It’s interesting that Augustus was given so much power but still refused to be called a king. Even when he basically ruled like one.

I find it interesting that the senators thought killing Caesar made them the good guy. Calling themselves liberators as they marched proudly with their bloody daggers.

I wonder how did Alexanders time studying with Aristotle help him when he started governing Macedon at just sixteen years old?

It is interesting that right before the final battle, te villain Ravana tells Rama that he’s actually god Vishnu in human form.

I find it interesting that after Alexander died, the Jewish land of Judea was taken over by the Seleucids. It shows how his empire broke into different parts.

I think its so interesting how this new faith appealed to totally different kinds of people! Educated elites liked its deep ideas, like how it connected to Plato's perfect forms, but it also gave hope and support to the poor and struggling. it really reached everyone in some way!

This man had a great adventure during his life and 6,000 pounds of gold! Wow!

I think this is interesting and I never knew that this was a religious thing.

This development simplified sending messages, surely influencing the future of the internet. If this has never been developed, who knows how the internet would have unfolded.

Topic discussed in class 10/15/2025

This case was about the Civil Rights Movement, specifically in Alabama. Its goal was to help support MLK and protect the First Amendment (freedom of speech and freedom of the press).

This sentence essentially calls out ChatGPT for what it really is - a tool that rephrases research found on the internet. You can always find the same information online with a bit of research, and word it more simply according to your understanding (making the content more suitable to you and your use of the knowledge). At the end of the day, ChatGPT is only a tool that summarizes multiple different answers found through research online. Though it's a useful tool to summarize topics at large, it doesn't delve into details of each website, article, etc.

This is an impressive example of collective support and online activism. I think utilizing social media "trolling" can be a effective way of stopping harmful initiatives I have seen things similar to this a few times.

I notice how the instructions weaponize ordinary tools (dating apps, webcams, public livestreams); nothing here is “high-tech,” which suggests that prevention is more about norms and platform rules than about advanced security. If we treat this as a design problem, I think dating sites could rate-limit new accounts, require stronger photo verification, and flag patterns like many “new users” inviting different men to the same place and time. Culturally, the meme also normalizes group bonding through dehumanizing outsiders; it is worth asking whether some online communities teach people that empathy is weakness. Personally, I feel angry and a bit scared, because the prank depends on making a private hope (a date) into public shame; it punishes people for trying to connect.

When looking at the example of the person mispronouncing Hors d'oeuvres, it is an act of a harmless troll. If someone does mispronounce this, the worst thing that would happen is someone correcting them. I think this type of trolling is both amusing and harmless. However, that is not the case for a lot of trolls who have harmful intent behind their messages on the internet.

eLife Assessment

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain activity, grounded in artificial network networks for Bayesian statistical inference. The authors have provided convincing evidence, across several case studies, for the utility and validity of the methods using simulated data from several commonly used models, but more thorough benchmarking could be used to demonstrate the practical utility of the toolkit. This work will be of interest to computational neuroscientists interested in modelling large-scale brain dynamics.

Reviewer #1 (Public review):

This work provides a new Python toolkit for combining generative modeling of neural dynamics and inversion methods to infer likely model parameters that explain empirical neuroimaging data. The authors provided tests to show the toolkit's broad applicability, accuracy, and robustness; hence, it will be very useful for people interested in using computational approaches to better understand the brain.

Strengths:

The work's primary strength is the tool's integrative nature, which seamlessly combines forward modelling with backward inference. This is important as available tools in the literature can only do one and not the other, which limits their accessibility to neuroscientists with limited computational expertise. Another strength of the paper is the demonstration of how the tool can be applied to a broad range of computational models popularly used in the field to interrogate diverse neuroimaging data, ensuring that the methodology is not optimal to only one model. Moreover, through extensive in-silico testing, the work provided evidence that the tool can accurately infer ground-truth parameters even in the presence of noise, which is important to ensure results from future hypothesis testing are meaningful.

Weaknesses

The paper still lacks appropriate quantitative benchmarking relative to other inference tools, especially with respect to performance accuracy and computational complexity and efficiency. Without this benchmarking, it is difficult to fully comprehend the power of the software or its ability to be extended to contexts beyond large-scale computational brain modelling.

Reviewer #2 (Public review):

Summary:

Whole-brain network modeling is a common type of dynamical systems-based method to create individualized models of brain activity incorporating subject-specific structural connectome inferred from diffusion imaging data. This type of model has often been used to infer biophysical parameters of the individual brain that cannot be directly measured using neuroimaging but may be relevant to specific cognitive functions or diseases. Here, Ziaeemehr et al introduce a new toolkit, named "Virtual Brain Inference" (VBI), offering a new computational approach for estimating these parameters using Bayesian inference powered by artificial neural networks. The basic idea is to use simulated data, given known parameters, to train artificial neural networks to solve the inverse problem, namely, to infer the posterior distribution over the parameter space given data-derived features. The authors have demonstrated the utility of the toolkit using simulated data from several commonly used whole-brain network models in case studies.

Strength:

Model inversion is an important problem in whole-brain network modeling. The toolkit presents a significant methodological step up from common practices, with the potential to broadly impact how the community infers model parameters.

Notably, the method allows the estimation of the posterior distribution of parameters instead of a point estimation, which provides information about the uncertainty of the estimation, which is generally lacking in existing methods.

The case studies were able to demonstrate the detection of degeneracy in the parameters, which is important. Degeneracy is quite common in this type of models. If not handled mindfully, they may lead to spurious or stable parameter estimation. Thus, the toolkit can potentially be used to improve feature selection or to simply indicate the uncertainty.

In principle, the posterior distribution can be directly computed given new data without doing any additional simulation, which could improve the efficiency of parameter inference on the artificial neural network is well-trained.

Weaknesses:

The z-scores used to measure prediction error are generally between 1-3, which seems quite large to me. It would give readers a better sense of the utility of the method if comparisons to simpler methods, such as k-nearest neighbor methods, are provided in terms of accuracy.

A lot of simulations are required to train the posterior estimator, which is computationally more expensive than existing approaches. Inferring from Figure S1, at the required order of magnitudes of the number of simulations, the simulation time could range from days to years, depending on the hardware. The payoff is that once the estimator is well-trained, the parameter inversion will be very fast given new data. However, it is not clear to me how often such use cases would be encountered. It would be very helpful if the authors could provide a few more concrete examples of using trained models for hypothesis testing, e.g., in various disease conditions.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

This work provides a new Python toolkit for combining generative modeling of neural dynamics and inversion methods to infer likely model parameters that explain empirical neuroimaging data. The authors provided tests to show the toolkit's broad applicability, accuracy, and robustness; hence, it will be very useful for people interested in using computational approaches to better understand the brain.

Strengths:

The work's primary strength is the tool's integrative nature, which seamlessly combines forward modelling with backward inference. This is important as available tools in the literature can only do one and not the other, which limits their accessibility to neuroscientists with limited computational expertise. Another strength of the paper is the demonstration of how the tool can be applied to a broad range of computational models popularly used in the field to interrogate diverse neuroimaging data, ensuring that the methodology is not optimal to only one model. Moreover, through extensive in-silico testing, the work provided evidence that the tool can accurately infer ground-truth parameters even in the presence of noise, which is important to ensure results from future hypothesis testing are meaningful.

We appreciate the positive feedback on our open-source tool that delivers rapid forward simulations and flexible Bayesian model inversion for a broad range of whole-brain models, with extensive in-silico validation, including scenarios with dynamical/additive noise.

Weaknesses

The paper still lacks appropriate quantitative benchmarking relative to non-Bayesian-based inference tools, especially with respect to performance accuracy and computational complexity and efficiency. Without this benchmarking, it is difficult to fully comprehend the power of the software or its ability to be extended to contexts beyond large-scale computational brain modelling.

Non-Bayesian inference methods were beyond the scope of this study, as we focused on full posterior estimation to enable uncertainty quantification and detection of degeneracy. Their advantages and disadvantages are briefly discussed in the Introduction and Discussion sections.

Reviewer #2 (Public review):

Whole-brain network modeling is a common type of dynamical systems-based method to create individualized models of brain activity incorporating subject-specific structural connectome inferred from diffusion imaging data. This type of model has often been used to infer biophysical parameters of the individual brain that cannot be directly measured using neuroimaging but may be relevant to specific cognitive functions or diseases. Here, Ziaeemehr et al introduce a new toolkit, named "Virtual Brain Inference" (VBI), offering a new computational approach for estimating these parameters using Bayesian inference powered by artificial neural networks. The basic idea is to use simulated data, given known parameters, to train artificial neural networks to solve the inverse problem, namely, to infer the posterior distribution over the parameter space given data-derived features. The authors have demonstrated the utility of the toolkit using simulated data from several commonly used whole-brain network models in case studies.

Strength:

Model inversion is an important problem in whole-brain network modeling. The toolkit presents a significant methodological step up from common practices, with the potential to broadly impact how the community infers model parameters.

Notably, the method allows the estimation of the posterior distribution of parameters instead of a point estimation, which provides information about the uncertainty of the estimation, which is generally lacking in existing methods.

The case studies were able to demonstrate the detection of degeneracy in the parameters, which is important. Degeneracy is quite common in this type of models. If not handled mindfully, they may lead to spurious or stable parameter estimation. Thus, the toolkit can potentially be used to improve feature selection or to simply indicate the uncertainty.

In principle, the posterior distribution can be directly computed given new data without doing any additional simulation, which could improve the efficiency of parameter inference on the artificial neural network is well-trained.

We thank the reviewer for the careful consideration of important aspects of the VBI tool, such as uncertainty quantification rather than point estimation, degeneracy detection, features selection, parallelization, and amortization strategy.

Weaknesses:

The z-scores used to measure prediction error are generally between 1-3, which seems quite large to me. It would give readers a better sense of the utility of the method if comparisons to simpler methods, such as k-nearest neighbor methods, are provided in terms of accuracy. - A lot of simulations are required to train the posterior estimator, which is computationally more expensive than existing approaches. Inferring from Figure S1, at the required order of magnitudes of the number of simulations, the simulation time could range from days to years, depending on the hardware. The payoff is that once the estimator is well-trained, the parameter inversion will be very fast given new data. However, it is not clear to me how often such use cases would be encountered. It would be very helpful if the authors could provide a few more concrete examples of using trained models for hypothesis testing, e.g., in various disease conditions.

We agree with the reviewer that for some parameters the z-score is large, which could be due to the limited number of simulations, the informativeness of the data features, or non-identifiability, and we do address these possible limitations in the Discussion. In line with our previous study, we stick to Bayesian metrics such as posterior z-scores and shrinkage. The application of an amortized strategy needs to be demonstrated in future work, for example in anonymized personalization of virtual brain twins (Baldy et al., 2025).

Ref: Baldy N, Woodman MM, Jirsa VK. Amortizing personalization in virtual brain twins. arXiv preprint arXiv:2506.21155.

Reviewer #1 (Recommendations for the authors):

(1) The authors want to keep the term "spatio-temporal" data features to make it consistent with the language they use in their code, even though they only refer to statistical and temporal features of the time series. I stand by my previous comment that this is misleading and should be avoided as much as possible because it doesn't take into account the actual spatial characteristics of the data. At the very least, the authors should recognize this in the text.

We have now recognized this point.

(2) There are still some things that need further clarification and/or explanation:

(a) It remains unclear why PCA needs to be applied to the FC/FCD matrices. It was also unclear how many PCs were kept as data features.

We aim to use as many features as possible as a battery of metrics to reduce the number of simulations. The role of each feature can be investigated in future studies.  For instance, PCA is used in the LEiDA approach (Cabral et al., 2017) to enhance robustness to high-frequency noise, thereby overcoming a limitation common to all quasi-instantaneous measures of FC. In this work, the default setting was two PCA components. 

Ref:  Cabral J, Vidaurre D, Marques P, Magalhães R, Silva Moreira P, Miguel Soares J, Deco G, Sousa N, Kringelbach ML. Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Scientific reports. 2017 Jul 11;7(1):5135.

(b) It was also unclear which features were used for each model. This is important for reproducibility and to make the users of the software aware of which features are most likely to work best for each model.

We have done our best to indicate the class of features used in each case. This is illustrated more clearly in the notebook examples provided in the repository.

Reviewer #2 (Recommendations for the authors):

Thanks for responding to my suggestions. Here is only one remaining point:

Section 2.1: Please mention the atlas used to parcellate the brain; without this information, readers won't know what area 88 is in Figure 1, for example. 

We have now mentioned this point. In this study we used AAL Atlas.

I have noticed this and it’s strange how far back this goes. I told my dad about this when i was younger and he told me about how it was even worse when he was in hs because there was often fights between the students in regards to race with people often gettting fatally hurt. I think at some point it became a thing of sittting with people who have faced the same hardships that you have

Even in environments free of overt discrimination, negative social stereotypes can induce anxiety and stress in Black students during exams or classroom performance, leading to declining grades. The interplay of “identity,” “academic disparities,” and “social prejudice” sheds light on how racial differences ultimately impact Black students' academic achievement.

Black students “gathering together” is not an act of segregation, but rather a psychological necessity. After experiencing racial prejudice, they often find emotional resonance and a sense of security among Black peers, rather than understanding from white friends. This is because certain cultural experiences and traumas cannot be fully acknowledged by people of other races. It's similar to how only some Asian students can truly relate to my struggles with pressure in public education or trauma stemming from my family of origin. The appearance of “self-segregation” can be interpreted as a coping strategy.

This passage reveals the author's central argument: Black adolescents' identity exploration differs from their white peers, as they must confront the “racial labels” imposed by society during adolescence. Black children are not only seeking personal identity but are also compelled to understand how society perceives their skin color. “Identity development” is not merely a matter of psychological growth but also the outcome of a socialization process. This reminds me of another somewhat similar topic. Some argue that gender is also a form of socialized symbol. Psychological gender and gender identity are actually shaped by an individual's social experiences and cognition—they are products of socialization. This perspective bears some resemblance to the author's view on racial cognition.

Museums vary in the things that they highlight and note as being important

Reminder to Self: put examples to the definition for better understanding

The construct you are trying to measure, or the effect of the program or treatment that's being implemented.

I think the tweet made by White was offensive. I think joking about someones death is a line no one should cross. MLK was one of the greatest figures of American history and for this person to just disrespect his legacy was beyond disrespectful.

This article tells us the joke that Jaboukie Young-White made about FBI and MLK. I think it is offensive because MLK is such a great figure in the US. There are people admiring him and respecting him. Making jokes about MLK's assassination by impersonating FBI offended the real FBI, and it also insulted MLK.

Phillips argues that trolling isn’t some fringe glitch—it feeds on the same attention economy that mainstream media uses, which is why hoaxes and outrage travel so well. A helpful detail from her piece is how “it’s just a joke” functions as a shield: ambiguity lets trolls toggle between sincerity and irony to avoid accountability while still harvesting attention. That framework reframes cases like the “Forever Alone Flashmob” as not only individual cruelty but also a media-system problem: amplification (retweets, headlines, livestreams) is the fuel. My takeaway is that platform and newsroom practices—e.g., not linking to troll content, slowing virality for unverifiable claims, and de-incentivizing engagement spikes—are as important as user education for reducing harm.

This article gauges public opinion on the tweet posted by Jaboukie Young-White about Martin Luther King Day and asks the question of "funny or offensive?" to the website's viewers. An aspect of the article I found particularly interesting was a tweet in response to Jaboukie's saying, "Jaboukie got his account deleted in minutes for a legit joke, but an account I reported with the n-word in its header and tweets is still up 46 hours later." This says a lot about what type of content social media companies like Twitter or Instagram put their effort into censoring or banning.

This link mainly explains that in 1993, Loyd Jowers claimed the Mafia and U.S. government were involved in Martin Luther King Jr.’s assassination, and in 1999, a civil trial supported the King family’s belief that there was a broader conspiracy beyond James Earl Ray.

A comedian named Jaboukie Young-White pretended to be the FBI on Twitter by changing his profile picture and name, and commented a pretty offensive joke about MLK. Even though Twitter deleted the tweet and suspended his account, a lot of people took screenshots, and the joke went viral. One fun detail was he also changed his account into a white girl with blonde hair and blue eyes as the "gun girl". It seems that it wasn't the first time for him doing pranks.

This source discusses one of the earliest moments of trolling. The BBC broadcast an April Fool's video showing spaghetti growing on trees. This surprisingly worked because the people of Britain were not yet familiar with spaghetti, convincing many that spaghetti actually grew on trees. This is considered one of the most significant instances of trolling by an official broadcasting network. However, this being such a big event, it is still considered harmless, especially in comparison to the trolling that goes on today.

