsequent resealing contribute to cytokinesis. They further discuss the proximity of these sites to vesicles and propose a role for vesicle-mediated membrane extension. 

Weaknesses:

(1) The possibility that the membrane discontinuity is an artifact

Although the authors focus on discontinuities in the plasma membrane, similar discontinuities are also observed in mitochondria, the nuclear envelope, and yolk granules. This raises concerns about whether the electron micrographs presented are suitable for assessing membrane continuity.

Electron micrographs result from a lengthy sample preparation process, including high-pressure freezing, freeze substitution in acetone containing OsO4, gradual warming, uranyl acetate staining, resin embedding, and ultrathin sectioning. In general, lipids are soluble in acetone at temperatures above −30 {degree sign}C, and preservation of membrane structures relies heavily on efficient OsO4 fixation.

Insufficient OsO4 treatment would be expected to reduce membrane contrast.

C. elegans embryos are encapsulated by an eggshell that forms at fertilization and gradually develops during the first few cell divisions. It is unclear how efficiently OsO4 in acetone penetrates the eggshell during freeze substitution, raising further concern about plasma membrane preservation under the conditions used.

We thank the reviewer for raising this important technical concern. We have taken this question seriously since first observing membrane discontinuities six years ago, and we have since conducted extensive controls to rule out fixation artifacts. Below, we present multiple lines of evidence—ranging from technical reproducibility to orthogonal imaging approaches—that collectively demonstrate the biological reality of these structures.

(1) Technical expertise and standard protocols

Our laboratory has extensive experience with electron microscopy across diverse biological systems, including neurons, muscle cells, and hypodermis in C. elegans, as well as tissues from Drosophila, mouse, bacteria, and cultured cells (Chen et al., 2013; Ding et al., 2018; Guan et al., 2022; Y. Li et al., 2018; Miao et al., 2024; Qin et al., 2014; Wang et al., 2026; J. Xu et al., 2022; M. Xu et al., 2021; L. Yang et al., 2020; X. Yang et al., 2019; Zhu et al., 2022). Importantly, we did not introduce any novel or unconventional steps in our EM preparation; all protocols were standard and well-established. Thus, the observed membrane discontinuities are unlikely to stem from technical inexperience or idiosyncratic methods.

In addition to membrane discontinuities, we would like to emphasize that a large number of single plasma membranes separating adjacent cytoplasmic domains were also detected under EM (Figure 1, 3 and 4, for instance). This observation is particularly significant because the invagination model cannot generate single plasma membrane barriers between adjacent cytoplasmic domains. Instead, independent extension of detached sister membranes could explain the formation of cytoplasm-enclosed membranes. Furthermore, as the morphology and continuity of these single cytoplasm-immersed membrane structures are well preserved, this indicates successful EM processing and argues against inefficient fixation or other technical issues.

(2) Reproducibility across independent preparations and techniques

To test whether the discontinuities were preparation-specific, we examined four independent sample batches collected in the lab over the years. Membrane discontinuities, as well as cytoplasm-immersed membranes, on embryonic cells were consistently observed across all batches, indicating that the phenomenon is not dependent on a single preparation method. Furthermore, we validated our findings using two EM techniques: transmission electron microscopy (HPF-TEM) and dualbeam scanning electron microscopy (SEM). Membrane discontinuities were clearly identifiable with both techniques, further supporting their robustness.

(3) Validation using an independent public dataset

We examined the publicly available C. elegans embryo EM collection (WormAtlas). In several instances, particularly at the embryonic periphery where plasma membrane discontinuities are more readily visualized (https://www.wormimage.org/image.php?id=140265&page=1), we identified similar structures. The presence of these features in an independent dataset generated by different researchers confirms that they are not artifacts unique to our sample preparation.

(4) Developmental regulation of membrane discontinuities

We analyzed embryos across multiple developmental stages. Membrane discontinuities were observed in both intrauterine and laid embryos at early stages. However, as embryos reached the comma stage—a period marked by the onset of elongation and reduced cell proliferation—the incidence of discontinuities dropped dramatically (0/13, 0/17, and 0/30 cells examined). This developmental specificity argues strongly against a general fixation artifact, which would be expected to occur randomly across stages. Additionally, the eggshell is present throughout the embryonic stage of C. elegans; therefore, the dramatic reduction of membrane discontinuities in comma-stage of embryo argues against the possibility that the eggshell poses a fixation problem.

(5) Rigorous criteria for identifying membrane discontinuities

To ensure unbiased analysis, we systematically collected images from early embryonic cells using the following criteria:

(1) Random section selection: For each sample, we randomly selected one section containing the largest number of embryos or cells (Sup Figure 2) for initial analysis. We found membrane discontinuities in 159 cells distributed across 57 embryos, representing 95% of the total sampled embryos This portion of the data is summarized in Figure 1.

(2) Whole-membrane examination: Each putative membrane discontinuity was identified only after examining the entire plasma membrane of the cell on a given section. Importantly, aside from the discontinuity, the remainder of the plasma membrane remained intact. Moreover, in most cells, only a single discontinuity was present per section, arguing against random, widespread membrane tearing during preparation.

(3) Neighboring section verification: Because EM preparation yields serial sections, we verified nearly all membrane discontinuities by examining adjacent sections. Again, the same membrane discontinuity was confirmed only after inspecting the entire plasma membrane on those neighboring sections as well. We will include this verification protocol in the revised Methods and additional imaging of consecutive sections would be provided if needed.

(4) Serial section reconstruction: To further determine whether a dividing cell indeed contains one membrane rupture, we performed two serial reconstruction experiments.

First, we used HPF-TEM to analyze 105 consecutive sections of a metaphase cell, reconstructing the entire plasma membrane and chromosome configuration. We found that one membrane rupture largely encircled the chromosomal disc (Figure 2 and Video S1), spatially aligning with the future segregation zone. Second, we used AutoCUTS-SEM to collect approximately 600 sections covering ~95% of a telophase cell containing three nuclei sharing a common cytoplasm. This tri-nucleated cell was enclosed by three distinct plasma membranes, each harboring a single rupture site. These three ruptures converged to form a Y-shaped exposed cytoplasmic region spanning >351 sections (Figure 5). Collectively, these reconstructions demonstrate that each cell contains only one discontinuity from a 3D point of view, further supporting that the phenomenon is not due to random sample preparation damage.

(6) Orthogonal validation by live imaging: In addition to EM, we performed live imaging of plasma membrane dynamics. While live imaging provides important temporal context, we recognize its limitations in resolving membrane ultrastructure. The rapid kinetics of membrane extension (approximately 20–30 seconds for metaphase and less than 3 minutes for cytokinesis), combined with embryo motility, introduces spatiotemporal ambiguities. To capture dynamic membrane events, our live imaging using the GFP::PH membrane marker was performed at 4-second intervals, approaching the practical limit for single-section scanning of the embryo. With single-plane live imaging, nevertheless, both membrane ruptures and free-ended sister membrane structures could be detected (Figures 6), providing additional evidence that membrane rupture and independent extension of detached sister membranes underlie cytokinesis in C. elegans embryos. Notably, 3D membrane dynamics analysis using light-sheet microscopy (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088) revealed membrane ruptures in dividing early C. elegans embryonic cells, including during telophase or metaphase. Therefore, live imaging further validates the membrane rupture phenomena in dividing embryonic cells in C. elegans. 

While future advances in imaging technology may enable real-time visualization at near-EM resolution, our extensive, multi-year effort to test the artifact hypothesis has convinced us that these membrane discontinuities are genuine biological features of dividing C. elegans embryonic cells.

We are confident that the cumulative evidence presented here addresses the reviewer's concerns and demonstrates that the observed membrane discontinuities, as well as cytoplasm-immersed membranes, are not procedural artifacts but rather reflect a previously underappreciated aspect of plasma membrane dynamics during embryonic cell division.

(2) Lack of evidence linking membrane discontinuity to cell division 

The reported plasma membrane discontinuities are not specific to mitotic cells. If this were a physiological process playing an important role in cytokinesis, it should occur in a temporally and spatially coordinated manner with nuclear division. However, it remains unclear at what stage of the cell cycle the membrane rupture occurs and where it is located relative to chromosomes and the mitotic spindle.

Thank you for this insightful comment. We agree that establishing a direct link between plasma membrane discontinuities and mitotic progression is critical, and we appreciate the opportunity to clarify this point.

In C. elegans embryos, the early stages of development are characterized by rapid and extensive cell division. Within approximately 100 minutes, a two-cell embryo develops into an embryo containing nearly 30 cells. The majority of the electron microscopy analyses in our study were performed on embryos at stages with fewer than 30 cells, where most cells are actively dividing. Thus, it is reasonable to infer that the cells exhibiting membrane discontinuities are predominantly mitotic cells.

Supporting this notion, as embryos reached the comma stage—a period marked by the onset of elongation and reduced cell proliferation—the incidence of membrane discontinuities dropped dramatically (0/13, 0/17, and 0/30 cells examined). This developmental specificity strongly suggests that membrane discontinuities are tightly linked to cell division.

Importantly, mitotic features such as metaphase chromosomes aligned at the equatorial plane or two (or more) nuclei sharing common cytoplasm can be identified in EM images. In our single random EM section analysis, we captured membrane discontinuities in cells at metaphase, anaphase (characterized by fewer than 10 chromosomal clumps), and telophase (defined by two nuclei sharing cytoplasm). Hence, membrane discontinuities are indeed present on mitotic cells. In addition, a published work by Fu et al (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088) using light-sheet microscopy captured similar membrane discontinuities in cells displaying classical mitotic features, including anaphase or telophase.

To further investigate the spatial relationship between membrane ruptures and chromosome organization, we performed three-dimensional reconstructions on a metaphase cell. As shown in Figure 2 and Video S1, the membrane discontinuities largely encircled the condensed chromosome disc and were spatially aligned with the future segregation zone, further revealing the relative location of membrane discontinuities to chromosomes, at least at metaphase.

We further collected 3D information for a telophase cell containing three nuclei. This tri-nucleated cell was enclosed by three distinct plasma membranes, each harboring a single rupture site that merged to form a single rupture. The observation that membrane ruptures are present in a tri-nucleated cell is particularly informative. The tri-nucleated feature indicates that this cell underwent two rounds of cell division and that both divisions were at telophase. The presence of a single membrane rupture suggests that membrane discontinuities may persist throughout the cell cycle, as the second cell cycle began from a mother cell that still shared cytoplasm with its sister cell and already had one membrane rupture. Therefore, in addition to the mitotic phase, membrane discontinuities—at least in this context—also exist during the DNA synthesis stage.

(3) Lack of evidence for extension of the separated membrane 

Although the authors speculate that resealing of the ruptured membrane occurs via extension of the separated membrane, no direct evidence supporting this mechanism is presented. Proximity to vesicles alone does not demonstrate that membrane extension occurs through vesicle fusion. More direct evidence is required to support this claim.

Thank you for raising this important point. We appreciate the opportunity to clarify our conclusion.

In our study, EM analysis revealed the presence of cellular vesicles in close proximity to both free membrane edges and the already separated sister plasma membranes (Figure 4). However, we acknowledge that without advanced live-cell imaging, it is not possible to conclusively determine whether the extension of these separated sister membranes occurs through vesicle fusion.

We realize that a statement in the Discussion section—“The expansion of the plasma membrane is generally driven by vesicle fusion”(page 16)—may have inadvertently led the reviewer to interpret this as our own conclusion regarding the mechanism of membrane extension in this context. In fact, that statement was intended to reflect the current general understanding of membrane expansion, not to imply that we had demonstrated such a mechanism for the free-ended sister membranes. As we subsequently noted, “However, this remains speculative and requires further experimental validation.”

To avoid any misunderstanding, we will revise this section to clearly state that the mechanism by which the separated sister membranes extend remains unknown and that further investigation is needed to determine how existing models of membrane expansion may apply to or be adapted for this novel context.

We thank the reviewer again for their thoughtful comment, which has helped us improve the clarity of our manuscript

(4) Inconsistency with published work

Numerous studies have examined cell division in developing C. elegans embryos using the GFP::PH(PLC1δ1) marker expressed from the ltIs38 transgene [pAA1; pie-1::GFP::PH(PLC1δ1) + unc-119(+)], generated by the Oegema lab (https://wormbase.org/species/c_elegans/transgene/WBTransgene00000911#01--10 ). To date, no study has reported membrane ruptures of the magnitude described here. The complexity of cell surface morphology from the 8- to 12-cell stages onward has been well documented, for example, by Fu et al. (2016) using light-sheet microscopy and 3D reconstruction (doi:10.1038/ncomms11088).

Supplementary Movies 5, 6, and 10 of this paper illustrate how single-plane images can easily produce apparent membrane discontinuities, for example, due to membrane orientations nearly parallel to the imaging plane.

The three single-plane images from only three embryos presented in Figure 6 are insufficient to support the authors' strong conclusions. Raw 3D data should be provided.

Thank you for this important comment. We fully agree that the GFP::PH(PLC1δ1) marker, generated by the Oegema lab, has been widely and effectively used to study various aspects of C. elegans embryonic development. In fact, we also employed this same marker in our study to assess membrane integrity.

However, while live imaging provides invaluable temporal resolution, its limitations in resolving membrane ultrastructure are substantial. In C. elegans embryos, early development is marked by rapid and extensive cell divisions. Within approximately 100 minutes, a two-cell embryo develops into one containing nearly 30 cells. During this fast-dividing stage, the rapid kinetics of membrane extension—approximately 20–30 seconds during metaphase and less than 3 minutes during cytokinesis— combined with embryo motility, introduce considerable spatiotemporal ambiguities. Furthermore, the longstanding invagination model of cytokinesis has shaped interpretations in the field, which may have led to ambiguous structures such as free-ended extensions being dismissed as potential artifacts rather than recognized as alternative morphological features. Theoretical and computational models have largely been built upon invagination-centric assumptions, which may have further constrained conceptual frameworks. Therefore, fluorescence protein-based live imaging analysis alone could not serve as a convincing approach to challenge the current dogma of cell division, nor did we intend it to.

However, when reexamined in light of our findings, previous studies using this same GFP marker have in fact revealed membrane discontinuities that went unnoticed. For example, Fu et al (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088) using light-sheet microscopy and 3D reconstruction, captured membrane discontinuities in cells undergoing mitotic phases such as anaphase or telophase. Similarly, an earlier study by Harrell and Goldstein (Harrell and Goldstein. 2011. Internalization of multiple cells during C. elegans gastrulation depends on common cytoskeletal mechanisms but different cell polarity and cell fate regulators. Developmental Biology. DOI:10.1016/j.ydbio.2010.09.012) showed regions where the GFP::PH signal appeared fuzzy and discontinuous.

Nevertheless, given the inherent limitations of fluorescence microscopy in resolving membrane ultrastructure, high-resolution electron microscopy—supported by rigorous controls and serial section analysis—remains the gold standard for definitively identifying such membrane discontinuities.

We acknowledge that our findings are surprising. We did not set out to challenge the long-held view of membrane integrity during cell division. In fact, this study began when our dedicated EM technician, Jingjing Liang, first observed membrane discontinuity phenomena in control samples—wild-type embryos. Had she not come across this observation, we likely would never have pursued this line of inquiry.

We appreciate the opportunity to clarify these points and thank the reviewer for thoughtful engagement with our work.

Reviewer #2 (Public review):

Summary:

Liang et al. explore an unusual observation of membrane discontinuities in dividing C. elegans embryonic cells. This report is the first to demonstrate that, instead of the classical invagination of membranes during cytokinesis, cells in the early embryos of C. elegans exhibit separation of sister membranes that extend independently. TEM images of high-pressure-frozen samples provide strong evidence for the presence of Membrane Openings (MOs) in cells at various stages of the cell cycle, predominantly during mitosis. High-resolution images (x 30,000) clearly show the wrinkled plasma membrane and smooth MOs.

The electron microscopy data are supported by the live cell imaging of strains with fluorescently tagged membrane markers. This study opens up the possibility of tracking MOs at other stages of C. elegans development, and also asks if it might be a common phenomenon in other species that exhibit rapid embryonic growth and divisions. 

Strengths:

(1) Thorough verification of Membrane Openings (MO) by several methods: 

(a) 4 independent sample batches.

(b) Examined historical collections.

(c) Analysed embryos at different stages of development. The absence of MOs in later stages (comma) serves as a negative control and gives confidence that MOs are genuine and not technical artifacts. 

(2) Live cell imaging of strain with fluorescently labelled membranes provides realtime dynamics of membrane rupture.

(3) After observing the membrane rupture, the next obvious question is - what prevents the cytosol from leaking out? The EM images showing PBL and PEL - extracellular matrix serving as barriers for the cytosol are convincing.

Thanks to the reviewer for the encouragement. Highly appreciated.

Weakness:

(1) The association of membrane discontinuities with cell division is not convincing, as there are 159 cells out of 425 showing MOs, but it is not mentioned clearly how many of these are undergoing cell division. Also, it's not clear whether the 20 dividing cells analysed for MOs are a part of the 159 cells or a separate dataset. A graphical representation of the number of samples and observed frequencies would be helpful to understand the data collection workflow.

We sincerely thank the reviewer for raising this important question and appreciate the opportunity to clarify these points.

(1) Relationship between membrane discontinuities and cell division

In C. elegans embryos, early development is characterized by rapid and extensive cell division: within approximately 100 minutes, a two-cell embryo develops into one containing nearly 30 cells. Most of our electron microscopy (EM) analyses were performed on embryos at stages with fewer than 30 cells, in which the majority of cells are actively dividing. Therefore, it is reasonable to infer that the cells exhibiting membrane discontinuities (MOs) are predominantly mitotic. Supporting this, as embryos reached the comma stage—when cell proliferation declines and elongation begins—the incidence of MOs dropped sharply (0/13, 0/17, and 0/30 cells examined. This developmental specificity strongly links MOs to cell division.

Moreover, in single random EM sections, we observed MOs in cells displaying clear mitotic features, such as metaphase chromosomes aligned at the equatorial plate, or anaphase/telophase configurations (fewer than 10 chromosomal clumps or two nuclei sharing common cytoplasm). Thus, MOs are indeed present in mitotic cells.

From our 3D reconstruction (Figure 5), we identified a telophase cell containing three nuclei, each enclosed by its own plasma membrane, with each membrane harboring a single rupture that converged into a single opening. This tri-nucleated configuration indicates that the cell had undergone two rounds of division and was at telophase in both. The presence of a single membrane rupture in this context suggests that MOs can persist beyond mitosis, as the second cell cycle initiated from a mother cell that already shared cytoplasm with its sister and already contained a rupture. Thus, in this case, MOs were also present during DNA synthesis stage.

(2) Clarification of sample numbers and datasets

In Figure 1, we present results from a single EM section per embryonic cell, with sections randomly selected per embryo as detailed in Sup Figure 2. This initial dataset (425 cells) forms the basis of Figure 1.

From the same pool of 425 cells, we used additional EM sections—distinct from those shown in Sup Figure 2—to locate 20 dividing cells for analysis of membrane discontinuities. Thus, while these 20 cells originated from the same set of embryos, they were not derived from the sections used in Figure 1 or Sup Figure 2.

A graphical summary of sample numbers from the single-section analysis is already provided in Figure 1. Notably, cells with two clearly visible nuclei are more likely to be sectioned through or near their maximal diameter. In contrast, the randomly selected sections used for Figure 1 captured cells at variable planes, reducing the likelihood of observing MOs. Consistent with this, in the three embryos where no MOs were detected (one example is Sup Figure 2N), the sections likely passed through peripheral regions of the cells. Consequently, the frequency of MOs in randomly sectioned cells (Figure 1) is not directly comparable to that observed in the 20 dividing cells, which were analyzed using sections more likely to capture cells near their maximal diameter. These 20 dividing cells should therefore be considered a separate analysis. We will add detailed explanations in the Methods section to ensure this distinction is clearly understood.

We are grateful for the reviewer’s thoughtful feedback and believe these clarifications will improve the clarity and rigor of the manuscript.

(2) In Figures 3A and 3B, the resolution of the images is not enough to verify 3A as classical membrane invagination and 3B as detached sister membranes.

Thank you for your valuable comment. In the revised manuscript, we will provide additional images at higher magnification to better illustrate the classical membrane invagination in Figure 3A and the detached sister membranes in Figure 3B.

(3) Figure 6 lacks controls. How does the classical invagination look in this strain? Also, adding nuclear dye would be informative, in order to correlate the nuclear division with membrane rupture, as claimed. 

Thank you for this important comment. As we addressed how we correlated nuclear division with membrane rupture in response to weakness (1), below we will focus on how we may distinguish classical invagination from membrane rupture.

While live imaging provides invaluable temporal resolution, its limitations in resolving membrane ultrastructure are substantial. In C. elegans embryos, early development is marked by rapid and extensive cell divisions. Within approximately 100 minutes, a two-cell embryo develops into one containing nearly 30 cells. During this fast-dividing stage, the rapid kinetics of membrane extension—approximately 20–30 seconds during metaphase and less than 3 minutes during cytokinesis— combined with embryo motility, introduce considerable spatiotemporal ambiguities. Furthermore, the longstanding invagination model of cytokinesis has shaped interpretations in the field, which may have led to ambiguous structures such as free-ended extensions being dismissed as potential artifacts rather than recognized as alternative morphological features. Theoretical and computational models have largely been built upon invagination-centric assumptions, which may have further constrained conceptual frameworks. Therefore, fluorescence protein-based live imaging analysis alone could not serve as a convincing approach to challenge the current dogma of cell division, nor did we intend it to.

However, when reexamined in light of our findings, previous studies using GFP::PH or similar markers have in fact revealed membrane discontinuities that went unnoticed. For example, using light-sheet microscopy and 3D reconstruction, Fu et al captured membrane discontinuities in cells undergoing division such as anaphase or telophase (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016.DOI:10.1038/ncomms11088)

Similarly, an earlier study by Goldstein et al. (Harrell and Goldstein. 2011. Internalization of multiple cells during C. elegans gastrulation depends on common cytoskeletal mechanisms but different cell polarity and cell fate regulators. Developmental Biology. DOI:10.1016/j.ydbio.2010.09.012) showed regions where the GFP::PH signal appeared fuzzy and discontinuous.

Here, to capture dynamic membrane events, our live imaging using the GFP::PH membrane marker was performed at 4-second intervals, approaching the practical limit for single-section scanning of the embryo. With single-plane live imaging, both membrane ruptures and free-ended sister membrane structures (Figures 6) could be detected, providing additional evidence that membrane rupture and independent extension of detached sister membranes underlie cytokinesis in C. elegans embryos.

However, given the inherent limitations of fluorescence microscopy in resolving membrane ultrastructure, high-resolution electron microscopy—supported by rigorous controls and serial section analysis—remains the gold standard for definitively distinguishing invagination from membrane discontinuities.

While future advances in imaging technology may enable real-time visualization at near-EM resolution, our extensive, multi-year effort to test the artifact hypothesis has convinced us that these membrane discontinuities are genuine biological features of dividing C. elegans embryonic cells.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors challenge a dogma in cell biology, namely that cells are at any time point engulfed by a continuous plasma membrane. Liang et al. find that during C elegans embryogenesis, a high number of cells are not entirely surrounded by a plasma membrane but show membrane openings (MOs). These openings are enriched at the embryo's periphery, towards the eggshell. The authors propose that plasma membrane discontinuities emerge during metaphase of mitosis and that independent extension of "sister membranes" engulfs the daughter cells.

Strengths:

On the positive side, the authors find plasma membrane discontinuities not only by electron microscopy but also by fluorescence microscopy and provide information about the dynamics of membrane openings and their emergence. While this is assuring, the authors conclude that MOs emerge during metaphase. From what the authors show, this particular information cannot be deduced, as there is no dynamic capture of a membrane scission event together with a chromatin marker that would indicate mitosis. The authors could, however, attempt to find such events in live movies, given the high incidence of MOs reported from their EM data.

Thanks to the reviewer for the encouragement. Highly appreciated.

Weaknesses:

In order to convincingly demonstrate the absence of any plasma membrane in the respective regions of the embryonic periphery or between cells of the embryo, the authors would have to show consecutive serial TEM sections where MOs are detected over more z-planes, beyond the mere 3D reconstructions. Although the authors state in the methods section that continuous ultrathin sections were cut for the metaphase sample (page 21, line 472), consecutive sections are never shown in TEM. While we do see the 3D reconstructions, better documentation of the underlying TEM data is missing. It would be necessary to show a membrane opening in consecutive z sections. Alternatively, the authors could seek the possibility to convincingly back up their claims with volume imaging by focused ion beam scanning EM (FIBSEM), where cellular volumes can be sectioned in almost isotropic resolution

We Thank the reviewer for raising these important technical concerns. We have taken this question seriously since first observing membrane discontinuities six years ago, and we have since conducted extensive controls to rule out fixation artifacts.

First of all, in addition to membrane discontinuities, we would like to highlight that a large number of single plasma membranes separating adjacent cytoplasmic domains were detected by EM (Figure 1, 3 and 4). This observation is particularly significant because the invagination model cannot account for the formation of single plasma membrane barriers between adjacent cytoplasmic domains. Instead, independent extension of detached sister membranes offers a plausible explanation for the generation of cytoplasm-immersed membranes. Furthermore, the morphology and continuity of these single cytoplasm-immersed membrane structures are well preserved, indicating successful EM processing and arguing against potential issues such as inadequate fixation or other technical limitations.

Second, we applied rigorous criteria for identifying membrane discontinuities:

(1) To test whether the discontinuities were preparation specific, we examined four independent sample batches and validated our findings using two EM techniques: transmission electron microscopy (HPF-TEM) and dual-beam scanning electron microscopy (SEM).

(2) We analyzed embryos across multiple developmental stages. Membrane discontinuities were observed in both intrauterine and laid embryos at early stages. However, as embryos reached the comma stage—a period marked by the onset of elongation and reduced cell proliferation—the incidence of discontinuities dropped dramatically (0/13, 0/17, and 0/30 cells examined). This developmental specificity argues strongly against a general fixation artifact, which would be expected to occur randomly across stages. Additionally, the eggshell is present throughout the embryonic stage of C. elegans; therefore, the dramatic reduction of membrane discontinuities in comma-stage of embryo argues against the possibility that the eggshell poses a fixation problem.

(3) Each putative membrane discontinuity was identified only after examining the entire plasma membrane of the cell on a given section. Importantly, aside from the discontinuity, the remainder of the plasma membrane remained intact. Moreover, in most cells, only a single discontinuity was present per section, arguing against random, widespread membrane tearing during preparation. Because EM preparation yields serial sections, we verified nearly all membrane discontinuities by examining adjacent sections. Again, the same membrane discontinuity was confirmed only after inspecting the entire plasma membrane on those neighboring sections as well. We will include this verification protocol in the revised Methods and additional imaging of consecutive sections would be provided if needed.

To further determine whether a dividing cell indeed contains one membrane rupture, we performed two serial reconstruction experiments using consecutive sections, as the reviewer suggested. First, we used HPF-TEM to analyze 105 consecutive sections of a metaphase cell, reconstructing the entire plasma membrane and chromosome configuration. We found that one membrane rupture largely encircled the chromosomal disc (Figure 2 and Video S1), spatially aligning with the future segregation zone. Second, we used AutoCUTS-SEM to collect approximately 600 sections covering ~95% of a telophase cell containing three nuclei sharing a common cytoplasm. This tri-nucleated cell was enclosed by three distinct plasma membranes, each harboring a single rupture site. These three ruptures converged to form a Yshaped exposed cytoplasmic region spanning >351 sections (Figure 5). Collectively, these reconstructions demonstrate that each cell contains only one discontinuity from a 3D point of view, further supporting that the phenomenon is not due to random sample preparation damage.

(4) In addition to EM, we performed live imaging of plasma membrane dynamics. While live imaging provides important temporal context, we recognize its limitations in resolving membrane ultrastructure. The rapid kinetics of membrane extension (approximately 20–30 seconds for metaphase and less than 3 minutes for cytokinesis), combined with embryo motility, introduces spatiotemporal ambiguities. To capture dynamic membrane events, our live imaging using the GFP::PH membrane marker was performed at 4-second intervals, approaching the practical limit for single-section scanning of the embryo. With single-plane live imaging, nevertheless, both putative membrane ruptures (Figure 6A) and free-ended sister membrane structures could be detected (Figures 6B and 6C), providing additional evidence that membrane rupture and independent extension of detached sister membranes underlie cytokinesis in C. elegans embryos. Notably, 3D membrane dynamics analysis using light-sheet microscopy (Fu et al. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nature Communications. 2016. DOI:10.1038/ncomms11088). revealed membrane ruptures in dividing early C. elegans embryonic cells, including during telophase and metaphase. Therefore, live imaging further validates the membrane rupture phenomena in dividing embryonic cells in C. elegans. 

We are confident that the cumulative evidence presented here addresses the reviewer's concerns and demonstrates that the observed membrane discontinuities, as well as cytoplasm-immersed membranes, are not procedural artifacts but rather reflect a previously underappreciated aspect of plasma membrane dynamics during embryonic cell division.

Another critical issue concerns the detection of the membrane discontinuities in electron micrographs, which, in my opinion, is ambiguous. How do the authors reliably discriminate in their TEM images whether there is a plasma membrane or not? The absence - or weak appearance - of the stain of the electron dense material at membranes, which seems to be their criterion for MOs, is also apparent at other, intracellular membranes, like at the NE or at the ER (for example, see Figure 1C). Also, the plasma membrane itself appears unevenly stained in regions that the authors delineate as intact (for example, Figure 1C, 2B/1).

We thank the reviewer for raising this important concern.

First, our laboratory has extensive experience with electron microscopy across diverse biological systems, including neurons, muscle cells, and hypodermis in C. elegans, as well as tissues from Drosophila, mouse, bacteria, and cultured cells (Chen et al., 2013; Ding et al., 2018; Guan et al., 2022; Y. Li et al., 2018; Miao et al., 2024; Qin et al., 2014; Wang et al., 2026; J. Xu et al., 2022; M. Xu et al., 2021; L. Yang et al., 2020; X. Yang et al., 2019; Zhu et al., 2022). Importantly, we did not introduce any novel or unconventional steps in our EM preparation; all protocols were standard and well established. Thus, the observed membrane discontinuities are unlikely to result from technical inexperience or idiosyncratic methods.

Second, because EM preparation yields serial sections, we verified nearly all membrane discontinuities by examining adjacent sections. Specifically, a membrane discontinuity was confirmed only after inspecting the entirety of the plasma membrane in neighboring sections. We will include this verification protocol in the revised Methods section, and additional images of consecutive sections can be provided if needed.

Third, in addition to membrane discontinuities, a large number of single plasma membranes separating adjacent cytoplasmic domains were detected by EM (Figure 1, 3 and 4). This observation is particularly significant because the invagination model cannot account for the formation of single plasma membrane barriers between adjacent cytoplasmic domains. Instead, independent extension of detached sister membranes offers a plausible explanation for the generation of cytoplasm-immersed membranes. Furthermore, the morphology and continuity of these single cytoplasm-immersed membrane structures are well preserved, indicating successful EM processing and arguing against potential issues such as inadequate fixation or other technical limitations.

EM-related publications by Jingjing Liang:

Chen D, Jian Y, Liu X, Zhang Y, Liang J, Qi X, Du H, Zou W, Chen L, Chai Y, Ou G, Miao L, Wang Y, Yang C. 2013. Clathrin and AP2 Are Required for Phagocytic Receptor-Mediated Apoptotic Cell Clearance in Caenorhabditis elegans. PLoS Genetics 9:e1003517. DOI: https://doi.org/10.1371/journal.pgen.1003517

Ding L, Yang X, Tian H, Liang J, Zhang F, Wang G, Wang Y, Ding M, Shui G, Huang X. 2018. Seipin regulates lipid homeostasis by ensuring calcium‐dependent mitochondrial metabolism. The EMBO Journal 37:e97572. DOI: https://doi.org/10.15252/embj.201797572

Guan L, Yang Y, Liang J, Miao Y, Shang A, Wang B, Wang Y, Ding M. 2022. ERGIC2 and ERGIC3 regulate the ER‐to‐Golgi transport of gap junction proteins in metazoans. Traffic 23:140–157. DOI: https://doi.org/10.1111/tra.12830

Li Y, Zhang Y, Gan Q, Xu M, Ding X, Tang G, Liang J, Liu K, Liu X, Wang X, Guo L, Gao Z, Hao X, Yang C. 2018. C . elegans -based screen identifies lysosome-damaging alkaloids that induce STAT3-dependent lysosomal cell death. Protein & Cell 9:1013–1026. DOI: https://doi.org/10.1007/s13238-018-0520-0

Miao Y, Du Y, Wang B, Liang J, Liang Y, Dang S, Liu J, Li D, He K, Ding M. 2024. Spatiotemporal recruitment of the ubiquitin-specific protease USP8 directs endosome maturation. eLife 13:RP96353. DOI: https://doi.org/10.7554/eLife.96353

Qin J, Liang J, Ding M. 2014. Perlecan Antagonizes Collagen IV and ADAMTS9/GON-1 in Restricting the Growth of Presynaptic Boutons. Journal of Neuroscience 34:10311–10324. DOI: https://doi.org/10.1523/JNEUROSCI.5128-13.2014

Wang Z, Zhang L, Zhou B, Liang J, Tian Y, Jiang Z, Tao J, Yin C, Chen S, Zhang W, Zhang J, Wei W. 2026. A single MYB transcription factor GmMYB331 regulates seed oil accumulation and seed size/weight in soybean. Journal of Integrative Plant Biology 68:470– 485. DOI: https://doi.org/10.1111/jipb.70101

Xu J, Chen S, Wang W, Man Lam S, Xu Y, Zhang S, Pan H, Liang J, Huang Xiahe, Wang Yu, Li T, Jiang Y, Wang Yingchun, Ding M, Shui G, Yang H, Huang Xun. 2022. Hepatic CDP-diacylglycerol synthase 2 deficiency causes mitochondrial dysfunction and promotes rapid progression of NASH and fibrosis. Science Bulletin 67:299–314. DOI: https://doi.org/10.1016/j.scib.2021.10.014

Xu M, Ding L, Liang J, Yang X, Liu Y, Wang Y, Ding M, Huang X. 2021. NAD kinase sustains lipogenesis and mitochondrial metabolism through fatty acid synthesis. Cell Reports 37:110157. DOI: https://doi.org/10.1016/j.celrep.2021.110157

Yang L, Liang J, Lam SM, Yavuz A, Shui G, Ding M, Huang X. 2020. Neuronal lipolysis participates in PUFA‐mediated neural function and neurodegeneration. EMBO reports 21:e50214. DOI: https://doi.org/10.15252/embr.202050214

Yang X, Liang J, Ding L, Li X, Lam S-M, Shui G, Ding M, Huang X. 2019. Phosphatidylserine synthase regulates cellular homeostasis through distinct metabolic mechanisms. PLOS Genetics 15:e1008548. DOI: https://doi.org/10.1371/journal.pgen.1008548

Zhu J, Lam SM, Yang L, Liang J, Ding M, Shui G, Huang X. 2022. Reduced phosphatidylcholine synthesis suppresses the embryonic lethality of seipin deficiency. Life Metabolism 1:175–189. DOI: https://doi.org/10.1093/lifemeta/loac02

At the time of the pilot and manuscript preparation, the product evaluated in this manuscript was referred to as “VoxiPlay.” This earlier prototype formed part of the development pathway for the clinician-led product now referred to as “VoxiPlay Pro.” References to VoxiPlay in this manuscript should therefore be understood as referring to that earlier prototype and not to the separate VoxiPlay product described in current Autsera materials.

Although the crowd is not fully black americans, there are a few white people throughout the crowd. Though none of the whites occupied the balconies and higher end better seats.Those seats were left to the hardworking and deserving black men that had attended the show.

I find it odd that urgent care facilities were only developed in the early 1980s. It seems like they should’ve been built earlier due to how they help in emergency situations.

This is an issue because for people who live in rural areas. People who live in the “country” often have to drive far to find the closest hospital to them. This is an inconvenience for emergency care.

I feel like the fact that the hospital was only used as a shelter for the “poor” shows a lot on how healthcare was back then. Most wealthy people had doctors at their own homes, and they did not have to go to the hospital to receive care. I feel like this is unjust in a way.

La citation n'est pas correcte. La phrase originale est : "Most of the times I’m actually dressed like super casual, just wear hoodie to work but on my Instagram, you see me dressing up and all that. I feel stressed like I’m not being who I am online."

Creo que esta es una forma esencial para no perderse en primera instancia en algo complejo, y es que este enfoque nos enseña a priorizar, pero obviamente no se trata de ignorar los detalles, sino de enfocarse en un aspecto central en primera instancia, para obtener una base sólida sobre la cual ir construyendo después

eLife Assessment

This important study explores how the phase of neural oscillations in the alpha band affects visual perception, indicating that perceptual performance varies due to changes in sensory precision rather than decision bias. The evidence is convincing in its experimental design and analytical approach. This work should interest cognitive neuroscientists who study perception and decision-making.

Reviewer #1 (Public review):

[Editors' note: This version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

Summary:

In their paper entitled "Alpha-Band Phase Modulates Perceptual Sensitivity by Changing Internal Noise and Sensory Tuning," Pilipenko et al. investigate how pre-stimulus alpha phase influences near-threshold visual perception. The authors aim to clarify whether alpha phase primarily shifts the criterion, multiplicatively amplifies signals, or changes the effective variance and tuning of sensory evidence. Six observers completed many thousands of trials in a double-pass Gabor-in-noise detection task while an EEG was recorded. The authors combine signal detection theory, phase-resolved analyses, and reverse correlation to test mechanistic predictions. The experimental design and analysis pipeline provide a clear conceptual scaffold, with SDT-based schematic models that make the empirical results accessible even for readers who are not specialists in classification-image methods.