I really appreciated the inclusion of Whitney Phillips’s “Internet Troll Sub-Culture’s Savage Spoofing of Mainstream Media” (Scientific American, 2015) — it gives a grounded, cultural perspective on trolling that complements the ethical discussions nicely. I wonder whether there are more recent studies that explore how trolling has shifted into coordinated “political influence” efforts that would update or extend Phillips’s observations.

Link leads to a website where people can disscuss, review, and rate video-games. They can also read about the lastest news, developments, and updates on current or future games they are interested in

Regarding the FBI–King suicide letter incident, I believe this is a classic case of government interference in citizens' rights. For every ordinary person, we should ensure our rights are not violated and courageously protect ourselves.

This was a prank done in 1957 however, I have heard about it in recent years. Basically, the BBC pranked British people into believing that spaghetti grew from trees. They were able to fool so many people since not many people in Britain knew about the origins of pasta, but this form of trolling is rather light compared to what's going on nowadays.

This article talks about how Jaboukie Young-White, a comedian who impersonated the FBI account on twitter by changing his profile picture and user name handle. Whom then proceeded to post an insulting "joke" about Martin Luther King Jr. assassination on MLK day. In attempts of some morbid humor he posted the tweet which immediately had twitter take down the tweet and his account --- leading to his suspension. This controversial tweet had some people questioning the boundaries of jokes/comedy with inappropriate insulting behavior. As his attempts were intentionally supposed to be funny but truly just came off offensive and disrespectful.

institutions are not only chnaged but create changes

Change in society and people bc fittest habits

we select the best things and change to that

Life is hard for us just as it is for animals, and those who adapt do better.

talks about change in society and people

talks about how the existence of a man in society is a struggle and a survival of the fittest

"still like a statue" - I liked the vocab word "Megalith"

I strongly endorse the main theme of most of the reviews, which is that the progression and underlying justifications for this article’s arguments needs a great deal of work. In my view, this article’s main contribution seems to be the evaluation of the three peer review models against the functions of scientific communication. I say ‘seems to be’ because the article is not very clear on that and I hope you will consider clarifying what your manuscript seeks to add to the existing work in this field.

In any case, if that assessment of the three models is your main contribution, that part is somewhat underdeveloped. Moreover, I never got the sense that there is clear agreement in the literature about what the tenets of scientific communication are. Note that scientific communication is a field in its own right.

I also agree that paper is too strongly worded at times, with limitations and assumptions in the analysis minimised or not stated. For example, all of the typologies and categories drawn could easily be reorganised and there is a high degree of subjectivity in this entire exercise. Subjective choices should be highlighted and made salient for the reader.

Note that greater clarity, rigour, and humility may also help with any alleged or actual bias.

Some more minor points are:

1. I agree with Reviewer 3 that the ‘we’ perspective is distracting.

2. The paragraph starting with ‘Nevertheless’ on page 2 is very long.

3. There are many points where language could be shortened for readability, for example:

   • Page 3: ‘decision on publication’ could be ‘publication decision’.

   • Page 5: ‘efficiency of its utilization’ could be ‘its efficiency’.

   • Page 7: ‘It should be noted…’ could be ‘Note that…’.

4. Page 7: ‘It should be noted that..’ – this needs a reference.

5. I’m not sure that registered reports reflect a hypothetico-deductive approach (page 6). For instance, systematic reviews (even non-quantitative ones) are often published as registered reports and Cochrane has required this even before the move towards registered reports in quantitative psychology.

6. I agree that modular publishing sits uneasily as its own chapter.

7. Page 14: ‘The "Publish-Review-Curate" model is universal that we expect to be the future of scientific publishing. The transition will not happen today or tomorrow, but in the next 5-10 years, the number of projects such as eLife, F1000Research, Peer Community in, or MetaROR will rapidly increase’. This seems overly strong (an example of my larger critique and that of the reviewers).

As an important corollary, and in the interest of transparency, I declare that I am a founding managing editor of MetaROR, which is a PRC platform. It may be advisable for the author to make a similar declaration because I understand that they are affiliated with one of the universities involved in the founding of MetaROR.

This article provides a brief history and review of peer review. It evaluates peer review models against the goals of scientific communication, expressing a preference for publish, review, curate (PRC) models. The review and history are useful. However, the article’s progression and arguments, along with what it seeks to contribute to the literature need refinement and clarification. The argument for PRC is under-developed due to a lack of clarity about what the article means by scientific communication. Clarity here might make the endorsement of PRC seem like less of a foregone conclusion.

I have no competing interests in the compilation of this review, although I do have specific interests as noted above.

In "Evolution of Peer Review in Scientific Communication", Kochetkov provides a point-of-view discussion of the current state of play of peer review for scientific literature, focussing on the major models in contemporary use and recent innovations in reform. In particular, they present a typology of three main forms of peer review: traditional pre-publication review; registered reports; and post-publication review, their preferred model. The main contribution it could make would be to help consolidate typologies and terminologies, to consolidate major lines of argument and to present some useful visualisations of these. On the other hand, the overall discussion is not strongly original in character.

The major strength of this article is that the discussion is well-informed by contemporary developments in peer-review reform. The typology presented is modest and, for that, readily comprehensible and intuitive. This is to some extent a weakness as well as a strength; a typology that is too straightforward may not be useful enough. As suggested at the end it might be worth considering how to complexify the typology at least at subordinate levels without sacrificing this strength. The diagrams of workflows are particularly clear.

The primary weakness of this article is that it presents itself as an 'analysis' from which they 'conclude' certain results such as their typology, when this appears clearly to be an opinion piece. In my view, this results in a false claim of objectivity which detracts from what would otherwise be an interesting and informative, albeit subjective, discussion, and thus fails to discuss the limitations of this approach. A secondary weakness is that the discussion is not well structured and there are some imprecisions of expression that have the potential to confuse, at least at first.

This primary weakness is manifested in several ways. The evidence and reasoning for claims made is patchy or absent. One instance of the former is the discussion of bias in peer review. There are a multitude of studies of such bias and indeed quite a few meta-analyses of these studies. A systematic search could have been done here but there is no attempt to discuss the totality of this literature. Instead, only a few specific studies are cited. Why are these ones chosen? We have no idea. To this extent I am not convinced that the references used here are the most appropriate. Instances of the latter are the claim that "The most well-known initiatives at the moment are ResearchEquals and Octopus" for which no evidence is provided, the claim that "we believe that journal-independent peer review is a special case of Model 3" for which no further argument is provided, and the claim that "the function of being the "supreme judge" in deciding what is "good" and "bad" science is taken on by peer review" for which neither is provided.

A particular example of this weakness, which is perhaps of marginal importance to the overall paper but of strong interest to this reviewer is the rather odd engagement with history within the paper. It is titled "Evolution of Peer Review" but is really focussed on the contemporary state-of-play. Section 2 starts with a short history of peer review in scientific publishing, but that seems intended only to establish what is described as the 'traditional' model of peer review. Given that that short history had just shown how peer review had been continually changing in character over centuries - and indeed Kochetkov goes on to describe further changes - it is a little difficult to work out what 'traditional' might mean here; what was 'traditional' in 2010 was not the same as what was 'traditional' in 1970. It is not clear how seriously this history is being taken. Kochetkov has earlier written that "as early as the beginning of the 21st century, it was argued that the system of peer review is 'broken'" but of course criticisms - including fundamental criticisms - of peer review are much older than this. Overall, this use of history seems designed to privilege the experience of a particular moment in time, that coincides with the start of the metascience reform movement.

Section 2 also demonstrates some of the second weakness described, a rather loose structure. Having moved from a discussion of the history of peer review to detail the first model, 'traditional' peer review, it then also goes on to describe the problems of this model. This part of the paper is one of the best - and best -evidenced. Given the importance of it to the main thrust of the discussion it should probably have been given more space as a Section all on its own.

Another example is Section 4 on Modular Publishing, in which Kochetkov notes "Strictly speaking, modular publishing is primarily an innovative approach for the publishing workflow in general rather than specifically for peer review." Kochetkov says "This is why we have placed this innovation in a separate category" but if it is not an innovation in peer review, the bigger question is 'Why was it included in this article at all?'.

One example of the imprecisions of language is as follows. The author also shifts between the terms 'scientific communication' and 'science communication' but, at least in many contexts familiar to this reviewer, these are not the same things, the former denoting science-internal dissemination of results through publication (which the author considers), conferences and the like (which the author specifically excludes) while the latter denotes the science-external public dissemination of scientific findings to non-technical audiences, which is entirely out of scope for this article.

A final note is that Section 3, while an interesting discussion, seems largely derivative from a typology of Waltman, with the addition of a consideration of whether a reform is 'radical' or 'incremental', based on how 'disruptive' the reform is. Given that this is inherently a subjective decision, I wonder if it might not have been more informative to consider 'disruptiveness' on a scale and plot it accordingly. This would allow for some range to be imagined for each reform as well; surely reforms might be more or less disruptive depending on how they are implemented. Given that each reform is considered against each model, it is somewhat surprising that this is not presented in a tabular or graphical form.

Beyond the specific suggestions in the preceding paragraphs, my suggestions to improve this article are as follows:

1. Reconceptualize this as an opinion piece. Where systematic evidence can be drawn upon to make points, use that, but don't be afraid to just present a discussion from what is clearly a well-informed author.

2. Reconsider the focus on history and 'evolution' if the point is about the current state of play and evaluation of reforms (much as I would always want to see more studies on the history and evolution of peer review).

3. Consider ways in which the typology might be expanded, even if at subordinate level.

The work ‘Evolution of Peer Review in Scientific Communication’ provides a concise and readable summary of the historical role of peer review in modern science. The paper categorises the peer review practices into three models: (1) traditional pre-publication peer review; (2) registered reports; (3) post-publication peer review. The author compares the three models and draws the conclusion that the “third model offers the best way to implement the main function of scientific communication”.

I would contest this conclusion. In my eyes the three models serve different aims - with more or less drawbacks. For example, although Model 3 is less chance to insert bias to the readers, it also weakens the filtering function of the review system. Let’s just think about the dangers of machine-generated articles, paper-mills, p-hacked research reports and so on. Although the editors do some pre-screening for the submissions, in a world with only Model 3 peer review the literature could easily get loaded with even more ‘garbage’ than in a model where additional peers help the screening.

Compared to registered reports other aspects can come to focus that Model 3 cannot cover. It’s the efficiency of researchers’ work. In the care of registered reports, Stage 1 review can still help researchers to modify or improve their research design or data collection method. Empirical work can be costly and time-consuming and post-publication review can only say that “you should have done it differently then it would make sense”.

Finally, the author puts openness as a strength of Model 3. In my eyes, openness is a separate question. All models can work very openly and transparently in the right circumstances. This dimension is not an inherent part of the models.

In conclusion, I would not make verdict over the models, instead emphasise the different functions they can play in scientific communication.

A minor comment: I found that a number of statements lack references in the Introduction. I would have found them useful for statements such as “There is a point of view that peer review is included in the implicit contract of the researcher.”

In this manuscript, the author provides a historical review of the place of peer review in the scientific ecosystem, including a discussion of the so-called current crisis and a presentation of three important peer review models. I believe this is a non-comprehensive yet useful overview. My main contention is that the structure of the paper could be improved. More specifically, the author could expand on the different goals of peer review and discuss these goals earlier in the paper. This would allow readers to better interpret the different issues plaguing peer review and helps put the costs and benefits of the three models into context. Other than that, I found some claims made in the paper a little too strong. Presenting some empirical evidence or downplaying these claims would improve the manuscript in my opinion. Below, you can find my comments:

1. In my view, the biggest issue with the current peer review system is the low quality of reviews, but the manuscript only mentions this fleetingly. The current system facilitates publication bias, confirmation bias, and is generally very inconsistent. I think this is partly due to reviewers’ lack of accountability in such a closed peer review system, but I would be curious to hear the author’s ideas about this, more elaborately than they provide them as part of issue 2.

2. I’m missing a section in the introduction on what the goals of peer review are or should be. You mention issues with peer review, and these are mostly fair, but their importance is only made salient if you link them to the goals of peer review. The author does mention some functions of peer review later in the paper, but I think it would be good to expand that discussion and move it to a place earlier in the manuscript.

3. Table 1 is intuitive but some background on how the author arrived at these categorizations would be welcome. When is something incremental and when is something radical? Why are some innovations included but not others (e.g., collaborative peer review, see https://content.prereview.org/how-collaborative-peer-review-can-transform-scientific-research/)?

4. “Training of reviewers through seminars and online courses is part of the strategies of many publishers. At the same time, we have not been able to find statistical data or research to assess the effectiveness of such training.” (p. 5)  There is some literature on this, although not recent. See work by Sara Schroter for example, Schroter et al., 2004; Schroter et al., 2008)

5. “It should be noted that most initiatives aimed at improving the quality of peer review simultaneously increase the costs.” (p. 7)  This claim needs some support. Please explicate why this typically is the case and how it should impact our evaluations of these initiatives.

6. I would rephrase “Idea of the study” in Figure 2 since the other models start with a tangible output (the manuscript). This is the same for registered reports where they submit a tangible report including hypotheses, study design, and analysis plan. In the same vein, I think study design in the rest of the figure might also not be the best phrasing.  Maybe the author could use the terminology used by COS (Stage 1 manuscript, and Stage 2 manuscript, see Details & Workflow tab of https://www.cos.io/initiatives/registered-reports). Relatedly, “Author submits the first version of the manuscript” in the first box after the ‘Manuscript (report)’ node maybe a confusing phrase because I think many researchers see the first version of the manuscript as the stage 1 report sent out for stage 1 review.

7. One pathway that is not included in Figure 2 is that authors can decide to not conduct the study when improvements are required. Relatedly, in the publish-review-curate model, is revising the manuscripts based on the reviews not optional as well? Especially in the case of 3a, authors can hardly be forced to make changes even though the reviews are posted on the platform.

8. I think the author should discuss the importance of ‘open identities’ more. This factor is now not explicitly included in any of the models, while it has been found to be one of the main characteristics of peer review systems (Ross-Hellauer, 2017). More generally, I was wondering why the author chose these three models and not others. What were the inclusion criteria for inclusion in the manuscript? Some information on the underlying process would be welcome, especially when claims like “However, we believe that journal-independent peer review is a special case of Model 3 (“Publish-Review-Curate”).” are made without substantiation.

9. Maybe it helps to outline the goals of the paper a bit more clearly in the introduction. This helps the reader to know what to expect.

10. The Modular Publishing section is not inherently related to peer review models, as you mention in the first sentence of that paragraph. As such, I think it would be best to omit this section entirely to maintain the flow of the paper. Alternatively, you could shortly discuss it in the discussion section but a separate paragraph seems too much from my point of view.

11. Labeling model 3 as post-publication review might be confusing to some readers. I believe many researchers see post-publication review as researchers making comments on preprints, or submitting commentaries to journals. Those activities are substantially different from the publish-review-curate model so I think it is important to distinguish between these types.

12. I do not think the conclusions drawn below Table 3 logically follow from the earlier text. For example, why are “all functions of scientific communication implemented most quickly and transparently in Model 3”? It could be that the entire process takes longer in Model 3 (e.g. because reviewers need more time), so that Model 1 and Model 2 lead to outputs quicker. The same holds for the following claim: “The additional costs arising from the independent assessment of information based on open reviews are more than compensated by the emerging opportunities for scientific pluralism.” What is the empirical evidence for this? While I personally do think that Model 3 improves on Model 1, emphatic statements like this require empirical evidence. Maybe the author could provide some suggestions on how we can attain this evidence. Model 2 does have some empirical evidence underpinning its validity (see Scheel, Schijen, Lakens, 2021; Soderberg et al., 2021; Sarafoglou et al. 2022) but more meta-research inquiries into the effectiveness and cost-benefits ratio of registered reports would still be welcome in general.

13. What is the underlaying source for the claim that openness requires three conditions?

14. “If we do not change our approach, science will either stagnate or transition into other forms of communication.” (p. 2)  I don’t think this claim is supported sufficiently strongly. While I agree there are important problems in peer review, I think would need to be a more in-depth and evidence-based analysis before claims like this can be made.

15. On some occasions, the author uses “we” while the study is single authored.

16. Figure 1: The top-left arrow from revision to (re-)submission is hidden

17. “The low level of peer review also contributes to the crisis of reproducibility in scientific research (Stoddart, 2016).” (p. 4)  I assume the author means the low quality of peer review.

18. “Although this crisis is due to a multitude of factors, the peer review system bears a significant responsibility for it.” (p. 4)  This is also a big claim that is not substantiated

19. “Software for automatic evaluation of scientific papers based on artificial intelligence (AI) has emerged relatively recently” (p. 5)  The author could add RegCheck (https://regcheck.app/) here, even though it is still in development. This tool is especially salient in light of the finding that preregistration-paper checks are rarely done as part of reviews (see Syed, 2023)

20. There is a typo in last box of Figure 1 (“decicion” instead of “decision”). I also found typos in the second box of Figure 2, where “screns” should be “screens”, and the author decision box where “desicion” should be “decision”

21. Maybe it would be good to mention results blinded review in the first paragraph of 3.2. This is a form of peer review where the study is already carried out but reviewers are blinded to the results. See work by Locascio (2017), Grand et al. (2018), and Woznyj et al. (2018).

22. Is “Not considered for peer review” in figure 3b not the same as rejected? I feel that it is rejected in the sense that neither the manuscript not the reviews will be posted on the platform.

23. “In addition to the projects mentioned, there are other platforms, for example, PREreview12, which departs even more radically from the traditional review format due to the decentralized structure of work.” (p. 11)  For completeness, I think it would be helpful to add some more information here, for example why exactly decentralization is a radical departure from the traditional model.

24. “However, anonymity is very conditional - there are still many “keys” left in the manuscript, by which one can determine, if not the identity of the author, then his country, research group, or affiliated organization.” (p.11)  I would opt for the neutral “their” here instead of “his”, especially given that this is a paragraph about equity and inclusion.

25. “Thus, “closeness” is not a good way to address biases.” (p. 11)  This might be a straw man argument because I don’t believe researchers have argued that it is a good method to combat biases. If they did, it would be good to cite them here. Alternatively, the sentence could be omitted entirely.

26. I would start the Modular Publishing section with the definition as that allows readers to interpret the other statements better.

27. It would be helpful if the Models were labeled (instead of using Model 1, Model 2, and Model 3) so that readers don’t have to think back what each model involved.

28. Table 2: “Decision making” for the editor’s role is quite broad, I recommend to specify and include what kind of decisions need to be made.

29. Table 2: “Aim of review” – I believe the aim of peer review differs also within these models (see the “schools of thought” the author mentions earlier), so maybe a statement on what the review entails would be a better way to phrase this.

30. Table 2: One could argue that the object of the review’ in Registered Reports is also the manuscript as a whole, just in different stages. As such, I would phrase this differently.

Good luck with any revision!

Olmo van den Akker (ovdakker@gmail.com)

References

Grand, J. A., Rogelberg, S. G., Banks, G. C., Landis, R. S., & Tonidandel, S. (2018). From outcome to process focus: Fostering a more robust psychological science through registered reports and results-blind reviewing. Perspectives on Psychological Science, 13(4), 448-456.

Ross-Hellauer, T. (2017). What is open peer review? A systematic review. F1000Research, 6.

Sarafoglou, A., Kovacs, M., Bakos, B., Wagenmakers, E. J., & Aczel, B. (2022). A survey on how preregistration affects the research workflow: Better science but more work. Royal Society Open Science, 9(7), 211997.

Scheel, A. M., Schijen, M. R., & Lakens, D. (2021). An excess of positive results: Comparing the standard psychology literature with registered reports. Advances in Methods and Practices in Psychological Science, 4(2), 25152459211007467.

Schroter, S., Black, N., Evans, S., Carpenter, J., Godlee, F., & Smith, R. (2004). Effects of training on quality of peer review: randomised controlled trial. Bmj, 328(7441), 673.

Schroter, S., Black, N., Evans, S., Godlee, F., Osorio, L., & Smith, R. (2008). What errors do peer reviewers detect, and does training improve their ability to detect them?. Journal of the Royal Society of Medicine, 101(10), 507-514.

Soderberg, C. K., Errington, T. M., Schiavone, S. R., Bottesini, J., Thorn, F. S., Vazire, S., ... & Nosek, B. A. (2021). Initial evidence of research quality of registered reports compared with the standard publishing model. Nature Human Behaviour, 5(8), 990-997.

Syed, M. (2023). Some data indicating that editors and reviewers do not check preregistrations during the review process. PsyArXiv Preprints.

Locascio, J. J. (2017). Results blind science publishing. Basic and applied social psychology, 39(5), 239-246.

Woznyj, H. M., Grenier, K., Ross, R., Banks, G. C., & Rogelberg, S. G. (2018). Results-blind review: A masked crusader for science. European Journal of Work and Organizational Psychology, 27(5), 561-576.

Overall thoughts: This is an interesting history piece regarding peer review and the development of review over time. Given the author’s conflict of interest and association with the Centre developing MetaROR, I think that this paper might be a better fit for an information page or introduction to the journal and rationale for the creation of MetaROR, rather than being billed as an independent article. Alternatively, more thorough information about advantages to pre-publication review or more downsides/challenges to post-publication review might make the article seem less affiliated. I appreciate seeing the history and current efforts to change peer review, though I am not comfortable broadly encouraging use of these new approaches based on this article alone.

Page 3: It’s hard to get a feel for the timeline given the dates that are described. We have peer review becoming standard after WWII (after 1945), definitively established by the second half of the century, an example of obligatory peer review starting in 1976, and in crisis by the end of the 20th century. I would consider adding examples that better support this timeline – did it become more common in specific journals before 1976? Was the crisis by the end of the 20th century something that happened over time or something that was already intrinsic to the institution? It doesn’t seem like enough time to get established and then enter crisis, but more details/examples could help make the timeline clear. 

Consider discussing the benefits of the traditional model of peer review.

Table 1 – Most of these are self-explanatory to me as a reader, but not all. I don’t know what a registered report refers to, and it stands to reason that not all of these innovations are familiar to all readers. You do go through each of these sections, but that’s not clear when I initially look at the table. Consider having a more informative caption. Additionally, the left column is “Course of changes” here but “Directions” in text. I’d pick one and go with it for consistency.

3.2: Considering mentioning your conflict of interest here where MetaROR is mentioned.

With some of these methods, there’s the ability to also submit to a regular journal. Going to a regular journal presumably would instigate a whole new round of review, which may or may not contradict the previous round of post-publication review and would increase the length of time to publication by going through both types. If someone has a goal to publish in a journal, what benefit would they get by going through the post-publication review first, given this extra time?

There’s a section talking about institutional change (page 14). It mentions that openness requires three conditions – people taking responsibility for scientific communication, authors and reviewers, and infrastructure. I would consider adding some discussion of readers and evaluators. Readers have to be willing to accept these papers as reliable, trustworthy, and respectable to read and use the information in them. Evaluators such as tenure committees and potential employers would need to consider papers submitted through these approaches as evidence of scientific scholarship for the effort to be worthwhile for scientists.

Based on this overview, which seems somewhat skewed towards the merits of these methods (conflict of interest, limited perspective on downsides to new methods/upsides to old methods), I am not quite ready to accept this effort as equivalent of a regular journal and pre-publication peer review process. I look forward to learning more about the approach and seeing this review method in action and as it develops.

Response to the Editors and the Reviewers

I am sincerely grateful to the editors and peer reviewers at MetaROR for their detailed feedback and valuable comments and suggestions. I have addressed each point below.

Handling editor

1. “However, the article’s progression and arguments, along with what it seeks to contribute to the literature need refinement and clarification. The argument for PRC is under-developed due to a lack of clarity about what the article means by scientific communication. Clarity here might make the endorsement of PRC seem like less of a foregone conclusion.”

The structure of the paper (and discussion) has changed significantly to address the feedback.

2. “I strongly endorse the main theme of most of the reviews, which is that the progression and underlying justifications for this article’s arguments needs a great deal of work. In my view, this article’s main contribution seems to be the evaluation of the three peer review models against the functions of scientific communication. I say ‘seems to be’ because the article is not very clear on that and I hope you will consider clarifying what your manuscript seeks to add to the existing work in this field. In any case, if that assessment of the three models is your main contribution, that part is somewhat underdeveloped. Moreover, I never got the sense that there is clear agreement in the literature about what the tenets of scientific communication are. Note that scientific communication is a field in its own right.”

I have implemented a more rigorous approach to argumentation in response. “Scientific communication” was replaced by “scholarly communication.”

3. “I also agree that paper is too strongly worded at times, with limitations and assumptions in the analysis minimised or not stated. For example, all of the typologies and categories drawn could easily be reorganised and there is a high degree of subjectivity in this entire exercise. Subjective choices should be highlighted and made salient for the reader. Note that greater clarity, rigour, and humility may also help with any alleged or actual bias.”

I have incorporated the conceptual framework and description of the research methodology. However, the Discussion section reflects my personal perspective in some points, which I have explicitly highlighted to ensure clarity.

4. “I agree with Reviewer 3 that the ‘we’ perspective is distracting.”

This has been fixed.

5. “The paragraph starting with ‘Nevertheless’ on page 2 is very long.”

The text was restructured.

6. “There are many points where language could be shortened for readability, for example:

Page 3: ‘decision on publication’ could be ‘publication decision’.

Page 5: ‘efficiency of its utilization’ could be ‘its efficiency’.

Page 7: ‘It should be noted…’ could be ‘Note that…’.”

I have proofread the text.

7. “Page 7: ‘It should be noted that..’ – this needs a reference.”

This statement has been moved to the Discussion section, paraphrased, and reference added

“It should be also noted that peer review innovations pull in opposing directions, with some aiming to increase efficiency and reduce costs, while others aim to promote rigor and increase costs (Kaltenbrunner et al., 2022).”

8. “I’m not sure that registered reports reflect a hypothetico-deductive approach (page 6). For instance, systematic reviews (even non-quantitative ones) are often published as registered reports and Cochrane has required this even before the move towards registered reports in quantitative psychology.”

I have added this clarification.

9. “I agree that modular publishing sits uneasily as its own chapter.”

Modular publishing has been combined with registered reports into the deconstructed publication group of models, now Section 5.1.

10. “Page 14: ‘The "Publish-Review-Curate" model is universal that we expect to be the future of scientific publishing. The transition will not happen today or tomorrow, but in the next 5-10 years, the number of projects such as eLife, F1000Research, Peer Community in, or MetaROR will rapidly increase’. This seems overly strong (an example of my larger critique and that of the reviewers).”

This part of the text has been rewritten.

Reviewer 1

11. “For example, although Model 3 is less chance to insert bias to the readers, it also weakens the filtering function of the review system. Let’s just think about the dangers of machine-generated articles, paper-mills, p-hacked research reports and so on. Although the editors do some pre-screening for the submissions, in a world with only Model 3 peer review the literature could easily get loaded with even more ‘garbage’ than in a model where additional peers help the screening.”

I think that generated text is better detected by software tools. At the same time, I tried and described the pros and cons of different models in a more balanced way in the concluding section.

12. “Compared to registered reports other aspects can come to focus that Model 3 cannot cover. It’s the efficiency of researchers’ work. In the care of registered reports, Stage 1 review can still help researchers to modify or improve their research design or data collection method. Empirical work can be costly and time-consuming and post-publication review can only say that ‘you should have done it differently then it would make sense’.”

Thank you very much for this valuable contribution, I have added this statement at P. 11.

13. “Finally, the author puts openness as a strength of Model 3. In my eyes, openness is a separate question. All models can work very openly and transparently in the right circumstances. This dimension is not an inherent part of the models.”

I think that the model, providing peer reviews to all the submissions, ensures maximum transparency. However, I have made effort to make the wording more balanced and distinguish my personal perspective from the literature.

14. “In conclusion, I would not make verdict over the models, instead emphasize the different functions they can play in scientific communication.”

This idea has been reflected now in the concluding section.

15. “A minor comment: I found that a number of statements lack references in the Introduction. I would have found them useful for statements such as ‘There is a point of view that peer review is included in the implicit contract of the researcher.’”

Thank you for your feedback. I have implemented a more rigorous approach to argumentation in response.

Reviewer 2

16. “The primary weakness of this article is that it presents itself as an 'analysis' from which they 'conclude' certain results such as their typology, when this appears clearly to be an opinion piece. In my view, this results in a false claim of objectivity which detracts from what would

otherwise be an interesting and informative, albeit subjective, discussion, and thus fails to discuss the limitations of this approach.”

I have incorporated the conceptual framework and description of the research methodology. However, the Discussion section reflects my personal perspective in some points, which I have explicitly highlighted to ensure clarity.

17. “A secondary weakness is that the discussion is not well structured and there are some imprecisions of expression that have the potential to confuse, at least at first.”

The structure of the paper (and discussion) has changed significantly.

18. “The evidence and reasoning for claims made is patchy or absent. One instance of the former is the discussion of bias in peer review. There are a multitude of studies of such bias and indeed quite a few meta-analyses of these studies. A systematic search could have been done here but there is no attempt to discuss the totality of this literature. Instead, only a few specific studies are cited. Why are these ones chosen? We have no idea. To this extent I am not convinced that the references used here are the most appropriate.”

I have reviewed the existing references and incorporated additional sources. However, the study does not claim to conduct a systematic literature review; rather, it adopts an interpretative approach to literature analysis.

19. “Instances of the latter are the claim that ‘The most well-known initiatives at the moment are ResearchEquals and Octopus’ for which no evidence is provided, the claim that ‘we believe that journal-independent peer review is a special case of Model 3’ for which no further argument is provided, and the claim that ‘the function of being the "supreme judge" in deciding what is "good" and "bad" science is taken on by peer review’ for which neither is provided.

Thank you for your feedback. I have implemented a more rigorous approach to argumentation in response.

20. “A particular example of this weakness, which is perhaps of marginal importance to the overall paper but of strong interest to this reviewer is the rather odd engagement with history within the paper. It is titled "Evolution of Peer Review" but is really focussed on the contemporary state-of-play. Section 2 starts with a short history of peer review in scientific publishing, but that seems intended only to establish what is described as the 'traditional' model of peer review. Given that that short history had just shown how peer review had been continually changing in character over centuries - and indeed Kochetkov goes on to describe further changes - it is a little difficult to work out what 'traditional' might mean here; what was 'traditional' in 2010 was not the same as what was 'traditional' in 1970. It is not clear how seriously this history is being taken. Kochetkov has earlier written that "as early as the beginning of the 21st century, it was argued that the system of peer review is 'broken'" but of course criticisms - including fundamental criticisms - of peer review are much older than this. Overall, this use of history seems designed to privilege the experience of a particular moment in time, that coincides with the start of the metascience reform movement.”

While the paper addresses some aspects of peer review history, it does not provide a comprehensive examination of this topic. A clarifying statement to this effect has been included in the methodology section.

“… this section incorporates elements of historical analysis, it does not fully qualify as such because primary sources were not directly utilized. Instead, it functions as an interpretative literature review, and one that is intentionally concise, as a comprehensive history of peer review falls outside the scope of this research”.

21. “Section 2 also demonstrates some of the second weakness described, a rather loose structure. Having moved from a discussion of the history of peer review to detail the first model, 'traditional' peer review, it then also goes on to describe the problems of this model. This part of the paper is one of the best - and best - evidenced. Given the importance of it to the main thrust of the discussion it should probably have been given more space as a Section all on its own.”

This section (now Section 4) has been extended, see also previous comment.

22. “Another example is Section 4 on Modular Publishing, in which Kochetkov notes "Strictly speaking, modular publishing is primarily an innovative approach for the publishing workflow in general rather than specifically for peer review." Kochetkov says "This is why we have placed this innovation in a separate category" but if it is not an innovation in peer review, the bigger question is 'Why was it included in this article at all?'.”

Modular publishing has been combined with registered reports into the deconstructed publication group of models, now Section 5.1.

23. “One example of the imprecisions of language is as follows. The author also shifts between the terms 'scientific communication' and 'science communication' but, at least in many contexts familiar to this reviewer, these are not the same things, the former denoting science-internal dissemination of results through publication (which the author considers), conferences and the like (which the author specifically excludes) while the latter denotes the science-external public dissemination of scientific findings to non-technical audiences, which is entirely out of scope for this article.”

Thank you for your remark. As a non- native speaker, I initially did not grasp the distinction between the terms. However, I believe the phrase ‘scholarly communication’ is the most universally applicable term. This adjustment has now been incorporated into the text.

24. “A final note is that Section 3, while an interesting discussion, seems largely derivative from a typology of Waltman, with the addition of a consideration of whether a reform is 'radical' or 'incremental', based on how 'disruptive' the reform is. Given that this is inherently a subjective decision, I wonder if it might not have been more informative to consider 'disruptiveness' on a scale and plot it accordingly. This would allow for some range to be imagined for each reform as well; surely reforms might be more or less disruptive depending on how they are implemented. Given that each reform is considered against each model, it is somewhat surprising that this is not presented in a tabular or graphical form.”

Ultimately, I excluded this metric due to its current reliance on purely subjective judgment. Measuring 'disruptiveness', e.g., through surveys or interviews remains a task for future research.