Strengths:

The study presents a coherent and well-executed investigation with several notable strengths. First, the main behavioral and EEG results in Figure 2 demonstrate robust pre-stimulus coupling between alpha phase and d′ across a substantial portion of the pre-stimulus interval, with little evidence that the criterion is modulated to a comparable extent. The inverse phasic relationship between hit and false-alarm rates maps clearly onto the variance-reduction account, and the response-consistency analysis offers an intuitive behavioral complement: when two identical stimuli are both presented at the participant's optimal phase, responses are more consistent than when one or both occur at suboptimal phases. The frontal-occipital phase-difference result suggests a coordinated rather than purely local phase mechanism, supporting the central claim that alpha phase is linked to changes in sensitivity that behave like changes in internal variability rather than simple gain or criterion shifts. Supplementary analyses showing that alpha power has only a limited relationship with d′ and confidence reassure readers that the main effects are genuinely phase-linked rather than a recasting of amplitude differences.

Second, the reverse-correlation results in Figure 3 extend this story in a satisfying way. The classification images and their Gaussian fits show that at the optimal phase, the weighting of stimulus energy is more sharply concentrated around target-relevant spatial frequencies and orientations, and the bootstrapped parameter distributions indicate that the suboptimal phase is best described by broader tuning and a modest change in gain rather than a pure criterion account. The authors' interpretation that optimal-phase perception reflects both reduced effective internal noise and sharpened sensory tuning is reasonable and well-supported. Overall, the data and figures largely achieve the stated aims, and the work is likely to have an impact both by clarifying the interpretation of alpha-phase effects and by illustrating a useful analytic framework that other groups can adopt.

Reviewer #2 (Public review):

Summary:

The study of Pilipenko et al evaluated the role of alpha phase in a visual perception paradigm using the framework of signal detection theory and reverse correlation. Their findings suggest that phase-related modulations in perception are mediated by a reduction in internal noise and a moderate increase in tuning to relevant features of the stimuli in specific phases of the alpha cycle. Interestingly, the alpha phase did not affect the criterion. Criterion was related to modulations in alpha power, in agreement with previous research.

Strengths:

The experiment was carefully designed, and the analytical pipeline is original and suited to answer the research question. The authors frame the research question very well and propose several models that account for the possible mechanisms by which the alpha phase can modulate perception. This study can be very valuable for the ongoing discussion about the role of alpha activity in perception.

Conclusion:

This study addresses an important and timely question and proposes an original and well-thought-out analytical framework to investigate the role of alpha phase in visual perception. While the experimental design and theoretical motivation are strong, the very limited sample size substantially constrains the strength of the conclusions that can be drawn at the group level.

Bibliography:

Button, K., Ioannidis, J., Mokrysz, C. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013). https://doi.org/10.1038/nrn3475

Tamar R Makin, Jean-Jacques Orban de Xivry (2019) Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript eLife 8:e48175 https://doi.org/10.7554/eLife.48175

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In their paper entitled "Alpha-Band Phase Modulates Perceptual Sensitivity by Changing Internal Noise and Sensory Tuning," Pilipenko et al. investigate how pre-stimulus alpha phase influences near-threshold visual perception. The authors aim to clarify whether alpha phase primarily shifts the criterion, multiplicatively amplifies signals, or changes the effective variance and tuning of sensory evidence. Six observers completed many thousands of trials in a double-pass Gabor-in-noise detection task while an EEG was recorded. The authors combine signal detection theory, phase-resolved analyses, and reverse correlation to test mechanistic predictions. The experimental design and analysis pipeline provide a clear conceptual scaffold, with SDT-based schematic models that make the empirical results accessible even for readers who are not specialists in classification-image methods.

Strengths:

The study presents a coherent and well-executed investigation with several notable strengths. First, the main behavioral and EEG results in Figure 2 demonstrate robust pre-stimulus coupling between alpha phase and d′ across a substantial portion of the pre-stimulus interval, with little evidence that the criterion is modulated to a comparable extent. The inverse phasic relationship between hit and false-alarm rates maps clearly onto the variance-reduction account, and the response-consistency analysis offers an intuitive behavioral complement: when two identical stimuli are both presented at the participant's optimal phase, responses are more consistent than when one or both occur at suboptimal phases. The frontal-occipital phase-difference result suggests a coordinated rather than purely local phase mechanism, supporting the central claim that alpha phase is linked to changes in sensitivity that behave like changes in internal variability rather than simple gain or criterion shifts. Supplementary analyses showing that alpha power has only a limited relationship with d′ and confidence reassure readers that the main effects are genuinely phase-linked rather than a recasting of amplitude differences.

Second, the reverse-correlation results in Figure 3 extend this story in a satisfying way. The classification images and their Gaussian fits show that at the optimal phase, the weighting of stimulus energy is more sharply concentrated around target-relevant spatial frequencies and orientations, and the bootstrapped parameter distributions indicate that the suboptimal phase is best described by broader tuning and a modest change in gain rather than a pure criterion account. The authors' interpretation that optimal-phase perception reflects both reduced effective internal noise and sharpened sensory tuning is reasonable and well-supported. Overall, the data and figures largely achieve the stated aims, and the work is likely to have an impact both by clarifying the interpretation of alpha-phase effects and by illustrating a useful analytic framework that other groups can adopt.

Weaknesses:

The weaknesses are limited and relate primarily to framing and presentation rather than to the substance of the work. First, because contrast was titrated to maintain moderate performance (d′ between 1.2 and 1.8), the phase-linked changes in sensitivity appear modest in absolute terms, which could benefit from explicit contextualization. Second, a coding error resulted in unequal numbers of double-pass stimulus pairs across participants, which affects the interpretability of the response-consistency results. Third, several methodological details could be stated more explicitly to enhance transparency, including stimulus timing specifications, electrode selection criteria, and the purpose of phase alignment in group averaging. Finally, some mechanistic interpretations in the Discussion could be phrased more conservatively to clearly distinguish between measurement and inference, particularly regarding the relationship between reduced internal noise and sharpened tuning, and the physiological implementation of the frontal-occipital phase relationship.

We appreciate the reviewer’s thoughtful and constructive feedback, particularly regarding clarity and framing. In response, we have made several revisions to improve transparency and contextualization throughout the manuscript.

First, we now explicitly contextualize the relatively modest change in sensitivity by adding discussion of the contrast-titration procedure and its implications for effect size interpretation. Second, we address the coding error that led to unequal numbers of double-pass stimulus pairs across participants sooner in the manuscript by reporting the average number of pairs per participant in the Results (as well as the Methods), allowing for readers to interpret the results more appropriately. Third, we have provided additional detail, including precise stimulus timing parameters, electrode selection criteria, and a clearer explanation of the rationale for phase alignment in the Results (in addition to the Methods) section. Finally, we have revised portions of the Discussion to adopt more conservative language when interpreting our results, which more clearly distinguishes between empirical observations and mechanistic inferences, along with offering additional interpretations for the frontal-occipital phase relationship.

We believe these revisions substantially improve the clarity, transparency, and interpretability of the manuscript.

Reviewer #2 (Public review):

Summary:

The study of Pilipenko et al evaluated the role of alpha phase in a visual perception paradigm using the framework of signal detection theory and reverse correlation. Their findings suggest that phase-related modulations in perception are mediated by a reduction in internal noise and a moderate increase in tuning to relevant features of the stimuli in specific phases of the alpha cycle. Interestingly, the alpha phase did not affect the criterion. Criterion was related to modulations in alpha power, in agreement with previous research.

Strengths:

The experiment was carefully designed, and the analytical pipeline is original and suited to answer the research question. The authors frame the research question very well and propose several models that account for the possible mechanisms by which the alpha phase can modulate perception. This study can be very valuable for the ongoing discussion about the role of alpha activity in perception.

Weaknesses:

The sample size collected (N = 6) is, in my opinion, too small for the statistical approach adopted (group level). It is well known that small sample sizes result in an increased likelihood of false positives; even in the case of true positives, effect sizes are inflated (Button et al., 2013; Tamar and Orban de Xivry, 2019), negatively affecting the replicability of the effect.

Although the experimental design allows for an accurate characterization of the effects at the single-subject level, conclusions are drawn from group-level aggregated measures. With only six subjects, the estimation of between-subject variability is not reliable. The authors need to acknowledge that the sample size is too small; therefore, results should be interpreted with caution.

Conclusion:

This study addresses an important and timely question and proposes an original and well-thought-out analytical framework to investigate the role of alpha phase in visual perception. While the experimental design and theoretical motivation are strong, the very limited sample size substantially constrains the strength of the conclusions that can be drawn at the group level.

Bibliography:

Button, K., Ioannidis, J., Mokrysz, C. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013). https://doi.org/10.1038/nrn3475

Tamar R Makin, Jean-Jacques Orban de Xivry (2019) Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript eLife 8:e48175 https://doi.org/10.7554/eLife.48175

We thank the reviewer for their supportive remarks on our design and analysis, and for raising this important statistical concern about our sample size (n=6). Our choice of a small sample size was driven by methodological considerations. Specifically, our reverse correlation analysis requires a large number of trials per participant, as it estimates perceptual tuning by regressing behavioral responses against fluctuations in the energy of stimulus features (orientation and spatial frequency). This approach, as well as the computation of signal detection theory (SDT) metrics such as d′ and criterion, depends on high trial counts to obtain reliable estimates, particularly given that our analysis further subdivides trials across eight phase bins. For this reason, we prioritized collecting a large number of trials per participant (∼5,000), which is consistent with established practices in psychophysical research.

Importantly, our approach means that our design is reliable on the individual level, which motivated us to include a new binomial probability testing in our revised paper. This binomial test helps address concerns about the generalizability of our results. Binomial testing considers each participant as an independent replication of the effect and then computes the p-value associated with the probability of having observed the given number of statistically significant participants by chance, with a false positive rate of 0.05. In our data, 3 out of 6 participants showed significant effects, which corresponds to a probability of 0.002 of having observed these effects by chance alone. We believe this converging evidence supports the replicability and generalizability of our results. To improve the transparency of the single-subject data, we have included single-participant results in the Supplemental Materials to allow readers to directly assess the consistency of effects across individuals and to better contextualize between-subject variability.

Thank you again for your suggestions, we believe that these additions have greatly improved our manuscript by demonstrating the robustness of our findings and increasing the transparency of our single-subject results.

Recommendations for the authors:

Reviewing Editor Comments:

The issue of generalizability arose during the review process, as your results are based on a small sample of participants who undertook a very large number of trials. In the revised version, it would be useful to discuss why this approach is valid, especially in the context of linking EEG with modeling (i.e., why it is more powerful than having many participants with fewer trials), and the extent to which your results can generalize to the population.

We sincerely appreciate all of the helpful comments provided by the reviewers and hope we can address the concerns of our experimental approach. In the introduction, we have emphasized the importance of our current small sample size design, which allows us to reliably compute our signal detection theory metrics across 8 phase bins in addition to including the reverse correlation analysis. In the methods section, we have added a description of the binomial probability statistical framework, which addresses the generalizability of our results. In this framework, each participant is viewed as an independent replication and the p-value reflects the probability of having observed the number of individually significant subjects from the total sample size by chance. In this regard, observing a significant effect in 3 out of 6 participants (as in our study) from chance alone has a 0.002 probability, which we believe is unlikely and instead reflects a true effect present in the general population.

Below I have copied our changes in the introduction and methods sections.

“... in a large number of trials (6,020 per observer, n = 6) across multiple EEG sessions. This approach ensures a sufficient number of trials in order to reliably compute signal detection theory (SDT) metrics across multiple alpha phase bins while also affording enough statistical power for reverse correlation analysis (Xue et al., 2024), making it preferred over having a larger sample size with fewer trials.”

“Additionally, we used a binomial probability testing framework that is designed for small sample sizes and treats each participant as an independent replication. As such, it computes the probability of having observed the number of statistically significant outcomes by chance given our sample size (Schwarzkopf & Huang, 2024).”

Reviewer #1 (Recommendations for the authors):

My suggestions are intended to be light-touch and focused on strengthening the clarity and durability of the Reviewed Preprint rather than on additional experimentation or major new analyses.

(1) Limitation statement for the double-pass coding error:

Add a short statement in the Methods or Results acknowledging that the coding error led to markedly fewer repeated stimulus pairs for the first three participants than for the last three. For the response-consistency result in Figure 2E, a simple acknowledgement that the available evidence is stronger for some participants than others will help readers calibrate their confidence without detracting from the main story.

Thank you for this suggestion, we have now added a statement to this effect in the Results section, in addition to the description already mentioned in the Methods section.

“To examine this, we implemented a double-pass stimulus presentation (~600 stimulus pairs for participants 1-3 and ~2,500 pairs for participants 4-6) and analyzed participant’s response consistency (Xue et al., 2024) to two identical stimuli.”

(2) Contextualizing the titrated performance level:

In the Discussion, explicitly note that contrast was titrated to keep d′ between approximately 1.2 and 1.8, which intentionally maintains moderate performance. This contextualization will help readers understand that while the phase-linked changes appear modest in absolute terms, they are mechanistically informative within this design.

Thank you, we have included a sentence to the Discussions speaking to this point.

“We also note that the observed modulation of d’ between optimal and suboptimal phases was relatively modest in absolute terms (0.21) in our study and could therefore require many trials per subject to detect. Two reasons for this modest effect size could be related to specific features of our task design. First, we titrated stimulus contrast to maintain consistent task performance. This titration could have reduced the magnitude of the phase effect on d’ that would otherwise be apparent if the stimulus intensity were kept constant. Additionally, the use of (relatively) high-contrast random noise likely means that trial-to-trial variability in perception is largely driven by random fluctuations in the noise properties and, to a lesser extent, internal brain state. Although both of these choices were necessary to perform SDT and reverse correlation analysis, they differ from many previous studies investigating alpha phase using only near-threshold detection in the absence of external noise and may contribute to an underestimation of the true effect size.”

(3) Methods clarifications:

(a) Replace placeholder text such as "{plus minus}" and "{degree sign}" with the appropriate symbols, and ensure that any equations implied in the reverse-correlation section are fully present.

Thank you for bringing this to our attention, these placeholder texts are an artifact of the conversion process and we will correct this.

(b) State explicitly that the 8 ms stimulus duration corresponds to a single frame on your 120 Hz display, which will clarify the timing in Figure 1A and the pre-stimulus windows in the phase analyses.

Thank you, we have added language to both the Method and Results sections explicitly indicating that the 8 ms stimulus choice corresponds to a single screen refresh. Additionally, we changed the text in Figure 1A to include inter-trial interval timing (as opposed to merely saying “Start Trial”):

“(A) Task design. Each trial contained a brief, filtered-noise stimulus (8 ms; one screen refresh) presented to the right or left of fixation with equal probability.”

“Each participant (n = 6) completed 5-6 EEG sessions of a Yes/No detection paradigm whereby participants reported the presence or absence of a brief (8 ms; one screen refresh) vertical Gabor target (2 cycles per degree) with concurrent confidence judgments (see Figure 1A), along with an additional imagination judgement (reported in the supplemental materials).”

(c) In the description of the post-stimulus taper, consider phrasing the rationale in terms of minimizing contamination from evoked responses rather than asserting that the taper ends before the earliest evoked response, which keeps the argument correct without committing to a precise latency boundary.

Thank you for this suggestion. We have changed our rationale for the taper to “minimizing”, rather than avoiding, the evoked response.

“This resulted in the post-stimulus data being flat after 70 ms, which is intended to minimize the evoked response in our data.”

(4) Analysis transparency:

(a) In the description of posterior electrode selection, explicitly note that channels were chosen solely on the basis of alpha power, independent of behavioural performance, and that the same electrodes were used for each participant across sessions.

We have gladly made this clarification to the methods.

“This was individually determined by rank-ordering 17 of the posterior channels (Pz, P3, P7, O1, Oz, O2, P4, P8, P1, P5, PO7, PO3, POz, PO4, PO8, P6, and P2) and algorithmically choosing the three with the highest power. This ensured that electrode selection was made independent of performance and instead was based upon maximizing alpha signal strength.”

(b) Describe the phase-alignment step used to center each participant's optimal bin before group averaging as a device for visualization and summary, and clarify that inferential statistics are based on the underlying, non-aligned data as appropriate. This will reassure readers who are cautious about circularity.

We agree that this should be made more explicit throughout the manuscript and have added statements clarifying this aspect in the Figure 2B caption, the Results, and Method sections.

“The data have been aligned across participants so that each individual's highest d’ was assigned to bin 8 (omitted from the plot), with the remaining data circularly shifted, and is averaged across -450 ms to stimulus onset. This graph is for visualization purposes only. Error bars represent ± 1 SEM. The pattern shows a clear phasic modulation of d’ across bins.”

“... requiring us to phase-align the performance data across participants in order to visualize the underlying phasic effects. To this end, we aligned all metrics (d’, c, HR, and FAR) by circularly shifting the data so that the bin with the highest d’ was assigned to bin 8, which was then omitted from further visualizations.”

“Bin 8 was then omitted from further visualizations. The shifted data were then averaged across all time points from -450 ms to 0 ms, based on significant effects at the group level, and averaged across participants. No statistics were conducted on these shifted variables and instead are for visualization purposes only.”

(c) Add a short note on the number of permutations and the cluster-forming threshold in the phase-coupling analyses, if not already stated in the Results or captions, to complete the description of your non-parametric testing procedure.

Thank you, we agree that reiterating this information in the Results section is helpful for the reader to clarify the analysis procedure.

“After smoothing the resultant vector length over time with a 50 ms moving average, we compared the observed vector lengths to a permuted threshold (95th percentile of 1,000 permutations) at each time point from –700 to 0 ms and performed cluster correction (95th percentile of the permuted cluster size) to account for multiple comparisons.”

(5) Discussion framing:

Make one or two small adjustments to your mechanistic phrasing so that the distinctions between measurement and interpretation are fully explicit:

(a) State that the combination of phase-d′ coupling, counterphased hit and false-alarm rates, response consistency, and phase-dependent classification images is "consistent with" a reduction in effective internal noise and sharper estimated tuning at optimal alpha phase, within the assumptions of your SDT and reverse-correlation framework.

Thank you for this suggestion. We have changed the language in the discussions to reflect this framing and interpretation of the results.

“Moreover, our data are consistent with a model in which the variability of internal responses changes systematically across the alpha cycle, as reflected in the inverse relationship between hit rate and false alarm rate.”

(b) Emphasize that reduced effective internal noise and sharpened sensory tuning are two complementary descriptions of a better match between sensory evidence and decision template rather than fully separable mechanisms.

Thank you, we have added this language for clarity of our interpretation.

“Together with decreases in the variance of sensory tuning during the optimal phase, our results suggest that alpha phase impacts sensitivity by shaping trial-to-trial variation in internal noise during perceptual decision making, leading to better matches between sensory evidence and decision templates as opposed to a change in the gain of internal sensory responses.”

(c) Note that the frontal-occipital phase relationship is consistent with a coordinated, possibly top-down component to the alpha-phase effect, while remaining agnostic about the precise physiological implementation.

Thank you for raising this additional interpretation. We have added this as a plausible alternative to the single-source account in the Discussion section.

“Moreover, our results suggest that prior literature reporting phasic effects in the alpha-band range from both frontal and occipital regions may plausibly be reporting the same effect from a single projected dipole source; however, these results are also consistent with two synchronized alpha sources which are anti-phase.”

Reviewer #2 (Recommendations for the authors):

Major issues:

Given that collecting more data may not be doable, the authors should take some actions to test the reliability of their results. For instance, simulations could be run to test the robustness of the results with such a small sample size (Zoefel, 2019). It would also be of interest to include in the report statistics and plots at the individual level, not only the aggregates. It is also important to report which electrodes were used in the analysis for each of the subjects, in the Methods section, it is clearly stated that these electrodes differed between subjects.

Thank you for these suggestions. To assess the reliability of our results at the single-subject level, we have included a new binomial probability test which is a framework suitable for small sample size experiments with large trail numbers (Schwarzkopf & Huang, 2024). Binomial testing views each individual as an independent replication and considers the probability of having observed the number of significant participants given the total number tested participants, and outputs the probability of having observed the results by chance. We believe this framework adequately addresses the reviewer’s concern of generalizability in addition to being well-suited to the design of our study.

To assess individual significance, we averaged the resultant vector length and permutations over the analysis window from -450 to 0 ms. If the resultant vector length exceeded the permutation for that participant, then they were considered to be a significant participant. In total, 3 out of 6 participants (participants 1, 4, and 5) showed significant d’ coupling. The binomial probability (equivalent to a p-value) of having observed this outcome as a result of three false positives at the individual-subject level is very small (p = 0.002), which is sufficiently low for psychological studies.

Below is the text which we have added to the Results and Methods sections.

“To interrogate the robustness of our findings at the single-subject level, we adopted a test of binomial probability, which is a statistical framework that treats each individual as an independent replication and is ideal for small sample size studies that utilize a large number of trials per observer (Schwarzkopf & Huang, 2024). For our data, we assessed individual significance by averaging the actual and permuted resultant vector lengths across time (-450 to 0ms) and comparing the real vector length to the 95% percentile of the permuted datasets. With this approach, 3 out of 6 participants showed significant d’-phase coupling which corresponds to a binomial probability of p = 0.002, indicating a very low probability that we observed these results by chance alone.”

“Additionally, we used a binomial probability testing framework that is designed for small sample sizes and treats each participant as an independent replication. As such, it computes the probability of having observed the number of statistically significant outcomes by chance given our sample size (Schwarzkopf & Huang, 2024). To assess significance at the participant level, we averaged the participant’s resultant vector length and permutations from -450 to 0 ms and obtained the 95th percentile of the time-averaged permutations. We then compared the averaged resultant vector lengths to the permutation thresholds for each subject, which revealed 3 out of 6 significant subjects. We then used the MATLAB function myBinomTest.m (Nelson, 2026) to compute the p-value associated with the probability of having observed 3 out of 6 significant subjects by chance (with a false-positive rate of 0.05).”

To address the reviewer's second request, we now include a supplemental figure which has each individual’s results for the main analysis (see Supplementary figure 3). These graphs, in addition to the methods, now provides the reader with each participant’s given set of analysis electrodes.

“Each participant had a different combination of electrodes which were used in the analyses; however, the same three channels were used across sessions within a participant (participant 1: POz, PO3, O1; participant 2: P7, PO7, PO4; participant 3: P2, P1, Pz; participant 4: O1, Oz, O2; participant 5: O2, PO8, PO4; participant 6: Oz, O2, O1).”

As an alternative approach, linear mixed models (LMM) could be used for statistics, as they are more suitable for small sample sizes (Wiley et al., 2019). LMM improve generalization by modelling subject-specific random effects. Although raw circular data is not suitable for LMM, the sine and cosine of the phases could have been used as predictors, for instance. Given that data were collected for 6 different sessions, sessions could be included as a factor in the model to improve statistical power.

We appreciate the suggestion but feel that LMMs would be a challenge in this case not only because the main predictor variables are circular, but because the main outcome variables are not defined on the single-trial level and require many trials to be computed (e.g., classification images, SDT measures, response consistency). As such, computing these measures within a session may also lead to noisier estimates than we had designed our experiment for. We therefore prefer the more straightforward approach we have taken in the paper, which has now been supplemented by a binomial test of individual-subject level significance.

Given that the number of subjects is quite small, I believe that individual data should be presented (either in the main text or supplementary materials) also for figures: 2A, B, C and D.

Thank you, we have included all of these results to the individual graphs in the Supplemental Materials (see Supplementary figure 3).

In plot 2B (HR and FAR) a p-value = 0.015 appears. However, in the text you write:

"Indeed, this showed that the difference between the HR and FAR vector angle was significantly clustered around a mean of 180{degree sign} (v = 3.78, p = 0.01), indicating that the phase angle associated with the greatest hits was counterphase to the phase angle associated with the greatest false alarms."

Which one is correct? Or do they refer to different tests?

We appreciate you catching this confusing discrepancy. The two values refer to the same test which has a p-value of 0.0145. In the figure, this value was rounded to the thousandths decimal place (i.e., 0.015), whereas in the text it was rounded to the hundredths value (0.01). We now consistently report p-values out to three decimal places throughout the manuscript.

Did you perform any statistical test for phasic modulation of dprime and criterion? I say that because in Figure 2B, you state that the data shows a "clear phasic modulation of d' across bins", but no statistic is mentioned. On the other hand, in Figure 2D, you state, "We did not & observe any significant phase-dependent relationship between phase and criterion." Is this sentence referring to both 2C and 2D panels or only to 2C?

Figure 2B and 2D show the phase-behavior relationship across bins after aligning the phase bins to each participant's “best” d’ bin. This bin is omitted from the plots because it is used for alignment, making the analysis circular. Accordingly, these panels were intended purely for visualization and were not used for statistical inference. Additional language has been added to the figure caption highlighting this aspect.

“The data have been aligned across participants so that each individual's highest d’ was assigned to bin 8 (omitted from the plot), with the remaining data circularly shifted, and is averaged across -450 ms to stimulus onset. This graph is for visualization purposes only.”

The primary statistical test for phase-behavior coupling was performed using permutation testing of the resultant vector length, which quantifies the magnitude of phase-dependent modulation. These results are shown in Figures 2A (for d′) and 2C (for criterion). In the original manuscript, we reported only the time points that survived cluster-based correction, but did not explicitly report the cluster p-values. We have now added these cluster p-values to the manuscript for completeness.

“The data revealed significant cluster-corrected coupling between alpha phase and d’ in the prestimulus window from -220 ms until stimulus onset (cluster p = 0.046),...”

Additionally, we have changed the caption of Figure 2 to be separate for C) and D).

“(C) No evidence for the coupling of criterion to pre-stimulus alpha-band phase. Graph C reveals the time course of the resultant vector lengths for alpha phase-criterion coupling, which shows no significant phase-dependent relationship between phase and criterion.

(D) The underlying shifted c across phase bins (shifted to participants’ optimal phase, as in graph B) did not visually demonstrate a phasic modulation pattern.”

Minor issues:

In general, the paper is very clear. I found a statement confusing in the Response consistency section:

"To quantify response consistency, we computed the proportion of trials in which participants provided the same response across the two identical trials. This procedure was done for each channel at each time point (from -450 to 0 ms) and then averaged."

Which makes no sense, as response consistency is independent of channel and time point. I believe here you refer to the phase, maybe by just changing the order (start with response consistency and then proceed to phase), the paragraph would be clearer.

We appreciate you catching this mistake. We have clarified the Methods section in the following way:

“To quantify response consistency, we computed the proportion of trials in which participants provided the same response across the two identical trials. Since the optimal phase changes over time, the set of trials were classified as either both having occurred during the optimal phase (or otherwise) for each time point (from -450 to 0 ms) and channel. The proportion of consistent responses was then averaged across channels and time.”

Could you include a plot of the power spectrum used for IAF estimation of all the subjects?

Thank you for the suggestion. In Supplemental Figure 3 we have included the power spectrum that was used to estimate IAF in addition to a topoplot of alpha power (IAF +/- 2 Hz) that has the analysis electrodes labelled.

Bibliography:

Wiley RW, Rapp B. Statistical analysis in Small-N Designs: using linear mixed-effects modeling for evaluating intervention effectiveness. Aphasiology. 2019;33(1):1-30. doi: 10.1080/02687038.2018.1454884.

Zoefel B, Davis MH, Valente G, Riecke L, How to test for phasic modulation of neural and behavioural responses, NeuroImage, Volume 202, 2019,116175, https://doi.org/10.1016/j.neuroimage.2019.116175.

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eLife Assessment

This is an important study that addresses the role of fever as a conserved response to viral infection. It demonstrates that the heat-shock factor, HSF1, is activated by increased temperature during fever to enhance the anti-viral immune response. The data provides compelling evidence for the conclusions and the work will be of interest to virologists, immunologists, and cell biologists.

Reviewer #1 (Public review):

Summary:

In the manuscript "Heat Shock Factor Regulation of Antimicrobial Peptides Expression Suggests a Conserved Defense Mechanism Induced by Febrile Temperature in Arthropods," Xiao and colleagues examine the role of the shrimp Litopenaeus vannamei HSF1 ortholog (LvHSF1) in the response to viral infection. The authors provide compelling support for their conclusions that the activation of LvHSF1 limits viral load at high temperatures. Specifically, the authors convincingly show that (i) LvHSF1 mRNA and protein are induced in response to viral infection at high temperatures, (ii) increased LvHSF1 levels can directly induce the expression of the nSWD (and directly or indirectly other antibacterial peptides, AMPs), (ii) nSWD's antimicrobial activities can limit viral load, and, (iv) LvHSF1 protects survival at high temperatures following virus infection. These data thus provide a model by which an increase in HSF1 levels limits viral load through the transcription of antimicrobial peptides, and provide a rationale for the febrile response as a conserved response to viral infection.

Strengths:

The large body of careful time series experiments, tissue profiling, and validation of RNA-seq data is convincing. Several experimental methodologies are used to support the author's conclusions that nSWD is an LvHSf1 target and increased LvHSF1 alone can explain increased levels of nSWD. Similar carefully conducted experiments also conclusively implicate nSWD protein in limiting WSSV viral loads.

Weaknesses:

As with any complex biological phenomenon, several aspects remain incompletely explained. Nevertheless, in their revision, the authors provide additional analyses supporting the authors model that losing LvHSF1 is not detrimental to survival, by more directly altering viral loads. In addition, their revised manuscript clarifies the complex interactions between infection, the role of HSF1, and hormesis. These revisions increase the impact of their findings.

Comments on revisions:

The authors have addressed all comments, and the manuscript is very much improved.

Reviewer #3 (Public review):

In the manuscript titled "Heat Shock Factor Regulation of Antimicrobial Peptides Expression Suggests a Conserved Defense Mechanism Induced by Febrile Temperature in Arthropods", the authors investigate the role of heat shock factor 1 (HSF1) in regulating antimicrobial peptides (AMPs) in response to viral infections, particularly focusing on febrile temperatures. Using shrimp (Litopenaeus vannamei) and Drosophila S2 cells as models, this study shows that HSF1 induces the expression of AMPs, which in turn inhibit viral replication, offering insights into how febrile temperatures enhance immune responses. The study demonstrates that HSF1 binds to heat shock elements (HSE) in AMPs, suggesting a conserved antiviral defense mechanism in arthropods. The findings are informative for understanding innate immunity against viral infections, particularly in aquaculture. However the logical flow of the paper can be improved.

Comments on revisions:

Some aspects of the initial study design, regarding the selection of representative candidate genes and the logical flow, raised concerns. However, these issues have been addressed in the revised manuscript through additional validations and clarifications. Most of my comments and concerns were sufficiently addressed in the revised manuscript. The results support the authors' conclusion that HSF1-dependent regulation of AMP expression contributes to antiviral defense under febrile conditions.

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Weaknesses:

Despite this compelling data regarding the protective role of HSF1 in the febrile response, what remains unexplained and complicates the authors' model is the observation that losing LvHSF1 at 'normal' temperatures of 25 ℃ is not detrimental to survival, even though viral loads increase and nSWD is likely still subject to LvHSF1 regulation. These observations suggest that WSSV infection may have other detrimental effects on the cell not reflected by viral load and that LvHSF1 may play additional roles in protecting the organism from these effects of WSSV infection, such as perhaps, perturbations to protein homeostasis. This is worth discussing, especially in light of the rather complicated roles of hormesis in protection from infection, the role of HSF1 in hormesis responses, and the findings from other groups that the authors discuss.

We are grateful for your unbiased advice by reviewer. And we have added the description about the role of HSF1 in hormesis responses in discussion in Lines 422-425 in the revised manuscript. Thank you.

Reviewer #2 (Public review):

Temperature is a critical factor affecting the progression of viral diseases in vertebrates and invertebrates. In the current study, the authors investigate mechanisms by which high temperatures promote anti-viral resistance in shrimp. They show that high temperatures induce HSF1 expression, which in turn upregulates AMPs. The AMPs target viral envelope proteins and inhibit viral infection/replication. The authors confirm this process in drosophila and suggest that there may be a conserved mechanism of high-temperature mediated anti-viral response in arthropods. These findings will enhance our understanding of how high temperature improves resistance to viral infection in animals.

The conclusions of this paper are mostly well supported by data, but some aspects of data analysis need to be clarified and extended. Further investigation on how WSSV infection is affected by AMP would have strengthened the study.

We are grateful for your unbiased advice by reviewer. We have provided additional experimental evidence and supplementary instructions in the revised manuscript. Thank you.

Reviewer #3 (Public review):

In the manuscript titled "Heat Shock Factor Regulation of Antimicrobial Peptides Expression Suggests a Conserved Defense Mechanism Induced by Febrile Temperature in Arthropods", the authors investigate the role of heat shock factor 1 (HSF1) in regulating antimicrobial peptides (AMPs) in response to viral infections, particularly focusing on febrile temperatures. Using shrimp (Litopenaeus vannamei) and Drosophila S2 cells as models, this study shows that HSF1 induces the expression of AMPs, which in turn inhibit viral replication, offering insights into how febrile temperatures enhance immune responses. The study demonstrates that HSF1 binds to heat shock elements (HSE) in AMPs, suggesting a conserved antiviral defense mechanism in arthropods. The findings are informative for understanding innate immunity against viral infections, particularly in aquaculture. However, the logical flow of the paper can be improved.

We are grateful for the positive comments and the unbiased advice by reviewer. We have improved the logical flow of the paper and added corresponding instructions in the revised manuscript. Thank you.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 1: The analysis compares Group TW to Group W (not the other way around).

Thank you very much. To uncover the molecular mechanisms by which high temperature restricts WSSV infection, two shrimp groups, Group TW and Group W, were cultured at 25 °C. Group W comprised shrimp injected with WSSV and maintained at 25 °C continuously. In contrast, Group TW was subjected to a temperature increase to 32 °C at 24 hours post-injection (hpi). Gill samples were collected for analysis 12 hours post-temperature rise (hptr) and subjected to Illumina sequencing (Figure 1A). RNA-seq was used to identify genes responsive to high temperature, particularly those encoding potential transcriptional regulators. Thank you.

(2) The RNA-seq data in Figure 1 focus only on the TFs. The manuscript would benefit from showing all the RNA-seq data and the differentially expressed genes. In particular, are the AMPs upregulated at the same time point? This should not be the case if LvHSF1 were responsible for the transcription of the AMPs, given the time lag between transcription and translation.

Thank you for your suggestion. In Author response image 1, our previous study has revealed that classical heat shock proteins (such as HSP21, HSP70, HSP60, HSP83, HSP90, HSP27, HSP10, and Bip) were induced by RNA-seq between Group TW and Group W, suggesting heat shock proteins exert a crucial role in enhancing the resistance of shrimp to WSSV at elevated temperatures (32 ℃) and underscoring the reliability of our transcriptomic findings (Xiao et al., 2024).

Additionally, we also analyzed the AMPs expression between Group TW and Group W, and the results show that some antimicrobial peptides such as Lysozyme and C-type lectin are upregulated between Group TW and Group W. Notably, we did not detect upregulated expression of SWD between Group TW and Group W. We agree with the reviewer's point of view that there is a time lag between transcription and translation. Supplementary experimental evidences show that the expression level of LvHSF1 is strongly induced by WSSV stimulation, and then the expression level of SWD begins to increase. We have added a description in Lines 136-138 in the revised manuscript.

Author response image 1.

The Figure of the heat shock proteins in Group TW and Group W

Author response image 2.

Transcriptional expression levels of HSF1 and SWD after WSSV stimulation

Reference:

Xiao, B., Wang, Y., He, J., Li, C., 2024. Febrile Temperature Acts through HSP70-Toll4 Signaling to Improve Shrimp Resistance to White Spot Syndrome Virus. J Immunol 213, 1187-1201.

(3) The data showing the tissue distribution of LvHSF1 and nSWD is a rigorous approach and adds to the manuscript. A similar approach to understanding the time course of expression of AMPs in relationship to LvHSF1 expression levels would strengthen the authors' conclusions that LvHSF1 induction in response to high temperatures and viral infection, in turn, upregulates SWD and other antibacterial genes.

Thank you for your suggestion. As you good suggestion, we detected the transcriptional expression levels of HSF1 and SWD after WSSV stimulation for 0, 2, 4, 6, 8, 12, 16, 20, and 24 hours. The transcriptional expression level of SWD was set to 1.00 at 0 h, in the early stage of WSSV infection (0-12 h, except 6 h), the expression level of LvHSF1 is strongly induced, and then the expression level of SWD begins to increase. Theses results show that LvHSF1 induction in response to viral infection, in turn, upregulates SWD and other antibacterial genes. Thank you.

(4) The data (Figures 3 and 4) show that LvHSF1 is necessary to survive WSSV infection at high temperatures but does not affect survival at lower temperatures, even though LvHSF1 limits VP28 levels, and viral load at both temperatures is confusing. Does this suggest that LvHSF1 is not primarily important for protection against the virus but instead, for protection from the heat-induced damage caused by high temperatures, which would not be surprising? The manuscript would benefit if the authors could address this point. How do the authors envision the protection conferred by LvHSF1 only at high temperatures?

Thank you for your comment. Although no significant difference in shrimp survival rates was observed between LvHSF1-silenced shrimp and GFP-silenced shrimp at low temperature (25 °C), shrimp with silenced LvHSF1 exhibited increased viral loads in hemocytes and gills, suggesting that upregulation of HSF1 expression can protect shrimp from WSSV infection.