25. “Reconceptualize this as an opinion piece. Where systematic evidence can be drawn upon to make points, use that, but don't be afraid to just present a discussion from what is clearly a well-informed author.”

I cannot definitively classify this work as an opinion piece. In fact, this manuscript synthesizes elements of a literature review, research article, and opinion essay. My idea was to integrate the strengths of all three genres.

26. “Reconsider the focus on history and 'evolution' if the point is about the current state of play and evaluation of reforms (much as I would always want to see more studies on the history and evolution of peer review).”

I have revised the title to better reflect the study’s scope and explicitly emphasize its focus on contemporary developments in the field.

“Peer Review at the Crossroads”

27. “Consider ways in which the typology might be expanded, even if at subordinate level.”

I have updated the typology and introduced the third tier, where it is applicable (see Fig.2).

Reviewer 3

28. “In my view, the biggest issue with the current peer review system is the low quality of reviews, but the manuscript only mentions this fleetingly. The current system facilitates publication bias, confirmation bias, and is generally very inconsistent. I think this is partly due to reviewers’ lack of accountability in such a closed peer review system, but I would be curious to hear the author’s ideas about this, more elaborately than they provide them as part of issue 2.

I have elaborated on this issue in the footnote.

29. “I’m missing a section in the introduction on what the goals of peer review are or should be. You mention issues with peer review, and these are mostly fair, but their importance is only made salient if you link them to the goals of peer review. The author does mention some functions of peer review later in the paper, but I think it would be good to expand that discussion and move it to a place earlier in the manuscript.”

The functions of peer review are summarized in the first paragraph of Introduction.

30. “Table 1 is intuitive but some background on how the author arrived at these categorizations would be welcome. When is something incremental and when is something radical? Why are some innovations included but not others (e.g., collaborative peer review, see https://content.prereview.org/how-collaborative-peer-review-can-transform-scientific-research/)?”

Collaborative peer review, namely, Prereview was mentioned in the context of Model 3 (Publish-Review-Curate). However, I have extended this part of the paper.

31“‘Training of reviewers through seminars and online courses is part of the strategies of many publishers. At the same time, we have not been able to find statistical data or research to assess the effectiveness of such training.’ (p. 5)  There is some literature on this, although not recent. See work by Sara Schroter for example, Schroter et al., 2004; Schroter et al., 2008)”

Thank you very much, I have added these studies and a few more recent ones.

32. “‘It should be noted that most initiatives aimed at improving the quality of peer review simultaneously increase the costs.’ (p. 7) This claim needs some support. Please explicate why this typically is the case and how it should impact our evaluations of these initiatives.”

I have moved this part to the Discussion section.

33. “I would rephrase “Idea of the study” in Figure 2 since the other models start with a tangible output (the manuscript). This is the same for registered reports where they submit a tangible report including hypotheses, study design, and analysis plan. In the same vein, I think study design in the rest of the figure might also not be the best phrasing. Maybe the author could use the terminology used by COS (Stage 1 manuscript, and Stage 2 manuscript, see Details & Workflow tab of https://www.cos.io/initiatives/registered-reports). Relatedly, “Author submits the first version of the manuscript” in the first box after the ‘Manuscript (report)’ node maybe a confusing phrase because I think many researchers see the first version of the manuscript as the stage 1 report sent out for stage 1 review.”

Thank you very much. Stage 1 and Stage 2 manuscripts look like suitable labelling solution.

34. “One pathway that is not included in Figure 2 is that authors can decide to not conduct the study when improvements are required. Relatedly, in the publish-review-curate model, is revising the manuscripts based on the reviews not optional as well? Especially in the case of

3a, authors can hardly be forced to make changes even though the reviews are posted on the platform.”

All the four models imply a certain level of generalization; thus, I tried to avoid redundant details. However, I have added this choice to the PRC model (now, Model 4).

35. “I think the author should discuss the importance of ‘open identities’ more. This factor is now not explicitly included in any of the models, while it has been found to be one of the main characteristics of peer review systems (Ross-Hellauer, 2017).”

This part has been extended.

36. “More generally, I was wondering why the author chose these three models and not others. What were the inclusion criteria for inclusion in the manuscript? Some information on the underlying process would be welcome, especially when claims like ‘However, we believe that journal-independent peer review is a special case of Model 3 (‘Publish-Review-Curate’).’ are made without substantiation.”

The study included four generalized models of peer review that involved some level of abstraction.

37. “Maybe it helps to outline the goals of the paper a bit more clearly in the introduction. This helps the reader to know what to expect.”

The Introduction has been revised including the goal and objectives.

38. “The Modular Publishing section is not inherently related to peer review models, as you mention in the first sentence of that paragraph. As such, I think it would be best to omit this section entirely to maintain the flow of the paper. Alternatively, you could shortly discuss it in the discussion section but a separate paragraph seems too much from my point of view.”

Modular publishing has been combined with registered reports into the fragmented publishing group of models, now in Section 5.

39. “Labeling model 3 as post-publication review might be confusing to some readers. I believe many researchers see post-publication review as researchers making comments on preprints, or submitting commentaries to journals. Those activities are substantially different from the publish-review-curate model so I think it is important to distinguish between these types.”

The label was changed into Publish- Review-Curate model.

40. “I do not think the conclusions drawn below Table 3 logically follow from the earlier text. For example, why are “all functions of scientific communication implemented most quickly and transparently in Model 3”? It could be that the entire process takes longer in Model 3 (e.g. because reviewers need more time), so that Model 1 and Model 2 lead to outputs quicker. The same holds for the following claim: ‘The additional costs arising from the independent assessment of information based on open reviews are more than compensated by the emerging opportunities for scientific pluralism.’ What is the empirical evidence for this? While I personally do think that Model 3 improves on Model 1, emphatic statements like this require empirical evidence. Maybe the author could provide some suggestions on how we can attain this evidence. Model 2 does have some empirical evidence underpinning its validity (see Scheel, Schijen, Lakens, 2021; Soderberg et al., 2021; Sarafoglou et al. 2022) but more meta-research inquiries into the effectiveness and cost-benefits ratio of registered reports would still be welcome in general.”

The Discussion section has been substantially revised to address this point. While I acknowledge the current scarcity of empirical studies on innovative peer review models, I have incorporated a critical discussion of this methodological gap. I am grateful for the suggested literature on RRs, which I have now integrated into the relevant subsection.

41. “What is the underlaying source for the claim that openness requires three conditions?”

I have made effort to clarify within the text that this reflects my personal stance.

42. “‘If we do not change our approach, science will either stagnate or transition into other forms of communication.’ (p. 2) I don’t think this claim is supported sufficiently strongly. While I agree there are important problems in peer review, I think would need to be a more in-depth and evidence-based analysis before claims like this can be made.”

The sentence has been rephrased.

43. “On some occasions, the author uses ‘we’ while the study is single authored.”

This has been fixed.

44. “Figure 1: The top-left arrow from revision to (re-)submission is hidden”

I have updated Figure 1.

45. “‘The low level of peer review also contributes to the crisis of reproducibility in scientific research (Stoddart, 2016).’ (p. 4) I assume the author means the low quality of peer review.”

This has been fixed.

46. “‘Although this crisis is due to a multitude of factors, the peer review system bears a significant responsibility for it.’ (p. 4) This is also a big claim that is not substantiated”

I have paraphrased this sentence as “While multiple factors drive this crisis, deficiencies in the peer review process remain a significant contributor.” and added a footnote.

47. “‘Software for automatic evaluation of scientific papers based on artificial intelligence (AI) has emerged relatively recently” (p. 5) The author could add RegCheck (https://regcheck.app/) here, even though it is still in development. This tool is especially salient in light of the finding that preregistration-paper checks are rarely done as part of reviews (see Syed, 2023)”

Thank you very much, I have added this information.

48. “There is a typo in last box of Figure 1 (‘decicion’ instead of ‘decision’). I also found typos in the second box of Figure 2, where ‘screns’ should be ‘screens’, and the author decision box where ‘desicion’ should be ‘decision’”

This has been fixed.

49. “Maybe it would be good to mention results blinded review in the first paragraph of 3.2. This is a form of peer review where the study is already carried out but reviewers are blinded to the results. See work by Locascio (2017), Grand et al. (2018), and Woznyj et al. (2018).”

Thanks, I have added this (now section 5.2)

50. “Is ‘Not considered for peer review’ in figure 3b not the same as rejected? I feel that it is rejected in the sense that neither the manuscript not the reviews will be posted on the platform.”

Changed into “Rejected”

51. “‘In addition to the projects mentioned, there are other platforms, for example, PREreview12, which departs even more radically from the traditional review format due to the decentralized structure of work.’ (p. 11) For completeness, I think it would be helpful to add some more information here, for example why exactly decentralization is a radical departure from the traditional model.”

I have extended this passage.

52. “‘However, anonymity is very conditional - there are still many “keys” left in the manuscript, by which one can determine, if not the identity of the author, then his country, research group, or affiliated organization.’ (p.11) I would opt for the neutral ‘their’ here instead of ‘his’, especially given that this is a paragraph about equity and inclusion.”

This has been fixed.

53. “‘Thus, “closeness” is not a good way to address biases.’ (p. 11) This might be a straw man argument because I don’t believe researchers have argued that it is a good method to combat biases. If they did, it would be good to cite them here. Alternatively, the sentence could be

omitted entirely.

I have omitted the sentence.

54. “I would start the Modular Publishing section with the definition as that allows readers to interpret the other statements better.”

Modular publishing has been combined with registered reports into the deconstructed publication group of models, now in Section 5, general definition added.

55. “It would be helpful if the Models were labeled (instead of using Model 1, Model 2, and Model 3) so that readers don’t have to think back what each model involved.”

All the models represent a kind of generalization, which is why non-detailed labels are used. The text labels may vary depending on the context.

56. “Table 2: ‘Decision making’ for the editor’s role is quite broad, I recommend to specify and include what kind of decisions need to be made.”

Changed into “Making accept/reject decisions”

57. “Table 2: ‘Aim of review’ – I believe the aim of peer review differs also within these models (see the ‘schools of thought’ the author mentions earlier), so maybe a statement on what the review entails would be a better way to phrase this.”

Changed into “What does peer review entail?”

58. “Table 2: One could argue that the object of the review’ in Registered Reports is also the manuscript as a whole, just in different stages. As such, I would phrase this differently.

Current wording fits your remark: “Manuscript in terms of study design and execution”

Reviewer 4

59. “Page 3: It’s hard to get a feel for the timeline given the dates that are described. We have peer review becoming standard after WWII (after 1945), definitively established by the second half of the century, an example of obligatory peer review starting in 1976, and in crisis by the end of the 20th century. I would consider adding examples that better support this timeline – did it become more common in specific journals before 1976? Was the crisis by the end of the 20th century something that happened over time or something that was already intrinsic to the institution? It doesn’t seem like enough time to get established and then enter crisis, but more details/examples could help make the timeline clear. Consider discussing the benefits of the traditional model of peer review.”

This section has been extended.

60. “Table 1 – Most of these are self-explanatory to me as a reader, but not all. I don’t know what a registered report refers to, and it stands to reason that not all of these innovations are familiar to all readers. You do go through each of these sections, but that’s not clear when I initially look at the table. Consider having a more informative caption. Additionally, the left column is “Course of changes” here but “Directions” in text. I’d pick one and go with it for consistency.”

Table 1 has been replaced by Figure 2. I have also extended text descriptions, added definitions.

61. “With some of these methods, there’s the ability to also submit to a regular journal. Going to a regular journal presumably would instigate a whole new round of review, which may or may not contradict the previous round of post-publication review and would increase the length of time to publication by going through both types. If someone has a goal to publish in a journal, what benefit would they get by going through the post-publication review first, given this extra time?”

Some of these platforms, e.g., F1000, Lifecycle Journal, replace conventional journal publishing. Modular publishing allows for step-by-step feedback from peers. An important advantage of RRs over other peer review models lies in their capacity to enhance research efficiency. By conducting peer review at Stage 1, researchers gain the opportunity to refine their study design or data collection protocols before empirical work begins. Other models of review can offer critiques such as "the study should have been conducted differently" without actionable opportunity for improvement. The key motivation for having my paper reviewed in MetaROR is the quality of peer review – I have never received so many comments, frankly! Moreover, platforms such as MetaROR usually have partnering journals.

62. “There’s a section talking about institutional change (page 14). It mentions that openness requires three conditions – people taking responsibility for scientific communication, authors and reviewers, and infrastructure. I would consider adding some discussion of readers and evaluators. Readers have to be willing to accept these papers as reliable, trustworthy, and respectable to read and use the information in them. Evaluators such as tenure committees and potential employers would need to consider papers submitted through these approaches as evidence of scientific scholarship for the effort to be worthwhile for scientists.”

I have omitted these conditions and employed the Moore’s Technology Adoption Life Cycle. Thank you very much for your comment!

63. Based on this overview, which seems somewhat skewed towards the merits of these methods (conflict of interest, limited perspective on downsides to new methods/upsides to old methods), I am not quite ready to accept this effort as equivalent of a regular journal and pre-publication peer review process. I look forward to learning more about the approach and seeing this review method in action and as it develops.

The Discussion section has been substantially revised to address this point. While I acknowledge the current scarcity of empirical studies on innovative peer review models, I have incorporated a critical discussion of this methodological gap.

Because it violates the law of nature

Impossibility as a defense for nonperformance coming through here

This line is from the Visuddhi-Magga, where a woman adorned in accessories (so much so that she looks like a goddess) leaves her husband's house to return home. On her way, she is met by an elder who bestows "saintship" upon her based on her teeth. He declares, "Was it a woman, or a man, / That passed this way? I cannot tell / But this I know, a set of bones / Is traveling on upon this road." The fact that the elder ignores physical beauty makes it an irrelevant indicator of divinity. As scholar Jeannie Yang notes, the ambiguity of gender suggests it is neither femininity nor masculinity that constitutes this third creature, but rather the skeletal core that remains when all external markers dissolve. The Visuddhi-Magga insists on the unification of human bodies: "while from the ninety-nine thousand pores of the skin an unclean sweat exudes attracting black flies and other insects." The text further claims that the king "would become as uncouth and unkempt as the moment he was born, and would in no wise differ in bodily offensiveness from the low-caste" whose occupation is to remove dead flowers. No matter how much beautifying or prestige is bestowed upon our bodies throughout our lives, all bodies are fundamentally the same. This Buddhist preoccupation with bones as the proof of human existence unifies The Waste Land. Bones are lost in rats' alley, they rattle by Sweet Thames, they lie in sunken ships. Of all the disguises a human wears, at their core they are all bones. Revealing that is where the truth lies, in that which cannot be decomposed.

The footnote for this line references Shackleton’s account of his journey, comparing the line to “it seemed to me often that we were four, not three. . . I had a curious feeling on the march that there was another person with us.” Though Eliot keeps the idea of “another person” unseen, unidentifiable, he breaks from the Shackleton by having two people with a mysterious third, not three with a mysterious fourth. Another place where Eliot changes his reference from four to three is from The Marudanayagam, where the original characters ask the question “who is the fourth one?” They are three, and the fourth is the unknown, which drew me originally to a Biblical interpretation of the line, with the Holy Trinity of God, Jesus, and the Holy Spirit (the unidentifiable omnipresent third). The other reference with an unknown other is Luke, where Jesus exists as the third person, the presence that the others are unable to identify as his true self. In this case, similar to Eliot, there are two, and the extra person makes them three. Even though Eliot is referencing the Bible, he is centering a story that centers the wrong member of the Trinity, moving away from the seeming reference of the Holy Spirit. For Christinas, the “third who walks always beside you,” is the Holy Spirit, which always “walks” beside people along their journey, not Jesus, whose presence is different in nature. Eliot takes a further step to make an intentional choice not to reference the Holy Spirit in his footnote, by taking the trinity away from the conversation by discussing the “four” and “the fourth” in his reference. All of this does not remove the Holy Spirit’s presence within the poem, but instead makes it similar to its presence in real life: detectable but not visible.

"The Fire Sermon" opens with "The river’s tent is broken" (line 173), a cut-throat declaration of decay in the scenery. The lush, protective canopy of nature is gone, leaving only the "last fingers of leaf" clutching at the "wet bank" (line 173-174). This establishes a scene of post-coital, post-festival exhaustion. The most telling line is the departure of the "nymphs."This is a direct and ironic invocation of Spenser's Prothalamion. In Spenser's poem, the "Sweet Thames" is a sacred, celebratory space, a "silver streaming" river where nymphs gather flowers for a noble wedding, and the poet's refrain, "Sweet Thames, run softly, till I end my song" (line 176), is a charm of harmony and blessing. Eliot steals this refrain but hollows it out. His Thames is a polluted dump, bearing the "testimony of summer nights": "empty bottles, sandwich papers, / Silk handkerchiefs, cardboard boxes, cigarette ends" (line 178). The nymphs are not gathering flowers; they are the modern, fleeting sexual partners of "the loitering heirs of City directors," who have "left no addresses" (line 180-181). Anonymous, meaningless liaisons have replaced the sacred marriage rite of Spenser. The beautiful refrain becomes a whispered, desperate plea, "for I speak not loud or long," as if the poet knows the charm no longer works.

Do you like these... * ...like I only checked boxes without asking why the boxes were there. * ...time, I only ever copied correct answers into the blank spaces for points, never trying to understand why those answers were correct."

I bring this up here because even though 97% of the people who read this will understand what your talking about, there is the possibility that someone may think you are actually checking a cardboard box, instead of discovering ideas. I won't be irritated if you decide to ignore this anote.

eLife Assessment

This is a valuable study that presents convincing evidence on the genesis of the CPSF6 condensates that form upon HIV-1 infection and the specific molecular determinants involved in their formation, as well as their interactions with SRRM. The study could be strengthened by assessing the relevance of their findings to infection, and in particular, with reverse transcription and gene expression

Reviewer #1 (Public review):

In recent years, our understanding of the nuclear steps of the HIV-1 life cycle has made significant advances. It has emerged that HIV-1 completes reverse transcription in the nucleus and that the host factor CPSF6 forms condensates around the viral capsid. The precise function of these CPSF6 condensates is under investigation, but it is clear that the HIV-1 capsid protein is required for their formation. This study by Tomasini et al. investigates the genesis of the CPSF6 condensates induced by HIV-1 capsid, what other co-factors may be required and their relationship with nuclear speckels (NS). The authors show that disruption of the condensates by the drug PF74, added post-nuclear entry, blocks HIV-1 infection, which supports their functional role. They generated CPSF6 KO THP-1 cell lines, in which they expressed exogenous CPSF6 constructs to map by microscopy and pull down assays the regions critical for the formation of condensates. This approach revealed that the LCR region of CPSF6 is required for capsid binding but not for condensates whereas the FG region is essential for both. Using SON and SRRM2 as markers of NS, the authors show that CPSF6 condensates precede their merging with NS but that depletion of SRRM2, or SRRM2 lacking the IDR domain, delays the genesis of condensates, which are also smaller.

The study is interesting and well conducted and defines some characteristics of the CPSF6-HIV-1 condensates. Their results on the NS are valuable. The data presented are convincing.

I have two main concerns.

Firstly, the functional outcome of the various protein mutants and KOs is not evaluated. Although Figure 1 shows that disruption of the CPSF6 puncta by PF74 impairs HIV-1 infection, it is not clear if HIV-1 infection is at all affected by expression of the mutant CPSF6 forms (and SRRM2 mutants), or KO/KD of the various host factors. The cell lines are available, and so it should be possible to measure HIV-1 infection and reverse transcription. Secondly, the authors have not assessed if the effects observed on the NS impact HIV-1 gene expression, which would be interesting to know given that NS are sites of highly active gene transcription. With the reagents at hand, it should be possible to investigate this too.

Comments on revisions:

The revised version of this paper addresses my concerns.

Reviewer #2 (Public review):

Summary:

HIV-1 infection induces CPSF6 aggregates in the nucleus that contain the viral protein CA. The study of the functions and composition of these nuclear aggregates have raised considerable interest in the field, and they have emerged as sites in which reverse transcription is completed and in the proximity of which viral DNA becomes integrated. In this work, the authors have mutated several regions of the CPSF6 protein to identify the domains important for nuclear aggregation, in addition to the already-known FG-region; they have characterized the kinetics of fusion between CPSF6 aggregates and SC35 nuclear speckles and have determined the role of two nuclear speckle components in this process (SRRM2, SUN2).

Strengths:

The work examines systematically the domains of CPSF6 of importance for nuclear aggregate formation in an elegant manner in which these mutants complement an otherwise CPSF6-KO cell line. In addition, this work evidences a novel role for the protein SRRM2 in HIV-induced aggregate formation, overall advancing our comprehension of the components required for their formation and regulation.

Reviewer #3 (Public review):

In this study, the authors investigate the requirements for the formation of CPSF6 puncta induced by HIV-1 under a high multiplicity of infection conditions. Not surprisingly, they observe that mutation of the Phe-Gly (FG) repeat responsible for CPSF6 binding to the incoming HIV-1 capsid abrogates CPSF6 punctum formation. Perhaps more interestingly, they show that the removal of other domains of CPSF6, including the mixed-charge domain (MCD), does not affect the formation of HIV-1-induced CPSF6 puncta. The authors also present data suggesting that CPSF6 puncta form individual before fusing with nuclear speckles (NSs) and that the fusion of CPSF6 puncta to NSs requires the intrinsically disordered region (IDR) of the NS component SRRM2. While the study presents some interesting findings, there are some technical issues that need to be addressed and the amount of new information is somewhat limited. Also, the authors' finding that deletion of the CPSF6 MCD does not affect the formation of HIV-1-induced CPSF6 puncta contradicts recent findings of Jang et al. (https://doi.org/10.1093/nar/gkae769).

Comments on revisions:

The authors have generally addressed my comments.

Author response:

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

Reviewer #1 (Public review): 

In recent years, our understanding of the nuclear steps of the HIV-1 life cycle has made significant advances. It has emerged that HIV-1 completes reverse transcription in the nucleus and that the host factor CPSF6 forms condensates around the viral capsid. The precise function of these CPSF6 condensates is under investigation, but it is clear that the HIV-1 capsid protein is required for their formation. This study by Tomasini et al. investigates the genesis of the CPSF6 condensates induced by HIV-1 capsid, what other co-factors may be required, and their relationship with nuclear speckels (NS). The authors show that disruption of the condensates by the drug PF74, added post-nuclear entry, blocks HIV-1 infection, which supports their functional role. They generated CPSF6 KO THP-1 cell lines, in which they expressed exogenous CPSF6 constructs to map by microscopy and pull down assays of the regions critical for the formation of condensates. This approach revealed that the LCR region of CPSF6 is required for capsid binding but not for condensates whereas the FG region is essential for both. Using SON and SRRM2 as markers of NS, the authors show that CPSF6 condensates precede their merging with NS but that depletion of SRRM2, or SRRM2 lacking the IDR domain, delays the genesis of condensates, which are also smaller. 

The study is interesting and well conducted and defines some characteristics of the CPSF6-HIV-1 condensates. Their results on the NS are valuable. The data presented are convincing. 

I have two main concerns. Firstly, the functional outcome of the various protein mutants and KOs is not evaluated. Although Figure 1 shows that disruption of the CPSF6 puncta by PF74 impairs HIV-1 infection, it is not clear if HIV-1 infection is at all affected by expression of the mutant CPSF6 forms (and SRRM2 mutants) or KO/KD of the various host factors. The cell lines are available, so it should be possible to measure HIV-1 infection and reverse transcription. Secondly, the authors have not assessed if the effects observed on the NS impact HIV-1 gene expression, which would be interesting to know given that NS are sites of highly active gene transcription. With the reagents at hand, it should be possible to investigate this too. 

We thank the reviewer for her/his valuable feedback on our manuscript. We are pleased to see her/his appreciation of our results, and we did our utmost to address the highlighted points to further improve our work.

To correctly perform the infectivity assay, we generated stable cell clones—a process that required considerable time, particularly during the selection of clones expressing protein levels comparable to wild-type (WT) cells. To accurately measure infectivity, it was essential to use stable clones expressing the most important deletion mutant, ∆FG CPSF6, at levels similar to those of CPSF6 in WT cells (new Fig.5 A-B). Importantly, we assessed the reproducibility of our experiments by freezing and thawing these clones.

Regarding SRRM2, in THP-1 cells we were only able to achieve a knockdown, which still retains residual SRRM2 protein, albeit at much lower levels. Due to the essential role of SRRM2 in cell survival, obtaining a complete knockout in this cell line is not feasible, making it difficult to draw definitive conclusions from these experiments.

In contrast, 293T cells carrying the endogenous SRRM2 deletion mutant (ΔIDR) cannot be infected with replication-competent HIV-1, as they lack expression of CD4 and either CCR4 or CCR5. These cells were instead used to monitor the dynamics of CPSF6 puncta assembly within nuclear speckles. However, they are not a suitable model for studying the impact of the depletion of SRRM2 in viral infection.

Thus, we performed infectivity assays in a more relevant cell line for HIV-1 infection, THP-1 macrophage-like cells, using both a single-round virus and a replication-competent virus. The new results, shown in Figure 5 C-D, indicate that complete depletion of CPSF6 reduces infectivity, as measured by luciferase expression in a single-round infection (KO: ~65%; ΔFG: ~74%; compared to WT: 100% on average). Notably, a more pronounced defect in viral particle production was observed when WT virus was used for infection (KO: ~21%; ΔFG: ~16%; compared to WT: 100% on average). These findings support the referee’s insightful suggestion that the absence of CPSF6 could also impair HIV-1 gene expression. 

Reviewer #2 (Public review): 

Summary: 

HIV-1 infection induces CPSF6 aggregates in the nucleus that contain the viral protein CA. The study of the functions and composition of these nuclear aggregates have raised considerable interest in the field, and they have emerged as sites in which reverse transcription is completed and in the proximity of which viral DNA becomes integrated. In this work, the authors have mutated several regions of the CPSF6 protein to identify the domains important for nuclear aggregation, in addition to the alreadyknown FG region; they have characterized the kinetics of fusion between CPSF6 aggregates and SC35 nuclear speckles and have determined the role of two nuclear speckle components in this process (SRRM2, SUN2). 

Strengths: 

The work examines systematically the domains of CPSF6 of importance for nuclear aggregate formation in an elegant manner in which these mutants complement an otherwise CPSF6-KO cell line. In addition, this work evidences a novel role for the protein SRRM2 in HIV-induced aggregate formation, overall advancing our comprehension of the components required for their formation and regulation. 

Weaknesses: 

Some of the results presented in this manuscript, in particular the kinetics of fusion between CPSF6aggregates and SC35 speckles have been published before (PMID: 32665593; 32997983). 

The observations of the different effects of CPSF6 mutants, as well as SRRM2/SUN2 silencing experiments are not complemented by infection data which would have linked morphological changes in nuclear aggregates to function during viral infection. More importantly, these functional data could have helped stratify otherwise similar morphological appearances in CPSF6 aggregates. 

Overall, the results could be presented in a more concise and ordered manner to help focus the attention of the reader on the most important issues. Most of the figures extend to 3-4 different pages and some information could be clearly either aggregated or moved to supplementary data. 

First, we thank the reviewer for her/his appreciation of our study and to give to us the opportunity to better explain our results and to improve our manuscript. We appreciate the reviewer’s positive feedback on our study, and we will do our best to address her/his concerns. In the meantime, we would like to clarify the focus of our study. Our research does not aim to demonstrate an association between CPSF6 condensates (we use the term "condensates" rather than "aggregates," as aggregates are generally non-dynamic (Alberti & Hyman, 2021; Banani et al., 2017; Scoca et al., JMCB 2022), and our work specifically examines the dynamic behavior of CPSF6 puncta formed during infection and nuclear speckles. The association between CPSF6 puncta and NS has already been established in previous studies, as noted in the manuscript (PMID: 32665593; 32997983). The previous studies (PMID: 32665593; 32997983) showed that CPSF6 puncta colocalize with SC35 upon HIV infection and in the submitted study we study their kinetics.

About the point highlighted by the reviewer: "Kinetics of fusion between CPSF6-aggregates and SC35 speckles have been published before."  

Our study differs from prior work PMID 32665593 because we utilize a full-length HIV genome, and we did not follow the integrase (IN) fluorescence in trans and its association with CPSF6 but we specifically assess if CPSF6 clusters form in the nucleus independently of NS factors and next to fuse with them. In the current study we evaluated the dynamics of formation of CPSF6/NS puncta, which it has not been explored before. Given this focus, we believe that our work offers a novel perspective on the molecular interactions that facilitate HIV / CPSF6-NS fusion.

We calculated that 27% of CPSF6 clusters were independent from NS at 6 h post-infection, compared to only 9% at 30 h. This likely reflects a reduction in individual clusters as more become fused with nuclear speckles over time. At the same time, these data suggest that the fusion process can begin even earlier. Indeed, it has been reported that in macrophages, the peak of viral nuclear import occurs before 6 h post-infection (doi: 10.1038/s41564-020-0735-8).

In addition, we have incorporated new experiments assessing viral infectivity in the absence of CPSF6, or in CPSF6-knockout cells expressing either a CPSF6 mutant lacking the FG peptide or the WT protein. As shown in our new Figure 5, these results demonstrate that the FG peptide is critical for viral replication in THP-1 cells.

For better clarity, we would like to specify that our study focuses on the role of SON, a scaffold factor of nuclear speckles, rather than SUN2 (SUN domain-containing protein 2), which is a component of the LINC (Linker of Nucleoskeleton and Cytoskeleton) complex.

As suggested by the reviewer, we have revised the text and combined figures to improve clarity and facilitate reader comprehension. We appreciate the constructive comment of the reviewer.

Reviewer #3 (Public review): 

In this study, the authors investigate the requirements for the formation of CPSF6 puncta induced by HIV-1 under a high multiplicity of infection conditions. Not surprisingly, they observe that mutation of the Phe-Gly (FG) repeat responsible for CPSF6 binding to the incoming HIV-1 capsid abrogates CPSF6 punctum formation. Perhaps more interestingly, they show that the removal of other domains of CPSF6, including the mixed-charge domain (MCD), does not affect the formation of HIV-1-induced CPSF6 puncta. The authors also present data suggesting that CPSF6 puncta form individual before fusing with nuclear speckles (NSs) and that the fusion of CPSF6 puncta to NSs requires the intrinsically disordered region (IDR) of the NS component SRRM2. While the study presents some interesting findings, there are some technical issues that need to be addressed and the amount of new information is somewhat limited. Also, the authors' finding that deletion of the CPSF6 MCD does not affect the formation of HIV-1-induced CPSF6 puncta contradicts recent findings of Jang et al. (doi.org/10.1093/nar/gkae769). 

We thank the reviewer for her/his thoughtful feedback and the opportunity to elaborate on why our findings provide a distinct perspective compared to those of Jang et al. (doi.org/10.1093/nar/gkae769).

One potential reason for the differences between our findings and those of Jang et al. could be the choice of experimental systems. Jang et al. conducted their study in HEK293T cells with CPSF6 knockouts, as described in Sowd et al., 2016 (doi.org/10.1073/pnas.1524213113). In contrast, our work focused on macrophage-like THP-1 cells, which share closer characteristics with HIV-1’s natural target cells. 

Our approach utilized a complete CPSF6 knockout in THP-1 cells, enabling us to reintroduce untagged versions of CPSF6, such as wild-type and deletion mutants, to avoid potential artifacts from tagging. Jang et al. employed HA-tagged CPSF6 constructs, which may lead to subtle differences in experimental outcomes due to the presence of the tag.

Finally, our investigation into the IDR of SRRM2 relied on CRISPR-PAINT to generate targeted deletions directly in the endogenous gene (Lester et al., 2021, DOI: 10.1016/j.neuron.2021.03.026). This approach provided a native context for studying SRRM2’s role.

We will incorporate these clarifications into the discussion section of the revised manuscript.  

Reviewer #1 (Recommendations for the authors): 

(1) Figure 2E: The statistical analysis should be extended to the comparison between the "+HIV" samples. 

We showed the statistics between only HIV+ cells now new Fig. 2D.  

(2) Figure 4A top panel is out of focus. 

We modified the figure now figure 6A.

Reviewer #2 (Recommendations for the authors): 

(1) Some of the sentences could be rewritten for the sake of simplicity, also taking care to avoid overstatement. 

We modified the sentences as best as we could.

(2) For instance: There is no evidence that "viral genomes in nuclear niches may be contributing to the formation of viral reservoirs" (lines 33-35). 

We changed the sentence as follows: “Despite antiretroviral treatment, viral genomes can persist in these nuclear niches and reactivate upon treatment interruption, raising the possibility that they could play a role in the establishment of viral reservoirs.”

(3) Line 53: unclear sentence. "The initial stages of the viral life cycle have been understood....." The authors certainly mean reverse transcription, but as formulated this is not clear. The authors should also bear in mind that reverse transcription starts already in budding/just released virions. 