Notably, the tolerance temperature for L. vannamei growth ranges from 7.5 to 42 °C. When infected with WSSV, shrimp use behavioral fever to elevate their body temperature (~32 °C), thereby inhibiting WSSV infection (Rakhshaninejad et al., 2023; Xiao et al., 2024). And this temperature (~32 °C) will not cause heat-induced damage to the shrimp. Our results demonstrate that febrile temperatures induce HSF1, which in turn upregulates antimicrobial peptides (AMPs) that target viral envelope proteins and inhibit viral replication.

Only at high temperatures, we observed that knockdown of HSF1 did not affect shrimp survival rate (Figure 4A). Thank you again for your valuable feedback.

Reference:

Rakhshaninejad, M., Zheng, L., Nauwynck, H., 2023. Shrimp (Penaeus vannamei) survive white spot syndrome virus infection by behavioral fever. Sci Rep 13, 18034.

Xiao, B., Wang, Y., He, J., Li, C., 2024. Febrile Temperature Acts through HSP70-Toll4 Signaling to Improve Shrimp Resistance to White Spot Syndrome Virus. J Immunol 213, 1187-1201.

(5) Related to the previous comment, the authors do not clearly distinguish between basal effects of LvHSF1 or nSWD induction and heat-induced effects and the differences related to the requirement of LvHSF1 for protection. Simply increasing LvHSF1 levels can result in increased nSWD. SWD levels increase upon WSSV infection even at 25 ℃, and the knockdown experiments suggest that this could also occur through LvHSF1. It would be useful to explicitly differentiate between basal functions of HSF1 and induced functions.

Thank you for your suggestion. In previous responses, we have distinguished between basal effects of LvHSF1 or nSWD induction and heat-induced effects.

As your good suggestion, we injected GST or rHSF1 protein into shrimp, the results showed that recombinant protein HSF1 could significantly induced the expression level of SWD (Supplementary Fig. 5C). Further, after knockdown of SWD, shrimp were injection with rLvHSF1 mixed with WSSV. The results showed that the viral load was significantly lower than the control group 48 hours post WSSV infection (Supplementary Fig. 5D). We have added these results to the Supplementary Figure 5C&5D and added a description in Lines 253-255 and Lines 290-293 in the revised manuscript. Thank you for your constructive comments.

Reviewer #2 (Recommendations for the authors):

(1) Two temperatures are used in the experiments of shrimp. It seems that HSF1 is also upregulated by WSSV infection at 25 ℃. However, this upregulation seems not to be able to protect the animals. The authors compare the infection at 25 and 32 ℃ but did not discuss the findings.

Thank you for your comment. Although no significant difference in shrimp survival rates was observed between LvHSF1-silenced shrimp and GFP-silenced shrimp at low temperature (25 °C), shrimp with silenced LvHSF1 exhibited increased viral loads in hemocytes and gills, suggesting that upregulation of HSF1 expression can protect shrimp from WSSV infection. We have added a discussion of this finding in Lines 461-464 in the revised manuscript. Thank you.

(2) In the abstract the authors say that "These insights provide new avenues for managing viral infections in aquaculture and other settings by leveraging environmental temperature control." However, this point has not been discussed in the main text.

We appreciated your comments. We have added a discussion about the environmental temperature control in Lines 512-514 in the revised manuscript. Thank you.

(3) Line 142: "These results suggest that LvHSF1 may play a key role in enhancing shrimp resistance to WSSV at elevated temperatures." Although this type of conclusion has been made in many studies, I think it is impossible to see a "KEY role" based mainly on change in expression.

Thank you for your suggestion. We have revised this conclusion in the revised manuscript. Thank you.

(4) Section 2.1 Induction of Heat Shock Factor 1 in Response to WSSV at High Temperature

Figure 1. Identification of HSF1 as a key factor induced by high temperature.

The two titles are confusing. Whether the upregulation of HSF1 is a response to high temperature or WSSV infection? I think it is more likely a response to high temperature. Did the authors see the difference in HSF1 expression in shrimp with and without WSSV infection at high temperatures?

Thank you for your comment. We have modified the title of Section 2.1 in the revised manuscript. As your good suggestion, we have measured the expression of LvHSF1 after WSSV challenge at high temperatures (32 ℃) in revised Figure 2F-2H in Line 122 in the revised manuscript. The results demonstrate that the expression of LvHSF1 is strongly induced by WSSV stimulation at high temperatures (32 ℃) in the revised manuscript. Thank you.

(5) Figure 2. Upregulation of LvHSF1 in shrimp challenged by WSSV at both low and high temperatures. Results for WSSV challenge at high temperatures are not included in this figure.

Thank you for your suggestion. As your good suggestion, we have measured the expression of LvHSF1 after Poly (I: C) and WSSV challenge at high temperatures (32 ℃) in revised Figure 2C-2H. The results demonstrate that the expression of LvHSF1 is strongly induced by Poly (I: C) and WSSV stimulation at high temperatures (32 ℃). And we have added a description in Lines 168-179 in revised manuscript. Thank you.

(6) Section 2.2 Expression Profiles of LvHSF1 in Shrimp Under Varied Temperature Conditions and WSSV Challenge. Did the authors try poly IC and WSSV challenge at 32℃, and compare with the un-challenge group? Why were only low temperature was analyzed?

Thank you for your suggestion. As your good suggestion, we have measured the expression of LvHSF1 after Poly (I: C) and WSSV challenge at high temperatures (32 ℃) in revised Figure 2C-2H. And we have added a description about the expression of LvHSF1 after Poly (I: C) and WSSV challenge at high temperatures (32 ℃) in Lines 168-179 in revised manuscript. Thank you.

(7) Figure 2: Please indicate the temperature used in C-E and F-H in the figure legend. Statistical significance: compared with which group? Please provide information in the legend or show it in the bar chart.

Thank you for your suggestion. We have added the description of temperature used in revised Figures 2C-2E. The expression changes of HSF1 were compared with those of PBS control group at the corresponding time and we modified the comparison method of significance in revised Figures 2C-2E. Thank you.

(8) Figure 3H: There are two groups (dsGFP+PBS; dsHSF1+PBS) showing with the same symbol (dot line).

Thank you for your comment. The revised Figure 3H has used different symbols to distinguish the two groups. Thank you.

(9) Line 205: qPCR

Thank you for your careful checks. We have corrected this error in the revised manuscript. Thank you.

(10) Figure 5d and f: Please indicate the sample in each row.

Thank you for your suggestion. We have marked the samples in each row in the revised Figures 5d&5f.

(11) Figure 3 and Figure 4: Why different tissues were analyzed in the two experiments? Low temperature: gill and hemocytes. High temperature: gill and muscle? It is better to use the same tissues so that they can be compared. Please indicate the tissue analyzed in D and d.

Thank you for your suggestion. We have repeated the experiment to detect the copy number of WSSV in hemocyte at high temperature (32 °C) after LvHSF1 knockdown. The results showed that knockdown LvHSF1 showed increased viral loads in shrimp hemocyte (Figure 4C). We have supplemented the tissue information in Figure 4D&4d. Thank you.

(12) Figure 2A The time for temperature treatment? hours or days?

Thank you for your comment. Transcriptional expression of LvHSF1 in different tissues of healthy shrimp subjected to low (25 °C) and high (32 °C) temperatures for 12 hours. We have supplemented this information in the legend of Figure 2A in Lines 840-841 in revised manuscript. Thank you.

(13) Line 249: purified by SDS-PAGE gel?

Thank you for your comment. We have modified this description in Lines 272-274 in current manuscript. Thank you.

(14) Line 258 "Next, to verify whether the anti-WSSV function of nSWD was mediated by LvHSF1 at high temperature". I think it is confusing to use "mediated" here. It seems that HSF1 is downstream of nSWD. Actually, HSF1 controls the expression of nSWD and thus regulates the anti-WSSV effect of shrimp at high temperatures.

We appreciated your comments. We have modified this description in Lines 282-283 in current manuscript. Thank you.

(15) Line 458 "The most probable anti-WSSV mechanism of nSWD is its direct interaction with WSSV envelope proteins VP24 and VP26, potentially inhibiting viral entry into target cells. I suggest the author analyze the entry of WSSV to see whether nSWD blocks this process.

Thank you for your comment. In general, the antimicrobial mechanism of action of AMPs is thought to involve direct membrane disruption, especially for enveloped virus (such as WSSV) (Wilson et al., 2013).

Thanks to the reviewers for their valuable comments. Our manuscript mainly focuses on the febrile temperature-inducible HSF in host antiviral immunity, and the role of HSF1 in regulating antimicrobial effectors (such as SWD). Due to the limitation of the manuscript's length, we will further investigate the functional mechanisms of SWD-specific anti-WSSV in future studies. Thank you.

Reference:

Wilson, S.S., Wiens, M.E., Smith, J.G., 2013. Antiviral Mechanisms of Human Defensins. Journal of Molecular Biology 425, 4965-4980.

(16) Line 435-456 The author discusses the difference between two shrimp species. Did the two studies measure the same immune parameters? I wonder whether the different observation is due to true differences or different methods they used to evaluate the response. If no immune response was promoted in the previous study, what's the possible anti-viral mechanism?

We appreciated your comments. Firstly, the shrimps in the two experimental groups have different adaptability to temperature. The optimal water temperature for M. japonicus growth ranges from 25 to 32 °C, and the tolerance temperature for L. vannamei growth ranges from 7.5 to 42 °C. Secondly, the experimental environmental factors are different in the two experimental groups. Ammonia is a key stress factor in aquatic environments that usually increases the risk of pathogenic diseases in aquatic animals, however, High temperatures (32°C) have been shown to inhibit the replication of WSSV and reduce mortality in WSSV-infected shrimp. Thirdly, the two studies tested different immune indicators. Ammonia-induced Hsf1 suppressed the production and function of MjVago-L, an arthropod interferon analog. In this study, our findings revealed the molecular mechanism through which the HSF-AMPs axis mediates host resistance to viruses induced by febrile temperature. Taken together, the benefits of HSF1 can be attributed to either the host or the pathogen, depending on the nature and context of the host-virus-environment interaction.

(17) Line 472 "directly bind to WSSV envelope proteins and inhibit WSSV proliferation"

I think it is confusing to use "proliferation" here. It seems that the binding of HSF affects the replication process. However, based on the authors' discussion, HSF may likely block viral entry.

Thank you for your suggestion. We have modified this description in Lines 505-507 in the current manuscript. Thank you.

Reviewer #3 (Recommendations for the authors):

In the manuscript titled "Heat Shock Factor Regulation of Antimicrobial Peptides Expression Suggests a Conserved Defense Mechanism Induced by Febrile Temperature in Arthropods", the authors investigate the role of heat shock factor 1 (HSF1) in regulating antimicrobial peptides (AMPs) in response to viral infections, particularly focusing on febrile temperatures. Using shrimp (Litopenaeus vannamei) and Drosophila S2 cells as models, this study shows that HSF1 induces the expression of AMPs, which in turn inhibit viral replication, offering insights into how febrile temperatures enhance immune responses. The study demonstrates that HSF1 binds to heat shock elements (HSE) in AMPs, suggesting a conserved antiviral defense mechanism in arthropods. The findings are informative for understanding innate immunity against viral infections, particularly in aquaculture. However, the logical flow of the paper can be improved. Following are my specific concerns.

Major comments

(1) The study design is pretty good, but the logical flow is not. The following should be improved.

(a) In Figure 1, the reason for selecting HSF1 as the focus of the study is not clearly explained.

Thank you for your comment. In a previous study, we have revealed that heat shock proteins exerted a significant role in enhancing the resistance of shrimp to WSSV at elevated temperature (32 ℃) (Xiao et al., 2024). GO functional enrichment analysis of DEGs between group TW and group W, indicating that most DEGs were involved in biological processes such as protein refolding, chaperone-mediated protein folding, and heat response. Therefore, special attention has been paid to heat shock factor 1 (HSF1), the master regulator of the heat shock response. We have added the description in Lines 136-138 in the revised manuscript. Thank you.

Reference:

Xiao, B., Wang, Y., He, J., Li, C., 2024. Febrile Temperature Acts through HSP70-Toll4 Signaling to Improve Shrimp Resistance to White Spot Syndrome Virus. J Immunol 213, 1187-1201.

(b) As the authors draw models in Figure 9, the established activation mechanism of HSF1 is via trimerization by the release of HSP90, which binds to misfolded proteins under stress conditions, such as heat shock. Therefore, the increase in the HSF1 mRNA level in Figure 1 is strange. The authors need to clarify this issue by explaining this established activation mechanism of HSF1 and also must provide the basis of upregulation of HSF1 by mRNA increase via citing papers in the Introduction.

We appreciated your comments. Under non-stress conditions, HSF monomers are retained in the cytoplasm in a complex with HSP90. During the stress response, such as high temperature, HSF dissociates from the complex, trimerizes, and converts into a DNA-binding conformation through regulatory upstream promoter elements known as heat shock elements (HSEs) (Andrasi et al., 2021). Previous studies have demonstrated that the expression of HSF1 was remarkably induced by stress response, such as high temperature (Ren et al., 2025), virus infection (Merkling et al., 2015), and ammonia stress (Wang et al., 2024). Our results also showed that the expression of LvHSF1 was significant induced by WSSV infection and high temperature (Figure 2). Therefore, this is not surprising that the increase in the HSF1 mRNA level in Figure 1.

In response, we have revised the proposed model to better reflect our experimental findings and the accompanying description. This revision ensures that the schematic is consistent with our data and accurately represents the proposed mechanism. We appreciate your careful review and constructive feedback.

Reference:

Andrasi, N., Pettko-Szandtner, A., Szabados, L., 2021. Diversity of plant heat shock factors: regulation, interactions, and functions. J Exp Bot 72, 1558-1575.

Ren, Q., Li, L., Liu, L., Li, J., Shi, C., Sun, Y., Yao, X., Hou, Z., Xiang, S., 2025. The molecular mechanism of temperature-dependent phase separation of heat shock factor 1. Nature Chemical Biology.

Merkling, S.H., Overheul, G.J., van Mierlo, J.T., Arends, D., Gilissen, C., van Rij, R.P., 2015. The heat shock response restricts virus infection in Drosophila. Sci Rep 5, 12758.

Wang, X.X., Zhang, H., Gao, J., Wang, X.W., 2024. Ammonia stress-induced heat shock factor 1 enhances white spot syndrome virus infection by targeting the interferon-like system in shrimp. mBio 15, e0313623.

(c) For RNA seq analysis in both in Figures 1 and 5, they need to provide changes in conventional HSF1 target chaperones (many HSPs) to validate their RNA seq data.

Thank you for your suggestion. In Authopr response image 1, our previous study has revealed that classical heat shock proteins (such as HSP21, HSP70, HSP60, HSP83, HSP90, HSP27, HSP10, and Bip) were induced by RNA-seq between Group TW and Group W, suggesting heat shock proteins exert a crucial role in enhancing the resistance of shrimp to WSSV at elevated temperatures (32 ℃) and underscoring the reliability of our transcriptomic findings (Xiao et al., 2024). We have added the description in Lines 136-138 in the revised manuscript.

In Figure 5, we have supplemented the heat shock proteins downregulated DEGs by transcriptome sequencing of dsGFP +WSSV (32 ℃) vs. dsLvHSF1 +WSSV (32 ℃) in Supplementary table 2. The results showed that the classical heat shock proteins were downregulated by the RNA-seq, underscoring the reliability of our transcriptomic findings. We have added the description in Lines 213-216 in the revised manuscript. Thank you.

Reference:

Xiao, B., Wang, Y., He, J., Li, C., 2024. Febrile Temperature Acts through HSP70-Toll4 Signaling to Improve Shrimp Resistance to White Spot Syndrome Virus. J Immunol 213, 1187-1201.

(d) In Figure 5, they did experiments by focusing on the changes by HSF1 knockdown at 32 ℃. However, the logical flow should be focusing on genes whose expression was increased by 32 ℃ compared with 25 ℃ (in figure 1), among them they need to characterize HSF1 target genes. Here as mentioned above, classical HSP genes must be included in addition to those AMP genes.

Thank you for your suggestion. As your good suggestion, we have supplemented the heat shock proteins downregulated DEGs by transcriptome sequencing of dsGFP +WSSV (32 ℃) vs. dsLvHSF1 +WSSV (32 ℃) in Supplementary table 2. The results showed that the classical heat shock proteins were downregulated by the RNA-seq, underscoring the reliability of our transcriptomic findings. We have added the description in Lines 213-216 in the revised manuscript. Thank you.

(e) What is the logical basis of just picking nSWD? It is another example of cherry-picking similar to picking HSF1 in Figure 1.

We appreciated your comments. To determine how temperature-induced LvHSF1 restricts WSSV infection, RNA-seq was performed to identify target genes regulated by HSF1. By analyzing the differentially expressed genes (DEGs), we screened eight candidate proteins for immunity-effector molecules, including SWD, CrustinⅠ, C-type lectin, Anti-lipopolysaccharide factor (ALF), and Vago. CrustinⅠ has been shown to play an important role in antiviral immunity (Li et al., 2020); C-type lectin (CTL1) can bind to the VP28, VP26, VP24, VP19, and VP14, thereby inhibiting the infection of WSSV (Zhao et al., 2009); Anti-lipopolysaccharide factor (ALF3) performs its anti-WSSV activity by binding to the envelope protein WSSV189 (Methatham et al., 2017); Vago can inhibit WSSV infection by activating the Jak/Stat pathway in shrimp (Gao et al., 2021). However, the detailed regulatory mechanism of SWD against WSSV was unclear, and particular attention was paid to the SWD. We have added the description in Lines 215-220 in the revised manuscript. Thank you for your valuable comments and the logic of the manuscript has been improved.

Reference:

Li, S., Lv, X., Yu, Y., Zhang, X., Li, F., 2020. Molecular and Functional Diversity of Crustin-Like Genes in the Shrimp Litopenaeus vannamei, Marine Drugs 18, 361.

Zhao, Z.Y., Yin, Z.X., Xu, X.P., Weng, S.P., Rao, X.Y., Dai, Z.X., Luo, Y.W., Yang, G., Li, Z.S., Guan, H.J., Li, S.D., Chan, S.M., Yu, X.Q., He, J.G., 2009. A novel C-type lectin from the shrimp Litopenaeus vannamei possesses anti-white spot syndrome virus activity. Journal of Virology 83, 347-356.

Methatham, T., Boonchuen, P., Jaree, P., Tassanakajon, A., Somboonwiwat, K., 2017. Antiviral action of the antimicrobial peptide ALFPm3 from Penaeus monodon against white spot syndrome virus. Dev Comp Immunol 69, 23-32.

Gao, J., Zhao, B.R., Zhang, H., You, Y.L., Li, F., Wang, X.W., 2021. Interferon functional analog activates antiviral Jak/Stat signaling through integrin in an arthropod. Cell Rep 36, 109761.

(f) Likewise, choosing Atta in S2 cells needs logic.

We appreciated your comments. Our manuscript revealed that febrile temperature inducible HSF1 confers virus resistance by regulating the expression of antimicrobial peptides (AMPs) in L. vannamei. Further, we want to know that whether HSF1 regulation of antimicrobial peptides is a conserved defense mechanism induced by elevated temperature in arthropods, and experiments were performed in an invertebrate model system (Drosophila S2 cells). Previous study showed that DmAMPs (such as Attacin A, Cecropins A, Defensin, Metchnikowin, and Drosomycin) exerted a significant role in the antiviral immunity in Drosophila (Zhu et al., 2013). Our results showed that the expression of Attacin A, Cecropins A and Defensin were remarkably induced by DmHSF, and the expression of Attacin A was the highest induced. Therefore, DmAtta was chosen as a representative to further demonstrate that DmHSF1 exerts its anti-DCV function by regulating DmAMPs. We have added the description in Lines 328-330 and Lines 361-364 in the revised manuscript. Thank you for your valuable comments and the logic of the manuscript has been improved.

Reference:

Zhu, F., Ding, H., Zhu, B., 2013. Transcriptional profiling of Drosophila S2 cells in early response to Drosophila C virus. Virol J 10, 210.

(2) From Figure 6I to 6K, the authors aimed to verify whether the anti-WSSV function of nSWD was mediated by LvHSF1 at high temperatures. However, what they showed was just showing that nSWD plays anti-WSSV function downstream of HSF1. The authors should show additional data for dsControl+rnSWD.

Thank you for your suggestion. As your suggestion, after knockdown of SWD, shrimp were injection with rLvHSF1 mixed with WSSV. The results showed that the viral load was significantly lower than the control group 48 hours post WSSV infection (Supplementary Fig. 5D). We have added these results to the Supplementary Figure 5C&5D and added a description in Lines 290-293 in the revised manuscript. Thank you for your constructive comments.

(3) For the physical interaction between nSWD and WSSV, it will be great if the authors perform Alphafold3 prediction analysis (Abramson et al PMID: 38718835).

Thank you for your suggestion. As you suggestion, we performed Alphafold3 prediction analysis on SWD and WSSV (VP24 and VP26). The predicted template modeling (pTM) score measures the accuracy of the entire structure. A pTM score above 0.5 means the overall predicted fold for the complex might be similar to the true structure. The Alphafold3 prediction results show that there is a possible interaction between SWD and WSSV. Notably, our manuscript demonstrated that rSWD could interact with VP24 and VP26 by pulldown assays and confocal analysis.

Author response image 3.

Alphafold3 prediction analysis of SWD&VP24 as follow (pTM = 0.64)

Author response image 4.

Alphafold3 prediction analysis of SWD&VP26 as follow (pTM = 0.53)

Minor comments

(1) In the Abstract and many other places, the authors need to specifically write "Drosophila S2 cells" instead of "Drosophila" because conventionally Drosophila implies fruit fly as an organism. We don't say cultured human cells as "human" or "Homo sapiens" in papers.

Thank you for your suggestion. We have modified the description of Drosophila in the revised manuscript. Thank you.

(2) Figure numbers can be reduced for better readability. I would combine Figures 1 and 2, and Figures 3 and 4. If the combined figures are too crowded, some can go to into supplementary figures.

Thank you for your suggestion. We have moved the Poly (I: C) data to Supplementary Figure 2 in the revised manuscript. However, we have added some experimental data to Figures 1, 2, 3, and 4. Therefore, we did not combine Figure 1 and Figure 2, and Figures 3 and 4. Thank you.

(3) One of the best-understood roles of HSF1 in physiology other than heat shock response is longevity, in particular with C. elegans. The authors need to mention this in the Discussion by citing the following recent review paper (Lee PMID: 36380728).

Thank you for your suggestion. We have supplemented the description of HSF1 regulating longevity and aging of organisms and cited the above reference in the revised manuscript (Lee and Lee, 2022). Thank you.

Reference:

Lee, H., Lee, S.V., 2022. Recent Progress in Regulation of Aging by Insulin/IGF-1 Signaling in Caenorhabditis elegans. Mol Cells 45, 763-770.

(4) Please make your own label for small letter panels or transfer small letter panels to supplementary figures.

Thank you for your suggestion. We have adjusted the relevant letter labels. The uppercase letters represent the main image of the Figure, and the small letter panels are the corresponding supplementary instructions in the revised manuscript. Thank you.

(5) In the introduction part, I recommend changing the references for HSFs and HSR with recent ones.

Thank you for your suggestion. We have added the latest references for HSFs and HSR in the Introduction part of the revised manuscript. Thank you.

(6) In Figure 1, it is not intuitive to understand the name groups W and TW.

We appreciated your comments. We have added the description of Group W and Group TW in revised Figure 1. Group W comprised shrimp injected with WSSV and maintained at 25 °C continuously. In contrast, Group TW was subjected to a temperature increase to 32 °C at 24 hours post-injection (hpi). Gill samples were collected for analysis 12 hours post-temperature rise (hptr) and subjected to Illumina sequencing. Thank you.

(7) Please add some kinds of sequence comparisons of SWD and nSWD for readers to understand the homology.

We appreciated your comments. We have added the multiple sequence alignment of SWD proteins in shrimp species in revised Supplementary Figure 3. Highly conserved amino acid residues and cysteine and residues are highlighted in red, indicating that LvSWD is a conserved antimicrobial peptide of the Crustin family. Thank you.

(8) Naming nSWD with "newly identified" is strange as it will not be new anymore as time goes by. Please change the name.

Thank you for your suggestion. We have modified the name of nSWD to SWD in the revised manuscript. Thank you.

(9) Please write the full name for Lv (Litopenaeus vannamei), Dm (Drosophila melanogaster), ds (double-stranded) before using LvHSF1, DmHSF1, and dsLvHSF1.

Thank you for your comments. We have added the full name of LvHSF1, DmHSF1, and dsLvHSF1 in the revised manuscript. Thank you.

(10) In Figure 2, it will be better to transfer poly I:C data to supplementary figures.

Thank you for your comments. We have moved the Poly (I: C) data to Supplementary Figure 2 in the revised manuscript. Thank you.

(11) The label for pGL3-nSWD-M12 is confusing. M1 and M2 are OK. Please change M12 with M1/2 or another one.

Thank you for your suggestion. We have changed pGL3-nSWD-M12 with pGL3-nSWD-M1/2 in the revised manuscript. Thank you.

its not a surprise to hear that the playing time for tetris was estimated at 20 hours because a game like that is designed to alluminate you and keep you occupied for longer periods of time.

The reason Atari was so much more successful around this time was that they already established roots in the tech industry from succeeding in the making of arcade games. The shift from arcade to home games was easier for them because they knew their target audience already.

The Magnavox was the first to be a home game but when Atari produced a home version of Pong, it sold more. i believe this was due to the development probably was more advanced it seems to me as though the Magnavox was just demos.

the TEA comparisons give different costs per liter, but I suppose they also give different cell densities. Should we interpret these as independent, fairly uncorrelated variables, or is the cell density more or less scaling with the price per liter in a way that using the actual cost per liter makes less sense?

crucial_uncertainty

Add a link to this paper and to other papers within the table (a hyperlink) to save people time.

the TEA comparisons give different costs per liter, but I suppose they also give different cell densities. Should we interpret these as independent, fairly uncorrelated variables, or is the cell density more or less scaling with the price per liter in a way that using the actual cost per liter makes less sense?

crucial_uncertainty

eLife Assessment

This article presents valuable findings on how the timing of cooling affects autumn bud set in European beech saplings. The study leverages extensive experimental data and provides an interesting conceptual framework for the various ways in which warming can affect bud set timing. The statistical analysis is very well considered, while indicating some factors that may temper the authors' claims. The factorial experiments offer solid support.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

Summary:

This study provided key experimental evidence for the "Solstice-as-Phenology-Switch Hypothesis" through two temperature manipulation experiments.

Strengths:

The research is data-rich, particularly in exploring the effects of pre- and post-solstice cooling, as well as daytime versus nighttime cooling, on bud set timing, showcasing significant innovation. The article is well-written, logically clear, and is likely to attract a wide readership.

Reviewer #2 (Public review):

In 'Developmental constraints mediate the reversal of temperature effects on the autumn phenology of European beech after the summer solstice', Rebindaine and co-authors report on two experiments on Fagus sylvatica where they manipulated temperatures of saplings between day and night and at different times of year. I think the experiments are interesting, but note that the treatments are extreme compared to natural conditions. Further, given that much of the experiment happened outside, I am not sure how much we can generalize from one year for each experiment, especially when conducted on one population of one species.

Author Response:

The following is the authors’ response to the previous reviews

eLife Assessment

This article presents useful findings on how the timing of cooling affects the timing of autumn bud set in European beech saplings. The study leverages extensive experimental data and provides an interesting conceptual framework for the various ways in which warming can affect but set timing. The statistical analysis is compelling, but indicates some factors that may temper the authors' claims, while the designs of experiments offer incomplete support for the current claims as they rely on one population under extreme conditions for only one year each while a confounding effect (time in a chamber) sometimes lacks a control.

We thank the editor and reviewers for their consideration of our revised manuscript and for their constructive suggestions. In response to the editor’s guidance, we have ensured that: 1) the experimental design is clearly presented as physiological forcing, 2) the Solstice-as-Phenology-Switch concept is explicitly defined, limited, and framed as inferred, 3) conclusions are strictly aligned with the scope of the evidence, and limitations are acknowledged transparently.

We hope these revisions fully address the remaining concerns and clarify both the conceptual framework and the appropriate scope of inference.

Public Review:

Reviewer #1 (Public review):

The authors identified the summer solstice (June 21) as a phenological "switch point", but the flexibility of this switch point remains poorly understood. A more precise explanation of what "flexibility" means in this context is needed, along with a description of the specific experimental results that would demonstrate this flexibility.

We agree that the concept of “flexibility” required clearer definition and a more explicit link to the experimental results. In the Introduction, we now explicitly define flexibility as the capacity for the effective timing of the phenological switch to shift earlier or later depending on developmental progression, rather than occurring at a fixed calendar date. This switch occurs at the compensatory point between the antagonistic influences of early-season development [ESD effect] and late-season temperature [LST effect](L92-98). We have extended and clarified our explanation of the summer solstice’s role in this framework (L69-90). We propose that the solstice acts as an environmental switch that initiates the LST effect, as declining daylengths signal trees to become responsive to late-season cooling (L92-94). The compensatory point then occurs where the advancing ESD effect is balanced by the delaying LST effect. This point should therefore not be fixed to a calendar date but instead vary with developmental progression each year (L75-95).

In the Discussion, we clarify that flexibility is demonstrated experimentally by the observation that the magnitude of July cooling effects (LST effect) on autumn phenology depend on prior developmental rate (ESD effect) [3.4 times greater delay in late-leafing trees], indicating that the position of the compensatory point is development-dependent rather than fixed to June 21 (L398-410). We have made consistent edits throughout the Discussion, in particular in the ‘Support for the Solstice-as-Phenology-Switch Hypothesis’ subsection (L514-530).

The experiment did not directly measure the specific date of the phenological switch point. Instead, it was inferred by comparing temperature effects before and after the solstice. The manuscript should clearly state that this switch point remains an inferred conceptual node rather than a directly measured variable.

We fully agree and have clarified this in the revised manuscript. In the Discussion, we now clearly state that the compensatory point is a conceptual node inferred from responses to cooling before the solstice (June), directly after it (July), or later in the growing season (August) rather than a directly observed phenological event (L352-358 & L405-406).

In Experiment 1, the effect of bud type (terminal vs. lateral) was inconsistent across the overall model and the different leafing groups. The authors should provide a more thorough discussion of potential reasons for this inconsistency.

This inconsistency reflects biological complexity. In the Discussion, we now expand our interpretation to note that terminal and lateral buds may differ in developmental status, resource allocation and hormonal context. We emphasize that bud-type effects are therefore expected to be context-dependent and to interact with wholeplant developmental state, which plausibly explains why effects differ across leafing groups and models (L390-396).

In addition, the statistical model for Experiment 1 indicates that the measured variables (summer cooling and leaf emergence date) explain only 23.4% of the variation in bud formation timing. This leaves over 76% of the variation unexplained, suggesting that other important factors are involved. The discussion should address this limitation in greater depth, moving beyond a focus on the measured variables.

We now discuss the explained and unexplained variance in more detail. We also make it clear that our experiment was designed to test specific mechanistic pathways rather than to fully explain all phenological variability or maximise predictive power L417-419).

In the Discussion, we acknowledge that a substantial fraction of variation remains unexplained (L419-421). We discuss the possibility of other physiological mechanisms, such as photosynthetic assimilation, contributing to the unexplained variation (L421-427). However, large inter-individual variability is commonplace in autumn phenology. A low intra-class correlation coefficient (ICC = 0.26; see L276-280 for methods) suggests much of the remaining variation is attributable to individual-level differences rather than missing explanatory variables (L429-431). In line with the literature, we suggest that genetic and epigenetic differences likely contributed significantly to inter-individual variation, even within a single provenance population (L431-434). In this context of high individual variability, leaf-out timing (ESD effect) and summer cooling treatment (LST effect) together explaining 23.4% of variation in bud set timing is biologically meaningful and demonstrates the mechanistic importance of these processes (L438-441). For completeness, we also briefly discuss alternate sources of within-treatment variability (L434-437).

Reviewer #2 (Public review):

I think the experiments are interesting, but I found the exact methods of them somewhat extreme compared to how the authors present them.

We appreciate this concern and have substantially revised the manuscript to clarify the experimental logic. In the Introduction, we now state explicitly that the study uses temperature regimes that were designed as strong physiological forcing treatments, intended to deeply constrain development and isolate mechanisms rather than to simulate natural or future climatic conditions (L113-115).

In the Methods, we have enhanced our description of the non-linear effects of temperatures below 10°C on physiological processes (L154-158).

At the start of the Discussion, we have added a dedicated paragraph clarifying the scope of inference: the experiment tests causality and constraint (i.e. whether specific physiological processes can drive phenological shifts), not quantitative responses under realistic climate scenarios (L346-363). Throughout the Discussion, we have revised language that could be read as scenario-based interpretation, replacing it with mechanistic phrasing.

Further, given that much of the experiment happened outside, I am not sure how much we can generalize from one year for each experiment, especially when conducted on one population of one species.

Given the large individual variation expected in phenological experiments, we used single experimental populations of single provenance beech saplings to minimise uncontrolled for variation arising from genetic differences (L358-360). This allowed us to elucidate mechanisms despite noisy biological heterogeneity associated with phenology.

In the last round of revision, we toned down statements of generalisation. In the Discussion, we now go further to clarify what mechanistic understanding can be gleamed directly from our findings and then cautiously make suggestions how these mechanisms may play out in natural systems. We repeatedly state the intention of the study as mechanistic inference rather than predictive power, e.g. “However, extrapolations to more complex natural ecosystems should be made with caution as our experimental design prioritised mechanistic inference over generalisability and predictive power.” (L417-419). Alongside our previous calls for tests on other species, we now additionally call for tests on other provenances of beech (L511-512).

I was also very concerned by the revisions.

If this concern stems from the confusion regarding line-numbers and the two submitted versions of the manuscript (with tracked changes and without tracked changes; as required by eLife), then we hope that situation is now clarified. Otherwise, the authors do not understand why our previous revisions would be perceived as being concerning. Regardless, we have made every attempt to address the remaining comments comprehensively.

Further, I am at a loss about their hypothesis, when they write in their letter: "Importantly, the Solstice-asPhenology-Switch hypothesis does not assume that the reversal is fixed to June 21." Why on earth reference the solstice if the authors do not mean to exactly reference the solstice?

We appreciate this important conceptual point. The Solstice-as-Phenology-Switch hypothesis is central to our conceptual model and therefore requires clear explanation. In concert with our changes in response to Reviewer 1’s comment regarding flexibility, we have substantially revised and improved our description of this hypothesis (L69-108).

Whilst the summer solstice is fixed to a calendar date (June 21), the timing of when trees change their autumn phenological responses to temperature is not (L88-90 & L515-517). This occurs when the compensatory point of two antagonistic effects is crossed. Higher early-season development rates (which are driven by temperature) have an advancing (negative) effect on autumn phenology, which we now refer to as the ESD effect (L71-78). Warmer late-season temperatures have a delaying (positive) effect because trees become phenologically susceptible to cooling, i.e. overwintering responses are induced in response to cooling, which we now refer to as the LST effect (L78-82). The point in time when these two effects balance each other out, i.e. the net effect = 0, is the compensatory point (L95-97 & L523-525). The reason this point occurs after the solstice, is because the LST effect only becomes active when days begin to shorten (L92-94 & L522-523). The solstice acts as an environmental switch, initiating trees’ susceptibility to cooling. Therefore, the solstice is referenced in the hypothesis because it forms a daylength barrier. In this framework, the compensatory point cannot occur earlier than the solstice because day lengths are still increasing (L517-519).

In the Introduction and Discussion, we clarify that the solstice is referenced as a biologically meaningful photoperiodic cue, not as a fixed threshold date. We now emphasise that the hypothesis concerns a seasonal reversal in responses to temperature structured around photoperiod, whose effective timing depends on developmental state, rather than a reversal occurring precisely on June 21. To avoid confusion, we have reworded phrases such as “summer solstice effect reversal” to “reversal of phenological responses to temperature after the summer solstice” (L371). In accordance, we have also changed the title to “Developmental constraints mediate the reversal of temperature effects on the autumn phenology of European beech after the summer solstice”.

The following comments stem from the first round of review. We have previously revised the manuscript in accordance with these comments. For most of these points we do not see further cause for changes except for any overlap with comments above. We therefore predominantly copy our previous responses in quotes for clarity, the exception being the comment regarding the framing of our results in relation to natural systems.

The comments below relate to my original review with many of them still applying.

Methods: As I read the Results I was surprised the authors did not give more info on the methods here. For example, they refer to the 'effect of July cooling' but never say what the cooling was. Once I read the methods I feared they were burying this as the methods feel quite extreme given the framing of the paper.

“We understand the concern regarding the structure of the manuscript and note that the methods section was moved to the end of the paper in accordance with eLife’s recommended formatting. We have now moved the methods section before the results to ensure that readers are familiar with the treatments before encountering the outcomes.

Regarding presentation, treatment details are now described in both the Methods and the relevant figure legends. Given this structure, we have chosen not to restate the full treatment conditions in the main Results text to avoid repetition.”

The paper is framed as explaining observational results of natural systems, but the treatments are not natural for any system in Europe of which I have worked in. For example a low of 2 deg C at night and 7 deg C during the day through end of May and then 7/13 deg C in July is extreme. I think these methods need to be clearly laid out for the reader so they can judge what to make of the experiment before they see the results.