We clarified the concept as follows: “the initial stages of the viral life cycle, such as the reverse transcription (the conversion of the viral RNA in DNA) and the uncoating (loss of the capsid), have been understood to mainly occur within the host cytoplasm.”

(4) Line 124: the results in Figure 1 are not at all explained in the text. PF74 does not act on CPSF6, it acts on CA and this in turn leads to CPS6 puncta disappearance. 

PF74 binds the same hydrophobic pocket of the viral core as CPSF6. However, when viral cores are located within CPSF6 puncta, treatment with a high dose of PF74 leads to a rapid disassembly of these puncta, while viral cores remain detectable up to 2 hours post-treatment (Ay et al., EMBO J. 2024). Here, we simply describe what we observed by confocal microscopy. Said that HIV-Induced CPSF6 Puncta include both CPSF6 proteins and viral cores as we have now specified.

(5) Line 130; 'hinges into two key ...' should be 'hinges on'. 

Thanks we modified it.

(6) Supplementary Figures are not cited sequentially in the text. 

We have now modified the numbers of the supplementary figures according to their appearance in the text.

(7) Line 44: define FG. 

We defined it.

Reviewer #3 (Recommendations for the authors): 

Specific comments that the authors should address are outlined below. 

(1) As mentioned in the summary above, the authors' findings seem to be in direct contradiction with recent work published by Alan Engelman's lab in NAR. The authors should address the possible reason(s) for this discrepancy. 

We mention the potential reasons for the differences in the results between our study and Engelman’s lab study in the discussion.

(2) The major finding here that deletion of the CFSF6 FG repeat prevents the formation of CFSP6 puncta is unsurprising, as the FG repeat is responsible for capsid binding. This has been reported previously and such mutants have been used as controls in other studies. 

Our study demonstrates that the FG domain is the sole region responsible for the formation of CPSF6 puncta, rather than the LCR or MCD domains. The unique role of the FG domain in CPSF6 that promotes the formation of CPSF6 puncta without the help of the other IDRs during viral infection is a finding particularly novel, as it has not yet been reported in the literature.

(3) Line 339, the authors state: "incoming viral RNA has been observed to be sequestered in nuclear niches in cells treated with the reversible reverse transcriptase inhibitor, NEV. When macrophage-like cells are infected in the presence of NEV, the incoming viral RNA is held within the nucleus (Rensen et al., 2021; Scoca et al., 2023). This scenario is comparable to what is observed in patients undergoing antiretroviral therapy". In what way is this comparable to what is observed in individuals on ART? I see no basis for this statement. Sequestration of viral RNA in the nucleus is not the basis for maintaining the viral reservoir in individuals on therapy. 

Thanks, we rephrased the sentence.

(4) General comment: analyzing single-cell-derived KO clones is very risky because of random clonal variability between individual cells in the population. If single-cell-derived clones are used, phenotypes could be confirmed with multiple, independent clones. 

We used a clone completely KO for CPSF6 mainly to investigate the role of a specific domain in condensate formation and it will be difficult that clone selection could have introduced artifacts in this context. Other available clones retain residual endogenous protein, which prevents us from accurately assessing CPSF6 cluster formation in the various deletion mutants. A complete CPSF6 knockout is essential for studying puncta formation, as it eliminates potential artifacts arising from protein tags that could alter the phase separation properties of the protein under investigation.

(5) Line 214. "It is predicted to form two short α helices and a ß strand, arranged as: α helix - FG - ß strand - α helix". What is this based on? No citation is provided and no data are shown. 

In fact, the statement "It is predicted to form two short α helices and a ß strand, arranged as: α helix - FG - ß strand - α helix" is based on the data shown in Figure 4E presenting data generated by PSIPRED. 

(6) Figure 1B. "Luciferase values were normalized by total proteins revealed with the Bradford kit". What does this mean? I couldn't find anything explaining how the viral inputs were normalized. 

The amount of the virus used is the same for all samples, we used MOI 10 as described in the legend of Figure 1. It is important to normalize the RLU (luciferase assay) with the total amount of proteins to be sure that we are comparing similar number of cells. Obviously, the cells were plated on the same amount on each well, the normalization in our case it is just an additional important control.

(7) I can't interpret what is being shown in the movies. 

We updated the movie 1B and rephrased the movie legends and we added a new suppl. Fig.4B.

(8) Figure 5B. The differences seen are very small and of questionable significance. The data suggest that by 6 hpi, around 75% of HIV-induced CPSF6 puncta are already fused with NSs. 

We calculated that 27% of CPSF6 clusters were independent from NS at 6 h post-infection, compared to only 9% at 30 h. This likely reflects a reduction in individual clusters as more become fused with nuclear speckles over time. At the same time, these data suggest that the fusion process can begin even earlier. Indeed, it has been reported that in macrophages, the peak of viral nuclear import occurs before 6 h post-infection (doi: 10.1038/s41564-020-0735-8).

(9) Figure 6. Immunofluorescence is not a good method for quantifying KD efficiency. The authors should perform western blotting to measure KD efficiency. This is an important point, because the effect sizes are small, quite likely due to incomplete KD. 

We performed WB and quantified the results, which correlated with the IF data and their imaging analysis. These new findings have been incorporated into Figure 8A. Of note, deletion of the IDR of SRRM2 does not affect the number of SON puncta (Fig.8C), but significantly reduces the number of CPSF6 puncta in infected cells compared to those expressing full-length SRRM2 (Fig.8D).

(10) There are a variety of issues with the text that should be corrected. 

The authors use "RT" to mean both the enzyme (reverse transcriptase) and the process (reverse transcription). This is incorrect and will confuse the reader. RT refers to the enzyme (noun, not verb). 

The commonly used abbreviation for nevirapine is NVP, not NEV. 

In line 60, it is stated that the capsid contains 250 hexamers. This number is variable, depending on the size and shape of the capsid. By contrast, the capsid has exactly 12 pentamers. 

Line 75. Typo: "nuclear niches containing, such as like". 

Line 82. Typo: "the mechanism behinds". 

Line 102. Typo: "we aim to elucidate how these HIV-induced CPSF6 form". 

Line 107. Type: "CPSF6 is responsible for tracking the viral core" ("trafficking the viral core"?). 

Thanks, we corrected all of them.

It’s crazy how much impact the White Huns had! By the late 4th century, they were raiding Sasanian lands like Bactria and Khorasan, and their attacks even helped cause the Gupta Empire’s decline. They really changed the course of history!

eLife Assessment

The results by Zhu et al provide valuable insights into the representation of border ownership in area V1. They used neuropixel recording to demonstrate the clustering of border ownership, and compared cross-correlation functions between neurons in different layers to demonstrate that they depend on the type of stimulus. The strength of the evidence is solid but can be improved by performing additional analyses and addressing some concerns (as raised in the previous and current review), and accounting for the differences in classical and non-classical receptive field stimulation conditions.

Reviewer #1 (Public review):

Zhu and colleagues used high-density Neuropixel probes to perform laminar recordings in V1 while presenting either small stimuli that stimulated the classical receptive field (CRF) or large stimuli whose border straddled the RF to provide nonclassical RF (nCRF) stimulation. Their main question was to understand the relative contribution of feedforward (FF), feedback (FB), and horizontal circuits to border ownership (B_own ), which they addressed by measuring cross-correlation across layers. They found differences in cross-correlation between feedback/horizontal (FH) and input layers during CRF and nCRF stimulation.

Comments on revisions:

In the revision, the authors have added a paragraph in the Discussion to address the question of layers 2/3 neurons leading layer 4 neurons, and have provided answers to the questions in the public review without making substantial changes in the paper. However, there were several other recommendations, which I am not sure why were not considered. I am adding those again below.

* For CRF stimulation, the zero lag between 4C and 4A/B with layer 5/6 (Figure 3D last two columns on the right) was surprising to me. I just felt that this could be because layer 6 may also be getting FF inputs. Perhaps better not to club layer 5 with 6, as mentioned earlier also.

* Interpreting the nCRF delays, with often negative delays, was very challenging for me. For example, 4C -> 5/6 (third column in Figure 3) has a significantly negative peak (although that does not show up in statistical analysis because it seems to be a signed test to just test if the median was greater than zero, not if the median was different from zero; line 285). What is the interpretation here? Are spikes in 5/6 causing spikes in 4C (which, as mentioned earlier, would require anatomical projections from 5/6 to 4C)? On the other hand, if FB inputs arrive in 5/6 but there are no inputs going to 4C, then why should there even be a significant cross-correlation?

The only explanation I could think of is somehow an alignment of inputs in these two layers such that FH inputs come in Layer 5/6 just before FF inputs arrive in 4C, each causing a spike in a neuron in each layer which are otherwise not anatomically interconnected. But this would require both a very precise temporal coupling between FF and FH inputs arriving in these areas AND neurons in layer 5/6 which very strongly respond to FH stimulation (I thought that FH inputs are mainly modulatory and not as strong). Anyway, it would be good to see some cross correlation functions which have a negative lag (all examples in Fig 3B has positive or zero lag).

* I think cross-correlation analysis would have been useful if there was data from a feedback area (say V2). In its absence, perhaps latency analysis (by just comparing the PSTH) could have revealed something interesting, given that the hypothesis is about differences in the timings in FH versus FF inputs. Do PSTHs across layers show the type of differences that are being claimed (e.g. in line 295-297)?

* Line 262-63: "Notably, the rates were nearly identical under the two stimulus conditions" - I would have thought CRF stimulation would produce higher rates. Can the authors explain this?

* Line 174-175: Isn't the proportion of border ownership cells in layer 4C higher than one would expect under the assumption that nCRF effects are mediated by horizontal and feedback connections which layer 4C does not receive? Can authors explain?

* Figure 3D: it would also be good to show the heatmaps stacked up in the increasing order of the interelectrode distance of the pairs so that it will be easy to see how the peak lag changes with distance as well.

* It will be good to show the shift in peak lag and CCG asymmetry between CRF and nCRF conditions for the same pairs, using a violin or bar plot with lines connecting each pair in Figure 3.

* Line 594, 603, 628 and 630: What procedure was used to determine the size, location of the CRF, and optimal orientation manually online?

* Line 733-734: Although a reference is cited, please explicitly mention the rationale for keeping the peak lag cutoff at 10 ms.

* It is unclear why a grating was used for the CRF condition, instead of just having the portion of the stimulus within the RF for the nCRF condition, as the comparisons for FHi with FF are with different FF drives in each case.

* Figure 5 - the scatter is enormous, can you please provide the R2 values?

Reviewer #2 (Public review):

Summary:

The authors present a study of how modulatory activity from outside the classical receptive field (cRF) differs from cRF stimulation. They study neural activity across the different layers of V1 in two anesthetized monkeys using Neuropixels probes. The monkeys are presented with drifting gratings and border-ownership tuning stimuli. They find that border-ownership tuning is organized into columns within V1, which is unexpected and exciting, and that the flow of activity from cell-to-cell (as judged by cross-correlograms between single units) is influenced by the type of visual stimulus: border-ownership tuning stimuli vs. drifting-grating stimuli.

Strengths:

The questions addressed by the study are of high interest, and the use of Neuropixels probes yields extremely high numbers of single-units and cross-correlation histograms (CCHs) which makes the results robust. The study is well-described.

Comments on revisions:

The results are interesting and seem robust. However, several of my main points were not addressed. The authors do not analyze or discuss the problem the border ownership stimuli do uniquely isolate feedback from feedforward influences. Here are my remaining points/recommendations:

(1) In my previous review I indicated that the border-ownership signal also provides a strong feedforward drive, a black-white edge, in addition to the border ownership signal. Calling this a "nCRF stimulus" is a misnomer. Please correct this terminology and replace it by something that is appropriate, e.g. changing it into "grating stimulation" (instead of CRF stimulation) and BO-stimulation (instead of nCRF stimulation).

(2) In my previous review I asked if the initial response for the border ownership stimulus show the feedforward signature. It is unclear to me why this suggestions did not lead to an analysis of the feedforward response. I repeat the text from my previous review: "The authors state that they did not look at cross-correlations during the initial response, but if they do, do they see the feedforward-dominated pattern? The jitter CCH analysis might suffice in correcting for the response transient." Can the authors address this point?

(3) In my previous review I asked the authors show the average time course of the response elicited by preferred and nonpreferred border ownership stimuli across all significant neurons. It remains unclear why this plot was not provided.

Reviewer #3 (Public review):

Summary:

The paper by Zhu et al is on an important topic in visual neuroscience, the emergence in the visual cortex of signals about figure and ground. This topic also goes by the name border ownership. The paper utilizes modern recording techniques very skillfully to extend what is known about border ownership. It offers new evidence about the prevalence of border ownership signals across different cortical layers in V1 cortex. Also, it uses pairwise cross correlation to study signal flow under different conditions of visual stimulation that include the border ownership paradigm.

Strengths: The paper's strengths are results of its use of multi-electrode probes to study border ownership in many neurons simultaneously across the cortical layers in V1. Also it provides new useful data about the dynamics of interaction of signals from the non-classical receptive field (NCRF) and the Classical receptive field (CRF).

Weaknesses:

The paper's weakness is that it does not challenge consensus beliefs about mechanisms. Also, the paper combines data about border ownership with data about the NCRF without making it clear how they are similar or different.

Critique:

The border ownership data on V1 offered in the paper replicate experimental results obtained by Zhou and von der Heydt (2000) and confirm the earlier results. The incremental addition is that the authors found border ownership in all cortical layers of V1, extending Zhou and von der Heydt's results that were only about layer 2/3 in V2 cortex. This is an interesting new result using the same stimuli but new measurement techniques.

The cross-correlation results show that the pattern of the cross correlogram (CCG) is influenced by the visual pattern being presented. However, in the initial submitted ms. the results were not analyzed mechanistically, and the interpretation was unclear. For instance, the authors show in Figure 3 (and in Figure S2) that the peak of the CCG can indicate layer 2/3 excites layer 4C when the visual stimulus is the border ownership test pattern, a large square 8 deg on a side. More than one reviewer asked, " how can layer 2/3 excite layer 4C"? . In the revised ms. the authors added a paragraph to the Discussion to respond to the reviewers about this point. The authors could provide an even better response to the reviewers by emphasizing that, consistently, layer 5/6 neurons lead neurons in layer 4, and for the CRF pattern and even more when the NCRF patterns are used.

The problems in understanding the CCG data are indirectly caused by the lack of a critical analysis of what is happening in the responses that reveal the border ownership signals, as in Fig.2. Let's put it bluntly--are border ownership signals excitatory or inhibitory? As the authors pointed out in their rebuttal, Zhang and von der Heydt (2010, JNS) did experiments to answer this question but I do not agree with the authors rebuttal letter about what Zhang and von der Heydt (2010) reported. If you examine Zhang and von der Heydt's Figure 6, you see that the major effect of stimulating border ownership neurons is suppression from the non-preferred side. That result is consistent with many papers on the NCRF (many cited by the authors) that indicate that it is mostly suppressive. That experimental fact about border ownership should be mentioned in the present paper.

What I should have pointed out in the first round, but didn't understand it then, is that there is a disconnect between the the border ownership laminar analysis (Figure 2) and the laminar correlations with CCGs (Figures 3-5) because the CCGs are not limited to border ownership neurons (or at least we are not told they were limited to them). So the CCG results are not mostly about border ownership--they are about the difference between signal flow in responses to small drifting Gabor patterns vs big flashed squares. Since only 21% of all recorded neurons were border ownership neurons, it is likely that most of the CCG statistics is based on neurons that do not show border ownership. Nevertheless, Figures 3 and 4 are very useful for the study of signal flow in the NCRF. It wasn't clear to me and I think the authors could make it clearer what those figures are about.<br /> And I wonder if it might be possible to make a stronger link with border ownership by restricting the CCG analysis to pairs of neurons in which one neuron is a border ownership neuron. Are there enough data?

My critique of the CCG analysis applies to Figure 5 also. That figure shows a weak correlation of CCG asymmetry with Border Ownership Index. Perhaps a stronger correlation might be present if the population were restricted to the much smaller population of neuron pairs that had at least one border ownership neuron.

Author response:

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

Reviewer #1 (Public review): 

Zhu and colleagues used high-density Neuropixel probes to perform laminar recordings in V1 while presenting either small stimuli that stimulated the classical receptive field (CRF) or large stimuli whose border straddled the RF to provide nonclassical RF (nCRF) stimulation. Their main question was to understand the relative contribution of feedforward (FF), feedback (FB), and horizontal circuits to border ownership (Bown), which they addressed by measuring crosscorrelation across layers. They found differences in cross-correlation between feedback/horizontal (FH) and input layers during CRF and nCRF stimulation. 

Although the data looks high quality and analyses look mostly fine, I had a lot of difficulty understanding the logic in many places. Examples of my concerns are written below. 