We appreciate the reviewer’s concern regarding the use of relatively extreme temperature treatments and the need to ensure that our conclusions are consistent with the motivation for using them. The manuscript was also revised in this regard in the previous round, and we copy the relevant responses at the bottom of this response. Despite this, we agree that further explanation of how our experimental treatments suited the aims of our study was still required.

The aim of these treatments was not to reproduce typical ambient conditions, but to act as a mechanistic probe. Such mechanisms are not readily identifiable from observations or mild manipulations, because the expected effects are small relative to natural variability; stronger perturbations are therefore required to generate a diagnostic contrast. By strongly constraining development in the early-season, and by providing a robust cooling signal in the late-season, we sought to reveal the causal structure underlying the observed solstice-related reversal in temperature effects on autumn phenology.

Temperatures below 10°C intensively slow down cell division and mitotic rates, these rates then rapidly and non-linearly approach 0 as temperatures drop towards 0°C (Körner, 2021). As reflected in L152-158 of the revised manuscript, we selected a spring cooling regime of 2–7 °C to strongly slow developmental processes while maintaining a clear thermal safety margin that eliminates the risk of frost damage. Although a milder cooling regime (e.g. 5–10 °C) would be less extreme, it would also be expected to produce only a comparatively small reduction in developmental rates, thereby substantially reducing our ability to generate distinct early- and late-developing individuals and to detect carry-over effects on autumn phenology. Applying strong cooling therefore increases signal-to-noise and allows us to detect the underlying mechanism, which would not be possible with temperature treatments that represent average contemporary climatic variation.

The use of conditions out with the norm is a standard practice to elucidate mechanisms in ecology, where organisms are often pushed to their physiological limits or transplanted into environments fundamentally different to those which they are adapted (Somero, 2010; Berend et al., 2019). Experiments targeting autumn phenology have utilised a broad range of environmental conditions from moderate to extreme manipulations (Tanino et al., 2010). For example, to test the controls of growth cessation and dormancy induction in Prunus species, one study applied a range of treatments including constant 9°C temperature and 24 hour photoperiod between April and July (Heide, 2008).

Our experimental design aimed to reduce rates of development, cell division and maturation. In the Methods, we describe this aim and clearly state that the experimental design was not intended to mimic natural climatic variation (L154-156 & L181-186). Importantly, our conclusions are framed at the level of direction, timing, and interaction of effects, rather than the magnitude expected under contemporary or future field conditions (L360-363).

This framing intends to reflect the primary inference of this study, which concerns when and why temperature effects reverse around the solstice, and how this timing depends on developmental state and diel temperature exposure, rather than making quantitative predictions for present-day or future climates. This aligns our conclusions with the experimental design. We have further revised the Discussion to explain these aims and conclusions more clearly, including the addition of a subsection at the beginning titled “Experimental forcing and scope of inference” (L346-363). We have also set up this expectation in the Introduction (L113-115).

Additionally, we have improved the Discussion in a number of related aspects.

We explicitly separate mechanistic conclusions and any relation to natural systems, remaining cautious to not overgeneralise or overstate our findings (L417-419).

We now include a dedicated paragraph explaining that, although these specific conditions are not likely to be found in beech’s range, analogous developmental constraints can arise during cold springs, late cold spells following budburst, or at high-elevation and continental sites where temperatures remain low despite increasing photoperiod (L540-545, L583-588). We further explain that because developmental progression integrates temperature cumulatively over time, even short episodes of strong cooling can exert lasting carry-over effects on seasonal timing, thereby linking the forced experimental responses to processes relevant under natural, fluctuating conditions (L545-550).

We explicitly state that the decoupling of day and night temperatures was not intended to represent realistic meteorological states (L458-460). We explain that this design was used diagnostically to isolate inherently diel physiological processes (e.g. nocturnal growth, cell division and expansion versus daytime carbon assimilation), and that the observed responses demonstrate the importance of diel timing of temperature exposure rather than the realism of the imposed cycles (L460-468).

Previous response:

We recognise that our temperature treatments were severe and do not mimic real world scenarios. They were deliberately designed to create large contrasts in developmental rates, thereby maximising our ability to detect the mechanisms underpinning the solstice switch. For example, the severe cooling between 4 April and 24 May was specifically designed to slow spring development as much as possible without damaging the plants. We have added text in the Methods to clarify this aim.

I also think the control is confounded with growth chamber experience in Experiment 1. That is, the control plants never experience any time in a chamber, but all the treatments include significant time in a chamber. The authors mention how detrimental chamber time can be to saplings (indeed, they mention an aphid problem in experiment 2) so I think they need to be more upfront about this. The study is still very valuable, but -- again -- we may need to be more cautious in how much we infer from the results.

We appreciate the reviewer’s concern about the potential confounding effect of chamber exposure in experiment 1. We have now discussed this limitation more explicitly, adding further explanation to the Methods and Discussion.

Note that chamber-related problems (e.g. aphid infestations) primarily occurred under warm chamber conditions, whereas our experiment 1 cooling treatments maintained low temperatures that suppressed such issues. This means that an equivalent “warm chamber control” could have been associated with its own artefacts, as trees kept under warm chamber conditions would have been exposed to additional stressors that were not present under natural growing conditions. To address this point, we included a chamber control in experiment 2. While aphid abundance was indeed higher in the warm chamber controls, chamber exposure itself had no detectable effect on autumn phenology. This suggests that the main findings of experiment 1 are unlikely to be artefacts of chamber conditions.

Nevertheless, we agree that chamber exposure remains a potential limitation of experiment 1, which requires clear acknowledgement. We now state this more explicitly in the manuscript while also emphasising that our results are supported by experiment 2 and by converging lines of external evidence.

Also, I suggest the authors add a figure to explain their experiments as they are very hard to follow. Perhaps this could be added to Figure 1?

We have now added figures to the methods section to depict the experimental timelines and settings more clearly (Figs. 2 and 3).

Finally, given how much the authors extrapolate to carbon and forests, I would have liked to see some metrics related to carbon assimilation, versus just information on timing.

We agree that carbon assimilation is an important component of forest carbon dynamics. However, the primary aim of this study was to identify how developmental state and diel cycles mediate temperature effects on autumn phenology, rather than to quantify carbon assimilation per se. Assessing photosynthetic controls on autumn phenology would require a substantially different experimental design and is therefore beyond the scope of the present study.

That said, we were able to include measurements of photosynthetic assimilation during pre-solstice cooling (now presented as Fig. S12 for all treatments). These data show that cooling strongly reduced assimilation across all treatments, despite their markedly different phenological outcomes. This supports our interpretation that variation in assimilation alone cannot explain the observed phenological responses, consistent with previous manipulative and observational studies reporting a weak role of late-season assimilation in controlling autumn phenology.

Fagus sylvatica: Fagus sylvatica is an extremely important tree to European forests, but it also has outlier responses to photoperiod and other cues (and leafs out very late) so using just this species to then state 'our results likely are generalisable across temperate tree species' seems questionable at best.

We agree that Fagus sylvatica has a stronger photoperiod dependence than many other European tree species. As we note in our response to Reviewer 1, our findings align with previous research across temperate northern forests. Within our framework, interspecific variation in leaf-out timing would not alter the overall response pattern, though it could shift the specific timing of effect reversals. For example, earlier-leafing species may approach completion of development sooner and thus show sensitivity to late-season cooling earlier than F. sylvatica. Nevertheless, we acknowledge the importance of not overstating generality. We have therefore revised the manuscript to phrase conclusions more cautiously and highlight the need for further research across species.

And the referenced response to Reviewer one:

We agree that extrapolation from our experiments on Fagus sylvatica to other species and natural forests requires caution. However, it is precisely the controlled nature of our design that allowed us to isolate the precise mechanisms that appear to underpin the solstice switch, highlighting the role of diel and seasonal temperature variation. In natural systems, additional variables such as competition, precipitation, and soil heterogeneity can strongly influence phenology, but they also make it difficult to disentangle causal mechanisms. By minimising these confounding factors, our experiment provided a clear test of how temperature before and after the solstice regulates growth cessation.

To acknowledge the limitation, we have toned down statements about generalisation (e.g. “likely generalisable” to “other temperate tree species may display similarities”) and explicitly call for follow-up studies across species and forest contexts. At the same time, we highlight that our findings align with independent evidence from manipulative experiments, satellite observations, flux measurements, and groundbased phenology, which suggests the mechanisms we report may extend beyond the specific populations studied here.”

As described in responses above, we have further clarified what can be directly concluded from our study, avoiding overgeneralisation.

Measuring end of season (EOS): It's well known that different parts of plants shut down at different times and each metric of end of season -- budset, end of radial expansion, leaf coloring etc. -- relate to different things. Thus I was surprised that the authors ignore all this complexity and seem to equate leaf coloring with budset (which can happen MONTHS before leaf coloring often) and with other metrics. The paper needs a much better connection to the physiology of end of season and a better explanation for the focus on budset. Relatedly, I was surprised the authors cite almost none of the literature on budset, which generally suggests is it is heavily controlled by photoperiod and population-level differences in photoperiod cues, meaning results may different with a different population of plants. 

We thank the reviewer for pointing out that our discussion of the responses of different EOS metrics needs more clarity. We agree with much of this perspective, and we have added an additional analysis of leaf chlorophyll content data to use leaf discolouration as an alternative EOS marker. On this we would like to make two important points:

Firstly, we agree that bud set often occurs before leaf discolouration, although this can depend on which definition of leaf discolouration is used. In experiment 1, budset occurred on average on day-of-year (DOY) 262 and leaf senescence (50% loss of leaf chlorophyll) occurred on DOY 320. However, we do not necessarily agree that this excludes the combined discussion of bud set and leaf senescence timing. Whilst environmental drivers can affect parts of plants differently, often responses from different end-of-season indicators (e.g. bud set and loss of leaf chlorophyll) are similar, even if only directionally. Figure S11 shows how, across both experiments, treatment effects were tightly conserved (R² = 0.49) amongst the two phenometrics. In accordance with these revisions, we have updated the manuscript title to “Developmental constraints mediate the summer solstice reversal of climate effects on the autumn phenology of European beech”.

Secondly, shifts in bud set timing remain the primary focus of the manuscript as these shifts are of direct physiological relevance to plant development and dormancy induction, whereas leaf discolouration may simply follow bud set as a symptom of developmental completion. This is supported by our results, which show stronger responses of bud set than leaf senescence (Figs. 4 & 5 vs. Figs. S9 & S10).

Following the reviewer’s suggestion, we have included more references on the topic of bud set and its environmental controls. The reviewer rightly stresses that photoperiod is considered the most important factor. Photoperiod is therefore key in our conceptual model. However, the responses we observed in F. sylvatica cannot be explained by photoperiod alone. For example, in experiment 1, July cooling delayed the autumn phenology of late-leafing trees but had negligible impact on early-leafing trees, even though both experienced the exact same photoperiod. Moreover, in experiment 2, day, night and full-day cooling showed substantial variations in their effects despite equal photoperiod across the climate regimes. This is why we suggest that the annual progression of photoperiod modulates the responses to temperature variations instead of eliciting complete control.

Following the addition of an analysis of leaf senescence data, we also revised the terminology in places (including the title) from “primary growth cessation/bud set” to the broader term “autumn phenology.” This term is intended to encompass two distinct but related physiological processes—bud set and leaf senescence—both of which are commonly used as markers of autumn phenology and the end of the growing season.

Somewhat minor comments:

(1) How can a bud type -- which is apical or lateral -- be a random effect? The model needs to try to estimate a variance for each random effect so doing this for n=2 is quite odd to me. I think the authors should also report the results with bud type as fixed, or report the bud types separately.

We have revised the analysis to include bud type as a fixed effect. There are only very minor numerical adjustments (e.g. rounding to 4.8 days instead of 4.9) and inferences are not altered. We also report the bud type effects for experiment 1 and experiment 2.

(2) I didn't fully see how the authors results support the Solstice as Switch hypothesis, since what timing mattered seemed to depend on the timing of treatment and was not clearly related to solstice. Could it be that these results suggest the Solstice as Switch hypothesis is actually not well supported (e.g., line 135) and instead suggest that the pattern of climate in the summer months affects end of season timing?

Our responses to the main comments in this new round of revision have comprehensively covered this topic.

References

Berend K, Haynes K, MacKenzie CM. 2019. Common garden experiments as a dynamic tool for ecological studies of alpine plants and communities in northeastern North America. Rhodora 121: 174.

Heide OM. 2008. Interaction of photoperiod and temperature in the control of growth and dormancy of Prunus species. Scientia Horticulturae 115: 309–314.

Körner C. 2021. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems. Cham: Springer International Publishing.

Somero GN. 2010. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. Journal of Experimental Biology 213: 912–920.

Tanino KK, Kalcsits L, Silim S, Kendall E, Gray GR. 2010. Temperature-driven plasticity in growth cessation and dormancy development in deciduous woody plants: a working hypothesis suggesting how molecular and cellular function is affected by temperature during dormancy induction. Plant Molecular Biology 73: 49–65.

eLife Assessment

This important study combined careful computational modeling, a large patient sample, and replication in an independent general population sample to provide convincing evidence in support of a computational account of a difference in risk-taking between people who have attempted suicide and those who have not. It is proposed that this difference reflects a general change in the approach to risky (high-reward) options and a lower emotional response to certain rewards. While the findings advance our understanding of cognitive mechanisms at the group level, the observation that computational phenotype is predictive of suicidal behavior only in the clinical sample and not in the online sample limits its applicability for individual prediction, early detection and prevention of suicidality.

Reviewer #1 (Public review):

Summary:

The authors use a gambling task with momentary mood ratings from Rutledge et al. and compare computational models of choice and mood to identify markers of decisional and affective impairments underlying risk-prone behavior in adolescents with suicidal thoughts and behaviors (STB). The results show that adolescents with STB show enhanced gambling behavior (choosing the gamble rather than the sure amount), and this is driven by a bias towards the largest possible win rather than insensitivity to possible losses. Moreover, this group shows a diminished effect of receiving a certain reward (in the non-gambling trials) on mood. The results were replicated in a general online sample where participants were divided into groups with or without STB based on their self-report of suicidal ideation on one question in the Beck Depression Inventory self-report instrument. The authors suggest, therefore, that adolescents diagnosed with depression or anxiety with decreased sensitivity to certain rewards may need to be monitored more closely for STB due to their increased propensity to take risky decisions aimed at (expected) gains (such as relief from an unbearable situation through suicide) regardless of the potential losses. However, such a result was only found in the clinical sample and cannot be generalized more broadly based on the current findings.

Strengths:

(1) The study uses a previously validated task design and replicates previously found results through well-explained model-free and model-based analyses.

(2) Sampling of adolescents at high risk can help target early preventative diagnoses and treatments for suicide.

(3) Replication of the results in an online cohort increases confidence in the findings.

(4) The models considered for comparison are thorough and well-motivated. The chosen models allow for teasing apart which decision and mood sensitivity parameters relate to risky decision-making across groups based on their hypotheses.

(5) Novel finding of mood (in)sensitivity to non-risky rewards and its relationship with risk behavior in STB.

Weaknesses:

(1) Sample size of 25 for S- group is low-powered, which is explicitly mentioned as a study limitation.

(2) Modeling in the mediation analysis focused on predicting risk behavior in this task from the model-derived bias for gains and suicidal symptom scores. Thus, the implications of this work are more relevant to a basic-science understanding of the etiology of suicidal behavior than they are useful as a predictor of suicidal behavior, and it is not clear that a psychiatrist or psychologist could use this task to potentially determine who is at higher risk of attempting suicide and must be more closely monitored. Indeed, relationships between task parameters and behavior and suicidal behavior was limited to the clinical sample with a diagnosis of depression or anxiety disorder, and did not extend to the online sample. Therefore, the claim that these findings provide "computational markers for general suicidal tendency among adolescents" is unwarranted.

Reviewer #2 (Public review):

Summary:

This article addresses a very pertinent question - what are the computational mechanisms underlying risky behaviour in patients having attempted suicide. In particular, it is impressive how the authors find a broad behavioral effect whose mechanisms they can then explain and refine through computational modeling. This work is important because currently, beyond previous suicide attempts, there has been a lack of predictive measures. This study is the first step towards that: understanding the cognition on a group level. Before then being able to include it in future predictive studies (based on the cross-sectional data, this study by itself cannot assess the predictive validity of the measure).

Strengths:

- Large sample size<br /> - Replication of their own findings<br /> - Well-controlled task with measures of behaviour and mood + precise and well-validated computational modeling

Questions, based on revised manuscript and replies to other reviewers:

(1) Replies to reviewers in general: Bayes Factors have been added, it would be good to also use common verbal terms to describe them (e.g. 'anecdotal', 'moderate' etc). For example, my reading of table S8 would be that for gambling rate there is only anecdotal evidence that it does not relate to PSWQ, BDI, and moderate evidence it does not relate to TAI.

(2) Reply to reviewer 1 Q2 (Predicting STB):<br /> For the regression predicting suicidal ideation, it seems to me that what you did was a regression STB ~ gambling behaviour + approach + mood? Could you clarify? I had expected as a test of whether the task can predict STB risk something slightly different - a cross-validation (LOO or maybe 5-fold in the large sample): STB ~ gambling behaviour + approach [parameter from model] + mood [parameter from model]; and then computing in the left out participants: predicted STB. Then checking correlation between STB and predicted STB. This would allow testing whether the diverse task measures together predict STB (with the caveat, that it's cross-validated, rather than hold-out sample, unless you could train on one sample (in lab) and test on the other (online).

(3) Reply to reviewer 2 Q1 (parameter recovery): I'm looking at S3, it seems to still show only the scatter plots and not the correlation matrices, which are now added as text notes. Can you actually show these matrices? An off-diagonal correlation of 0.63 appears quite high. I think it needs to be discussed exactly which parameters those are, and whether that impacts the interpretation of the results.

(4) Reply to reviewer 3 Q3 (mood model): I would have imagined that the response would involve changing the mood equations (equation 8 main text) to include a term for whether the participant gambled or not, independent of the gamble value.

Reviewer #3 (Public review):

This manuscript investigates computational mechanisms underlying increased risk-taking behavior in adolescent patients with suicidal thoughts and behaviors. Using a well-established gambling task that incorporates momentary mood ratings and previously established computational modeling approaches, the authors identify particular aspects of choice behavior (which they term approach bias) and mood responsivity (to certain rewards) that differ as a function of suicidality. The authors replicate their findings on both clinical and large-scale non-clinical samples.

The main problem, however, is that the results do not seem to support a specific conclusion with regard to suicidality. The S+ and S- groups differ substantially in the severity of symptoms, as can be seen by all symptom questionnaires and the baseline and mean mood, where S- is closer to HC than it is to S+. The main analyses control for illness duration and medication but not for symptom severity. The supplementary analysis in Figure S11 is insufficient as it mistakes the absence of evidence (i.e., p > 0.05) for evidence of absence. Therefore, the results do not adequately deconfound suicidality from general symptom severity.

The second main issue is that the relationship between an increased approach bias and decreased mood response to CR is conceptually unclear. In this respect, it would be natural to test whether mood responses influence subsequent gambling choices. This could be done either within the model by having mood moderate the approach bias or outside the model using model-agnostic analyses.

Additionally, there is a conceptual inconsistency between the choice and mood findings that partly results from the analytic strategy. The approach bias is implemented in choice as a categorical value-independent effect, whereas the mood responses always scale linearly with the magnitude of outcomes. One way to make the models more conceptually related would be to include a categorical value-independent mood response to choosing to gamble/not to gamble.

The manuscript requires editing to improve clarity and precision. The use of terms such as "mood" and "approach motivation" is often inaccurate or not sufficiently specific. There are also many grammatical errors throughout the text.

Claims of clinical relevance should be toned down, given that the findings are based on noisy parameter estimates whose clinical utility for the treatment of an individual patient is doubtful at best.

Comments on revisions:'

The authors adequately addressed my comments and I find the manuscript substantially strengthened.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This valuable study combined careful computational modeling, a large patient sample, and replication in an independent general population sample to provide a computational account of a difference in risk-taking between people who have attempted suicide and those who have not. It is proposed that this difference reflects a general change in the approach to risky (high-reward) options and a lower emotional response to certain rewards. Evidence for the specificity of the effect to suicide, however, is incomplete, which would require additional analyses.

We thank the editors and reviewers for this important assessment. Based on clinical interviews, we included patients with and without suicidality (S⁺ and S^- groups). However, in line with suicidal-related literature (e.g., Tsypes et al., 2024), two groups also differed substantially in the severity of symptoms (see Table 1). To address the request for evidence on specificity to suicidality beyond general symptom severity, we performed separate linear regressions to explain in gambling behaviour, value-insensitive approach parameter (β_gain), and mood sensitivity to certain rewards (β_CR) with group as a predictor (1 for S⁺ group and 0 for S^- group) and scores for anxiety and depression as covariates. Results remained significant after controlling anxiety and depression (ps < 0.027; Table S8). Given high correlations among anxiety and depression questionnaires (rs > 0.753, ps < 0.001), we performed Principal Components Analysis (PCA) on the clinical questionnaire to extract the orthogonal components, where each component explained 86.95%, 7.09%, 3.27%, and 2.68% variance, respectively. We then performed linear regressions using these components as covariates to control for anxiety and depression. Our main results remained significant (ps < 0.027; Table S9). We believe that these analyses provide evidence that the main effects on gambling and on mood were specific to suicide.

Moreover, as Reviewer 3 pointed out, these “absence of evidence” cannot provide insights of “evidence of absence”. Although we median-split patients by the scores of general symptoms (e.g., depression and anxiety-related questionnaires) and verified no significant differences in these severities (Figure S11), we additionally conducted Bayesian statistics in gambling behavior, value-insensitive approach parameter, and mood sensitivity to certain rewards. BF₀₁ is a Bayes factor comparing the null model (M₀) to the alternative model (M₁), where M₀ assumes no group difference. BF₀₁ > 1 indicates that evidence favors M₀. As can be seen in Table S7, most results supported null hypothesis, suggesting that general symptoms of anxiety and depression overall did not influence our main results. Overall, we believe that these analyses provide compelling evidence for the specificity of the effect to suicide, above and beyond depression and anxiety.

Beyond these specific findings, this work highlights the broader utility of computational modelling and mood to better understand behavioral effect, showing how to use both mood and choice data to better comprehend a psychiatric issue. 

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors use a gambling task with momentary mood ratings from Rutledge et al. and compare computational models of choice and mood to identify markers of decisional and affective impairments underlying risk-prone behavior in adolescents with suicidal thoughts and behaviors (STB). The results show that adolescents with STB show enhanced gambling behavior (choosing the gamble rather than the sure amount), and this is driven by a bias towards the largest possible win rather than insensitivity to possible losses. Moreover, this group shows a diminished effect of receiving a certain reward (in the non-gambling trials) on mood. The results were replicated in an undifferentiated online sample where participants were divided into groups with or without STB based on their self-report of suicidal ideation on one question in the Beck Depression Inventory self-report instrument. The authors suggest, therefore, that adolescents with decreased sensitivity to certain rewards may need to be monitored more closely for STB due to their increased propensity to take risky decisions aimed at (expected) gains (such as relief from an unbearable situation through suicide), regardless of the potential losses.

Strengths:

(1) The study uses a previously validated task design and replicates previously found results through well-explained model-free and model-based analyses.

(2) Sampling choice is optimal, with adolescents at high risk; an ideal cohort to target early preventative diagnoses and treatments for suicide.

(3) Replication of the results in an online cohort increases confidence in the findings.

(4) The models considered for comparison are thorough and well-motivated. The chosen models allow for teasing apart which decision and mood sensitivity parameters relate to risky decision-making across groups based on their hypotheses.

(5) Novel finding of mood (in)sensitivity to non-risky rewards and its relationship with risk behavior in STB.

Weaknesses:

(1) The sample size of 25 for the S- group was justified based on previous studies (lines 181-183); however, all three papers cited mention that their sample was low powered as a study limitation.

We thank the Reviewer for rising this concern. We agree that the sample size for S^- group (n=25) is modest, and the prior studies we cited also acknowledged limited power. We wanted to point out that we obtained a comparable sample size to a prior study. In the revision, we therefore updated the section to justify this sample size in which we acknowledge the limited power of our study in the limitation section. Please see our clarification below:

Page 32:

“Third, despite replicating our main results in an independent dataset (n=747), the modest S^- subgroup size (n=25) has a limited statistical power.”

(2) Modeling in the mediation analysis focused on predicting risk behavior in this task from the model-derived bias for gains and suicidal symptom scores. However, the prediction of clinical interest is of suicidal behaviors from task parameters/behavior - as a psychiatrist or psychologist, I would want to use this task to potentially determine who is at higher risk of attempting suicide and therefore needs to be more closely watched rather than the other way around (predicting behavior in the task from their symptom profile). Unfortunately, the analyses presented do not show that this prediction can be made using the current task. I was left wondering: is there a correlation between beta_gain and STB? It is also important to test for the same relationships between task parameters and behavior in the healthy control group, or to clarify that the recommendations for potential clinical relevance of these findings apply exclusively to people with a diagnosis of depression or anxiety disorder. Indeed, in line 672, the authors claim their results provide "computational markers for general suicidal tendency among adolescents", but this was not shown here, as there were no models predicting STB within patient groups or across patients and healthy controls.

Thank you for these thoughtful comments. Our study focuses on why adolescent patients with suicidality have increased risk behavior, aiming to provide a mechanism-based target for suicide prevention. Therefore, our dependent variable in the mediation model was gambling behavior. We also agree that the clinically relevant question is whether suicidality can be predicted from task-derived behavior/parameters. We thus used risky behavior and the potential mental parameters to predict STB. Linear regressions showed that gambling behavior, as well as the value-insensitive approach parameter, can predict suicidal symptom scores among patients (former: β = 9.189, t = 2.004, p = 0.048; latter: β = 5.587, t = 2.890, p = 0.005). In healthy controls, these predictions failed (gambling behavior: β = 1.471, t = 0.825, p = 0.411; approach: β = 0.874, t = 1.178, p = 0.241). These results suggest that clinical relevance of these findings apply exclusively to people with a diagnosis of depression or anxiety disorder. We found same patterns for the mood parameter (mood sensitivity to certain rewards: patients: β = -28.706, t = -2.801, p = 0.006; healthy controls: β = -2.204, t = -0.528, p = 0.599). In sum, we believe that our statement of “computational markers for general suicidal tendency among adolescents” is reasonable now. Please see our revisions below:

Page 17:

“Furthermore, linear regression showed that gambling rate can predict the current suicidal ideation score (BSI-C, β = 9.189, t = 2.004, p = 0.048) among patients, but not among HC (β = 1.471, t = 0.825, p = 0.411), suggesting that gambling behavior has patient-specific predictive utility for suicidal symptoms.”

Page 19:

“Furthermore, linear regression showed that approach parameter can predict the current suicidal ideation score (β = 5.587, t = 2.890, p = 0.005) among patients, but not among HC (β = 0.874, t = 1.178, p = 0.241), suggesting that value-insensitive approach parameter has patient-specific predictive utility for suicidal symptoms.”

Page 21:

“Furthermore, linear regression showed that mood sensitivity to CR can predict the current suicidal ideation score (β = -28.706, t = -2.801, p = 0.006) among patients, but not among HC (β = -2.204, t = 0.528, p = 0.599), suggesting that mood sensitivity to CR has patient-specific predictive utility for suicidal symptoms.”

(3) The FDR correction for multiple comparisons mentioned briefly in lines 536-538 was not clear. Which analyses were included in the FDR correction? In particular, did the correlations between gambling rate and BSI-C/BSI-W survive such correction? Were there other correlations tested here (e.g., with the TAI score or ERQ-R and ERQ-S) that should be corrected for? Did the mediation model survive FDR correction? Was there a correction for other mediation models (e.g., with BSI-W as a predictor), or was this specific model hypothesized and pre-registered, and therefore no other models were considered? Did the differences in beta_gain across groups survive FDR when including comparisons of all other parameters across groups? Because the results were replicated in the online dataset, it is ok if they did not survive FDR in the patient dataset, but it is important to be clear about this in presenting the findings in the patient dataset.

Thank you for raising the important issue of multiple testing and for asking us to clarify exactly which tests were covered by the FDR procedure. In the clinical dataset we conducted a large number of inferential tests (χ², t-tests, ANOVAs, regressions) spanning: (i) group differences in demographic/clinical characteristics; (ii) sanity checks (e.g., anxiety/depression questionnaires); (iii) primary hypotheses (e.g., group differences in risky behavior); (iv) model-based analyses (parameter checks and between-group contrasts); and (v) control/sensitivity analyses. Post-hoc t-tests were performed only when the three-group ANOVA was significant. This yielded >150 p-values. FDR was applied using all these p-values. Please see our clarification below:

Supplementary Page 4:

“Supplementary Note 8: Clarification for FDR correction.

In the clinical dataset we conducted a large number of inferential tests (χ<sup2\</sup>, t-tests, ANOVAs, regressions) spanning: (i) group differences in demographic/clinical characteristics; (ii) sanity checks (e.g., anxiety/depression questionnaires); (iii) primary hypotheses (e.g., group differences in risky behavior); (iv) model-based analyses (parameter checks and between-group contrasts); and (v) control/sensitivity analyses. Post-hoc t-tests were performed only when the three-group ANOVA was significant. This yielded >150 p-values. FDR was applied using all these p-values.”

(4) There is a lack of explicit mention when replication analyses differ from the analyses in the patient sample. For instance, the mediation model is different in the two samples: in the patient sample, it is only tested in S+ and S- groups, but not in healthy controls, and the model relates a dimensional measure of suicidal symptoms to gambling in the task, whereas in the online sample, the model includes all participants (including those who are presumably equivalent to healthy controls) and the predictor is a binary measure of S+ versus S- rather than the response to item 9 in the BDI. Indeed, some results did not replicate at all and this needs to be emphasized more as the lack of replication can be interpreted not only as "the link between mood sensitivity to CR and gambling behavior may be specifically observable in suicidal patients" (lines 582-585) - it may also be that this link is not truly there, and without a replication it needs to be interpreted with caution.

Thank you for these important comments. This study focused on cognitive and affective computational mechanisms underlying increased risky behavior in STB. Accordingly, we compared patients with STB (S⁺) with patients without STB (S^-) and healthy controls (HC) to examine the effects of STB on risky behavior. Therefore, group comparison, instead of dimensional measure of suicidal symptoms by Beck Scale for Suicidal Ideation, can answer our research questions directly.

To enhance consistency between the clinical and replication datasets, we included all participants in each dataset when performing the mediation analysis. Given that S^- and HC did not differ in gambling behavior or the approach parameter in the clinical dataset, we merged these two groups. In the replication dataset, to mirror the S⁺ vs. S^- contrast used clinically, we categorized the general sample into S+ and S^- based on BDI item 9. The mediation results remained significant in both datasets (the clinical dataset: a×b = 0.321, 95% CI = [0.070, 0.549], p = 0.016; the replication dataset: a×b = 0.143, 95% CI = [0.016, 0.288], p = 0.031), suggesting that STB is associated with increased risk behavior via stronger approach motivation.

We also acknowledge the non-replication of the correlation between gambling behavior and mood sensitivity to certain rewards in the online sample. While this pattern might indicate that the link is specific to suicidal patients, it may also reflect sample-specific or unstable effects; thus, we now state this explicitly and interpret the finding with caution. Please see our revisions below:

Page 15:

“We next verified our results in an independent dataset, including the same task and BDI questionnaire in 747 general participants (500 females; age: 20.90±2.41) (46). One item in BDI involves the measurement of STB. In item 9 of BDI, participants chose one option that describes them best: Option 1, “I don't have any thoughts of killing myself.”; Option 2, “I have thoughts of killing myself, but I would not carry them out.”; Option 3, “I would like to kill myself.”; Option 4, “I would kill myself if I had the chance.”. In line with the current definition of S⁺/S^- in the clinical dataset, we identified S⁺ group as choosing Option 2, 3, or 4, while participants selecting Option 1 were categorized as S^- group.”

Page 19:

“Given significant correlations between group, approach parameter, and gambling rate for gain trials (ps < 0.017), we further conducted a mediation analysis with the assumption of the mediating effect of approach motivation of suicidality on the risk behavior. Given that we aimed to test the effect of STB, with S^- and HC as controls, and given that S^- and HC did not differ in gambling behavior or in the approach parameter, we merged these two groups for the mediation analysis. Results supported our hypothesis (a×b = 0.321, 95% CI = [0.070, 0.549], p = 0.016; Figure 2C), confirming that suicidal thoughts and behavior increase risk behavior through stronger approach motivation.”

Page 26:

“However, we did not observe any significant correlation between mood sensitivity to CR and gambling behavior (ps > 0.389), which suggests that the link between mood sensitivity to CR and gambling behavior may be specifically observable in suicidal patients. Alternatively, this non-replicated result may also reflect sample-specific or unstable effects, which needs to be interpreted with caution.”

(5) In interpreting their results, the authors use terms such as "motivation" (line 594) or "risk attitude" (line 606) that are not clear. In particular, how was risk attitude operationalized in this task? Is a bias for risky rewards not indicative of risk attitude? I ask because the claim is that "we did not observe a difference in risk attitude per se between STB and controls". However, it seems that participants with STB chose the risky option more often, so why is there no difference in risk attitude between the groups?

Thank you for pointing out the ambiguity. In our manuscript, “motivation” and “risk attitude” are defined at the computational level. Following prior work with this task Rutledge et al., (2015, 2016), we decompose observed gambling into (i) value-dependent valuation parameters that capture risk attitude (e.g., risk aversion and loss aversion, which scale the subjective value of outcomes), and (ii) value-insensitive, valence-dependent biases that capture approach/avoidance motivation. Accordingly, a higher gambling rate does not imply a change in risk attitude per se: it can arise from an increased value-insensitive approach bias even when risk-attitude parameters are comparable between groups—which is what we observe for S⁺ vs. controls. We have clarified this point in the computational modeling section.

Pages 12-13:

“Please note that a higher gambling rate does not imply a change in risk attitude per se: it can arise from an increased value-insensitive approach bias even when risk-attitude parameters are comparable between groups. Risk attitude is indeed conceptualized in economics as the curvature of the utility function (i.e., the subjective value) of the objective outcomes, with concave curves associated with risk aversion, and convex curves associated with risk seeking (54,56). By contrast, the approach or avoidance bias apply to all the value. A possible interpretation of the approach bias is that participant approach the option with the highest possible gain (the lottery) in the gain frame; the avoidance bias would then reflect a tendency to systematically avoid the highest potential losses (the lottery) in the loss frame.”

Reviewer #2 (Public review):

Summary:

This article addresses a very pertinent question: what are the computational mechanisms underlying risky behaviour in patients who have attempted suicide? In particular, it is impressive how the authors find a broad behavioural effect whose mechanisms they can then explain and refine through computational modeling. This work is important because, currently, beyond previous suicide attempts, there has been a lack of predictive measures. This study is the first step towards that: understanding the cognition on a group level. This is before being able to include it in future predictive studies (based on the cross-sectional data, this study by itself cannot assess the predictive validity of the measure).

Strengths:

(1) Large sample size.

(2) Replication of their own findings.

(3) Well-controlled task with measures of behaviour and mood + precise and well-validated computational modeling.

Weaknesses:

I can't really see any major weakness, but I have a few questions:

(1) I can see from the parameter recovery that the parameters are very well identified. Is it surprising that this is the case, given how many parameters there are for 90 trials? Could the authors show cross-correlations? I.e., make a correlation matrix with all real parameters and all fitted parameters to show that not only the diagonal (i.e., same data is the scatter plots in S3) are high, but that the off-diagonals are low.

Thank you for raising these thoughtful concerns. The current task consisted of 90 choices and 36 mood ratings. There were 5 choice parameters and 4 mood parameters. The apparently strong identifiability is not unexpected, as 90 choice trials and 36 mood ratings are comparable to those in prior computational modeling literature (Blain & Rutledge, 2022).

As suggested, we computed cross-correlations between all generating (“true”) and recovered (“fitted”) parameters. The resulting matrix showed high diagonal (choice winning model: rs > 0.91; mood winning model: rs > 0.90) and low off-diagonal (choice winning model: abs(rs) < 0.63; mood winning model: abs(rs) > 0.40) correlations, further supporting parameter recovery. Please see our clarifications below:

Supplementary Pages 2-3:

“Parameter recovery: Figure S3 shows good parameter recovery for both choice and mood winning model (choice: rs > 0.91, ps < 0.001; intraclass coefficients > 0.78; mood: rs > 0.90, ps < 0.001; intraclass coefficients > 0.86). Moreover, we computed cross-correlations between all generating (“true”) and recovered (“fitted”) parameters. The resulting matrix showed high diagonal (choice winning model: rs > 0.91; mood winning model: rs > 0.90) and low off-diagonal (choice winning model: abs(rs) < 0.63; mood winning model: abs(rs) > 0.40) correlations, further supporting parameter recovery.”

Page 10：

“The numbers of choice trials and mood ratings were comparable to those in prior computational modeling studies (34,35).”

(2) Could the authors clarify the result in Figure 2B of a correlation between gambling rate and suicidal ideation score, is that a different result than they had before with the group main effect? I.e., is your analysis like this: gambling rate ~ suicide ideation + group assignment? (or a partial correlation)? I'm asking because BSI-C is also different between the groups. [same comment for later analyses, e.g. on approach parameter].

Thank you for pointing out the lack of clarity. We performed group difference analysis and correlation of suicidal ideation analysis, separately. We first performed group difference analysis to test our hypothesis of STB effects. We then conducted correlational analysis to further specify our findings.