(1) What is the main question? The authors refer to nCRF stimulation emerging from either feedback from higher areas or horizontal connections from within the same area (e.g. lines 136 to 138 and again lines 223-232). I initially thought that the study would aim to distinguish between the two. However, the way the authors have clubbed the layers in 3D, the main question seems to be whether Bown is FF or FH (i.e., feedback and horizontal are clubbed). Is this correct? If so, I don't see the logic, since I can't imagine Bown to be purely FF. Thus, just showing differences between CRF stimulation (which is mainly expected to be FF) and nCRF stimulation is not surprising to me. 

We thank the reviewer for their thoughtful comments. As explained in the discussion, we grouped cortical layers to reduce uncertainty in precisely assigning laminar boundaries and to increase statistical power. Consequently, this limits our ability to distinguish the relative contributions of feedback inputs, primarily targeting layers 1 and 6, and horizontal connections, mainly within layers 2/3 and 5. Nevertheless, previous findings, especially regarding the rapid emergence of B_own signals, suggest that feedback is more biologically plausible than horizontal-based mechanisms.

Importantly, the emergence of B_own signals in the primate brain should not be taken for granted. Direct physiological evidence that distinguishes feedforward from feedback/horizontal mechanisms has been lacking. While we agree it is unlikely that B_own is mediated solely by feedforward processing, we felt it was necessary to test this empirically, particularly using highresolution laminar recordings.

As discussed, feedforward models of B_own have been proposed (e.g., Super, Romeo, and Keil, 2010; Saki and Nishimura, 2006). These could, in theory, be supported by more general nCRF modulations arising through early feedforward inhibitions, such as those observed in the retinogeniculate pathway (e.g., Webb, Tinsley, Vincent and Derrington, 2005; Blitz and Regehr, 2005; Alitto and Usrey, 2008). However, most B_own models rely heavily on response latency, yet very few studies have recorded across layers or areas simultaneously to address this directly. Notably, recent findings in area V4 show that B_own signals emerge earlier in deep layers than in granular (input) layers, suggesting a non-feedforward origin (Franken and Reynolds, 2021).

Furthermore, although previous studies have shown that the nCRF can modulate firing rates and the timing of neuronal firing across layers, our findings go beyond these effects. We provide clear evidence that nCRF modulation also alters precise spike timing relationships and interlaminar coordination, and that the magnitude of nCRF modulation depends on these interlaminar interactions. This supports the idea that B_own , or more general nCRF modulation, involves more than local rate changes, reflecting layer-specific network dynamics consistent with feedback or lateral integration.

(2) Choice of layers for cross-correlation analysis: In the Introduction, and also in Figure 3C, it is mentioned that FF inputs arrive in 4C and 6, while FB/Horizontal inputs arrive at "superficial" and "deep", which I take as layer 2/3 and 5. So it is not clear to me why (i) layer 4A/B is chosen for analysis for Figure 3D (I would have thought layer 6 should have been chosen instead) and (ii) why Layers 5 and 6 are clubbed. 

We thank the reviewer for raising this important point. The confusion likely stems from our use of the terms “superficial” and “deep” layers when describing the targets of feedback/horizontal inputs. To clarify, by “superficial” and “deep,” we specifically refer to layers 1–3 and layers 5–6, respectively, as illustrated in Figure 3C. Feedback and horizontal inputs relatively avoid entire layer 4, including both 4C and 4A/B.

We also emphasize that the classification of layers as feedforward or feedback/horizontal recipients is relative rather than absolute. For example, although layer 6 receives both feedforward and feedback/horizontal inputs, it contains a higher proportion of feedback/horizontal inputs compared to layers 4C and 4A/B. 

We had addressed this rationale in the Discussion, but recognize it may not have been sufficiently emphasized. We have revised the main text accordingly to clarify this point for readers in the final manuscript version.

(3) Addressing the main question using cross-correlation analysis: I think the nice peaks observed in Figure 3B for some pairs show how spiking in one neuron affects the spiking in another one, with the delay in cross-correlation function arising from the conduction delay. This is shown nicely during CRF stimulation in Figure 3D between 4C -> 2/3, for example. However, the delay (positive or negative) is constrained by anatomical connectivity. For example, unless there are projections from 2/3 back to 4C which causes firing in a 2/3 layer neuron to cause a spike in a layer 4 neuron, we cannot expect to get a negative delay no matter what kind of stimulation (CRF versus nCRF) is used. 

We thank the reviewer for the insightful comment. The observation that neurons within FH_i laminar compartments (layers 2/3, 5/6) can lead those in layer 4 (4C, 4A/B) during nCRF stimulation may indeed seem unexpected. However, several anatomical pathways could mediate the propagation of B_own signals from FH_i compartments to layer 4. We have revised the Discussion section in the final version of the manuscript to address this point explicitly.

In Macaque V1, projections from layers 2/3 to 4A/B have been documented (Blasdel et al., 1985; Callaway and Wiser, 1996), and neurons in 4A/B often extend apical dendrites into layers 2/3 (Lund, 1988; Yoshioka et al., 1994). Although direct projections from layers 2/3 to 4C are generally sparse (Callaway, 1998), a subset of neurons in the lower part of layer 3 can give off collateral axons to 4C (Lund and Yoshioka, 1991). Additionally, some 4C neurons extend dendrites into 4B, enabling potential dendritic integration of inputs from more superficial layers (Somogyi and Cowey, 1981; Mates and Lund, 1983; Yabuta and Callaway, 1998). Sparse connections from 2/3 to layer 4 have also been reported in cat V1 (Binzegger, Douglas and Martin, 2004). Moreover, layers 2/3 may influence 4C neurons disynaptically, without requiring dense monosynaptic connections. 

Importantly, while CCGs can suggest possible circuit arrangements, functional connectivity may arise through mechanisms not fully captured by traditional anatomical tracing. Indeed, the apparent discrepancy between anatomical and functional data is not uncommon. For example, although 4B is known to receive anatomical input primarily from 4Cα, but not 4Cβ, photostimulation experiments have shown that 4B neurons can also be functionally driven by 4Cβ (Sawatari and Callaway, 1996). Our observation of functional inputs from layers 2/3 to layer 4 is also consistent with prior findings in rodent V1, where CCG analysis (e.g., Figure 7 in Senzai, Fernandez-Ruiz and Buzsaki, 2019) or photostimulation (Xu et al., 2016) revealed similar pathways. 

Layers 5/6 provide dense projections to layers 4A/B (Lund, 1988; Callaway, 1998). In particular, layer 6 pyramidal neurons, especially the subset classified as Type 1 cells, project substantially to layer 4C (Wiser and Callaway, 1996; Fitzpatrick et al., 1985). 

Reviewer #2 (Public review): 

Summary: 

The authors present a study of how modulatory activity from outside the classical receptive field (cRF) differs from cRF stimulation. They study neural activity across the different layers of V1 in two anesthetized monkeys using Neuropixels probes. The monkeys are presented with drifting gratings and border-ownership tuning stimuli. They find that border-ownership tuning is organized into columns within V1, which is unexpected and exciting, and that the flow of activity from cellto-cell (as judged by cross-correlograms between single units) is influenced by the type of visual stimulus: border-ownership tuning stimuli vs. drifting-grating stimuli. 

Strengths: 

The questions addressed by the study are of high interest, and the use of Neuropixels probes yields extremely high numbers of single-units and cross-correlation histograms (CCHs) which makes the results robust. The study is well-described. 

Weaknesses: 

The weaknesses of the study are (a) the use of anesthetized animals, which raises questions about the nature of the modulatory signal being measured and the underlying logic of why a change in visual stimulus would produce a reversal in information flow through the cortical microcircuit and (b) the choice of visual stimuli, which do not uniquely isolate feedforward from feedback influences. 

(1) The modulation latency seems quite short in Figure 2C. Have the authors measured the latency of the effect in the manuscript and how it compares to the onset of the visually driven response? It would be surprising if the latency was much shorter than 70ms given previous measurements of BO and figure-ground modulation latency in V2 and V1. On the same note, it might be revealing to make laminar profiles of the modulation (i.e. preferred - non-preferred border orientation) as it develops over time. Does the modulation start in feedback recipient layers? 

(2) Can the authors show the average time course of the response elicited by preferred and nonpreferred border ownership stimuli across all significant neurons? 

We thank the reviewer for the insightful comment—this is indeed an important and often overlooked point. As noted in the Discussion, B_own modulation differs from other forms of figure-ground modulation (e.g., Lamme et al., 1998) in that it can emerge very rapidly in early visual cortex—within ~10–35 ms after response onset (Zhou et al., 2000; Sugihara et al., 2011). This rapid emergence has been interpreted as evidence for the involvement of fast feedback inputs, which can propagate up to ten times faster than horizontal connections (Girard et al., 2001). Moreover, interlaminar interactions via monosynaptic or disynaptic connections can occur on very short timescales (a few milliseconds), further complicating efforts to disentangle feedback influences based solely on latency.

Thus, while the early onset of modulation in our data may appear surprising, it is consistent with prior B_own findings, and likely reflects a combination of fast feedback and rapid interlaminar processing. This makes it challenging to use conventional latency measurements to resolve laminar differences in B_own modulation. Latency comparisons are well known to be susceptible to confounds such as variability in response onset, luminance, contrast, stimulus size, and other sensory parameters. 

Although we did not explicitly quantify the latency of B_own modulation in this manuscript, our cross-correlation analysis provides a more sensitive and temporally resolved measure of interlaminar information flow. We therefore focused on this approach rather than laminar modulation profiles, as it more directly addresses our primary research question.

(3) The logic of assuming that cRF stimulation should produce the opposite signal flow to borderownership tuning stimuli is worth discussing. I suspect the key difference between stimuli is that they used drifting gratings as the cRF stimulus, the movement of the stimulus continually refreshes the retinal image, leading to continuous feedforward dominance of the signals in V1. Had they used a static grating, the spiking during the sustained portion of the response might also show more influence of feedback/horizontal connections. Do the initial spikes fired in response to the borderownership tuning stimuli show the feedforward pattern of responses? The authors state that they did not look at cross-correlations during the initial response, but if they do, do they see the feedforward-dominated pattern? The jitter CCH analysis might suffice in correcting for the response transient. 

We thank the reviewer for the insightful comment. As noted in the final Results section, our CRF and nCRF stimulation paradigms differ in respects beyond the presence or absence of nonclassical modulation, including stimulus properties within the CRF.

We agree with the reviewer’s speculation that drifting gratings may continually refresh the retinal image, promoting sustained feedforward dominance in V1, whereas static gratings might allow greater influence from feedback/horizontal inputs during the sustained response. Likewise, the initial response to the B_own stimulus could be dominated by feedforward activity before feedback/horizontal influences arrive. 

This contrast was a central motivation for our experimental design: we deliberately used two stimulus conditions — drifting gratings to emphasize feedforward processing, and B_own stimuli, which are known to engage feedback modulation — to test whether these two conditions yield different patterns of interlaminar information flow. Our results confirm that they do. While we did not separately analyze the very initial spike period, our focus is on interlaminar information flow during the sustained response, which serves as the primary measure of feedback/horizontal engagement in this study.

Finally, beyond this direct comparison, we show in Figure 5 that under nCRF stimulation alone, the direction and strength of interlaminar information flow correlate with the magnitude of B_own modulation, further supporting the idea that our cross-correlation approach reveals functionally meaningful differences in cortical processing.

(4) The term "nCRF stimulation" is not appropriate because the CRF is stimulated by the light/dark edge. 

We thank the reviewer for the comment. As noted in the Introduction, nCRF effects described in the literature invariably involve stimulation both inside and outside the CRF. Our use of the term “nCRF stimulation” refers to this experimental paradigm, rather than suggesting that the CRF itself is unstimulated. We hope this clarifies our use of the term.

Reviewer #3 (Public review): 

Summary: 

The paper by Zhu et al is on an important topic in visual neuroscience, the emergence in the visual cortex of signals about figures and ground. This topic also goes by the name border ownership. The paper utilizes modern recording techniques very skillfully to extend what is known about border ownership. It offers new evidence about the prevalence of border ownership signals across different cortical layers in V1 cortex. Also, it uses pairwise cross-correlation to study signal flow under different conditions of visual stimulation that include the border ownership paradigm. 

Strengths: 

The paper's strengths are its use of multi-electrode probes to study border ownership in many neurons simultaneously across the cortical layers in V1, and its innovation of using crosscorrelation between cortical neurons -- when they are viewing border-ownership patterns or instead are viewing grating patterns restricted to the classical receptive field (CRF). 

Weaknesses: 

The paper's weaknesses are its largely incremental approach to the study of border ownership and the lack of a critical analysis of the cross-correlation data. The paper as it is now does not advance our understanding of border ownership; it mainly confirms prior work, and it does not challenge or revise consensus beliefs about mechanisms. However, it is possible that, in the rich dataset the authors have obtained, they do possess data that could be added to the paper to make it much stronger. 

Critique: 

The border ownership data on V1 offered in the paper replicates experimental results obtained by Zhou and von der Heydt (2000) and confirms the earlier results using the same analysis methods as Zhou. The incremental addition is that the authors found border ownership in all cortical layers extending Zhou's results that were only about layer 2/3. 

The cross-correlation results show that the pattern of the cross-correlogram (CCG) is influenced by the visual pattern being presented. However, the results are not analyzed mechanistically, and the interpretation is unclear. For instance, the authors show in Figure 3 (and in Figure S2) that the peak of the CCG can indicate layer 2/3 excites layer 4C when the visual stimulus is the border ownership test pattern, a large square 8 deg on a side. But how can layer 2/3 excite layer 4C? The authors do not raise or offer an answer to this question. Similar questions arise when considering the CCG of layer 4A/B with layer 2/3. What is the proposed pathway for layer 2/3 to excite 4A/B? Other similar questions arise for all the interlaminar CCG data that are presented. What known functional connections would account for the measured CCGs? 

We thank the reviewer for raising this important point. As noted in our response to a previous comment, several anatomical pathways could mediate apparent functional inputs from layers 2/3 to 4C and 4A/B. In macaque V1, projections from layers 2/3 to 4A/B have been documented (Blasdel et al., 1985; Callaway and Wiser, 1996), and neurons in 4A/B often extend apical dendrites into layers 2/3 (Lund, 1988; Yoshioka et al., 1994). Although direct projections from layers 2/3 to 4C are generally sparse (Callaway, 1998), a subset of lower layer 3 neurons can give off collateral axons to 4C (Lund and Yoshioka, 1991). Some 4C neurons also extend dendrites into 4B, potentially allowing dendritic integration of inputs from more superficial layers (Somogyi and Cowey, 1981; Mates and Lund, 1983; Yabuta and Callaway, 1998). Sparse connections from 2/3 to layer 4 have also been reported in cat V1 (Binzegger et al., 2004).

Moreover, layers 2/3 may influence 4C neurons disynaptically, without requiring dense monosynaptic connections. While CCGs suggest possible circuit arrangements, functional connectivity may arise through mechanisms not fully captured by anatomical tracing, and apparent discrepancies between anatomical and functional data are not uncommon. For example, although 4B is known to receive anatomical input primarily from 4Cα, 4B neurons can also be functionally driven by 4Cβ using photostimulation (Sawatari and Callaway, 1996). Our observation of functional inputs from layers 2/3 to layer 4 is also consistent with prior findings in rodent V1, where CCG analysis (e.g., Figure 7 in Senzai, Fernandez-Ruiz and Buzsaki, 2019) or photostimulation (Xu et al., 2016) revealed similar pathways. 

Layers 5/6 also provide dense projections to layers 4A/B (Lund, 1988; Callaway, 1998). In particular, layer 6 pyramidal neurons, especially the subset classified as Type 1 cells, project substantially to layer 4C (Wiser and Callaway, 1996; Fitzpatrick et al., 1985). 

We have revised the Discussion section to explicitly address these points and clarify the potential anatomical and functional pathways underlying the measured interlaminar CCGs, highlighting how inputs from layers 2/3 and 5/6 to layer 4 can be mediated via both direct and indirect connections.

The problems in understanding the CCG data are indirectly caused by the lack of a critical analysis of what is happening in the responses that reveal the border ownership signals, as in Figure 2. Let's put it bluntly - are border ownership signals excitatory or inhibitory? The reason I raise this question is that the present authors insightfully place border ownership as examples of the action of the non-classical receptive field (nCRF) of cortical cells. Most previous work on the nCRF (many papers cited by the authors) reveal the nCRF to be inhibitory or suppressive. In order to know whether nCRF signals are excitatory or inhibitory, one needs a baseline response from the CRF, so that when you introduce nCRF signals you can tell whether the change with respect to the CRF is up or down. As far as I know, prior work on border ownership has not addressed this question, and the present paper doesn't either. This is where the rich dataset that the present authors possess might be used to establish a fundamental property of border ownership. 