(3) The authors correlate the impact of certain rewards on mood with the % gambling variable. Could there not be a more direct analysis by including mood directly in the choice model?

Thank you for this insightful suggestion. As suggested, we tried to integrate mood into choice models by adding mood bias component(s) in line with previous literature (Vinckier et al., 2018). The first model (mcM1) assumes that mood biases choice, building on cM3 (the winning choice model). cmM2 further separated the mood bias parameter into two components according to participants’ choices.

However, model comparison using BIC supported cM3 (Table S6), that is, without consideration of mood in choice modeling. This can be due to the lack of block design in our experimental design unlike e.g., Vinckier et al., (2018) and Eldar & Niv, (2015). Please see our clarifications below:

Supplementary Pages 3-4:

“Supplementary Note 6: integration of mood into choice models

Although we modeled choice and mood separately to examine cognitive and affective mechanisms underlying increased risk behavior in adolescent suicidal patients, one interesting question was whether mood responses influence subsequent gambling choices and how to model them. First, we median-split mood responses (except the final rating) to compare gambling rate. Results showed a trend for less gambling rate in higher mood (t = -1.971, p = 0.050). However, there was no significant group difference (F = 0.680, p = 0.507). Second, with the assumption that mood biases choice, we constructed mcM1 based on cM3 (the winning choice model).

Based on our finding of the negative correlation between mood sensitivity to certain rewards and gambling rate in S⁺, we separated β_Mood parameter into β_Mood-CR and β_Mood-GR (cmM2).

Model comparison using BIC supported cM3 (Table S6), that is, without consideration of mood in choice modeling. The mood bias parameters in neither cM2 nor cM3 reached significance (ps > 0.091), which may be due to the absence of a blocked design in our experiment, unlike in Vinckier et al. (2018) and Eldar and Niv (2015).”

(4) In the large online sample, you split all participants into S+ and S-. I would have imagined that instead, you would do analyses that control for other clinical traits. Or, for example, you have in the S- group only participants who also have high depression scores, but low suicide items.

Thank you for this insightful suggestion. Following prior suicide-related literature (Tsypes et al., 2024), we controlled for depression by including them as covariates. Note that depression scores were derived from our established bifactor model (Wang et al., 2025), which decomposed depression from the anxiety. These results remained largely significant (ps ≤ 0.050), except a marginally significant effect of group on gambling behavior (p = 0.059). Despite a trend, this effect with covariates of depression-related questionnaires is strong in our clinical cohort (p = 0.024; Table S8). This suggests that the link between suicidality and risky behavior persists above and beyond general depressive symptoms.

Please see our clarifications below:

Page 26:

“After controlling for depression severity using our established bifactor model (see ref 60 for details), these results remained significant (ps ≤ 0.050), except a marginally significant effect of group on gambling behavior (p = 0.059). Despite a trend, this effect with covariates of depression-related questionnaires is strong in our clinical cohort (p = 0.024; Table S8). This suggests that the link between suicidality and risky behavior persists above and beyond general depressive symptoms.”

Reviewer #3 (Public review):

This manuscript investigates computational mechanisms underlying increased risk-taking behavior in adolescent patients with suicidal thoughts and behaviors. Using a well-established gambling task that incorporates momentary mood ratings and previously established computational modeling approaches, the authors identify particular aspects of choice behavior (which they term approach bias) and mood responsivity (to certain rewards) that differ as a function of suicidality. The authors replicate their findings on both clinical and large-scale non-clinical samples.

(1) The main problem, however, is that the results do not seem to support a specific conclusion with regard to suicidality. The S+ and S- groups differ substantially in the severity of symptoms, as can be seen by all symptom questionnaires and the baseline and mean mood, where S- is closer to HC than it is to S+. The main analyses control for illness duration and medication but not for symptom severity. The supplementary analysis in Figure S11 is insufficient as it mistakes the absence of evidence (i.e., p > 0.05) for evidence of absence. Therefore, the results do not adequately deconfound suicidality from general symptom severity.

Thank you for this important comment. Based on clinical interviews, we included patients with and without suicidality (S⁺ and S^- groups). However, in line with suicidal-related literature (e.g., Tsypes et al., 2024), two groups also differed substantially in the severity of symptoms (see Table 1). To address the request for evidence on specificity to suicidality beyond general symptom severity, we performed separate linear regressions to explain in gambling behaviour, value-insensitive approach parameter (β_gain), and mood sensitivity to certain rewards (β_CR) with group as a predictor (1 for S⁺ group and 0 for S^- group) and scores for anxiety and depression as covariates. Results remained significant after controlling anxiety and depression (ps < 0.027; Table S8). Given high correlations among anxiety and depression questionnaires (rs > 0.753, ps < 0.001), we performed Principal Components Analysis (PCA) on the clinical questionnaire to extract the orthogonal components, where each component explained 86.95%, 7.09%, 3.27%, and 2.68% variance, respectively. We then performed linear regressions using these components as covariates to control for anxiety and depression. Our main results remained significant (ps < 0.027; Table S9). We believe that these analyses provide evidence that the main effects on gambling and on mood were specific to suicide.

As pointed out, these “absence of evidence” cannot provide insights of “evidence of absence”. Although we median-split patients by the scores of general symptoms (e.g., depression and anxiety-related questionnaires) and verified no significant differences in these severities (Figure S11), we additionally conducted Bayesian statistics in gambling behavior, value-insensitive approach parameter, and mood sensitivity to certain rewards. BF₀₁ is a Bayes factor comparing the null model (M₀) to the alternative model (M₁), where M₀ assumes no group difference. BF₀₁ > 1 indicates that evidence favors M₀. As can be seen in Table S7, most results supported null hypothesis, suggesting that general symptoms of anxiety and depression overall did not influence our main results. Overall, we believe that these analyses provide compelling evidence for the specificity of the effect to suicide, above and beyond depression and anxiety.

Please see our revisions below:

Page 17:

“Within patients, this group effect on gambling rate remained significant after controlling for sex, illness duration, family history, diagnosis, and various medications use (ps < 0.05), as well as general symptoms (e.g., depression and anxiety; p = 0.024; also see Figure S11, Table S7 and Table S8). Given high correlations among anxiety and depression questionnaires (rs > 0.753, ps < 0.001), we performed Principal Components Analysis (PCA) to extract main components, where each component explained 86.95%, 7.09%, 3.27%, and 2.68% variance, respectively. To further control for anxiety and depression, linear regression using these components as covariates revealed that the group effect on gambling rate remained significant (p = 0.024; Table S9).”

Pages 18-19:

“Within patients, this group effect on the approach parameter remained significant after controlling for sex, illness duration, family history, diagnosis, and various medications use (ps < 0.05), as well as general symptoms (e.g., depression and anxiety; p = 0.027; also see Figure S11, Table S7 and Table S8). Linear regression using PCA components as covariates revealed that the group effect on approach parameter remained significant (p = 0.027; Table S9).”

Page 21:

“Within patients, this group effect on βCR remained significant after controlling for gambling rate, earnings, mood-related outcome effect, mood drift effect, sex, illness duration, family history, diagnosis, and various medications use (ps < 0.032), as well as general symptoms (e.g., depression and anxiety; p = 0.001; also see Figure S11, Table S7 and Table S8). Linear regression using PCA components as covariates revealed that the group effect on this mood parameter remained significant (p = 0.001; Table S9).”

(2) The second main issue is that the relationship between an increased approach bias and decreased mood response to CR is conceptually unclear. In this respect, it would be natural to test whether mood responses influence subsequent gambling choices. This could be done either within the model by having mood moderate the approach bias or outside the model using model-agnostic analyses.

Thank you for this important suggestion. As suggested, one interesting question was whether mood responses influence subsequent gambling choices and how to model them. First, we median-split mood responses (except the final rating) to compare gambling rate. Results showed a trend for less gambling rate in higher mood (t = -1.971, p = 0.050). However, there was no significant group difference (F = 0.680, p = 0.507). Second, with the assumption that mood biases choice, we constructed mcM1 based on cM3 (the winning choice model). Based on our finding of the negative correlation between mood sensitivity to certain rewards and gambling rate in S⁺, we separated β_Mood parameter into β_Mood-CR and β_Mood-GR (cmM2). Model comparison using BIC supported cM3 (Table S6), that is, without consideration of mood in choice modeling. This can be due to the lack of block design in our experimental design unlike e.g., Vinckier et al., (2018) and Eldar & Niv, (2015). Please see Supplementary Pages 3-4:

(3) Additionally, there is a conceptual inconsistency between the choice and mood findings that partly results from the analytic strategy. The approach bias is implemented in choice as a categorical value-independent effect, whereas the mood responses always scale linearly with the magnitude of outcomes. One way to make the models more conceptually related would be to include a categorical value-independent mood response to choosing to gamble/not to gamble.

We apologise for the unclear statement. The approach bias is implemented in choice as a continuous value-independent effect, ranging from -1 to 1.

It was true that the mood responses always scale with the magnitude of outcomes, since mood ratings were request after the outcomes. Therefore, mood parameters and the approach bias were both continuous.

We also attempted to integrate mood into choice modelling. See Response 2 for Reviewer 3 for details.

(4) The manuscript requires editing to improve clarity and precision. The use of terms such as "mood" and "approach motivation" is often inaccurate or not sufficiently specific. There are also many grammatical errors throughout the text.

Thank you for this important suggestion. We have now explained motivation and mood in the Introduction section and the computational modeling section. Please see our clarifications below:

Pages 3-4:

“A growing literature indeed shows that risky behavior can be far better explained after adding value-insensitive approach and avoidance components to prospect theory(18,19), that is by including a decision bias in favor of the highest gain (approach) and another decision bias against the lowest loss (avoidance), above and beyond options value difference. This class of models highlights the important role of value-insensitive motivational components in decision making in addition to risk attitude-driven valuation (e.g., loss/risk aversion)(20).”

Page 5:

“Although mood is thought to persist for hours, days, or even weeks(30-33), momentary mood, measured over the timescale in the laboratory setting, represents the accumulation of the impact of multiple events at the scale of minutes(30,32,34-38). Momentary mood external validity is demonstrated e.g., through its association with depression symptoms(37). Mood is different from emotions, which reflect immediate affective reactivity and is more transient (e.g., from surprise to fear)(31-33,39).”

We have corrected grammatical errors throughout the manuscript.

5) Claims of clinical relevance should be toned down, given that the findings are based on noisy parameter estimates whose clinical utility for the treatment of an individual patient is doubtful at best.

Thank you for this comment. We agree that we did not evaluate the noise in our estimate e.g., by assessing the test-retest reliability on the task parameters, which is outside the scope of the study, and it is indeed possible that parameter estimate is somehow noisy. Therefore, we tone down the clinical relevance of our results. Please see our revision below:

Page 32:

“Next, we did not evaluate the noise in our estimate e.g., by assessing the test-retest reliability on the task parameters and it is indeed possible that parameter estimate is somehow noisy.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Title: I believe "aberrant mood dynamics" is both too general and overstating the results of this study, which did not measure mood dynamics longitudinally. "Aberrant" is also overly pathologizing. I would suggest sticking more directly to the results, for instance, "Insensitivity of momentary mood to non-risky rewards in adolescent suicidal patients".

Thank you for this suggestion. We have now corrected it.

(2) Abstract: in line 61, "Our study uncovers the cognitive and affective mechanisms" suggests that these are the only ones, and you uncovered them. Of course, there could be more mechanisms contributing to risk behavior in STB, so I would suggest removing the word "the" or adding "one of the".

Thank you for this suggestion. We have now corrected it.

(3) One major weakness of this study is that suicidal thoughts and behaviors were not assessed via a clinical instrument such as the Columbia Suicide Severity Rating Scale - this should be mentioned upfront.

Thank you for this comment. According to medical records and information from family and friends by the researcher and psychiatrists, patients with suicidal thoughts and behaviors were categorized as suicidal group (S⁺), while patients without suicidal thoughts and behaviors were identified as control group (S^-). Note that medical records and information were recorded from clinical interviews where the psychiatrists were vigilant for signs of suicidal ideation and inquired about suicidal-related thoughts and behaviors from both the patients and their families. Therefore, the current group operation was possibly comparable to Columbia Suicide Severity Rating Scale.

(4) Table 1: female/male are sex, not gender (gender is man/woman/transgender/non-binary).

Thank you for this suggestion. We have now corrected it.

(5) Equation 1: It would be good to clarify what happens in gain-only or loss-only trials (the other value is then 0, but this can be clarified as it is not technically a loss or a gain).

Thank you for this suggestion. We have now corrected it. Please see below for our revision:

Page 12:

“Please note that V_gain is 0 in gain trials and V_loss is 0 in loss trials.”

(6) Figure 1E: The model prediction is not informative here. Given the linear regression model, there is no other option except that the mean prediction would overlap with the mean empirical measurement (unless the model was specified incorrectly). The same is true in Figure 2A.

Thank you for this suggestion. We have now removed plots for model prediction.

(7) Figure 1G: There was no analysis of the differences between groups in terms of earnings, given that the ANOVA was not significant. Still, if the claim is that risky behavior is sometimes suboptimal in this task, it would be good to show that there is a correlation between, say, symptoms of STB across groups and 1) risky behavior and 2) earnings.

Thank you for this insightful comment. In the patient cohort, risky behavior (gambling rate)—but not earnings—predicted the current suicidal ideation score (BSI-C, β = 9.189, t = 2.004, p = 0.048; earnings, β = 0.001, t = 0.582, p = 0.562). The lack of association for earnings is consistent with the task design, in which there is no stable optimal policy and payouts are only a coarse proxy for decision quality. Future work in learning paradigms, where optimality is well defined, may be better suited to test earnings-based links to STB. We have clarified this point below:

Page 32:

“Second, although we assumed that increased risky behavior in STB was suboptimal, the current task was not suited to test this, given the task design of random feedback for gambling option. Future work in learning paradigms, where optimality is well defined, may be better suited to test earnings-based links to STB.”

(8) Line 290: "beta_gain: -1-1" is unclear. I believe you meant beta_gain \in [-1,1].

Thank you for this suggestion. We have now corrected it to make it clear.

(9) The gain and loss biases are modeled as minimum and maximum probabilities for choosing the gamble. This is a legitimate choice for value-agnostic biases, but it is not the traditional choice (as far as I know). I wonder if the same results would hold with the more traditional formulation of the bias as an added constant to the utility of the gamble, i.e., p(gamble) = 1/(1+ exp(-mu(U_gamble + beta_gain - U_certain)). I believe in this case, you would also not have to specify different equations for positive or negative biases, or to limit the bias to the range of [-1,1] (indeed, the bias would be in reward-equivalent units).

Thank you for this suggestion. The winning choice model we used here was consistent with previous literature (Rutledge et al., 2015 & 2016), which decomposed the decision process into risk-attitude-driven valuation (e.g., loss and risk aversion) and value-insensitive motivational components. These approach/avoidance parameters are a decision bias in favor of the highest gain (approach) and another decision bias against the lowest loss (avoidance), above and beyond options value difference.

As suggested, we also compared the traditional bias choice model. Model comparison did not support this. Please see our revision below:

Supplementary Page 4:

“We also considered the traditional bias parameter (cM4), rather than approach/avoidance parameters. We limited the bias to the range of [-100, 100], which was in reward-equivalent units.

However, model comparison did not support cM4 (Table S6).”

(10) Also, for equations 5-8, it seems that 5-6 are identical to 7-8 except for the use of beta_gain versus beta_loss. You might want to consider simplifying by putting beta in the equations and specifying in the text that, depending on the trial type (loss or gain), the relevant beta is used.

Thank you for this suggestion. We have now simplified it. Please see response to Reviewer 2, point 3.

(11) It is not clear what equations are applied to mixed trials in cM3.

Sorry for the confusion. We have now clarified this point.

Page 12:

“Approach/avoidance parameters are not applied to in mixed trials.”

(12) Model comparison: the mood models are nested within each other (e.g., mM3 can be derived from mM1 by setting beta_EV = beta_RPE). In this case, model comparison can use the likelihood ratio test instead of BIC, which can be too conservative (and therefore does not support the extra beta parameter for RPE, different from previous results in the literature). I wonder if a likelihood ratio test would lead to results more in line with previous findings with this task?

Thanks for this suggestion. We agree that mM1 (CR+EV+RPE) and mM3 (CR+GR) are nested. However, our model space also included unnested models, such as mM5 (CR+GR_better+GR_worse). Therefore, it was not reasonable in our model space to use likelihood ratio tests.

(13) Line 346: The replication sample is described as "healthy participants," however, their health (or mental health) status was not assessed, and they may as well have mental health concerns. I would suggest calling this a general sample or an undifferentiated sample - but not a healthy sample.

Sorry for the confusion. We have now corrected this phrase.

(14) Line 363: "in addition to the replication of previous findings in the validation dataset" is unclear. Are those tests not two-tailed?

Sorry for the unclear statement. In the replication analyses, we used one-tailed t-tests because the direction of the effect was revealed on the clinical dataset. Please see our clarification below:

Page 15:

“For the replication of previous findings in the validation dataset, we used one-tailed tests in line with our clinically motivated directional hypothesis.”

(15) Line 372: "validating our group manipulation" - the presented work does not have a manipulation. Maybe you meant "validating our grouping of participants"?

Thank you for this suggestion. We have now corrected it to make it clear.

(16) Figure 2B: It is not clear how the data were binned for illustration purposes only, and why this binning is necessary (I have not seen it in other papers) - presenting the data from each subject and the correlation line with error margins (as is done here) should be sufficient.

Thank you for flagging this. For illustration only, we binned the data proportional to group sizes: in the patient sample (S^- n = 25; S⁺ n = 58; ≈1:2), we displayed 3 bins for S^- and 6 bins for S⁺. We agree that binning is not necessary; all statistics were computed on raw, unbinned data. The binned panel was included solely for visualization, consistent with our prior work (Blain et al., 2023).

(17) Table 2: delta BIC should be presented per subject (that is, divided by the number of subjects in each group), as the groups are of different sizes, so as presented now, the columns are not comparable across groups.

Thank you for the helpful suggestion. Our goal in Table 2 is not to compare ΔBIC magnitudes across groups, but to identify the winning model within each group. The ΔBICs are aggregated at the group level solely to rank models for that group. Dividing by the number of participants would rescale each group’s column by a constant and would therefore not affect the within-group ranking or the conclusion that cM3 is the best model in all groups. For this reason, we retain the current presentation and interpret each column within group rather than across groups.

(18) Line 640 - the effect of expectations and prediction errors on mood was not only shown in healthy people, but also in people with depression (Rutledge et al., 2007, https://pubmed.ncbi.nlm.nih.gov/28678984/)

Thank you for this comment. Indeed, Rutledge et al., (2017) showed evidence for CR+EV+RPE mood model in adult people with depression. However, our study recruited adolescents with depression or anxiety, given that adolescent period might provide a developmental window for opportunities for early intervention of suicidality. Therefore, it is also possible that the current winning model was specific to adolescents. Please see our clarifications below:

Page 28:

“It is also possible that the current winning model was specific to adolescents. Given that Rutledge et al., (2017) supported the “CR-EV-RPE model” in adults with depression, our study with adolescent populations may suggest a developmental change for mood sensitivities.”

(19) Supplemental material: Is the R2 section about R-squared? Perhaps you can use superscript on the 2 to make that clearer? For Figure S2, how was model recovery determined? Should I interpret the confusion matrix as suggesting that the winning model for each and every simulated subject was the generating model, or was the winning model determined for the whole simulated population in each of the 100 simulations? Traditionally, confusion matrices use the former measure, but the results of 100% recoverability make me suspect the latter was used here. In Figure S3, should we not be looking at simulated parameters and recovered parameters? What are "real parameters" here?

Thank you for these important comments. We now consistently denote the coefficient of determination as R² (with a superscript 2) throughout the manuscript and Supplementary Materials.

For the model recovery analysis in Figure S2, we have clarified that the confusion matrix is computed at the population level. Specifically, for each of the 100 simulations we generated a full dataset under each candidate model, fit all models to that dataset, and selected the winning model based on group-level model evidence (BIC). Each cell in the confusion matrix therefore reflects the proportion of simulations in which model j was selected as the best-fitting model when the data were generated by model i. This operation was reasonable because the decision of the winning model is made on the population-level dataset rather than on individual subjects.

In Figure S3, the term “real parameters” referred to the parameters used to generate the simulated data. To avoid confusion, we now relabel these as “simulated (generating) parameters” and explicitly describe the figure as showing the relationship between simulated (generating) parameters and recovered parameters. Please see our revisions below:

Supplementary Pages 2-3:

“Model recovery: We generated 100 simulated datasets for each model (3 choice models and 8 mood models) using the fitted parameters of each model as the ground truth. Each dataset contained 201 trials and included 3 (or 8) sets of simulated data corresponding to the respective models. For each simulated dataset, we then fit all models and determined the winning model at the population level based on group-level BIC, yielding a confusion matrix in which each entry represents the proportion of simulations in which model j was selected as the best-fitting model when the data were generated by model i. As shown in Figure S2, all models are highly identifiable, indicating excellent recovery performance for both the choice and mood models.”

“Parameter recovery: Figure S3 shows good parameter recovery for both choice and mood winning model (choice: rs > 0.91, ps < 0.001; intraclass coefficients > 0.78; mood: rs > 0.90, ps < 0.001; intraclass coefficients > 0.86). Moreover, we computed cross-correlations between all generating (“generating”) and recovered (“fitted”) parameters. The resulting matrix showed high diagonal (choice winning model: rs > 0.91; mood winning model: rs > 0.90) and low off-diagonal (choice winning model: abs(rs) < 0.63; mood winning model: abs(rs) > 0.40) correlations, further supporting parameter recovery.”

Typos:

(1) Line 90: original → originate

(2) Line 596-598 - the same phrase is repeated twice.

(3) Line 616: on the other word → hand.

Sorry for the mistakes. We have now corrected them throughout the manuscript.

Reviewer #2 (Recommendations for the authors):

For people unfamiliar with interpersonal theory or motivational-volitional model, or three-step theory (lines 105-106), could you briefly explain the key idea of mood and suicide before going to the decision-making tasks? And from this, maybe motivate the predictions in your task? In particular, in the abstract and introduction, the phrasing could be a bit more concise and simpler. In the abstract, sentences were sometimes quite long. In the introduction, some paragraphs are somewhat repetitive. In the discussion, there were some typos.

Thank you for these suggestions. We have now explained the key idea of mood and suicide before going to the decision-making tasks in the introduction, which can be seen below:

Pages 4-5:

“Contemporary theories of suicide converge on the idea that STB is initially caused by low mood experience. The interpersonal theory of suicide proposes that suicidal desire arises when people simultaneously feel socially disconnected (“thwarted belongingness”) and like a burden on others (“perceived burdensomeness”), experiences that are tightly linked to chronically low mood(25). The motivational–volitional model(26) and the three-step theory(27,28) similarly emphasize that when negative mood and feelings of defeat or entrapment are experienced as inescapable, they can give rise to suicidal ideation, and that the progression from ideation to suicide attempts depends on additional factors such as reduced fear of death, increased pain tolerance, and a tendency to act impulsively under intense affect. Some official organizations, e.g., National Institute of Mental Health, have also listed mood problems as warning signals(8). Interestingly, within the framework of decision making under uncertainty, gambling on lotteries with a revealed outcome has been found to induce high mood variance(29), providing an opportunity to assess the relationship between deficient mood and increased gambling decisions in STB.”

We have also refined the wording and corrected typos throughout the manuscript.

Reviewer #3 (Recommendations for the authors):

(1) Since many readers might only read the abstract, it is important that it is both informative and accurate. I have two suggestions in this respect. First, for the abstract to be more informative, it may be helpful to indicate already there that these are value-insensitive approach-avoidance parameters, in the sense that they favor/disfavor the gamble regardless of the potential outcomes' magnitude or probability. This issue is also present throughout the text, where the phrases "approach and avoidance motivation" are referred to as if they have established and precise computational definitions. In my view, these terms could just as easily be interpreted as parameters that multiply the value of potential gains or losses, which is not what the authors mean. It would be helpful to clarify this terminology.

Thank you for these suggestions. In line with previous literature (Rutledge et al., 2015 & 2016), approach and avoidance motivation are indeed defined at the computational level, referring to a decision bias in favor of the highest gain (approach) and another decision bias against the lowest loss (avoidance), above and beyond options value difference. We have cited these papers in the manuscript. We also make it clear to further clarify approach and avoidance parameters in the abstract and introduction. Please see our revisions below:

Page 2 (Abstract):

“Using a prospect theory model enhanced with value-insensitive approach-avoidance parameters revealed that this rise in risky behavior resulted only from a heightened approach parameter in S⁺.Altogether, model-based choice data analysis indicated dysfunction in the approach system in S⁺, leading to greater propensity for gambling in the gain domain regardless of the lottery expected value.”

Page 3 (Introduction):

“A growing literature indeed shows that risky behavior can be far better explained after adding value-insensitive approach and avoidance components to prospect theory(18,19), that is by including a decision bias in favor of the highest gain (approach) and another decision bias against the lowest loss (avoidance), above and beyond options value difference. This class of models highlights the important role of value-insensitive motivational components in decision making in addition to risk attitude-driven valuation (e.g., loss/risk aversion)(20).”

(2) The statement "our study uncovers the cognitive and affective mechanisms contributing to increased risk behavior in STB" is overstating the findings, as the study may have uncovered some contributing mechanisms, but likely not all of them. Removing the word "the" would fix this issue.

Thank you for this suggestion. We have now corrected it.

(3) Since mood is typically defined as lasting hours, it's inappropriate to refer to ratings that only reflect the last few trials as self-reports of mood. To be sure, I view the distinction between emotions and moods as quantitative, not qualitative, so I do not think there is a problem studying the former to understand the latter, but to avoid confusion, the terminology should follow common usage.

Thank you for this suggestion. We follow previous work and operational definitions regarding mood (Rutledge et al., 2014, Eldar & Niv, 2015, Vinckier et al., 2018). Emotion is usually a very brief response to a specific stimulus (Emanuel & Eldar, 2023), e.g., leading to rapid changes like surprise then fear. In contrast, mood is defined as a diffuse state that is not specific to one stimulus. Here, we operationally and computationally define mood as an affective state reflecting the recent history of safe and gamble outcomes. We now clarify that point in the main text. Please see our revision below:

Page 5:

“Although mood is thought to persist for hours, days, or even weeks(30-33), momentary mood, measured over the timescale in the laboratory setting, represents the accumulation of the impact of multiple events at the scale of minutes(30,32,34-38). Momentary mood external validity is demonstrated e.g., through its association with depression symptoms(37). Mood is different from emotions, which reflect immediate affective reactivity and is more transient (e.g. from surprise to fear)(31-33,39).”

(4) Line 78: The phrases "increase in risk attitude", "decrease in loss attitude", and "decrease in value-independent choice biases" are unclear to me in terms of their directionality. An attitude might be avoidant or embracing. If it is the former then increasing it would decrease risk-taking.

Thank you for pointing out the ambiguity. We have now corrected them throughout the manuscript. Please see our revision below:

Page 4:

“We therefore hypothesized that heightened approach motivation, or weakened avoidance motivation, would account for increased risk behavior in STB.”

(5) Line 125: I was not sure why one would expect the mood response to gamble-related quantities (EV and RPE) to be lower in STB and not higher.

Sorry for the typo. We hypothesized that mood would respond more strongly to gambling-related quantities—expected value (EV) and reward prediction error (RPE)—in adolescents with STB than in controls, given prior evidence that STB is associated with greater risk-taking.

(6) The text could use proofreading, as there are many typos. These are from the first 100 lines alone:

a) Abstract: regardless the lotteries -> regardless of the lotteries'.

b) Line 78: it remains whether.

c) Line 80: can each -> each can.

d) Line 90: may original from.

Sorry for the mistakes. We have now corrected them throughout the manuscript.

(7) The rationale for focusing on the S+ group for mood model comparison is incorrect. The purpose is to identify parameters that vary as a function of suicidality, and for that, the S- group is just as important.

Thank you for this comment. We agree that the S^- group is as important as the S⁺ group. A direct comparison was complicated because the winning mood models differed (S⁺: mM3; S^-: mM5; Table 3). To ensure comparability, we checked results from both model specifications (mM3 and mM5). The conclusions were convergent: mood sensitivity to certain rewards (CR) was lower in S⁺ than in S^- (see Fig. 3 for mM3 and Fig. S8 for mM5).

(8) There appears to be a contradiction between the inclusion criteria, which include having experienced suicidal thoughts and behaviors, and the definition of the S- group as not having suicidality.

Thank you for pointing out this mistake. The corrected version of inclusion criteria can be seen on Page 7:

“Patients were included if they met the following criteria: 1) both the researcher and psychiatrists agreed on their group classification; 2) they had a current diagnosis of major depressive disorder (MDD; unipolar depression), generalized anxiety disorder (GAD), or bipolar disorder with depressive episodes (BD), confirmed by two experienced psychiatrists using the Structured Clinical Interview for DSM-IV-TR-Patient Edition (SCID-P, 2/2001 revision; see Supplementary Note 1 for details); 3) they were between 10 and 19 years of age; 4) they had no organic brain disorders, intellectual disability, or head trauma; 5) they had no history of substance abuse; 6) they had no experience of electroconvulsive therapy.”

(9) It would be helpful to specify whether mood modeling was based on objective or subjective values, and why.

Thank you for this helpful suggestion. We have now clarified whether mood modeling was based on objective or subjective values, and why. Specifically, we constructed two model families: one in which mood was driven by objective monetary outcomes (objective values) and one in which mood was driven by subjective values derived from each participant’s fitted choice model (subjective values). We then used the VBA_groupBMC function in the VBA toolbox to perform family-wise model comparison, with 8 candidate mood models within each family. Consistent with previous literature, the objective-value family provided a clearly superior fit to the data (exceedance probability, EP = 1.000). Based on this result and for parsimony, we report and interpret the mood modeling results from the objective-value family in the main text. We have clarified this point below:

Supplement Pages 4-5:

“Supplementary Note 9: Mood model comparison using subjective values.

To identify whether mood modeling was based on objective or subjective values, we constructed two model families: one in which mood was driven by objective monetary outcomes (objective values) and one in which mood was driven by subjective values derived from each participant’s fitted choice model (subjective values). We then used the VBA_groupBMC function in the VBA toolbox (Daunizeau et al., 2014) to perform family-wise model comparison, with 8 candidate mood models within each family. Consistent with previous literature, the objective-value family provided a clearly superior fit to the data (exceedance probability, EP = 1.000).”

En gras

eLife Assessment

Using single-cell transcriptomic data from mouse inner ear hair cells, the authors compare for the first time gene expression across the four recognized hair cell types in adults, generating information fundamental to understanding hair cell relationships between the ancient vestibular compartment and the more recent cochlea. Among observed differences, compelling evidence is provided for the expression in vestibular hair cells but not cochlear hair cells of certain ciliary motility-related genes, suggesting that the kinocilium of vestibular hair cells may function as an active force generator to increase sensitivity.

Reviewer #1 (Public review):

Summary

From transcriptomic comparisons of adult mouse cochlear and vestibular hair cells, Xu et al. provide a broad and well-organized overview of differences across 4 established hair cell types (2 cochlear and 2 vestibular). They go on to demonstrate the power of such analyses to provide functional insights by focusing on the differentiated expression of ciliary genes, building to the hypothesis that kinociliary motility occurs in adult vestibular hair cells.

Background

Cilia are prominent in sensory receptors, including vertebrate photoreceptors, olfactory neurons and mechanosensitive hair cells of the inner ear and lateral line. Cilia can be motile or nonmotile depending on their axonemal structure: motile cilia require dynein and the inner 2 singlet microtubules of the 9+2 array. Primary cilia, present early in development, are considered to have sensory functions and to be nonmotile (Mill et al., Nature Rev Gen 2023).

In hair cells, the kinocilium anchors and polarizes the mechanosensitive hair bundle of specialized microvilli. The kinocilium matures from the primary cilium of a newborn hair cell; behind it the bundle of mechanosensory microvilli rises in a descending staircase of rows. During maturation of the mammalian cochlea, all hair cells lose the kinocilium, though not the associated basal body. The consensus for many years has been that most vertebrate kinocilia, and especially mammalian kinocilia, are nonmotile, based largely on the lack of spontaneous motility in excised mammalian vestibular organs, but also on the impression that the rare examples of spontaneous beating motility even in non-mammalian hair cells are associated with deterioration of the preparation (Rüsch & Thurm 1990).

Strengths

In comparing RNA expression across the 4 major types of mouse hair cells - 2 cochlear and 2 vestibular - Xu et al. provide rich data sets for exploration of structure-function differences between these highly specialized cell types. The revised paper significantly improves the organization, interpretation and readability of the presentation of overall findings. smFISH and immuno-staining back up key RNA data, and comparisons are made with published data.

The ciliary motility focus of the rest of the paper is creative and highly interesting. The authors curated the ciliary genes into types associated with different aspects of beating motility, and also investigated the expression of genes typical of primary cilia, which are considered to have sensory and cell signaling functions and to be nonmotile. Their data justify suggesting a role for kinociliary motility (or force generation) in adult mammalian vestibular hair cells, in opposition to a long-held assumption. The results should stimulate investigation of the implications for mechanosensitivity.

Weaknesses

Data

Functional data on kinocilia motility: The technical difficulty in making such measurements in small mouse hair bundles led the authors to work with bullfrog crista bundles. Though not extensively studied here, the ciliary motility shown is convincing. Mouse hair bundle motions are also shown but the evidence connecting the data to kinociliary motion are more suggestive than convincing. But the authors are not dogmatic about these data, and it is reasonable to show them.

Interpretation

The authors take the view that kinociliary motility is likely to be normally present but is rare in their observations because conditions are not right. But while others have described some (rare) kinociliary motility in fish organs (Rusch & Thurm 1990), they interpreted its occurrence as a sign of pathology. Indeed, in this paper, it is not clear what role kinociliary motility would play in mature hair bundles. The authors have added a discussion of this question in the revision.

An underlying rationale for the hypothesis that ciliary motility manifests in mammalian vestibular hair cells seems to rest on the presence of the necessary mRNA and its contrasting absence in cochlear hair cells. Another way to look at this difference could be that evolution acted on cochlear hair cells to shed kinocilia as one of many changes to improve mechanosensitivity at much higher sound frequencies. In vestibular hair cells, kinociliary motion might be useful to enhance mechanostimulation in the developing vestibule (as suggested in this revision) and not so active in maturity. Nevertheless, with their scholarly analysis of the expression of ciliary genes, the authors make a significant argument for further investigation of when and why hair cell kinocilia show active motility.

Reviewer #2 (Public review):

Summary:

In this study the authors compared the transcriptomes of the various different types of hair cells contained in the sensory epithelia of the cochlea and vestibular organs of the mouse inner ear. The analysis of their transcriptomic data lead to novel insights into the potential function of the kinocilium.

Strengths:

The novel findings for the kinocilium gene expression along with the demonstration that some kinocilia demonstrate rhythmic beating as would be seen for known motile cilia is fascinating. It is possible that perhaps the kinocilium known to play a very important role in the orientation of the stereocilia, may have a gene expression pattern that is more like a primary cilium early in development and later in mature hair cells more like a motile cilium. Since the kinocilium is retained in vestibular hair cells it makes sense that it is playing a different role in these mature cells than its role in the cochlea.

Another major strength of this study which cannot be overstated is that for the transcriptome analysis they are using mature mice. To date there is a lot of data from many labs for embryonic and neonatal hair cells but very little transcriptomic data on the mature hair cells. They do a nice job in presenting the differences in marker gene expression between the 4 hair cell types. This information is very useful to those labs studying regeneration or generation of hair cells from ES cell cultures. One of the biggest questions these labs confront is what type of hair cell develop in these systems. The more markers available the better. These data will also allow researchers in the field to compare developing hair cells with mature hair cell to see what genes are only required during development and not in later functioning hair cells.

Comments on revision:

I am satisfied with the revision, the authors made an effort to incorporate the changes requested.

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Weaknesses:

(1) Data:

(a) The main weakness in the data is the lack of functional and anatomical data from mouse hair bundles. While the authors compensate in part for this difficulty with bullfrog crista bundles, those data are also fragmentary - one TEM and 2 exemplar videos. Much of the novelty of the EM depends on the different appearance of stretches of a single kinocilium - can we be sure of the absence of the central microtubule singlets at the ends?

Our single-cell RNA-seq findings show that genes related to motile cilia are specifically expressed in vestibular hair cells. This has not been demonstrated before. We have also provided supporting evidence using electrophysiology and imaging from bullfrogs and mice. Although no ultrastructural images of mouse vestibular kinocilia were provided in our study, transmission electron micrograph of mouse vestibular kinocilia has been published (O’Donnell and Zheng, 2022). The mouse vestibular kinocilia have a “9+2” microtubule configuration with nine doublet microtubules surrounding two central singlet microtubules. This finding contrasts with a previous study, which demonstrated that the vestibular kinocilia from guinea pigs lack central singlet microtubules and inner dynein arms, whereas outer dynein arms and radial spokes are present (Kikuchi et al., 1989). The central pair of microtubules is absent at the end of the bullfrog saccular kinocilium (Fig. 7A). We would like to point out that the dual identity of primary and motile cilia is not just based on the TEM images. The kinocilium has long been considered a specialized cilium, and its role as a primary cilium during development has been demonstrated before (Moon et al., 2020; Shi et al., 2022).

In most motile cilia, the central pair complex (CPC) does not originate directly from the basal body; instead, it begins a short distance above the transition zone, a feature that already illustrates variation in CPC assembly across systems (Lechtreck et al., 2013). The CPC can also show variation in its spatial extent: for example, in mammalian sperm axonemes, it can terminate before reaching the distal end of the axoneme (Fawcett and Ito, 1965). In addition, CPC orientation differs across organisms: in metazoans and Trypanosoma, the CPC is fixed relative to the outer doublets, whereas in Chlamydomonas and ciliates it twists within the axoneme (Lechtreck et al., 2013). Such variation has been described in multiple motile cilia and flagella and is therefore not unique to vestibular kinocilia. What appears more unusual in our data is the organization at the distal tip, where a distinct distal head is present, similar to cilia tip morphologies recently described in human islet cells (Polino et al., 2023). Although this feature is intriguing, we interpret it primarily as a structural signature rather than as evidence for a specialized motile adaptation, and we have moderated our interpretation accordingly in the revision.

(b) While it was a good idea to compare ciliary motility expression in published P2 datasets for mouse cochlear and vestibular hair cells for comparison with the authors' adult hair cell data, the presentation is too superficial to assess (Figure 6C-E; text from line 336) - it is hard to see the basis for concluding that motility genes are specifically lower in P2 cochlear hair cells than vestibular hair cells. Visually, it is striking that CHCs have much darker bands for about 10 motility-related genes.

While these genes (e.g., Dynll1, Dynll2, Dynlrb1, Cetn2, and Mdh1) appear more highly expressed in P2 cochlear hair cells, they are not uniquely associated with the axoneme. For example, Dynll1/2 and Dynlrb1 are components of the cytoplasmic dynein-1 complex (Pfister et al., 2006), Cetn2 has multiple basic cellular functions beyond cilia (e.g., centrosome organization, DNA repair), and Mdh1 encodes a cytosolic malate dehydrogenase involved in central metabolic pathways such as the citric acid cycle and malate–aspartate shuttle. This contrasts with axonemal dyneins, which are uniquely required for cilia motility. To avoid ambiguity, we have marked such cytoplasmic or multifunctional genes with red asterisks in both Fig. 5G and Fig. 6D in the revised manuscript.

Our comparison showed that key genes for motile machinery are not detected in cochlear hair cells. For example, Dnah6 and Dnah5 are not expressed in the P2 cochlear hair cells. Dnah6 and Dnah5 encode axonemal dynein and are part of inner and outer dynein arms. Importantly, we did not detect the expression of CCDC39 and CCDC40 in kinocilia of P2 cochlear hair cells. Furthermore, axonemal CCDC39 and CCDC40, the molecular rulers that organize the axonemal structure in the 96-nm repeating interactome were not detected in cochlear hair cells. We have revised the text to emphasize key differences.

(2) Interpretation:

The authors take the view that kinociliary motility is likely to be normally present but is rare in their observations because the conditions are not right. But while others have described some (rare) kinociliary motility in fish organs (Rusch & Thurm 1990), they interpreted its occurrence as a sign of pathology. Indeed, in this paper, it is not clear, or even discussed, how kinociliary motility would help with mechanosensitivity in mature hair bundles. Rather, the presence of an autonomous rhythm would actively interfere with generating temporally faithful representations of the head motions that drive vestibular hair cells.

Spontaneous flagella-like rhythmic beating of kinocilia in vestibular HCs in frogs and eels (Flock et al., 1977; Rüsch and Thurm, 1990) and in zebrafish early otic vesicle (Stooke-Vaughan et al., 2012; Wu et al., 2011) has been reported previously. Based on Rüsch and Thurm (1990), spontaneous kinocilia motility occurred under non-physiological conditions and was interpreted as a sign of cellular deterioration rather than a normal feature. We speculate that deterioration under non-physiological conditions may lead to the disruption of lateral links between the kinocilium and the stereociliary bundle, effectively unloading the kinocilium and allowing it to move more freely. Additionally, fluctuations in intracellular ATP levels may contribute, as ciliary motility is highly ATP-dependent; when ATP is depleted, beating ceases. Similar phenomena have been documented in respiratory epithelia, where ciliary activity can temporarily pause. Nevertheless, the fact that kinocilia can exhibit spontaneous motility under these conditions indicates that they possess the motile machinery necessary for such beating. Irrespective of the condition, cilia without the molecular machinery required for motility will not be able to move.

We agree with the reviewer that, based on the present data, it is difficult to know the functional role of kinocilia and whether the presence of such autonomous rhythm would interfere with temporal fidelity. Spontaneous bundle motion, driven by the active process associated with mechanotransduction, was observed in bullfrog saccular hair cells (Benser et al., 1996; Martin et al., 2003). We have revised the discussion to clarify this important point of the reviewer. Specifically, we will emphasize that our observations of ciliary beating in the ex vivo conditions may not reflect its properties in the mature in vivo context, but rather a byproduct of motile machinery clearly present in the kinocilia. We speculate that this machinery in mature hair cells could operate in a more subtle mode—modulating the rigor state of dynein arms or related axonemal structures to influence kinociliary mechanics and, in turn, bundle stiffness in response to stimuli or signaling cues. Such a mechanism could either enhance sensitivity or introduce filtering properties, thereby contributing to the fine control of mechanosensory function without compromising temporal fidelity. Future studies using loss-of-function approach will be needed to reveal the unexplored role(s) of kinocilia for vestibular hair cells in vertebrates.

We note that spontaneous activity exits throughout nervous system. It allows the nervous system to maintain baseline activity and interpret signals. Retinal cells are spontaneously active even in the dark and spiral ganglion neurons also fire spontaneously. Spontaneous hair bundle motion driven by mechanotransduction-related mechanism has been observed in bullfrog saccular hair cells. So, it is unlikely that spontaneous kinocilia beating would interfere with generating temporally faithful representations.

Could kinociliary beating play other roles, possibly during development - for example, by interacting with forming accessory structures (but see Whitfield 2020) or by activating mechanosensitivity cell-autonomously, before mature stimulation mechanisms are in place? Then a latent capacity to beat in mature vestibular hair cells might be activated by stressful conditions, as speculated regarding persistent Piezo channels that are normally silent in mature cochlear hair cells but may reappear when TMC channel gating is broken (Beurg and Fettiplace 2017). While these are highly speculative thoughts, there is a need in the paper for more nuanced consideration of whether the observed motility is normal and what good it would do.

We thank the reviewer for these excellent suggestions. We agree that kinociliary motility could plausibly serve roles during development, for example by guiding hair bundle formation or by contributing to early mechanosensitivity and spontaneous neural activity before mature stimulation mechanisms are established. It is also possible that the motility machinery represents a latent capacity in mature vestibular hair cells that could be reactivated under stress or pathological conditions. We have revised the Discussion to address these possibilities and to provide a more nuanced consideration of whether the observed motility is normal and what potential functions it might serve.

Reviewer #2 (Public review):

Summary:

In this study, the authors compared the transcriptomes of the various types of hair cells contained in the sensory epithelia of the cochlea and vestibular organs of the mouse inner ear. The analysis of their transcriptomic data led to novel insights into the potential function of the kinocilium.

Strengths:

The novel findings for the kinocilium gene expression, along with the demonstration that some kinocilia demonstrate rhythmic beating as would be seen for known motile cilia, are fascinating. It is possible that perhaps the kinocilium, known to play a very important role in the orientation of the stereocilia, may have a gene expression pattern that is more like a primary cilium early in development and later in mature hair cells, more like a motile cilium. Since the kinocilium is retained in vestibular hair cells, it makes sense that it is playing a different role in these mature cells than its role in the cochlea.

Another major strength of this study, which cannot be overstated, is that for the transcriptome analysis, they are using mature mice. To date, there is a lot of data from many labs for embryonic and neonatal hair cells, but very little transcriptomic data on the mature hair cells. They do a nice job in presenting the differences in marker gene expression between the 4 hair cell types. This information is very useful to those labs studying regeneration or generation of hair cells from ES cell cultures. One of the biggest questions these labs confront is what type of hair cells develop in these systems. The more markers available, the better. These data will also allow researchers in the field to compare developing hair cells with mature hair cells to see what genes are only required during development and not in later functioning hair cells.

We would like to thank reviewer 2 for his/her comments and hope that the datasets provided in this manuscript will be a useful resource for researchers in the auditory and vestibular neuroscience community.

Joint Recommendations for the authors:

(1) Figure 1 - Explain how hair cell types are recognized after dissociation. Figure 1 will not be clear in this regard for non-aficionados. Some of the dissociated cells shown appear quite distorted and even unhealthy - e.g., the bottom right crista type II hair cell; the second from left crista type I hair cell; can you address why this doesn't matter for the purposes of this study?

HC types in Fig. 1C were identified based on their morphological features: Type I HCs are flask-shaped with a narrow neck while type II HCs are cylindrical and short. We have replaced those cells with new images. In our study, HCs were identified based on their marker genes. Although some HCs such as those shown in Fig. 3C were impossible to avoid during preparation of single cells for library (most people did not examine their morphology), quality of mRNA and sequencing was high, better than those datasets published in previous studies.

(2) Line 98 - Explain accessory cells (as opposed to supporting cells).

We changed accessory cells to other cell types.

(3) Line 246 - The primary cilium is...

Changed.

(4) Figure 6D - The scale bar is missing. Please use arrows to point to the genes you call out in the text. Also, the genes called out in the text as differently expressed (line 342) are quite faint bands in both cell types. It would be a service to the reader to point them out in the panel.

A scale bar has been added. We also marked those genes as suggested and edited the text accordingly.

(5) Figure 7 - mixes frog crista and mouse middle ear images with waveforms and FFTs from frog crista, mouse middle ear, and mouse crista. Related to these still images are 2 videos of frog kinocilium beating (2 hair cells). The mouse images must be underwhelming, or we would have been shown those, yet they were considered adequate to analyze.

Yes, the spontaneous kinocilia motion of mouse crista HCs is very small. The peak motion is about 40 nm, which is very close to the resolution of our camera. That is why we used photodiode technique to detect its motion. Photodiode is more sensitive, and this technique allows us to observe dynamic response waveform.

(6) I recommend labeling each figure panel with the tissue of origin to avoid confusion.

Labeled as suggested.

(7) I suggest dropping the mouse middle ear data, as they are not directly adequate as a positive control (or no more so than the more beautiful frog data).

We keep the waveforms of middle ear cilia movement in Fig. 7. The main reason is that we would like to show the magnitude difference between airway cilia and kinocilia. The kinocilia movement was at least an order of magnitude less than the movement of airway cilia. This has led to our effort to generate a model to predict the 96-nm modular repeat and explain why kinocilia movement in mice is much smaller than airway cilia and bullfrog kinocilia.

(8) Focus on the hair bundle motions:

(a) Show the waveforms for the frog crista hair cells and their FFTs.

These images were captured many years ago using camera. The kinocilia motion is between 5 and 10 Hz. We did not present any waveforms of kinocilia motion since we no longer have access to bullfrogs. However, although we did not present response waveforms, the videos are very powerful for visualization of kinocilia beat of bullfrog saccular HCs.

(b) Find some way to show us how you measured the mouse hair bundle beating.

Photodiode technique was used to measure spontaneous kinocilia motion in mice. More details are now included in the text.

(c) Does EGTA break links between kinocilium and stereocilia? (Could that contribute to the higher beat frequency?) Just applying the same treatment and viewing from above could clarify whether kinocilia dissociate from stereocilia rows. This would likely be more straightforward with an otolith organ.

All these links (tip links, side links) are vulnerable to Ca concentration and Ca-free medium is often used to break these links as shown in many previous studies. Breaking the kinocilia links leads to reduced load to the kinocilia, which may result in larger motion of the kinocilia. The frequency is inherent to motile machinery and subject to temperature and intracellular ATP concentration. When facing upward, the hair bundles in otolith organ do not have a good contrast against HCs in the background. This makes measurement of their motion difficult, especially when the motion is small and random and can’t be averaged to improve signal to noise ratio. Besides, unlike cochlear HCs whose hair bundles are short and can easily be oriented in parallel with light path, the long hair bundle of vestibular HCs is more difficult to orient and image. For these reasons, we chose to use crista hair bundles for our measurements since they can be oriented in perpendicular to the light path without interference from background HCs. The lateral motion of the entire bundle is also relatively easy to measure in this preparation.

(6) Is there no reason to cite McInturff et al. (2018), given that they compared type I and II VHC transcriptomes at P12 and P100? This database is also available on gEAR.

Their studies are now cited. We also compared their datasets with ours.

(7) Line 374 - Eatock et al., 1998 citation does not work for this purpose. Eatock & Songer (2011) would be better, or Li, Xue, Peterson (2008): mouse utricle anatomy; significant discussion of relative heights of kinocilia and tallest stereocilia.

Changed and cited.

(8) In Figure 3, 2 of the 18 panels in B are missing labels.

The bar, applied to all panels, was there at the bottom of Fig. 3B. The bar is bigger and more visible in the revision.

(9) Line 187 should "Sppl1" be Spp1?

Corrected.

(10) Define BBSome on line 244.

Added.

(11) Looking at Figure 5, it seems that all the motile genes are expressed in the vestibular hair cells and not the cochlear hair cells. It is surprising that there are any cilia-related genes expressed in these adult cochlear hair cells, given that they do not retain their cilia into adulthood. Could the authors make a comment on this finding in the discussion? Also, are there any ciliopathies that show a vestibular defect but normal hearing in mice or humans? Have you compared the cilia-related gene expression in neonatal/embryonic vestibular hair cells to your dataset?

There are many kinocilia related genes still expressing adult cochlear HCs. It is not surprising to see many kinocilia related genes in cochlear HCs. Most of these genes are related to primary cilia structure including the basal body and transporters in cilia. The basal body is still present in cochlear HCs. Many other primary cilia-related proteins are also expressed in soma, especially those related to signal transduction, microtubule cytoskeleton, actin cytoskeleton, vesicle transport, metabolic enzyme, protein folding, translation, nuclear transport, ubiquitination, RNA binding, mitochondrial proteins and transcription factors. Of course, some of them are vestigial. We added discussion of this in the text. Comparison between neonatal cochlear and vestibular was presented in Fig. 6D. We compared those genes related to the axonemal repeat (96 nm repeat complex). Due to quality of mRNA, the total genes and genes related to kinocilia detected in previous developmental studies were much less than our datasets. While we detected 112 out of 128 genes related to axonemal repeat, only 90 genes were detected in previous studies (Burns et al., 2015; McInturff et al., 2018). Therefore, we only compared neonatal cochlear and vestibular HCs using their datasets. As far as we know, no ciliopathies with vestibular defects but normal hearing have been reported in mice or humans. But we plan to use a Ccdc39 mutant mouse model to examine how loss of function of a key motile cilia signature gene would affect kinocilia motility and vestibular function.

(12) How is "expression level" in the violin plots being calculated? Is this a measure of read count? The normalization is cursorily explained in the methods. Is this value comparable across genes? Did the authors switch to z-score by Figure 6?

We dissected the auditory and vestibular sensory epithelia from the same groups of mice and prepared libraries and sequenced them at the same time. All parameters are the same. The violin Plots are based on values presented in Supplementary Table 1. Each dot in the plot reflects an aggregated number of reads across all cells for each gene. They are all normalized across different HC types and biological repeats. The details for normalization are now provided.

(13) The authors comment on the 16/128 motile cilia axonemal repeat genes that are not expressed in the vestibular hair cells. Listing these somewhere may be helpful to the readers.

We thank the reviewer for this helpful suggestion. Most of the 128 motile cilia axonemal repeat genes were listed in Figs 8C and S5, along with known loss-of-function mutations and ciliopathy associations identified in human diseases or observed in animal models. To improve clarity, we have now included Table S2, which provides the complete list of all 128 motile cilia axonemal repeat genes, including those not expressed in vestibular HCs.

(14) Figure 5D needs some refinement. While the authors used databases, including CiliaCarta, SYSCILIA gold standard, and CilioGenics, to identify the primary cilia-related genes, they have included many genes that are not highly specific to primary cilia function (e.g., HSP90, HSPA8, DNAJA4, GNAS...). Perhaps the authors would be able to do a better job of specifically querying primary cilia function by using genes that are common to these three databases.

We presented comparison and analysis based on three major cilia databases, which are generated from proteomics of cilia from different tissues/organisms. In addition, we have provided more comprehensive list of primary cilia-related genes in Fig. S2. While majority of cilia-related genes/proteins are highly conserved, some genes/proteins are tissue-/organism-specific. Majority of the genes presented in Fig. 5D of our manuscript are shared among all three databases. The cilium is a complex structure, composed of proteins for microtubule cytoskeleton, actin cytoskeleton, vesicle transport, metabolic enzyme, signaling, and protein folding. It also contains proteins for translation, nuclear transport, ubiquitination, RNA binding as well as mitochondrial proteins and transcription factors (https://ciliogenics.com/?page=Home). Proteins such as HSP90 and HSPA8 are important for protein folding. HSPA8 also functions as an ATPase in the disassembly of clathrin-coated vesicles during transport of membrane components through the cell. GNAS is part of a G protein complex that transmits signals. DNAJA4 is one of the high-confidence cilia proteins (mean score of 1.26, expression rank is 938). These proteins are detected in cilia according to CilioGenics (https://ciliogenics.com/?page=Home). These proteins are not highly specific to cilia and are expressed in soma as well. Most of these proteins for signaling such as WNT (Supplementary Fig. 2) are detected in both cilia and soma.

(15) The authors state, "Furthermore, we observed robust spontaneous kinocilia motility in bullfrog crista HCs and small spontaneous bundle motion in mouse crista HCs." This statement should be moderated by acknowledging that this motility was observed in only some cells. The authors favor the hypothesis that the lack of motility in some crista HCs is due to depolarization or damage to the sample. The authors should also acknowledge the possibility that there may be cell-to-cell variability in the motility of the kinocilia.

We address these issues in public review section. We modified the statement as suggested.

(16) The first few pages of the Results section include many lists of genes. Readability may be improved if this is curtailed modestly.

Changed as suggested. We removed comparison among different types of HCs and replotted Fig. 2B. This has reduced the number of genes mentioned in the text.

Why does AI has the best supporting chorus?

Need Civic Computing, then we may have a chance to go towards Civic AI.

We need Civic Web Infrastructure that is People Centered, Personal First, Local first

Burn InterPersonal MultiPlayer

and born Interplayable

Permanent, Evergreem future Compatible in fact Future Perfect through co-evolution with full provenance and re0capitulable, measurable history of all contributions, making all efforts attributed, re-sumable, re-factorable, exaptable and built upon with strong guarantees that are baked into the very concept of the seed system auto- - nomous - caling - poietic

Commons based, Peer produced born interplayeable scaling Infrastructures buitl from trust for trust

easy to emulate/enhance compelling to do

No exit strategy, not even as "exit to community" But contiually entering, interplaying with, without and across autonomous communities

and yes local-first

il manque ça : " ## Bibliographie " avant la bibliographie

eLife Assessment

This important work delineates layered glucose-responsive neuropeptidergic mechanisms that regulate sugar intake. Using a combination of genetic, physiological, and behavioral experiments, the authors convincingly show that Hugin- and Allatostatin A-releasing neurons suppress sugar feeding by reducing the sensitivity of Gr5a-expressing gustatory neurons. They further demonstrate that Neuromedin U neurons share key physiological properties with fly Hugin neurons, highlighting conserved peptide functions across animal phyla.

Reviewer #1 (Public review):

This revised manuscript by Qin and colleagues delineates an important neural mechanism that suppresses the intake of sugar solution in response to internal glucose level (the "brake" mechanism for sugar consumption). They identified a three-step neuropeptidergic system that downregulates the sensitivity of sweet-sensing gustatory sensory neurons, primarily in response to elevated level of circulating glucose. First, neurons that release a neuropeptide Hugin (which is an insect homolog of vertebrate Neuromedin U (NMU)) are activated by a high concentration of hemolymph glucose, which is directly sensed by Hugin-releasing neurons in a cell-autonomous mechanism. Next, Hugin neuropeptides activate Allatostatin A (AstA)-releasing neurons via one of Hugin receptors, PK2-R1. Finally, the released AstA neuropeptide suppresses sugar response in sweet-sensing Gr5a-expressing gustatory sensory neurons through the AstA-R1 receptor. Suppression of sugar response in Gr5a-expressing neurons reduces fly's sugar intake motivation. They also found that NMU-expressing neurons in the ventromedial hypothalamus (VMH) of mice (which project to the rostal nucleus of the solitary tract (rNST)) are also activated by high concentration of circulating glucose, independent of synaptic transmission, and that injection of NMU reduces the glucose-induced activity in the downstream of NMU-expressing neurons in rNST. These data suggest that the function of Hugin neuropeptides in the fly is analogous to the function of NMU in the mouse.

The authors have provided multiple lines of compelling evidence generated through rigorous and comprehensive experiments, which spans genetic abrogation, neuronal manipulation, pharmacology, and functional imaging. The authors are also receptive to the critiques and reframed the central message, such that their conclusions are soundly supported by the presented data. Importantly, the parallel study in mice adds a unique comparative perspective that makes the paper of interest to a wide range of readers.

Reviewer #2 (Public review):

Summary:

The question of how caloric and taste information interact and consolidate remains both active and highly relevant to human health and cognition. The authors of this work sought to understand how nutrient sensing of glucose modulates sweet sensation. They found that glucose intake activates hugin signaling to AstA neurons to suppress feeding, which contributes to our mechanistic understanding of nutrient sensation. They did this by leveraging the genetic tools of Drosophila to carry out nuanced experimental manipulations, and confirmed the conservation of their main mechanism in a mammalian model. This work builds on previous studies examining sugar taste and caloric sensing, enhancing the resolution of our understanding.

Strengths:

Fully discovering neural circuits that connect body state with perception remains central to understanding homeostasis and behavior. This study expands our understanding of sugar sensing, providing mechanistic evidence for a hugin/AstA circuit that is responsive to sugar intake and suppresses feeding. In addition to effectively leveraging the genetic tools of Drosophila, this study further extends their findings into a mammalian model with the discovery that NMU neural signaling is also responsive to sugar intake.

Weaknesses:

The effect of Glut1 knockdown on PER in hugin neurons is modest in both fed and starved flies, suggesting that glucose intake through Glut1 may only be part of the mechanism. The authors address this in their discussion.

Author Response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

In this revised manuscript, Qin and colleagues aim to delineate a neural mechanism that is engaged specifically in the sated flies to suppress the intake of sugar solution (the "brake" mechanism for sugar consumption). They identified a three-step neuropeptidergic system that downregulates the sensitivity of sweet-sensing gustatory sensory neurons in sated flies. First, neurons that release a neuropeptide Hugin (which is an insect homolog of vertebrate Neuromedin U (NMU)) are in active state when the concentration of glucose is high. This activation depends on the cell-autonomous function of Hugin-releasing neurons that sense hemolymph glucose levels directly. Next, the Hugin neuropeptides activate Allatostatin A (AstA)-releasing neurons via one of Hugin receptors, PK2-R1. Finally, the released AstA neuropeptide suppresses sugar response in sugar-sensing Gr5a-expressing gustatory sensory neurons through AstA-R1 receptor. Suppression of sugar response in Gr5a-expressing neurons reduces fly's sugar intake motivation. They also found that NMU-expressing neurons in the ventromedial hypothalamus (VMH) of mice (which project to the rostal nucleus of the solitary tract (rNST)) are also activated by high concentrations of glucose independent of synaptic transmission, and that injection of NMU reduces the glucose-induced activity in the downstream of NMU-expressing neurons in rNST. These data suggest that the function of Hugin neuropeptide in the fly is analogous to the function of NMU in the mouse.

The shift of the narrative, which focuses specifically on the hugin-AstA axis as the "brake" on the satiety signal and feeding behavior, clarified the central message of the presented work. The authors have provided multiple lines of compelling evidence generated through rigorous experiments. The parallel study in mice adds a unique comparative perspective that makes the paper interesting to a wide range of readers.

While I deeply appreciate the authors' efforts to substantially restructure the manuscript, I have a few suggestions for further improvements. First, there remains room for discussion whether the "brake" function of the hugin-AstA axis is truly satiety state-dependent. The fact that neural activation (Fig. Supp. 8), peptide injection (Fig. 3A, 4A), receptor knockdown (Fig. 3C,G, 4E), and receptor mutants (Fig. Supp. 10, 12) all robustly modulate PER irrespective of the feeding status suggests that the hugin-AstA axis influences feeding behaviors both in sated and hungry flies. Additionally, their new data (Fig. Supp. 13B, C) now shows that synaptic transmission from hugin-releasing neurons is necessary for completely suppressing feeding even in sated flies. If the hugin-AstA axis engages specifically in sated (high glucose) state, disruption of this neuromodulatory system is expected to have relatively little effect in starved flies (in which the "brake" is already disengaged).

We thank the reviewer for pointing out this inconsistency. We have corrected this interpretation. Specifically:

(1) We removed statements suggesting that the circuit is fully disengaged during starvation.

(2) We now state that endogenous hugin activity is reduced during starvation, but the circuit retains modulatory capacity when experimentally perturbed.

(3) The Discussion now emphasizes that the system operates as a state-modulated inhibitory tone rather than a strictly fed-state switch.

We believe this revised framing resolves the discrepancy.

In this context, it is intriguing that the knockdown of PK2-R2 hugin receptor modestly but consistently decreases proboscis extension reflex specifically in starved flies (Fig. 3D, H). The manuscript does not discuss this interesting phenotype at all. Given the heterogeneity of hugin-releasing neurons (Fig. Supp. 7), there remains a possibility that a subset of hugin-releasing neurons and/or downstream neurons can provide a complementary (or even opposing) effect on the feeding behavior.

We agree that this is an important observation. Although the effect size is modest, it is reproducible and suggests that hugin signaling may not operate as a strictly linear pathway.

To address this:

(1) We added a paragraph in the Results acknowledging the PK2-R2-dependent phenotype.

(2) We included a discussion noting the potential functional heterogeneity of hugin neurons.

(3) The schematic model (now Figure Supplementary 17, previously Figure Supplementary 16) includes a dashed line indicating a possible parallel PK2-R2-dependent branch.

Given these intriguing yet unresolved issues, it is important to acknowledge that whether this system is "selectively engaged in fed states to dampen sweet sensation (in Discussion)" requires further functional investigations. Consistent effects of manipulation of the hugin-AstA system across multiple experimental approaches underscores the importance of this molecular circuitry axis for controlling feeding behaviors. Moderation of conclusions to accommodate alternative interpretation of data will be beneficial for field to determine the precise mechanism that controls feeding behaviors in future studies.

We fully agree with the reviewer. Our original description of the circuit as a “satiety brake” implied exclusive engagement in fed states, which is not strictly supported by the behavioral data. Although endogenous hugin activity is elevated under fed conditions (as shown by CaMPARI), experimental manipulations demonstrate that the circuit retains functional capacity to modulate feeding behavior across feeding states.

To address this concern, we have:

(1) Removed the term “satiety-specific brake” throughout the manuscript.

(2) Reframed the circuit as a glucose-responsive, state-modulated inhibitory module.

(3) Revised the Discussion to explicitly state that the hugin–AstA pathway biases sweet sensitivity according to circulating glucose levels rather than functioning as an on/off switch.

(4) Substantially revised Supplementary Figure 17 to reflect graded modulation across metabolic states rather than binary state engagement.

These changes better align our conclusions with the experimental observations.

Reviewer #2 (Public review):

Summary:

The question of how caloric and taste information interact and consolidate remains both active and highly relevant to human health and cognition. The authors of this work sought to understand how nutrient sensing of glucose modulates sweet sensation. They found that glucose intake activates hugin signaling to AstA neurons to suppress feeding, which contributes to our mechanistic understanding of nutrient sensation. They did this by leveraging the genetic tools of Drosophila to carry out nuanced experimental manipulations, and confirmed the conservation of their main mechanism in a mammalian model. This work builds on previous studies examining sugar taste and caloric sensing, enhancing the resolution of our understanding.

Strengths:

Fully discovering neural circuits that connect body state with perception remains central to understanding homeostasis and behavior. This study expands our understanding of sugar sensing, providing mechanistic evidence for a hugin/AstA circuit that is responsive to sugar intake and suppresses feeding. In addition to effectively leveraging the genetic tools of Drosophila, this study further extends their findings into a mammalian model with the discovery that NMU neural signaling is also responsive to sugar intake.

Weaknesses:

The effect of Glut1 knockdown on PER in hugin neurons is modest in both fed and starved flies, suggesting that glucose intake through Glut1 may only be part of the mechanism.

We agree that the modest PER phenotype suggests that Glut1-mediated glucose uptake represents one component of glucose sensing in hugin neurons. We have clarified this in the Discussion and now explicitly state that additional glucose-sensing mechanisms may contribute to hugin activation.

Additionally, many of the manipulations testing the "brake" circuitry throughout the study show similar effects in both fed and starved flies. This suggests that the focus of the discussion and Supplemental Figure 16 on a satiety-specific "brake" mechanism may not be fully supported by the data.

We fully agree that the previous framing overstated state specificity.

As described above, we have:

(1) Removed “satiety-specific brake” terminology.

(2) Reframed the circuit as a glucose-responsive inhibitory module.

(3) Revised the Discussion to explicitly acknowledge modulation across feeding states.

(4) Updated the schematic model (Figure Supplementary 17, formerly Figure Supplementary 16) accordingly.

Recommendations for the authors:

Reviewing Editor (Recommendations for the authors):

Both the reviewers and I agree that the conclusion about a "satiety-dependent" brake needs to be modified to discuss the phenotypes that are also observed under starved conditions. Reviewer 1 would further like to emphasize that the authors are not required to follow through with the specific recommendations suggested by them. Modifying the conclusion and Supplementary Figure 16 should suffice.

We sincerely thank the Reviewing Editor for the clear guidance. We fully agree that our previous framing of the hugin–AstA circuit as a strictly “satiety-dependent” brake may have overstated the state specificity of the system.

In response to this recommendation, we have:

(1) Revised the Abstract, Results, and Discussion to moderate the conclusion and explicitly acknowledge the phenotypes observed under starved conditions.

(2) Reframed the circuit as a glucose-responsive, state-modulated inhibitory module, rather than a satiety-exclusive brake.

(3) Supplementary Figure 17 (formerly Figure Supplementary 16) has been substantially revised to illustrate graded modulation across metabolic states rather than binary engagement.

We appreciate the clarification that no additional experiments were required and are grateful for the opportunity to improve the conceptual framing of our work.

Please include full statistical reporting in the main manuscript (e.g., figure legends or results).

We have revised all figure legends to include full statistical reporting.

Reviewer #1 (Recommendations for the authors):

By re-framing their finding as the "brake" mechanism on satiety-induced suppression of feeding behavior and sensitivity to sweet taste, the authors substantially improved the clarity of their findings and their significance. The additional data (Fig. Supp. 13B, C) allows "apple-to-apple" comparisons of behavioral data. I support the publication of this manuscript with no further experiments, although I have several suggestions for the text.

As I write in the public review, I have a reservation on the authors' argument that hugin-AstA system is the "'satiety brake' - that is selectively engaged in fed states to dampen sweet sensation (lines 392-394)". Manipulation of both hugin system (Fig. 2C, Fig. 3A, C, D, G, Fig. Supp. 8A, C, Fig. Supp. 10A-C, Fig. Supp. 13B, C) and AstA system (Fig. 4A, E, Fig. Supp., 8C, D, Fig. Supp. 12A-C, Fig. Supp. 13D) all indicate that hugin-AstA system suppresses feeding regardless of the satiety state. Specifically, Fig. Supp. 13B shows that synaptic blockade does further increases PER, causing contradictions to authors' statements ("silencing hugin+ neurons led to enhanced sweet-driven feeding behavior (line 299-300)" and "...further silencing has little additional effect (line 402)"). The CaMPARI data (Fig. 1J) provides the link between the activity levels of hugin-releasing neurons and satiety state. However, the fact that eliminating hugin-AstA signal can promote further PER in starved flies suggests that this brake is not completely satiety-dependent. I ask authors to at least discuss this perceived discrepancy between their data and conclusions.

Also, the authors' finding that PK2-R2 reduction actually suppresses PER specifically among starved flies (Fig. 3D, H), albeit with relatively small effect size, suggests that hugin-AstA axis is not a singular, linear pathway as authors suggest in Fig. Supp. 16. While delineating the PK2-R2-dependent pathway is beyond the scope of this study, at least a line of discussion would be helpful.

Minor comments:

(1) Fig. Supp. 8 (dTRPA1 activation of hugin and AstA neurons), and Fig. Supp. 13B-D (inhibition of hugin and AstA neurons) should be in the main figure given its relevance to the narrative of this manuscript.

We agree with the reviewer regarding their importance. The key behavioral panels from these figures have now been moved to the main figures to strengthen the narrative flow.

(2) Fig. Supp. 11 (PER and imaging using decapitated heads only), despite its creativity, leaves me wonder how PER of fly heads looks like. It is a highly artificial and invasive experiment. Supplementary movies would be helpful.

We apologize for the lack of clarity in our description. In this experiment, flies were not decapitated. Instead, we surgically severed the connection between the brain and the ventral nerve cord (VNC), while keeping the body and proboscis musculature intact. Thus, the flies remained physically intact, and PER was measured using the same behavioral protocol as in intact animals.

We have revised the figure legend to clarify this point and avoid confusion. Because the behavioral procedure was identical to standard PER assays and the flies retained normal proboscis motor function, we did not include supplementary videos.

(3) Expression patterns of PK2-R1 and AstA-R2 in proboscis are mentioned in text but with no data (lines 229 and 279). I strongly encourage authors to show images.

We have now included the relevant expression images in the revised manuscript.

(4) A citation for the "previous study (line 486)" describing PER method is required.

The appropriate citation has been added.

eLife Assessment

This important study developed a new sensor for TDP-43 activity that is sensitive and robust that should strongly impact the field's ability to monitor whether TDP-43 is functional or not. The evidence, though limited to cell culture, is compelling and is the first demonstration that a GFP on/off system can be used to assess genetic TDP-43 mutants as well as loss of soluble TDP-43.

Reviewer #2 (Public review):

Summary:

The authors goals is to be develop a more accurate system that reports TDP-43 activity as a splicing regulator. Prior to this, most methods employed western blotting or QPCR based assays to determine whether targets of TDP-43 were up or down regulated. The problem with that is the sensitivity. This approach uses an ectopic delivered construct containing splicing elements from CFTR and UNC13A (two known splicing targets) fused to a GFP reporter. Not only does it report TDP-43 function well, but it operates at extremely sensitive TDP-43 levels, requiring only picomolar TDP-43 knockdown for detection. This reporter should supersede the use of current TDP-43 activity assays, its cost-effective, its rapid and reliable.

Strengths:

In general, the experiments are convincing and well designed. The rigor, number of samples and statistics, and gradient of TDP-43 knockdown were all viewed as strengths. In addition, the use of multiple assays to confirm the splicing changes were viewed as complimentary (ie PCR and GFP-fluorescence) adding additional rigor. The final major strength i'll add is the very clever approach to tether TDP-43 to the loss of function cassette such that when TDP-43 is inactive it would autoregulate and induce wild-type TDP-43. This has many implications for the use of other genes, not just TDP-43, but also other protective factors that may need to be re-established upon TDP-43 loss of function.

Weaknesses:

Admittedly, one needs to initially characterize the sensor and the use of cell lines is an obvious advantage, but it begs the question of whether this will work in neurons. Additional future experiments in primary neurons will be needed. The bulk analysis of GFP-positive cells is a bit crude. As mentioned in the manuscript, flow sorting would be an easy and obvious approach to get more accurate homogenous data. This is especially relevant since the GFP signal is quite heterogenous in the image panels, for example Figure 1C, meaning the siRNA is not fully penetrant. Therefore, stating that 1% TDP-43 knockdown achieves the desired sensor regulation might be misleading. Flow sorting would provide a much more accurate quantification of how subtle changes in TDP-43 protein levels track with GFP fluorescence.

Some panels in the manuscript would benefit from additional clarity to make the data easier to visualize. For example, Figure 2D and 2G could be presented in a more clear manner, possibly split into additional graphs since there are too many outputs. Sup Figure 2A image panels would benefit from being labeled, its difficult to tell what antibodies or fluorophores were used. Same with Figure 4B.

Figure 3 is an important addition to this manuscript and in general is convincing showing that TDP-43 loss of function mutants can alter the sensor. However, there is still wild-type endogenous TDP-43 in these cells, and its unclear whether the 5FL mutant is acting as a dominant negative to deplete the total TDP-43 pool, which is what the data would suggest. This could have been clarified. Additional treatment with stressors that inactivate TDP-43 could be tested in future studies.

Overall, the authors definitely achieved their goals by developing a very sensitive readout for TDP-43 function. The results are convincing, rigorous, and support their main conclusions. There are some minor weaknesses listed above, chief of which is the use of flow sorting to improve the data analysis. But regardless, this study will have an immediate impact for those who need a rapid, reliable, and sensitive assessment of TDP-43 activity, and it will be particularly impactful once this reporter can be used in isolated primary cells (ie neurons) and in vivo in animal models. Since TDP-43 loss of function is thought to be a dominant pathological mechanism in ALS/FTD and likely many others disorders, having these type of sensors is a major boost to field and will change our ability to see sub-threshold changes in TDP-43 function that might otherwise not be possible with current approaches.

Comments on revisions:

In the revised version, most of the reviewer's comments have been appropriately addressed with the exception of 1) the use of flow sorting to improve the data analysis and 2) testing this sensor in primary neurons. The latter is the focus of an ongoing separate study. Though flow sorting would significantly strengthen this study and help others in the field to use this sensor, it is still an impactful and innovative study without it.

Reviewer #3 (Public review):

The DNA and RNA binding protein TDP-43 has been pathologically implicated in a number of neurodegenerative diseases including ALS, FTD, and AD. Normally residing in the nucleus, in TDP-43 proteinopathies, TDP-43 mislocalizes to the cytoplasm where it is found in cytoplasmic aggregates. It is thought that both loss of nuclear function and cytoplasmic gain of toxic function are contributors to disease pathogenesis in TDP-43 proteinopathies. Recent studies have demonstrated that depletion of nuclear TDP-43 leads to loss of its nuclear function characterized by changes in gene expression and splicing of target mRNAs. However, to date, most readouts of TDP-43 loss of function events are dependent upon PCR based assays for single mRNA targets. Thus, reliable and robust assays for detection of global changes in TDP-43 splicing events are lacking. In this manuscript, Xie, Merjane, Bergmann and colleagues describe a biosensor that reports on TDP-43 splicing function in real time. Overall, this is a well-described unique resource that would be of high interest and utility to a number of researchers validated in multiple cell types as a sensitive readout of TDP-43 loss of function. Future studies validating the utility of this biosensor in models of TDP-43 loss of function (e.g. disease iPSNs) that do not rely on TDP-43 knockdown will be of further interest.

Author Response:

The following is the authors’ response to the previous reviews

Public Review:

We thank the editor and reviewers for their thoughtful and constructive feedback, which has enabled us to greatly strengthen the manuscript. We apologize for the delay in resubmitting this as we were dealing with a large turnover in the lab due to trainee graduations which has We have carefully revised the text, figures, and supplementary materials in response to these comments. Below, we summarize the key revisions made followed by a point-by-point response to the reviewers’ critiques.

(1) Performed CUTS analyses in human neuronal system: In the revised manuscript, we included new data demonstrating that the CUTS system can be applied to additional cellular models, specifically neuronal cells (Figure 5, Figure S4). To address whether CUTS functions effectively in neuronal contexts, we generated stable CUTS-expressing lines in differentiated BE(2)-C and ReN VM–derived differentiated neurons (Figure 5A-D, Figure S4 A-C). To ensure this was neuronal expression, we developed a new Tet-On3G system construct where the Tet-On3G transactivating protein is driven by the SYN1 promoter to ensure neuron-specific inducible expression for these experiments.

(2) Define the relationship between CUTS and endogenous/physiological cryptic exons inclusion: To evaluate how well the CUTS system reflects physiological cryptic exon regulation, we performed RT-PCR analysis of several cryptic exons previously reported by us and evaluated CUTS activation at the RNA level in parallel (Figure S2E) . CUTS is sensitive to low-mild reductions in TDP-43 levels, whereas the tested endogenous cryptic exons exhibit variable responses to TDP-43 knockdown.

(3) Defining stress-induced TDP-43 loss of function: We included new data demonstrating that the CUTS system can detect TDP-43 loss of function induced by acute sodium arsenite (NaAsO₂) treatment in HEK cells (Figure 3D–I). We have also tested additional stressor as part of a separate ongoing study where this work will be expanded upon (Xie et al., 2025). We selected this paradigm since TDP-43 loss of function in response to acute NaAsO₂ treatment is also supported by work from other labs(Huang et al., 2024).

(4) Implications of using a TDP-43 Loss-of-Function sensor for therapeutic applications: In the revised manuscript, we clarify that CUTS-TDP43 is auto-regulated and we highlight two potential therapeutic applications: i) TDP-43 Knockdown-and-replacement: CUTS-TDP43 provides a strategy for simultaneous depletion of pathological TDP-43 species while enabling autoregulated re-expression of wild-type TDP-43. This design mitigates the risk of supraphysiologic overexpression, a known liability in conventional replacement approaches, by restoring TDP-43 within a self-limiting regulatory network that maintains homeostatic control. ii) Aggregation-independent correction: Because CUTS is autoregulatory, it can be repurposed to regulate alternative downstream effectors, including splicing modifiers or TDP-43 functional interactors, without expressing TDP-43 itself. This approach provides a potential aggregation-independent strategy to compensate for TDP-43 loss-of-function (LOF) by restoring downstream splicing. We are evaluating this work in a follow up study (Xie et al., 2025). In these ongoing studies, we show that CUTS-regulated expression of splicing proteins in response to TDP-43 loss restored subsets of cryptic exon events (24/28 events evaluated). These findings suggest CUTS as a versatile tool for both autoregulated TDP-43 replacement and trans-regulatory therapeutic correction. We expanded on this concept in the discussion section of this revised manuscript. We also note that autoregulatory TDP-43 biosensor strategies have been proposed in related systems, including TDP-Reg, underscoring broader interest in self-regulated TDP-43 systems (Wilkins et al., 2024).

(5) Clarified mechanism of TDP-43 5FL causing strong loss of function: The TDP-43 5FL exhibits reduced RNA binding capacity, and we previously showed that the lack of RNA binding promotes aberrant homotypic phase separation of TDP-43 (Mann et al., 2019). Expression of RNA-deficient TDP-43 variant forms nuclear “anisomes” (Yu et al., 2021), which evidence suggests sequesters endogenous TDP-43 protein into insoluble structures. We expanded on this in our results section in this revised manuscript.

(6) Improved figure clarity and data presentation: To enhance clarity and organization, we maintained the main structure of the manuscript while reorganizing figures and improved data visualization. Some examples include:

Figure 1: We revised the schematic layout for greater clarity and simplicity. The figure now focuses more specifically on the CUTS data, with additional data on the UNC13A-TS and CFTR-TS moved to Figure S1. To improve readability, titles were added to all schematic panels. Visual consistency was also improved by refining the color labelling for each sensor in Figures 1C and 1D and adjusting the corresponding bar graphs accordingly.

Figure 2: We reorganized the figure to clearly distinguish between protein and mRNA analyses for greater clarity. In the revised layout, western blot quantifications of TDP-43 and CUTS (GFP) signals are shown in Figures 2D and 2E, respectively, while the corresponding qPCR analyses are presented in Figures 2H and 2I. Minor edits include removing the percentage knockdown and fold-change annotations from the graphs and incorporating these values into a mini-table in Figure S2E.

The original Figure 2D and 2G were reincorportated as reference panels in Figure S2A–B, while new graphs showing CUTS protein-level changes as a function of TDP-43 knockdown were added (Figure S2C–D). We also incorporated new data showing the behavior of endogenous cryptic exons under low siTDP-43 treatment (Figure S2E).

Figure 3: We added new data demonstrating that the application of the CUTS system in detecting TDP-43 loss of function induced by stress conditions. Specifically, we show that sodium arsenite (NaAsO₂) treatment leads to TDP-43 functional impairment detectable by CUTS and supported with endogenous cryptic exon via RT-PCR (Figure 3D-I).

Figure 5 and Figure S4: We introduced a new figure that demonstrates the effective application of the CUTS system in differentiated neuronal systems, thereby extending its usability to disease-relevant cell types.

Figures 2SA and 4B were edited to include the corresponding labels on the sides of each image for clarity. Sup Figure 2A was moved to Sup Figure 3A, while Figure 4B remains in its original configuration.

We thank the reviewers again for their insightful critiques and helpful suggestions, which have enabled us to substantially improve the manuscript. Please find our detailed response to each review below:

Reviewer #1 (Public review):

Summary:

The authors create an elegant sensor for TDP -43 loss of function based on cryptic splicing of CFTR and UNC13A. The usefulness of this sensor primarily lies in its use in eventual high throughput screening and eventual in vivo models. The TDP-43 loss of function sensor was also used to express TDP-43 upon reduction of its levels.

Strengths:

The validation is convincing, the sensor was tested in models of TDP-43 loss of function, knockdown and models of TDP-43 mislocalization and aggregation. The sensor is susceptible to a minimal decrease of TDP-43 and can be used at the protein level unlike most of the tests currently employed,

Weaknesses:

Although the LOF sensor described in this study may be a primary readout for high-throughput screens, ALS/TDP-43 models typically employ primary readouts such as protein aggregation or mislocalization. The information in the two following points would assist users in making informed choices.

(1) Testing the sensor in other cell lines

We thank the reviewer for raising this important point. In agreement with this suggestion, we generated ReN VM cell lines and used a neuroblastoma cell line model (BE(2)-C) expressing the TetOn3G CUTS system under a human synapsin I (hSYN1) promoter. In this construct the transactivator protein is under the control of a neuronal specific hSYN1 promoter whereas the classical TetOn3G system uses a CMV-like promoter. Several studies have reported reduced activity or silencing of CMV and PGK-driven transgenes in neurons. Therefore, we for our neuronal experiments, we removed this promoter to generate a new version of a doxycycline-inducible CUTS system in which Tet-On 3G transactivator is now driven by the hSYN1 promoter which will express CUTS in response to doxycycline treatment. In this improved construct, we also replaced mCherry with mScarlet to enhance the fluorescent signal.

To test this neuronal-adapted system, we established stable CUTS expression in undifferentiated BE(2)-C cells, a subclone of the SK-N-BE(2) neuroblastoma line that has been used to study TDP-43–dependent splicing function(Brown et al., 2022). This model can be differentiated into neuron-like cells within 10 days, as shown in Supplementary Figure 4A. Using this model, we confirmed that TDP-43 knockdown leads to robust activation of the CUTS system (Figure 5B-E). We additionally tested this in in a stable polyclonal ReN VM cells following differentiation into cortical-like neurons (Figure 5D, Figure S4B-C).

(2) Establishing a correlation between the sensor's readout and the loss of function (LOF) in the physiological genes would be useful given that the LOF sensor is a hybrid structure and doesn't represent any physiological gene. It would be beneficial to determine if a minor decrease (e.g., 2%) in TDP-43 levels is physiologically significant for a subset of exons whose splicing is controlled by TDP43.

We agree with the reviewer that correlating the sensor’s readout with physiological TDP-43 splicing targets is essential to validate its biological relevance. To this end, we complemented our sensor expression profile with endogenous cryptic exons (CEs) sensitive to TDP-43 depletion. We tested a panel of five physiological cryptic exons regulated by TDP-43 (LRP8, EPB41L4A, ARHGAP32, HDGFL2, and ACBD3). To address the reviewer’s concerned, we performed RT-PCR on samples from the low-dose siTDP-43 experiment shown in Figure S2E.

The endogenous CEs used in the panel were selected based on our own and others’ preliminary observations. Among these, HDGFL2 showed a particularly robust increase in cryptic exon inclusion at very low siTDP-43 concentrations (38 pM), while untreated samples showed almost no CE inclusion. This finding strongly supports a direct mechanism linking mild TDP-43 reduction to loss of physiological splicing control.

(3) Considering that most TDP-LOF pathologically occurs due to aggregation and or mislocalization, and in most cases the endogenous TDP-43 gene is functional but the protein becomes non-functional, the use of the loss of function sensor as a switch to produce TDP-43 and its eventual use as gene therapy would have to contend with the fact that the protein produced may also become nonfunctional. This would eventually be easy to test in one of the aggregation modes that were used to test the sensor.. However, as the authors suggest, this is a very interesting system to deliver other genetic modifiers of TDP-43 proteinopathy in a regulated fashion and timely fashion.

We thank the reviewer for this thoughtful point and agree that in the disease-relevant context where endogenous TDP-43 is intact but TDP-43 function is lost due to mislocalization and/or aggregation, a re-supply of TDP-43 risks sequestration and loss of activity. In our manuscript, the CUTS-TDP43 module was presented as a control circuit proof-of-concept rather than a stand-alone approach: it demonstrates that CUTS can (i) sense LOF with high dynamic range and proportionality, and (ii) drive a payload under negative feedback such that total TDP-43 remains near baseline while partially rescuing a splicing readout (CFTR minigene) under knockdown conditions.

Importantly, we evaluated CUTS in aggregation/mislocalization-prone contexts: ΔNLS, 5FL, and ΔNLS+5FL variants trigger CUTS activation (ref), allowing us to quantify LOF arising from these aggregation modes. This confirms that CUTS can operate precisely in the very settings where sequestration is likely to occur.

To directly address the reviewer’s suggestion, in the revision we (i) clarify in the Discussion that CUTS-TDP43 is a circuit demonstration and not our proposed monotherapy in aggregation-dominant disease; and (ii) expand our therapeutic framing into two approaches:

Knockdown-and-replacement: concurrently deplete aggregation-prone/endogenous pathologic TDP-43 species (i.e., mutant TDP-43) while using CUTS to re-deliver wild-type TDP-43 under autoregulation. Aggregation-independent correction: use of CUTS to deliver modifiers that bypass TDP-43 sequestration (e.g., downstream effectors or splicing correctors that restore LOF consequences without expressing TDP-43 itself).

(4) I don't think the quantity of siRNA is directly proportional to the degree of TDP-43 knockdown/extent of TDP-43 loss. Therefore, to enhance the utility of the dose-response curves, I'd suggest using TDP-43 levels as the variable on the x-axis, rather than the amount of siRNA administered or even just adding a plot alongside the current plots would enable readers to quickly evaluate LOF response levels concerning the protein. While I understand that the sensitivity of Western blots for quantification might be why the authors have not created the graphs in this manner, having this information would be useful.

We appreciate the reviewer’s insightful comment. As noted, in the original version of the graph, we incorporated the percentage of TDP-43 knockdown corresponding to each siTDP-43 concentration (indicated in red text). However, we agree that this format was not easy to interpret, given the amount of information presented. To address this, we generated two new plots in which the x-axis represents TDP-43 levels (percentage of remaining protein or mRNA), and the y-axis shows the fold change in CUTS signal measured by (i) TDP-43 protein pixel intensity and (ii) TDP-43 mRNA levels, respectively. These new plots are now included as Supplementary Figures 2C–D, which allow a clearer visualization of CUTS readout in relation to actual TDP-43 levels rather than siRNA dose. As the reviewer anticipated, the reason we did not originally present the data in this format was that at low siTDP-43 concentrations, the fold change is minimal and more difficult to quantify by Western blot. Nevertheless, we have now incorporated the revised plots to strengthen the interpretation of the dose–response relationship. Additionally, we experience batch effects across siRNA lots. We believe this revised format should enhance the clarity of the result.

(5) p3 line 74: one of the reasons cited as a pitfall of using the endogenous cryptic exons exhibit variable responses to TDP-43 loss and may be cell type-specific. has the sensor been used in different cell lines?

We tested the CUTS system in differentiated neuronal models using two differentiated neuronal cell types, BE(2)C and ReN VM cells. The results are presented in Figure 5 and Figure S4 of the revised manuscript.

(6) The order of the text describing 1A and 1B is confusing. The text starts describing the TS cassettes referring to 1A using the CUTS cassettes which haven't been introduced yet as an example. I'd suggest reorganising this section. The graph, always in 1A showing readout proportional to GFP should be taken out or highlighted in the figure legend that it is theoretical.

We agree with the reviewer’s point. In the original schematic (Figure 1A), we included the CUTS system as an example to introduce the TS cassette design, since it contains the three possible sensor configurations. However, we recognize that this could be confusing. Therefore, we have removed the CUTS cassette from Figure 1A, along with the theoretical graph showing GFP readout proportional to the degree of TDP-43 LOF. In agreement with this change, we also restructured Figure 1. As the focus is the CUTS system, we have moved the Western blot and quantification of UNC13A-TS and CFTR-TS to Supplementary Figure 1.

Reviewer #2 (Public review):

Summary:

The authors goal is to develop a more accurate system that reports TDP-43 activity as a splicing regulator. Prior to this, most methods employed western blotting or QPCR-based assays to determine whether targets of TDP-43 were up or down-regulated. The problem with that is the sensitivity. This approach uses an ectopic delivered construct containing splicing elements from CFTR and UNC13A (two known splicing targets) fused to a GFP reporter. Not only does it report TDP-43 function well, but it operates at extremely sensitive TDP-43 levels, requiring only picomolar TDP-43 knockdown for detection. This reporter should supersede the use of current TDP-43 activity assays, it's cost-effective, rapid and reliable.

Strengths:

In general, the experiments are convincing and well designed. The rigor, number of samples and statistics, and gradient of TDP-43 knockdown were all viewed as strengths. In addition, the use of multiple assays to confirm the splicing changes were viewed as complimentary (ie PCR and GFPfluorescence) adding additional rigor. The final major strength I'll add is the very clever approach to tether TDP-43 to the loss of function cassette such that when TDP-43 is inactive it would autoregulate and induce wild-type TDP-43. This has many implications for the use of other genes, not just TDP-43, but also other protective factors that may need to be re-established upon TDP-43 loss of function.

Weaknesses:

(1) Admittedly, one needs to initially characterize the sensor and the use of cell lines is an obvious advantage, but it begs the question of whether this will work in neurons. Additional future experiments in primary neurons will be needed.

We thank the reviewer for highlighting the importance of validating the sensor in neuronal models, given the central role of TDP-43 dysfunction in ALS/FTD and related neurodegenerative disorders. While initial characterization in established cell lines provides experimental control and scalability, we agree that demonstrating functionality in neuronal systems is essential. To address this, we adapted the CUTS platform for neuronal application by incorporating the human synapsin-1 (hSYN1) promoter into the Tet-On 3G system to enable inducible, neuronal specific expression. We validated this configuration in differentiated BE(2)-C cells (Figures 5A-C, S4A-C), where CUTS retained robust responsiveness to TDP-43 perturbation. In parallel, we generated stable CUTS-expressing ReN VM neural progenitor cells and differentiated them for three weeks prior to functional assessment (Figures 5A-C, S4A-C). In both neuronal models, CUTS was functional and responsive to TDP-43 siRNA. We are currently optimizing promoter selection and expression paradigms for fully differentiated iPSC-derived neuronal models and will be the subject of future studies.

(2) The bulk analysis of GFP-positive cells is a bit crude. As mentioned in the manuscript, flow sorting would be an easy and obvious approach to get more accurate homogenous data. This is especially relevant since the GFP signal is quite heterogeneous in the image panels, for example, Figure 1C, meaning the siRNA is not fully penetrant. Therefore, stating that 1% TDP-43 knockdown achieves the desired sensor regulation might be misleading. Flow sorting would provide a much more accurate quantification of how subtle changes in TDP-43 protein levels track with GFP fluorescence.

We thank the reviewer for this thoughtful suggestion. We agree that flow cytometry and sorting of GFP-positive populations would provide a higher-resolution, single-cell–level relationship between TDP-43 abundance and sensor output. Such an approach would reduce heterogeneity arising from incomplete siRNA penetrance and allow more precise quantification of how incremental changes in TDP-43 protein levels track with GFP fluorescence. In the present study, our goal was to establish proof-of-principle functionality of the CUTS circuit and to demonstrate that graded TDP-43 depletion produces a proportional sensor response at the population level. While GFP signal heterogeneity is visible in imaging panels, we hypothesize that this variability likely reflects known differences in siRNA uptake and transfection efficiency rather than instability of the circuit itself. Importantly, bulk measurements consistently demonstrated dose-dependent sensor regulation across independent experiments, supporting the robustness of the system despite cellular heterogeneity. Furthermore, we were able to quantify CUTS activation in HeLa TARDBP^-/- cells. We also note that CUTS was developed as a practical tool for rapid assessment of TDP-43 LOF in standard laboratory settings. Although flow cytometry increases resolution, the ability to detect functional perturbation using bulk fluorescence measurements supports the utility of the system for routine and high-throughput applications.

We agree that flow cytometry would provide a more refined analysis of the dynamic range and sensitivity of CUTS, particularly for defining thresholds such as minimal TDP-43 knockdown required for measurable activation. We plan to include this work in future studies. Specifically, we have implemented FACs sorting of CUTS-expressing cells in a parallel study in which we are conducting a CRISPR knockout screen to identify modifiers of TDP-43 splicing function. For this, we incorporate TDP-43 knockdown followed by FACs to stratify cells based on CUTS activation. This strategy enables direct evaluation of the relationship between the extent of TDP-43 LOF and CUTS sensor activation. These analyses are ongoing and provide a more quantitative analyses linking TDP-43 depletion to CUTS activation and address the reviewer’s concern regarding heterogeneity in bulk measurements. We plan to include this in a future study.

(3) Some panels in the manuscript would benefit from additional clarity to make the data easier to visualize. For example, Figure 2D and 2G could be presented in a more clear manner, possibly split into additional graphs since there are too many outputs.

We thank the reviewer for this suggestion. In response, we have split the graphs previously shown in Figures 2D and 2G to improve clarity, as we agree that these panels contained an extensive amount of data. We Specifically split Figure 2D into two separate graphs showing TDP-43 and GFP pixel intensity from Western blots on the Y-axis, plotted against low siTDP-43 treatment on the X-axis. Please see this data as Figure 2 D and Figure 2E in the new manuscript.

Furthermore, for Figure 2G we also split into graphs showing the fold change of mRNA for TDP-43 and the CUTS cryptic exon plotted against low siTDP-43 treatment on the X-axis. Please see this data as Figure 2 H and Figure 2I in the new manuscript. We have maintained the previous graphs in Supplementary Figure 2 to preserve the full dataset for reference.

(4) Sup Figure 2A image panels would benefit from being labeled, its difficult to tell what antibodies or fluorophores were used. Same with Figure 4B.

We appreciate the reviewer’s careful observation. In both figures, we are showing mCherry and GFP signals. In the revised version, we have added the corresponding labels to the side of each image for clarity. Therefore, Sup Figure 2A has been moved and is now Sup Figure 3A, while Figure 4B remains in its original configuration.

(5) Figure 3 is an important addition to this manuscript and in general is convincing showing that TDP43 loss of function mutants can alter the sensor. However, there is still wild-type endogenous TDP-43 in these cells, and it's unclear whether the 5FL mutant is acting as a dominant negative to deplete the total TDP-43 pool, which is what the data would suggest. This could have been clarified.

The TDP-43 5FL variant exhibits reduced RNA-binding capacity, and we previously demonstrated that impaired RNA binding promotes aberrant homotypic phase separation of TDP-43. Consistent with this mechanism, expression of RNA-binding–deficient TDP-43 variants induces the formation of nuclear “anisomes” which have been shown to sequester endogenous TDP-43 into insoluble fractions via dominant-negative mechanisms (Cohen et al., 2015; Keating et al., 2023; Mann et al., 2019; Yu et al., 2021). These findings support a model in which disruption of RNA engagement alters TDP-43 biophysical behavior and promotes functional depletion through self-association. We have expanded this mechanistic explanation in the Results section of the revised manuscript to better contextualize the behavior of the 5FL construct and its impact on endogenous TDP-43.

(6) Additional treatment with stressors that inactivate TDP-43 could be tested in future studies.

We appreciate this suggestion and agree with this important point. Due to the lack of methods to directly induce endogenous TDP-43 aggregation and loss of function, the use of stressors has become a partial solution to address this issue. In line with this, our group has tested several stressors in follow-up research, including sodium arsenite (NaAsO₂), puromycin, KCl, MG132, sorbitol, and tunicamycin, using HEK cells expressing the CUTS system(Xie et al., 2025). We were able to show a dose-response relationship in relative GFP intensity under these conditions, with sodium arsenite showing the strongest effect, consistent with previous reports(Huang et al., 2024). To provide additional relevant findings in the current manuscript, we expanded this analysis by testing sodium arsenite in the CUTS system while also including endogenous cryptic exons. We therefore added a new figure showing the effect of sodium arsenite on the CUTS system, including GFP intensity measurements, qPCR using CUTS cryptic exon primers, and three endogenous cryptic exon reporters (ATG4B, GPSM2, and KCNQ2).

Overall, the authors definitely achieved their goals by developing a very sensitive readout for TDP-43 function. The results are convincing, rigorous, and support their main conclusions. There are some minor weaknesses listed above, chief of which is the use of flow sorting to improve the data analysis. But regardless, this study will have an immediate impact for those who need a rapid, reliable, and sensitive assessment of TDP-43 activity, and it will be particularly impactful once this reporter can be used in isolated primary cells (ie neurons) and in vivo in animal models. Since TDP-43 loss of function is thought to be a dominant pathological mechanism in ALS/FTD and likely many other disorders, having these types of sensors is a major boost to the field and will change our ability to see sub-threshold changes in TDP-43 function that might otherwise not be possible with current approaches.

(7) Regarding the methods, they seem a bit sparse and would benefit from additional detail. For example, I do not see a section in the methods where microscopy images were quantified (%GFP positive cells for example). This information is important and is lacking in the current form.

We thank the reviewers, and we add the following information in the method section: For live imaging quantification, we measured the mean GFP signal intensity for each group. The values were averaged, and the fold change was calculated and plotted. For immunofluorescent imaging, we first created maximum intensity projection images. We then applied masks to the GFP, mCherry, and Hoechst signals. By overlapping the GFP and mCherry signals, we identified the number of GFP-positive cells. Similarly, by overlapping the mCherry signal with the Hoechst mask, we identified the CUTS-expressing cells. We then calculated the ratio of GFPpositive cells to CUTS-expressing cells and plotted it as a percentage of GFP-positive cells. All analyses were performed using the Nikon NIS software. This information is included in the methods of the revised manuscript.

Reviewer #3 (Public review):

The DNA and RNA binding protein TDP-43 has been pathologically implicated in a number of neurodegenerative diseases including ALS, FTD, and AD. Normally residing in the nucleus, in TDP-43 proteinopathies, TDP-43 mislocalizes to the cytoplasm where it is found in cytoplasmic aggregates. It is thought that both loss of nuclear function and cytoplasmic gain of toxic function are contributors to disease pathogenesis in TDP-43 proteinopathies. Recent studies have demonstrated that depletion of nuclear TDP-43 leads to loss of its nuclear function characterized by changes in gene expression and splicing of target mRNAs. However, to date, most readouts of TDP-43 loss of function events are dependent upon PCR-based assays for single mRNA targets. Thus, reliable and robust assays for detection of global changes in TDP-43 splicing events are lacking. In this manuscript, Xie, Merjane, Bergmann and colleagues describe a biosensor that reports on TDP-43 splicing function in real time. Overall, this is a well described unique resource that would be of high interest and utility to a number of researchers. Nonetheless, a couple of points should be addressed by the authors to enhance the overall utility and applicability of this biosensor.

(1) While the rationale for selecting UNC13A CE as the reporting CE species is understood given the relevance to disease, could the authors please comment on whether other CE sequences would behave similarly or as robustly? This is particularly critical given the multitude of different splicing changes that can occur as a result of TDP-43 loss of function (ie cryptic exons of differing sensitivity, skiptic exons, premature polyadenylation).

We thank the reviewer for this question regarding generalizability beyond the UNC13A CE. While UNC13A was selected due to its strong disease relevance and well-characterized sensitivity to TDP-43 loss-of-function (LOF), our platform is not intrinsically restricted to this sequence. In the manuscript, we directly compared three architectures: UNC13A-TS, CFTR-TS, and the combined CUTS sensor incorporating additional UG motif optimization. Under matched conditions in stable HEK293 lines, CUTS demonstrated superior specificity and sensitivity, exhibiting near-zero baseline activity and a proportional, log-linear response across low-dose siTDP43 (38–1200 pM) (Figures 1–2). Importantly, this head-to-head comparison demonstrates that sensor performance can be engineered and optimized beyond a single CE species.

TDP-43 LOF is known to induce a spectrum of RNA processing defects, including cryptic exons with differing sensitivities and cell-type dependence, premature polyadenylation events (e.g., STMN2), and, under conditions of excess nuclear TDP-43, exon skipping (“skiptic exons”). This diversity supports the concept in which alternative CE elements, or other TDP-43 regulated RNAs, can be incorporated into the same sensor backbone and tuned for specific biological scenarios (cell type, specific stress responses, etc...). Consistent with this, the recently described TDP-REG system (Wilkins et al., 2024) designed and AI-generated de novo CE sequences to express reporters or gene payloads, and screened multiple candidates to identify the appropriate RNA elements required for this response. These findings demonstrate that CE sequences beyond UNC13A can serve as robust TDP-43 sensing elements when optimized. Our results complement this work by demonstrating that CUTS achieves tight baseline control and a steep dynamic range (>110,000-fold induction over baseline in HEK293 cells), while maintaining compatibility across both non-neuronal and neuronal model systems, as shown in the revised manuscript.

In the revised manuscript, we show direct comparisons indicating that CUTS outperforms single-CE sensors such as UNC13A-TS and CFTR-TS under identical conditions. This supports independent work from other groups that alternative CE sequences can be engineered into effective sensors, depending on their paradigm and model systems. We have clarified this in the revised Discussion and now note that CUTS is adaptable to alternative CE inserts.

(3) Could the authors provide evidence of the utility of their biosensor in disease relevant systems that do not rely on TDP-43 KD? For example, does this biosensor report on TDP-43 loss of function in C9orf72 iPSNs in a time-dependent manner? Alternatively, groups have modeled TDP-43 proteinopathy in wildtype iPSNs via MG132 treatment.

We thank the reviewer for this important suggestion. We agree that demonstrating CUTS responsiveness in disease-relevant models independent of artificial TDP-43 knockdown would further strengthen its translational relevance. In the current study, our primary objective was to establish the sensitivity, dynamic range, and autoregulatory properties of the CUTS circuit under controlled perturbation of TDP-43 levels. siRNA-mediated depletion provides a reliable approach to establish the relationship between graded TDP-43 LOF and the CUTS sensor sensitivity/specificity. That said, CUTS is designed to detect functional TDP-43 loss irrespective of the upstream cause. As the reviewer notes, disease-relevant systems, such as C9orf72 iPSC-derived neurons and proteotoxic stress paradigms (e.g., MG132-induced impairment of TDP-43 nuclear function), are important for future studies. We are currently evaluating CUTS in iPSC-derived neuronal models of TDP-43 proteinopathy, but are optimizing the induction system, promoters, and timing. It should be noted that C9orf72 iPSC neurons do not exhibit TDP-43 LOF using standard differentiation protocols. Regarding pharmacological stress, we have shown that acute sodium arsenite treatment can activate CUTS (Figure 3). In a concurrent study under revision, we show that MG132 similarly causes TDP-43 LOF and CUTS activation (Xie et al., 2025). Notably, none of these induce complete nuclear loss of TDP-43; instead, they show nuclear TDP-43 retention or modest mislocalization. This suggests that TDP-43 LOF may also result from nuclear redistribution and dysfunction under these stress conditions, rather than from complete nuclear loss. We look forward to presenting these ongoing studies in the future.

References

Brown A-L, Wilkins OG, Keuss MJ, Kargbo-Hill SE, Zanovello M, Lee WC, Bampton A, Lee FCY, Masino L, Qi YA, Bryce-Smith S, Gatt A, Hallegger M, Fagegaltier D, Phatnani H, NYGC ALS Consortium, Newcombe J, Gustavsson EK, Seddighi S, Reyes JF, Coon SL, Ramos D, Schiavo G, Fisher EMC, Raj T, Secrier M, Lashley T, Ule J, Buratti E, Humphrey J, Ward ME, Fratta P. 2022. TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A. Nature 603:131–137. doi:10.1038/s41586-022-04436-3

Cohen TJ, Hwang AW, Restrepo CR, Yuan C-X, Trojanowski JQ, Lee VMY. 2015. An acetylation switch controls TDP-43 function and aggregation propensity. Nat Commun 6:5845. doi:10.1038/ncomms6845

Huang W-P, Ellis BCS, Hodgson RE, Sanchez Avila A, Kumar V, Rayment J, Moll T, Shelkovnikova TA. 2024. Stress-induced TDP-43 nuclear condensation causes splicing loss of function and STMN2 depletion. Cell Rep 43:114421. doi:10.1016/j.celrep.2024.114421

Keating SS, Bademosi AT, San Gil R, Walker AK. 2023. Aggregation-prone TDP-43 sequesters and drives pathological transitions of free nuclear TDP-43. Cell Mol Life Sci 80:95. doi:10.1007/s00018-023-04739-2

Mann JR, Gleixner AM, Mauna JC, Gomes E, DeChellis-Marks MR, Needham PG, Copley KE, Hurtle B, Portz B, Pyles NJ, Guo L, Calder CB, Wills ZP, Pandey UB, Kofler JK, Brodsky JL, Thathiah A, Shorter J, Donnelly CJ. 2019. RNA Binding Antagonizes Neurotoxic Phase Transitions of TDP-43. Neuron 102:321-338.e8. doi:10.1016/j.neuron.2019.01.048

Wilkins OG, Chien MZYJ, Wlaschin JJ, Barattucci S, Harley P, Mattedi F, Mehta PR, Pisliakova M, Ryadnov E, Keuss MJ, Thompson D, Digby H, Knez L, Simkin RL, Diaz JA, Zanovello M, Brown A-L, Darbey A, Karda R, Fisher EMC, Cunningham TJ, Le Pichon CE, Ule J, Fratta P. 2024. Creation of de novo cryptic splicing for ALS and FTD precision medicine. Science 386:61–69. doi:10.1126/science.adk2539

Xie L, Zhu Y, Hurtle BT, Wright M, Robinson JL, Mauna JC, Brown EE, Ngo M, Bergmann CA, Xu J, Merjane J, Gleixner AM, Grigorean G, Liu F, Rossoll W, Lee EB, Kiskinis E, Chikina M, Donnelly CJ. 2025. Contextdependent Interactors Regulate TDP-43 Dysfunction in ALS/FTLD. BioRxiv. doi:10.1101/2025.04.07.646890

Yu H, Lu S, Gasior K, Singh D, Vazquez-Sanchez S, Tapia O, Toprani D, Beccari MS, Yates JR, Da Cruz S, Newby JM, Lafarga M, Gladfelter AS, Villa E, Cleveland DW. 2021. HSP70 chaperones RNA-free TDP-43 into anisotropic intranuclear liquid spherical shells. Science 371. doi:10.1126/science.abb4309.

Please give sample response too.

are we not using the full create order body here? Some merchants pass IP too as per the standard create order body

this is to be written more clearly to reflect that Apple Pay and other methods will load in the location of the script.

eLife Assessment

This valuable study addresses mechanisms of feedback inhibition between planar cell polarity protein complexes during convergent extension movements in Xenopus embryos. The authors propose a conceptually new model, in which non-canonical Wnt ligand stimulates transition of Dishevelled from its complex with Vangl to Frizzled, with essential roles of Prickle and Ror in this process. The main observations supporting molecular interactions rely on modest but significant changes in protein association in response to Wnt11. While the study is limited due to insufficient phenotypic analysis at the cellular level and the use of exogenously supplied proteins, this work is convincing and will be of broad interest to cell and developmental biologists.

Reviewer #1 (Public review):

Summary:

Planar cell polarity core proteins Frizzled (Fz)/Dishevelled (Dvl) and Van Gogh-like (Vangl)/Prickle (Pk) are localized on opposite sides of the cell and engage in reciprocal repression to modulate cellular polarity within the plane of static epithelium. In this interesting manuscript, the authors explore how the anterior core proteins (Vangl/Pk) inhibit the posterior core protein (Dvl). The authors propose that Pk assists Vangl2 in sequestering both Dvl2 and Ror2, while Ror2 is essential for Dvl to transition from Vangl to Fz in response to non-canonical Wnt signaling.

Strengths:

The strengths of the manuscript are found in the very interesting and new concept along with supportive data for a model of how non-canonical Wnt induces Dvl to transition from Vangl to Fz with an opposing role for PK and Vangl2 to suppress Dvl during convergent extension movements. Ror is key player required for the transition and antagonizes Vangl.

Weaknesses:

In addition to general whole embryo morphology that is used as evidence for CE defects, two forms of data are presented: co-expression and IP, as well as IF of exogenously expressed proteins. The microscopy would benefit from super-resolution microscopy since in many cases the differences in protein localization are not very pronounced, and Western analysis data often show relatively subtle differences. Thus, future work will determine the strength of the interactions of the model.

Major points.

Overexpression conditions

A possible concern is that most analyses were performed with overexpression conditions. PCP core proteins (Vangl2, Pk, Dvl, and Fz receptors) are known to display polarized subcellular localization in both the neural epithelium and DMZ explants (Ref: PCP and Septins govern the polarized organization of the actin cytoskeleton during convergent extension, Current Biology, 2024). However, in this study, overexpressed PCP core proteins failed to show polarized localization. Thus, one must be careful in interpreting data.

Subtle effects

Several of the reported results show quite modest changes in imaging and immunoprecipitation analyses, which are supportive of the proposed molecular model, but future experiments will be needed to robustly test the model.

Author Response:

The following is the authors’ response to the previous reviews

Public Review:

Reviewer #1 (Public review):

The weaknesses are in the clarity and resolution of the data that forms the basis of the model. In addition to general whole embryo morphology that is used as evidence for CE defects, two forms of data are presented, co-expression and IP, as well as a strong reliance on IF of exogenously expressed proteins. Thus, it is critical that both forms of evidence be very strong and clear, and this is where there are deficiencies; 1) For vast majority of experiments general morphology and LWR was used as evidence of effects on convergent extension movements rather than keller explants or actual cell movements in the embryo. 2) the microscopy would benefit from super resolution microscopy since in many cases the differences in protein localization are not very pronounced. 3) the IP and Western analysis data often shows very subtle differences, and some cases not apparent.

Major points.

(1) Assessment of CE movement

The authors conducted an analysis of the subcellular localization of PCP core proteins, including Vangl2, Pk, Fz, and Dvl, within animal cap explants (ectodermal explants). The authors primarily used the length-to-width ratio (LWR) to evaluate CE movement as a basis for their model. However, LWR can be influenced by multiple factors and is not sufficient to directly and clearly represent CE defects. While the author showed that Prickle knockdown suppresses animal cap elongation mediated by Activin treatment, they did not test their model using standard assays such as animal cap elongation or dorsal marginal zone (DMZ) Keller explants. Furthermore, although various imaging analyses were performed in Wnt11-overexpressing animal caps and DMZ explants, the Wnt11-overexpressing animal caps did not undergo CE movement. Given that this study focuses on the molecular mechanisms of Vangl2 and Ror2 regulation of Dvl2 during CE, the model should be validated in more appropriate tissues, such as DMZ explants.

(2) Overexpression conditions

Another concern is that most analyses were performed with overexpression conditions. PCP core proteins (Vangl2, Pk, Dvl, and Fz receptors) are known to display polarized subcellular localization in both the neural epithelium and DMZ explants (Ref: PCP and Septins govern the polarized organization of the actin cytoskeleton during convergent extension, Current Biology, 2024). However, in this study, overexpressed PCP core proteins failed to show polarized localization. Previous studies, such as those from the Wallingford lab, typically used 10-30 pg of RNA for PCP core proteins, whereas this study injected 100-500 pg, which is likely excessive and may have created artificial conditions that confound the imaging results.

(3) Subtle and insufficient effects

Several of the reported results show quite modest changes in imaging and immunoprecipitation analyses, which are not sufficient to strongly support the proposed molecular model. For example, most Dvl2 remained localized with Fz7 even under Vangl2 and Pk overexpression (Fig. 4). Similarly, Wnt11 overexpression only slightly reduced the association between Vangl2 and Dvl2 (Sup. Fig. 8), and the Ror2-related experiments also produced only subtle effects (Fig. 8, Sup. Fig. 15).

We thank reviewer 1 for careful reading of our revised manuscript, and additional constructive criticisms. Since the two reviewers had divergent opinions towards our revised manuscript, we think that it might be more productive to request a Version of Record at this point, and have our proposed model debated/ tested by others in the field. We will keep the reviewer’s suggestions in mind while design ongoing studies. We would like to address the criticisms collectively below:

(1) The primary goal of our current manuscript is to build a mechanistic model for non-canonical Wnt signaling through elucidating the functional relationships between Dvl, Vangl, PK and Ror during CE. They each have been studied extensively in prior literature using DMZ injected embryos, and DMZ, Keller and animal cap explants, so there is little doubt that the reduced LWR following their over-expression or knockdown in DMZ is due to disruption of CE. In the context of our study in the current manuscript, we primarily performed their co-injections in different combinations to differentiate synergistic vs. antagonistic relationship, and in the majority cases we relied on epistatsis to draw conclusions (e.g. Fig. 1; Fig. 2h, I; Suppl. Fig. 6; Suppl. Fig. 14). Nevertheless, we did follow the reviewer’s suggestion and used animal cap elongation as an additional assay to confirm that Pk and Vangl2 did synergize to disrupt CE, and their synergy could be blocked by Dvl2 co-overexpression; the new data is added to Fig. 1 (Fig. 1h, h’). Therefore, given the prior literature, our new animal cap explant data, and the specific scope of our current study, we feel that the LWR measurement is a reasonable assay to determine CE phenotype in this manuscript. We fully agree with the reviewer that our model will need to be tested at the cellular level through live imaging of DMZ explants; it is indeed the direction of our future study, but is beyond the scope of the current manuscript.

(2) A salient feature of non-canonical Wnt signaling is that loss or over-expression of any components can often cause identical CE defects at the tissue/ embryo level. We used many co-injection experiments to demonstrate that this is due, at least in part, to a counterbalance between Dvl/Ror and Vangl/PK (e.g. Fig. 1; Fig. 2h, I; Suppl. Fig. 6; Suppl. Fig. 14). It is in this context that we planned the imaging and biochemical experiments to determine the possible molecular mechanisms underlying their functional interaction, and we feel that the moderate over-expression used is reasonable in this case for us to build the first integrated model. We do plan to test our model using lower expression in the future. To acknowledge the limitation of our study, we also added the following sentences in the Discussion:

“We acknowledge, however, that our model explains primarily the potential molecular actions underlying the regulation of CE at the tissue level. Whether and how our model may explain the cellular behavior during CE, such as polarized remodeling of cell junction or extension of cell protrusions, will require further study.”

(3) The Wnt11 induced reduction of Dvl2-Vangl2 co-IP (Suppl. Fig. 8, 15) may be moderate, but is statistically significant and reproducible, and we have reported similar findings in two other publications (DOI: 10.1093/hmg/ddx095; DOI: 10.1038/s41467-025-57658-0). Given the limitation of co-IP, we had to rely on high level over-expression to make the experiments feasible. We are building proximity based assays such as NanoBRET, and plan to verify the result with lower level expression in the future.

Reviewer #2 (Public review):

We thank the reviewer for the encouraging comments, and the suggestion to clarify the description related to Suppl. Fig. 15. We made revision according to the reviewer’s suggestion, and added Suppl. Fig. 16 to further examine the effect of Ror2 knockdown on the steady state interaction between Dvl2 and Vangl2 using imaging approach.

This is more common on movies, where the starts at the moment everything changes, with some movies starting after the first plot point, presenting past elements from the normal life as small clips.

Lots of novels have the habit to "start too early", sometimes even making the start of the story abnormally forgettable.

the percentage of customers or subscribers who cancel or stop using a service over a specific period

I think it could work but people could just choose the first one anyway and this is not a good measurement at this point.

eLife Assessment

In this important study, a new multi-scale imaging workflow promises to accelerate and democratize comparative connectomics, with projectome-level data informing synapse-level connectivity. While the pipeline and time savings are convincing, the evidence for the segmentation methodology as a reusable community resource is incomplete, with key metrics like error rates, annotation times, and proof-reading times not reported. Furthermore, the evidence on the utility of projectome-level information for analysing brains appears misleading. By clarifying the findings and ensuring that the complete software pipeline is available in online open source repositories alongside precise documentation, the authors would deliver on their vision to enable any laboratory to map and analyse brain connectomes.

Reviewer #1 (Public review):

Summary:

This manuscript presents an end-to-end pipeline, intended to accelerate EM-based connectomics by combining low-resolution imaging for large volumes with synapse-level imaging only in selected regions of interest. In principle, this strategy can substantially reduce imaging time, computational demands, analysis time, and overall cost.

General note:

Overall, I found the manuscript interesting and valuable, particularly as a description of how one laboratory has assembled and applied a practical workflow to reconstruct and analyze the central complex across multiple insect species. In that sense, the work is compelling as an account of a real, functioning strategy for comparative connectomics, and I appreciated reading it. My main reservation is not about the relevance of the biological problem or the utility of the pipeline in the authors' own hands, but about whether the manuscript, in its current form, fully meets the expectations of a paper that is focused on tools and resources. The expectation would be that this paper would be a venue for sharing new techniques, software tools, datasets, and other resources intended to be usable by the community. Here, because much of the pipeline appears to build on existing methods and software, the key value added should be a particularly clear demonstration of how these components were adapted, integrated, validated, and documented for this specific use case in a way that others could realistically reproduce and adopt. At present, that translational and reproducibility-oriented component does not yet seem sufficiently developed, despite the clear promise of the overall approach.

Major comments:

(1) The work is valuable as a practical integration and application of multiple existing tools into a coherent pipeline, together with a new multi-resolution imaging strategy. However, the manuscript at times reads as though it introduces an entirely novel workflow. I would encourage the authors to clarify the contribution more explicitly: which components are genuinely new (for example, the acquisition strategy and the end-to-end integration/validation), and which are adaptations of already established methods or software. This would make the scope and novelty of the paper easier to assess.

(2) The most distinctive element is the multi-resolution acquisition strategy. However, as described, the selection of high-resolution regions seems to be decided a priori based on anatomy (guided by xCT localization of the CX), rather than being determined automatically from the data (i.e., ROI placement is anatomy-driven rather than data-driven). A more data-driven or machine learning-guided ROI strategy would strengthen the methodological contribution and the adaptability to new scenarios, along the lines of approaches such as SmartEM [1].

(3) The manuscript emphasizes open-source availability and reduced barriers to entry, but the current software release, as referenced, does not yet appear to support straightforward external reuse. Since much of the pipeline builds on existing methods, the main added value lies in how these technologies were adapted, combined, and validated for the present problem. A clear and complete explanation of this adaptation is therefore essential, but is currently missing. I would suggest the following concrete improvements:<br /> a) Provide a single landing page or umbrella repository that links each pipeline step in the paper to the corresponding codebase, including version tags/commits and expected inputs/outputs for each step.<br /> b) Include step-by-step tutorials for each component.<br /> c) Provide an example dataset together with a full reproduction walkthrough in a controlled environment.<br /> d) Clearly explain the required parameters and configuration for each step, including how they should be adjusted for other datasets or scenarios.<br /> e) Follow packaging and distribution best practices (for example, PyPI/conda releases, Docker containers, and version pinning).

(4) In my own attempt to set up and run parts of the released code, I encountered issues that currently limit reproducibility. For example, when creating an environment for EMalign (https://github.com/Heinze-lab/EMalign), the required Python version is not specified, and installation did not succeed under Python 3.12 due to dependency constraints. Additionally, synful_312 (https://github.com/Heinze-lab/synful_312) and SegToPCG (https://github.com/Heinze-lab/SegToPCG) appear to be empty despite being referenced in the manuscript. These are fixable issues, but addressing them is important if the paper is to deliver on its "low entry cost" claim.

(5) Table 1 reports acquisition times, which is helpful. However, the multi-resolution approach adds essential processing steps that appear due to the strategy followed (e.g., "XY alignment high-res" and "high-res to low-res alignment"). Please include registration/alignment (and other major post-processing) runtimes and resource requirements, such as storage, in a comparable table so readers can assess true end-to-end cost.

References:

[1] Meirovitch, Y., et al. "SmartEM: machine learning-guided electron microscopy." Nature Methods (2025).

Reviewer #2 (Public review):

Summary:

The paper proposes a workflow to accelerate EM connectomics by combining multi-scale imaging with image processing and analysis (image alignment, registration, neuron tracing, automated segmentation and synapse prediction, proof-reading) to derive a brain region connectome. The paper argues and (partially) demonstrates that this approach facilitates comparative connectomics.

The data acquisition pipeline uses a well-established sample preparation protocol, uCT guided acquisition, and SBEM imaging at cellular and synaptic resolution.

Data processing and analysis combine existing state-of-the-art components and focus on the alignment and complementary analysis of the two SBEM resolution levels. The paper applies the workflow to the central complex of six different insects and performs some preliminary analysis based on this (which is acceptable for a resource/tool).

Disclaimer for the rest of the review: I am an expert in image analysis and segmentation, so I have mainly focused on these aspects as I am not qualified to analyze the details of image acquisition.

Strengths:

The paper addresses an important problem and promises an acceleration and democratization of comparable connectomics. The time savings of the imaging approach are well-motivated and derived. The methods used for image alignment, segmentation, synapse detection, and proofreading are state-of-the-art.

Weaknesses:

I see two major weaknesses in the paper:

(1) The paper introduces the (approximate) equivalence of the projectome and connectome in the insect brain very prominently in the introduction and uses this as a central motivation for the multi-resolution image acquisition protocol. But - to me - it is unclear how this principle is really used in the analysis presented in the last results and if this assumption is evaluated at all. Specifically, Figure 4 a shows the anatomical neuron reconstructions (from cellular resolution SBEM), d-g show connectome-level analysis from the synaptic resolution data. The only link I can see between the two is that the neural processes in the synapse-resolution data can be mapped to the neurons from the cellular resolution data, thanks to the image alignment. This is certainly important, BUT it is only tangentially related to the projectome vs. connectome claim from the introduction. This claim implies that a tentative connectome is derived from projectome-level data (e.g. by assuming a uniform probability of synapse-formation given surface or distance between projections) that is then validated by the "true" connectome data from synaptic resolution. Instead, what is actually solved - to my understanding - is mapping the local connectome to the projectome. While related, these are different things and the current framing of the paper and the quite brief description of the section on comparative connectomics (also no corresponding Methods section) make this claim inadequately supported.

(2) Reporting on segmentation and proofreading is purely qualitative. Given that this is claimed as a core contribution of the paper (e.g. statement in line 497 and following), I would expect substantially more reporting and evaluation of this claim:<br /> a) Report the actual time needed for proofreading the segmentations in CAVE. I could not find any numbers on this.<br /> b) Report the initial segmentation quality of the model: How many errors does it make? Note: There is a brief mention of VoI-based quantification in Methods (around line 1060), but the results are not reported.

What should be done: Report the error rates (with an accurate measure such as skeleton VoI) independently for all 6 volumes. Given that the authors have the proofread versions, this is feasible. Only then can the claims be made here be evaluated. Note that the F1-score of synapse prediction is quantified. This is a good starting point, but could also be extended to further species in order to assess the actual transferability. Furthermore, none of the data from the study seems to be available. The training data of the network has to be made available. If possible, high-resolution data should be proofread too.

Further points:

(1) Why isn't reconstruction at the cellular level addressed with ML? This is surely possible and should be easier than the full connectome analysis. Similar to before, the actual times needed for tracing with CATMAID are not reported; the manuscript only states that this can be done in minutes for a neuron, but it's unclear if this is the best or average case. It would help to have quantitative numbers to assess whether automation would bring any benefits.

(2) Finally, regarding the underlying software. I did not try this myself due to time constraints, but did check the repositories. They seem to be in an ok state with some documentation in a README. However, given the central role of the software contribution, I would expect a centralized doc page that explains how to use the different parts of the software, including a full example with sample data. Without this, application by other labs - a central claim - will be difficult.

Author Response:

Public Review:

On behalf of all authors I would like to thank the reviewers for highly constructive and helpful comments, which, once addressed fully, will make the paper stronger and more useful as a tools and resources contribution.

Besides addressing all minor issues that were pointed out by the reviewers, we see three main lines of changes we will need to pursue in order to address all major concerns. We plan to do all of these as fast as possible. Given that new alignments, segmentation and tracing is needed, this will take between one and three months.

(1) Availability of code, software documentation and accessibility of pipeline. 

Both reviewers and the editorial summary agreed that we need to improve the availability of our code, provide more instructions and examples of how to use the code, and make our methods more reusable to outsiders. To achieve this we will follow the suggestions made by the reviewers, in particular the list presented by reviewer 1 (point three of weaknesses in the public review).

We firstly would like to apologize for the faulty link to the SegToPCG (https://github.com/Heinzelab/SegToPCG) repository (the correct name and link is: LSDtoPCG and https://github.com/Heinze-lab/LSDtoPCG) as well as the missing code in the https://github.com/Heinze-lab/synful_312 repository; these issues have already been fixed and will be included in an updated bioRxiv version.

Second, we will generate an overarching umbrella page that will serve as a go-to site for any user who would like to implement our pipeline. To enable implementation, we will expand the documentation, provide detailed instructions, and include an example dataset with these instructions.

(2) Quantification of analysis steps, including segmentation, alignment and manual tracing, to validate our claims of increased efficiency and transferability across species.

As for point 1, both reviewers as well as the editorial summary highlighted the need for more comprehensive quantification of the workflow, especially with respect to segmentation quality as well as time investment into manual tracing and high resolution alignments. In particular, these data should validate the transferability of the segmentation models across species, and support the claims made about the time savings resulting from using our multiresolution workflow compared to a whole sample synaptic resolution approach.

To this aim, we will generate all analyses according to the reviewer suggestions and incorporate the resulting data in new figures and tables. To make the data fully comparable across species, we will apply the latest version of our alignment and segmentation scripts to at least one high resolution data stack of each species, quantify manual tracing of a comparable, defined set of neurons in each species, and perform VOI analyses of each species segmentation against manually traced neurons in identically sized testing volumes in each dataset. Additionally, we will proof-read identical branches of homologous neurons in each species and quantify the required number of edits from raw segmentation output to completion.

As the segmentation pipeline has evolved over the last years, a fair comparison between all datasets requires fresh analysis based on the latest version of our machine learning models (cannot be done with existing data) and will therefore take a few weeks of time.

(3) Clarification of aims for multi-resolution pipeline and how projectomes and connectomes inform each other

Reviewer 2 highlighted that there is not sufficient clarity about the aims of combining projectome and connectome. Judging from the reviewer comment, we might have inadvertently left the impression that we aimed at predicting a connectome from projectome data, by using spatial proximity of neurons as a proxy for connectivity. In fact, our data show that this is not possible, and that projection level data cannot predict connectivity. For instance, in the head direction system, the projectivity data suggests identical circuits for bees and flies (except at the edges of the ring), but connectivity data shows that the components of the ring attractor circuit are forming circuits that are distinctly different between the species (despite the same neurons with the same projection patterns being involved).

What we aim to do is slightly different. We define global patterns of information flow using the projectome, and then define circuits in a part of this global circuit at synaptic level. Then, we extrapolate the global connectivity by assuming that the circuits identified in one or two computational units (columns) are repeated in each column. This rests on the assumption that the same neurons form the same connections in each repeated module, as long as the cellular repertoire is identical (verified by the projectome), but does not use proximity data to predict connectivity. This method thus only applies to brain regions that consist of repeated computational modules, i.e. where we can assume that knowing the connectivity in one of them allows extrapolation to the entire brain region. While this is a simplification, the Drosophila CX has in principle confirmed this assumption.

We will generate a new figure in which we illustrate the process of combining local connectomes and global projectomes using examples from our data, but illustrating this schematically also for other brain regions, e.g. the insect optic lobe or the cerebral cortex of mammals. We will also carefully rewrite the relevant text passages to avoid misunderstandings.

Overall, we would like to thank the reviewers again for their thorough and detailed comments, which will help to make our connectomics workflow more accessible and reproducible.

eLife Assessment

This manuscript demonstrates the feasibility and potential value of using functional MRI in awake, behaving mice, enabling assessment of distributed brain activity during ongoing behavior in a manner analogous to human fMRI. The valuable findings suggest that the periaqueductal gray (PAG), a midbrain structure classically linked to threat processing and aversive learning, also contributes to reversal learning. If supported, this result would carry theoretical and practical implications for our subfield by expanding the computational roles attributed to the PAG and motivating cross-species circuit-level investigations. However, the strength of evidence is, at present, incomplete, and several key claims are only partially supported by the current analyses.

Reviewer #1 (Public review):

Summary:

The authors aimed to determine the neural networks involved in updating behaviour by training mice on a 'go / no go' odour discrimination task, and measuring their brain activity using functional MRI.

Strengths:

The use of the translationally relevant 'go / no go' task is a major strength, as this is a task that can be used as readily in humans as in animals such as mice. The use of fMRI in awake, behaving mice is also a major strength, as this allows the activation of multiple brain regions to be measured while behaviour is ongoing, and also facilitates comparison to human studies. The computational modelling approaches further support these translational aims, again being as readily applied to human data as to animal data.

Weaknesses:

The major weakness of the paper - and one that is potentially addressable - is that the key analysis of the paper, showing that the periaqueductal gray (PAG) is recruited for reversal learning, is only partially supported by the data presented in the paper as it stands. The authors have used a sophisticated way of analysing the behavioural data using 'signal detection theory', in which they collected behavioural data showing correct 'go' responses ('hits'), correct 'no go' responses ('correct rejections'), missed 'go' responses ('misses') and go responses when mice should have withheld a response ('false alarms'). The data presented showing a double dissociation in the activation of the nucleus accumbens for 'hits' but not 'correct rejections' and the PAG for 'correct rejections' but not 'hits' is very interesting; however, it is confounded by the fact that the nucleus accumbens may activate when the animal makes a response, and the PAG when the animal withholds a response. If the authors also included the analysis of nucleus accumbens and PAG activation for 'misses' and 'false alarms', this would allow them to determine whether the activation of these regions reflects the behavioural response or the expectation of reinforcement from the response.

Thus, the paper includes very interesting data and is impressive in its approach to analysing behaviour in a manner that is highly translatable between species. The additional analyses would markedly strengthen the paper and would add depth to the finding that the PAG appears to be involved in behavioural flexibility.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors test the hypothesis that whole-brain functional magnetic resonance imaging in behaving mice, coupled with reinforcement-learning modeling, can dissociate neural substrates of initial cue-reward acquisition versus contingency reversal, and potentially reveal underappreciated contributors to cognitive flexibility. Using a head-fixed go/no-go odor discrimination task with subsequent rule reversal in a subset of mice, they model trial-by-trial state-action values with a model-free Q-learning algorithm (hierarchical Bayesian fit) and use the model-derived decision variable as a parametric regressor in whole-brain analyses. They report that acquisition-related signals prominently involve ventral and dorsal striatal regions, whereas reversal learning additionally recruits the periaqueductal gray (negative correlation with the decision variable) and shows an apparent double dissociation between nucleus accumbens and periaqueductal gray responses for hit versus correct-rejection outcomes during reversal.

Strengths:

(1) The reversal manipulation is implemented without explicit punishment, targeting suppression of previously rewarded actions under reward omission - an underexplored regime for midbrain contributions beyond canonical threat/pain framing.

(2) The manuscript provides a credible MR-compatible olfactory/licking platform with synchronized sniff/lick/valve/reward timing and high-field imaging, supporting feasibility and broader utility for mesoscale systems neuroscience in rodents.

(3) Trial-by-trial value estimates from a Q-learning variant are fit via hierarchical Bayesian inference and explicitly integrated into subject-level general linear models with a mouse hemodynamic response function, which is appropriate for leveraging within-subject dynamics in small-N rodent fMRI.

(4) The decision-variable maps during acquisition recover expected basal ganglia involvement (including nucleus accumbens and dorsal striatum), providing face validity; the reversal-stage map yields an interpretable set of cortical/striatal/pallidal regions plus periaqueductal gray/hippocampus.

(5) The finite impulse response analysis stratified by behavioral outcomes (hit, false alarm, correct rejection, miss) adds interpretability beyond the model regressor alone, and the reported crossover interaction between nucleus accumbens and periaqueductal gray is potentially impactful if robust.

Weaknesses:

(1) The core claim regarding selective periaqueductal gray engagement rests on a subset of n = 6 mice for reversal. With permutation-based whole-brain inference and very small cluster sizes, the robustness of the periaqueductal gray effect to reasonable analytic perturbations is not yet convincing. I would suggest providing leave-one-animal-out analyses for the periaqueductal gray cluster/ROI effects and reporting how often the key findings survive.

(2) The authors note that due to temporal resolution and hemodynamics, they cannot separate stimulus, choice, and feedback and therefore model "whole trials." This limitation creates ambiguity about whether periaqueductal gray signals reflect value updating, action inhibition (no-lick), reward omission, autonomic arousal, or motor preparation/withholding, especially given the strong hit versus correct-rejection opponency. I would suggest adding targeted analyses that disambiguate "withholding" from "reversal-related updating".

(3) ROIs are defined from the whole-brain decision-variable maps and then interrogated by outcome types; the manuscript acknowledges non-independence. This can inflate apparent dissociations. It would be better if the authors define ROIs independently (anatomical periaqueductal gray/nucleus accumbens masks, or split-half ROI definition with held-out data) and repeat the key ROI conclusions.

(4) The reversal group is a subset of the acquisition cohort and also experiences a different task phase structure and additional sessions; the paper attempts to address exposure differences descriptively. I would suggest that the authors formally test whether periaqueductal gray effects are explained by session count, time-in-scanner, or learning rate differences (e.g., include these as covariates, or match sessions more strictly).

(5) The platform records sniffing and licking, but the imaging models described include motion, global, and ventricle regressors and do not clearly include trialwise lick/sniff covariates. Given the periaqueductal gray's known autonomic and defensive coordination roles, physiological state confounding is a major concern. Could the authors incorporate sniff and lick metrics (and their derivatives) as nuisance regressors and show whether the periaqueductal gray effects persist?

eLife Assessment

This multi-omics study provides a comprehensive characterization of the context-dependent roles of the JAK-STAT pathway (JSP) across different cellular compartments within the breast cancer microenvironment. The authors present convincing evidence that high JSP activity paradoxically drives anti-tumor cytotoxicity in T cells but promotes malignancy and immunosuppression in tumor epithelial cells, leading to the fundamental discovery that broad JAK-STAT inhibition could be therapeutically counterproductive. Ultimately, the identification of the immune-related JSP score and the STAT4 axis as predictive biomarkers for anti-PD-1 immunotherapy response, particularly in triple-negative breast cancer, offers critical insights for precise patient stratification and targeted therapeutic interventions.

Reviewer #1 (Public review):

Summary:

In their manuscript, Zhou and colleagues present a detailed look at how the JSP functions differently in the various cells of a breast tumor. The authors have effectively shown that the JSP acts as a double-edged sword, as it helps T cells fight cancer but also allows tumor cells to grow and avoid ferroptosis. These findings are important because they identify a useful biomarker to predict how TNBC patients might respond to PD-1 inhibitors.

Strengths:

This work is important because it provides a clear explanation for the conflicting roles of the JSP in the tumor environment. The evidence is solid, as it combines data from thousands of patients with single-cell analysis and lab experiments to confirm the role of STAT4 in cancer progression and immunity.

Weaknesses:

However, there are areas for improvement in the scope of the review, the depth of analysis, and the potential for broader clinical implications. The authors are encouraged to address these issues to enhance the scientific and clinical impact of the study.

Major Issues:

(1) The authors demonstrate that STAT4 upregulates SLC47A1, but this is currently supported only by expression correlation and western blot data. To confirm a direct link, the authors are encouraged to perform ChIP-qPCR or luciferase reporter assays to show that STAT4 binds directly to the SLC47A1 promoter.

(2) The conclusion that the MIF-CD74 axis drives immunosuppression is based on computational inference. To support this, the authors could consider mining publicly available breast cancer spatial transcriptomics data to show the co-localization of MIF and CD74. Alternatively, performing simple dual-color immunofluorescence staining on a few clinical sections would effectively demonstrate the physical proximity of these cells.

(3) TNBC is highly heterogeneous and includes subtypes like mesenchymal and immunomodulatory groups. The authors should analyze whether the JSP score or STAT4 levels vary significantly between these subtypes, as this could further refine the selection of patients for JAK1 inhibitors.

(4) While the JSP score works well in the current datasets, the authors should consider validating its predictive accuracy in additional independent immunotherapy cohorts, such as the TONIC trial, to ensure the biomarker is robust across different treatment settings.

Minor Issue:

The manuscript mentions a U-shaped trajectory of JSP activity during tumor transition. A more detailed biological explanation of why the pathway activity initially drops and then rises would add depth to the discussion.

Reviewer #2 (Public review):

Summary:

The JAK-STAT pathway (JSP) exhibits cell-type-specific functional heterogeneity in breast cancer. This study investigates the JSP in breast cancer and its response to anti-PD‑1 immunotherapy. JSP displays distinct cell‑type heterogeneity: it promotes malignant phenotypes and immunosuppression in tumor cells, while enhancing cytotoxicity and reducing exhaustion in T cells. Elevated JSP expression correlates with improved immunotherapy responses, especially in triple‑negative breast cancer. These findings highlight the paradoxical roles of JSP, indicating that broad inhibition may compromise anti‑tumor immunity.

Strengths:

The major strengths of this study include the comprehensive characterization of JSP heterogeneity across epithelial, tumor, and T cells in breast cancer. The identification of JSP and STAT4 as predictive biomarkers for immunotherapy response, particularly in triple‑negative breast cancer, provides clinically relevant insights for patient stratification.

Weaknesses:

The findings rely heavily on public dataset analyses.

Reviewer #3 (Public review):

Summary:

This multi-omics study by Zhou et al elucidates the context-dependent roles of the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway (JSP) across different cellular compartments in the breast cancer tumor microenvironment. While bulk JSP activity is associated with a favorable prognosis, single-cell analysis reveals a paradoxical landscape: high JSP in T cells drives anti-tumor cytotoxicity and reduces exhaustion, whereas high activity in tumor epithelial cells promotes malignancy and immunosuppression via the MIF-CD74 signaling axis. The JSP score (immune-related) serves as a robust predictive biomarker for response to anti-PD-1 immunotherapy, particularly in triple-negative breast cancer (TNBC). Furthermore, the study identifies the STAT4/SLC47A1 axis as a critical mechanism through which tumor cells resist ferroptosis, facilitating disease progression. These findings suggest that broad JAK-STAT inhibition may be counterproductive in cancer therapeutics; instead, therapeutic success depends on precise modulation and carefully timed interventions to preserve its T-cell-associated functions. This study may inspire future studies to explore specific factors that selectively modulate JAK-STAT activity in immune cells to achieve favorable therapeutic outcomes.

Strengths:

Significant therapeutic implications.

Weaknesses:

Limited molecular mechanisms.

eLife Assessment

It remains unclear how human antibody-secreting cells (ASCs) differentiate. In this study, the authors discovered a CD30⁺ intermediate subset that appears during the transition from B cells to ASCs, providing a potential ontogeny for extra-germinal center B cell differentiation. This study is useful because it identifies novel intermediate markers that enable tracking of human ASC ontogeny, offering new insights into ASC development. However, the evidence is incomplete, and we see three major limitations: (1) the data are largely representative, requiring additional reproducibility; (2) the bioinformatics analysis is limited; and (3) step-wise phenotypic validation would require lineage-tracing experiments on sorted populations.

Reviewer #1 (Public review):

Summary:

Fields et al. investigated the heterogeneity and kinetics of human antibody secreting cell (ASC) differentiation by analyzing ex vivo tonsil samples and using in vitro differentiation modeling. They discovered that a CD30+ intermediate subset emerges in transition from B cell to ASC in both contexts, but not from germinal centers, and they identified cytokines that promote this state. They also identified an isoform of CD44, CD44v9, that is expressed on some ASCs.

Strengths:

The strengths are the novelty of the findings and the identification of two new markers that may be useful for tracking ASC heterogeneity.

Weaknesses:

However, some of this work seems preliminary and would need to be further validated. Some of the data presented was only representative, with limited controls and biological repeats, limiting the interpretation. For example, the role of Mef2c for CD30 expression was not robustly demonstrated. It was not clear if Figure 1 scRNAseq/ATACseq was from multiple donors or just one. Future studies may extend these novel findings and determine the functional relevance of these factors, CD30, and CD44v9 for ASC differentiation and physiology.

Reviewer #2 (Public review):

Summary:

Bhattacharya and colleagues here use cell culture, single-cell RNA and ATACseq sequencing of such in vitro cultures and of ex vivo isolated B-lineage cells to infer an ontogeny for extra-germinal centre B cell differentiation. The manuscript presents a useful potential ontogeny for plasma cells, wherein in vitro cultured naïve human B cells enter a CD30+ intermediate state before moving in subsequent days through a CD44v9+ state before ultimately obtaining a 'mature' antibody-secreting plasma cell phenotype. Ex vivo isolated germinal centre B cells obtain the plasma cell state without expressing CD30 in their development. Phenotype analysis of tonsillar B-lineage cells supports the same phenotype conversion in vivo, although the intermediate cell population was smaller in vivo. The link to CD44v9 expression on developing plasma cells is inferred to be for extra-GC (T-independent) responses, but the data presented leave this equivocal, and the functional importance of developing via a CD30+CD44v9+ intermediate is not investigated.

Strengths:

The article presents a solid potential ontogeny for PC development, wherein some differentiating B cells acquire a CD30+ state, transition through a CD44v9+CD30+ state, then downmodulate CD30 before obtaining canonical CD38+ 'PC' status. A strength is the integration of in vitro cultured B cell results with tonsillar B-lineage cell data sets, and careful flow cytometry of the in vitro cultures over several days to infer lineage. The data provide reasonable support for the concept. CD30+ cells are shown to develop readily from naïve B cells in culture, but uncommonly from GC B cell cultures. A nice piece of data is Figure 6B, which shows reasonably strong correlative changes in phenotype through the assumed ontogeny, and this fits with the expected trajectory of maturation.

Weaknesses:

The most important weakness throughout is the non-absolute nature of the relationship. An example is seen in that the sorted ex vivo GC B cells also give rise to the 'extra-GC' phenotype of plasma cell, suggesting that while the profile is enriched, it is not absolute. There is a further weakness, as while cultures are run for several days, division-associated shifts in PC phenotype are not mapped; such would greatly strengthen the weight of the argument, and show conditional shifts in phenotype associated with division, an uncontrolled parameter in the mix. For example, for the MEF2C A388 inhibition experiments, it would be strong evidence of the pathway/process contributing if a by-division peak increase in CD30+ population was demonstrated in the early days of culture.

There are some basic sort experiments performed (e.g. 3C-3F), which show that the CD30+ cells do give rise to PC preferentially, but what is missing is the step-wise phenotype shifts in these sorted populations, which should support the trajectory shown in Figure 3B and (the in vitro equivalent of) 6B. It would emphatically support the trajectory to show the cellular phenotypes on the PC with sorting based on CD30, CD44v9, CD27, and CD20 expression, and following outcome phenotypes 24-48 hours later, if the inferred maturation trajectory is true.

There are also specific weaknesses with the bioinformatics, in that, while the analyses are likely appropriate, unpresented data is necessarily used to shape the argument. For example, Figure 1C shows bubble plots for two plasma cell sets, yet, of archetypal PC-expressed genes, only IRF4 is demonstrated to confirm they are true PC, and the gene is not universally expressed in cells in the clusters. For this figure, it would help to expand the bubble plot to show J-CHAIN, XBP-1, CIITA and PRDM1 or other appropriate PC demarcating molecules. Similarly, in Fig 2B, more evidence of a bifurcation in state is needed than that CD44v9 distinguishes PC1 from PC2 clusters-this is the stated conclusion, but 2A depicts that 50% of PC1 relatively weakly express CD44, while <25% of PC2 express it. Demonstrating additional molecules or genes distinguishing the clusters would improve veracity. Figure 2F shows clonal lineages, but it would be helpful to see somatic hypermutation burdens and learn if they differ between the demarcated subsets. I also find the pseudotime analyses of limited value, as some of the branches follow trajectories that are unrealistic biologically, so less weight should be placed on the pathways to which they do or do not point (i.e., the notion that GC B cells do or do not give rise to particular PC subsets).

Statistically, some of the experiments are single wells from single donors, so there is a low level of confidence and no reproducibility demonstrated for some aspects of the study, which is a weakness.

Paradoxical to the argument that it is the TI response process being modelled, it is presented that CpG stimulation, plus proxy T cell help (CD40L), drives the CD30+ phenotype best with the addition of the GC-associated cytokine IL-21. This should be carefully considered and discussed.

Overall, in addition to presenting more contextual information from the bioinformatics, the best way to solidify the data set, in my vie,w would be to revisit the hypothesis with two additional experimental approaches: (1) to incorporate division tracing into the ontogeny studies and (2) to perform lineage tracing on sort-purified populations at different stages of the maturation process.

Je trouve qu’il y a un peu trop d’informations visuelles (mots en gras, citations, liens..)

DOI: 10.1038/s41590-026-02458-x

Resource: Nonhuman Primate Reagent Resource (RRID:SCR_012986)

Curator: @giovanni.decastro

SciCrunch record: RRID:SCR_012986

What is this?

DOI: 10.1016/j.chembiol.2026.03.010

Resource: (IMSR Cat# JAX_003627,RRID:IMSR_JAX:003627)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:003627

What is this?

DOI: 10.1158/1535-7163.MCT-25-0392

Resource: SILVA (RRID:SCR_006423)

Curator: @scibot

SciCrunch record: RRID:SCR_006423

What is this?

DOI: 10.1158/1535-7163.MCT-25-0392

Resource: VSEARCH (RRID:SCR_024494)

Curator: @scibot

SciCrunch record: RRID:SCR_024494

What is this?

DOI: 10.1158/1535-7163.MCT-25-0392

Resource: (IMSR Cat# JAX_019104,RRID:IMSR_JAX:019104)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:019104

What is this?

DOI: 10.1158/1535-7163.MCT-25-0392

Resource: phyloseq (RRID:SCR_013080)

Curator: @scibot

SciCrunch record: RRID:SCR_013080

What is this?

DOI: 10.1158/1535-7163.MCT-25-0340

Resource: (JCRB Cat# JCRB0721, RRID:CVCL_0259)

Curator: @scibot

SciCrunch record: RRID:CVCL_0259

What is this?

DOI: 10.1158/1535-7163.MCT-25-0340

Resource: (ICLC Cat# ATL95003, RRID:CVCL_0213)

Curator: @scibot

SciCrunch record: RRID:CVCL_0213

What is this?

DOI: 10.1158/1535-7163.MCT-25-0340

Resource: None

Curator: @scibot

SciCrunch record: RRID:CVCL_2251

What is this?

no direct quptes sa column

no direct quptes sa column

Mention the phase of the game in this point. We already lost the sauce.

again, tactical highlight, you are not yet here yet.

we need a tactical POV of the game

this whole thing is just not relatable to the intended audience

mga quotation na umuulit lamang sa sinasabi ng naunang talata

strengthen the tone

This part is already mentioned on the primary angle....

either connect it to the recent paragraph OR cite the person here.

Again, this is a backgrounder data. Should be on the last paragraph.

There's no quotation paragraphs on columns

It'll enable them to have time....

MINIMIZE the preaching devices for columns (must, should)

add more quotation paragraphs on your feature! It holds the article together.

the backgrounder feels lacking. kinsa pay lahi nga nakabuhat ani?

Also before this, any emotional statement from the winner? Any indirect quotation gikan sa atoang losing player

remove, since u already made the style earlier on the first para

this solutions section seems so one dimensional and should be adding more. maybe adding more guardrails in the competition.