Then we must go back to consider what the consequences of knowing the sign of the border ownership signal would mean for interpreting the CCG data. If the border ownership signals from extrastriate feedback or, alternatively, from horizontal intrinsic connections, are excitatory, they might provide a shared excitatory input to pairs of cells that would show up in the CCG as a peak at 0 delay. However, if the border ownership manuscript signals are inhibitory, they might work by exciting only inhibitory neurons in V1. This could have complicated consequences for the CCG.The interpretation of the CCG data in the present version of the m is unclear (see above). Perhaps a clearer interpretation could be developed once the authors know better what the border ownership signals are. 

We thank the reviewer for raising this fundamental and thought-provoking question. As noted, B_own signals arise from nCRF, which has often been associated with suppressive effects. However, Zhang and von der Heydt (2010) provided important insight into this issue by systematically varying the placement of figure fragments outside the CRF while keeping an edge centered within the CRF. They found that contextual fragments on the preferred side of B_own produce facilitation, while those on the non-preferred side produce suppression. Thus, the nCRF contribution to B_own reflects both excitatory and inhibitory modulation, depending on the spatial configuration of the figure.

These effects were well explained by their model in which feedback from grouping cells in higher areas selectively enhances or suppresses V1/V2 neuron responses, depending on their B_own preference. In this framework, the B_own signal itself is not inherently excitatory or inhibitory; rather, it results from the net effect of feedback, which can be either facilitative or suppressive. Importantly, it is the input that is modulated — not that the receiving neurons are necessarily inhibitory themselves.

In the current study, our analysis focused on CCGs showing excessive coincident spiking, i.e., positive peaks, which are typically interpreted as evidence for shared excitatory input or excitatory connections. Due to the limited number of connections, we did not analyze inhibitory interactions, such as anti-correlations or delayed suppression in the CCGs, which would be expected if the reference neuron were inhibitory. Therefore, the CCGs we report here likely reflect the excitatory component of the B_own signal, and possibly its upstream drive via feedback. While a full separation of excitatory and inhibitory components remains an important goal for future work, our data suggest that B_own modulation is at least partially mediated through excitatory feedback input.

My critique of the CCG analysis applies to Figure 5 also. I cannot comprehend the point of showing a very weak correlation of CCG asymmetry with Border Ownership Index, especially when what CCG asymmetry means is unclear mechanistically. Figure 5 does not make the paper stronger in my opinion. 

We thank the reviewer for this comment. As described in the Results section for Figure 5, the observation that interlaminar information flow correlates with B_own modulation is important because it demonstrates that these flow patterns are specifically related to the magnitude of B_own signals, independent of the comparisons between CRF and nCRF stimulation. 

In Figure 3, the authors show two CCGs that involve 4C--4C pairs. It would be nice to know more about such pairs. If there are any 6--6 pairs, what they look like also would be interesting. The authors also in Figure 3 show CCG's of two 4C--4A/B pairs and it would be quite interesting to know how such CCGs behave when CRF and nCRF stimuli are compared. In other words, the authors have shown us they have many data but have chosen not to analyze them further or to explain why they chose not to analyze them. It might help the paper if the authors would present all the CCG types they have. This suggestion would be helpful when the authors know more about the sign of border ownership signals, as discussed at length above. 

We thank the reviewer for the insightful comment. The rationale for selecting specific laminar pairs is described in the Results section after Figure 3C and further discussed in the Discussion. In brief, we focused on CCGs computed from pairs in which one neuron resided in laminar compartments receiving feedback/horizontal inputs (layers 2/3 and 5/6) and the other within compartments relatively devoid of these inputs (layers 4C and 4A/B).

To mitigate uncertainty in defining exact laminar boundaries and to maximize statistical power, we combined some anatomical layers into distinct laminar compartments. This approach allowed us to compare the relative spike timing between neuronal pairs during CRF and nCRF stimulation. If feedback/horizontal inputs contribute more during nCRF than CRF stimulation, we expect this to be reflected in the lead-lag relationships of the CCGs. While other pairs (e.g., 5/6–5/6 or 4C– 4A/B) could in principle be analyzed, the hypothesized patterns for these pairs are less clear, and thus they were not the focus of our study. Nonetheless, these additional pairs represent interesting directions for future work.

Food and shared rituals become equalizers —moments where social barriers fade.

The simple language captures the narrator's awe and newfound awareness. Carver leaves the ending open, implying quiet but profound personal growth. The narrator's transformation feels genuine, yet fragile.

The narrator's tone is dismissive and uncomfortable. His use of "this blind man" rather than a name reveals his prejudice and emotional detachment. Carver begins by exposing the narrator's ignorance and narrow worldview.

Never knew that about undergarments!

for - stats - climate crisis - decarbonization - 2025 - 2.5 to 4 years of carbon budget remaining for 1.5 Deg C - 20% per annum decarbonization rate

Steps missed in combining everything.

I think at some point there was a chapter describing the new ACL concept and basic info for existing customer. I am missing this, either here either on migration instructions.

for - climate crisis - major barrier - unwillingness to eliminate fossil fuel subsidies - Johan Rockstrom

for - metaphor - Tour de France - decarbonization - Science in lead - business second - policy distant 3rd - Johan Rockstrom

for - quote - Paris Accord - 2025 - policy lagged but business stayed on track - Johan Rockstrom

These areas experience distinct wet and dry seasons with high rainfall and temperature variation between elevations. How might differences in elevation, temperature, and rainfall influence mammal behavior and activity patterns in the study?

The authors found that while their results support aspects of both the predation risk and visual acuity models, neither model alone fully explains how mammals respond to moonlight. How might these findings help guide conservation strategies to reduce the negative impacts of human-caused shifts in animal activity?

The ability to anticipate the changes in the lunar cycles allow for the regulation of biological processes that require specific light conditions to protect from predators

This is interesting to note as it shows how common an adaptation that evolved 100 million years prior to mammals became within the class.

This shows a key limitation in behavioral ecology research. Without an objective and a quantifiable/ clear metric for visual acuity. Hypothesis, about how moonlight levels affect predation remain largely speculative. Mammalian predators and prey exhibit complex sensory adaptations that directly affect foraging, avoidance, and movement patterns at night and are much more complex than visual or non-visual oriented. However in the absence of standardized measures that link these visual traits to actual ecological behaviors, it becomes difficult to draw meaningful comparisons across species or environments. Developing a rigorous, objective framework for assessing visual acuity in natural contexts is an essential step toward clarifying the sensory constraints that shape nocturnal predatory-prey dynamics and improving the predictive power of ecological models.

That is actually really interesting to find out. Obviously, animals seeing in the dark is an adaptation for some animals to survive but I didn't know it became so common because of a bottleneck effect. What catastrophe occurred to drastically reduce the population leaving mainly nocturnal animals reproducing?

Important to note: Tapetum lucidum is a reflective layer in the eyes of many vertebrates, enhancing night vision by reflecting the light back through the retina.

Researchers tested two ideas about how moonlight affects animal behavior but didn’t find full support for either. Only half of the prey acted as expected others were more active or showed no pattern at all.

It is fascinating that neither the Predation Risk nor the Visual Acuity Hypothesis completely explains the results. That suggests nocturnal behavior is influenced by more complex interactions than just moonlight intensity. I am curious about how human disturbance or artificial light at night might add another layer to these patterns, especially as ecosystems become more affected by human activity.

Statistical tests showed that only coyote, ocelot, and puma had meaningful activity patterns. The others didn’t, likely due to small sample sizes.

Using the tapetum lucidum as a proxy for night vision is such a creative way to make the Visual Acuity Hypothesis measurable. Still, I wonder if this structure alone fully represents visual ability across species. Other factors like eye size, neural processing, or even habitat openness might also influence how animals perceive moonlight. It would be interesting if future studies incorporated those aspects to give a more complete understanding of nocturnal vision.

nostr self hosted public relay

Good Idea for the /tmp directory! isn't it?

the disputed amount is deducted from your account and is credited back to the customer's account.

If you want to contest the dispute and show the transaction was legitimate, you'll have to submit supporting evidence documents.

Check the details required by the Issuing Bank for Dispute Representment.

Also: Immediately after leaving, have the interviewer write down any notes about how the interview went.

e2ee

seen it before

Nowadays, trolls are very common on the internet, and this paragraph really made me worried about the situation. There are so many ways people can express themselves on social media, such as posts and comments. Vicious words on the internet can really hurt people in real life. And I agree with the point that some trolls would be more dangerous when they are ignored, so instead of ignoring them, I think some rules and policies are needed to prevent them from acting that way, such as kicking them out of the platforms and arresting them when it's necessary.

I think this is an interesting distinction. I believe that women, particularly famous women or women in the public eye, probably experience trolling on social media more often than their male counterparts. There are clear similarities in behavior between trolls and abusers or stalkers, as this segment states, which makes me believe women are more directly affected by trolls.

I believe the best way to deal with trolling is to not acknowledge them. Trolls thrice off of others reaction and hostility you may give them. To combat this the best way is to act like it doesn't bother you at all. From there in silence report them, block them, or do anything in your will power to restrict them from your own life. If it get's even more serious in terms of personal attacks or death threats that's when I would suggest reaching out for help and talking to an adult for further action.

საინტერესო სტატიაა, ირემი უწყინარ ბალახისმჭამელ ცხოველთან ასოცირდება, ჩინური წყლის ირემიც არაა გამონაკლისი, თუმცა მისი შესახედაობა და პირის აპარატი მტაცებლის იერს ქმნის, ზედმეტსახელი ვამპირი ირემი ნამდვილად მასზეა მორგებული.

The set of questions on a survey cannot be considered a scale unless a scaling process was followed to identify the questions and determine how the responses would be combined. So, just because a set of questions on a survey looks like a scale, it collects data using the same response scale, and it is even analyzed like a scale, it isn’t a real scale unless some type of scaling process was used to create it.

eLife Assessment

This study presents SegPore, a valuable new method for processing direct RNA nanopore sequencing data, which improves the segmentation of raw signals into individual bases and boosts the accuracy of modified base detection. The evidence presented to benchmark SegPore is solid, and the authors provide a fully documented implementation of the method. SegPore will be of particular interest to researchers studying RNA modifications.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors describe a new computational method (SegPore), which segments the raw signal from nanopore direct RNA-Seq data to improve the identification of RNA modifications. In addition to signal segmentation, SegPore includes a Gaussian Mixture Model approach to differentiate modified and unmodified bases. SegPore uses Nanopolish to define a first segmentation, which is then refined into base and transition blocks. SegPore also includes a modification prediction model that is included in the output. The authors evaluate the segmentation in comparison to Nanopolish and Tombo (RNA002) as well as f5c and Uncalled 4 (RNA004), and they evaluate the impact on m6A RNA modification detection using data with known m6A sites. In comparison to existing methods, SegPore appears to improve the ability to detect m6A, suggesting that this approach could be used to improve the analysis of direct RNA-Seq data.

Strengths:

SegPore address an important problem (signal data segmentation). By refining the signal into transition and base blocks, noise appears to be reduced, leading to improved m6A identification at the site level as well as for single read predictions. The authors provide a fully documented implementation, including a GPU version that reduces run time. The authors provide a detailed methods description, and the approach to refine segments appears to be new.

Weaknesses:

The authors show that SegPore reduces noise compared to other methods, however the improvement in accuracy appears to be relatively small for the task of identifying m6A. To run SegPore, the GPU version is essential, which could limit the application of this method in practice.

Reviewer #2 (Public review):

Summary:

The work seeks to improve detection of RNA m6A modifications using Nanopore sequencing through improvements in raw data analysis. These improvements are said to be in the segmentation of the raw data, although the work appears to position the alignment of raw data to the reference sequence and some further processing as part of the segmentation, and result statistics are mostly shown on the 'data-assigned-to-kmer' level.<br /> As such, the title, abstract and introduction stating the improvement of just the 'segmentation' does not seem to match the work the manuscript actually presents, as the wording seems a bit too limited for the work involved.<br /> The work itself shows minor improvements in m6Anet when replacing Nanopolish' eventalign with this new approach, but clear improvements in the distributions of data assigned per kmer. However, these assignments were improved well enough to enable m6A calling from them directly, both at site-level and at read-level.

A large part of the improvements shown appear to stem from the addition of extra, non-base/kmer specific, states in the segmentation/assignment of the raw data, removing a significant portion of what can be considered technical noise for further analysis. Previous methods enforced assignment of (almost) all raw data, forcing a technically optimal alignment that may lead to suboptimal results in downstream processing as datapoints could be assigned to neighbouring kmers instead, while random noise that is assigned to the correct kmer may also lead to errors in modification detection.

For an optimal alignment between the raw signal and the reference sequence, this approach may yield improvements for downstream processing using other tools.<br /> Additionally, the GMM used for calling the m6A modifications provides a useful, simple and understandable logic to explain the reason a modification was called, as opposed to the black models that are nowadays often employed for these types of tasks.

Weaknesses:

The manuscript suggests the eventalign results are improved compared to Nanopolish. While this is believably shown to be true (Table 1), the effect on the use case presented, downstream differentiation between modified and unmodified status on a base/kmer, is likely limited for during downstream modification calling the noisy distributions are often 'good enough'. E.g. Nanopolish uses the main segmentation+alignment for a first alignment and follows up with a form of targeted local realignment/HMM test for modification calling (and for training too), decreasing the need for the near-perfect segmentation+alignment this work attempts to provide. Any tool applying a similar strategy probably largely negates the problems this manuscript aims to improve upon. Should a use-case come up where this downstream optimisation is not an option, SegPore might provide the necessary improvements in raw data alignment.

Appraisal:

The authors have shown their methods ability to identify noise in the raw signal and remove their values from the segmentation and alignment, reducing its influences for further analyses. Figures directly comparing the values per kmer do show a visibly improved assignment of raw data per kmer. As a replacement for Nanopolish' eventalign it seems to have a rather limited, but improved effect, on m6Anet results. At the single read level modification modification calling this work does appear to improve upon CHEUI.

Impact:

With the current developments for Nanopore based modification calling largely focusing on Artificial Intelligence, Neural Networks and the likes, improvements made in interpretable approaches provide an important alternative that enables deeper understanding of the data rather than providing a tool that plainly answers the question of wether a base is modified or not, without further explanation. The work presented is best viewed in context of a workflow where one aims to get an optimal alignment between raw signal data and the reference base sequence for further processing. For example, as presented, as a possible replacement for Nanopolish' eventalign. Here it might enable data exploration and downstream modification calling without the need for local realignments or other approaches that re-consider the distribution of raw data around the target motif, such as a 'local' Hidden Markov Model or Neural Networks. These possibilities are useful for a deeper understanding of the data and further tool development for modification detection works beyond m6A calling.

Reviewer #3 (Public review):

Summary:

Nucleotide modifications are important regulators of biological function, however, until recently, their study has been limited by the availability of appropriate analytical methods. Oxford Nanopore direct RNA sequencing preserves nucleotide modifications, permitting their study, however many different nucleotide modifications lack an available base-caller to accurately identify them. Furthermore, existing tools are computationally intensive, and their results can be difficult to interpret.

Cheng et al. present SegPore, a method designed to improve the segmentation of direct RNA sequencing data and boost the accuracy of modified base detection.

Strengths:

This method is well described and has been benchmarked against a range of publicly available base callers that have been designed to detect modified nucleotides.

Weaknesses:

However, the manuscript has a significant drawback in its current version. The most recent nanopore RNA base callers can distinguish between different ribonucleotide modifications, however, SegPore has not been benchmarked against these models.

The manuscript would be strengthened by benchmarking against the rna004_130bps_hac@v5.1.0 and rna004_130bps_sup@v5.1.0 dorado models, which are reported to detect m5C, m6A_DRACH, inosine_m6A and PseU.

A clear demonstration that SegPore also outperforms the newer RNA base caller models will confirm the utility of this method.

Author response:

The following is the authors’ response to the original reviews

We thank all the reviewers for their constructive comments. We have carefully considered your feedback and revised the manuscript accordingly. The major concern raised was the applicability of SegPore to the RNA004 dataset. To address this, we compared SegPore with f5c and Uncalled4 on RNA004, and found that SegPore demonstrated improved performance, as shown in Table 2 of the revised manuscript.

Following the reviewers’ recommendations, we updated Figures 3 and 4. Additionally, we added one table and three supplementary figures to the revised manuscript:

· Table 2: Segmentation benchmark on RNA004 data

· Supplementary Figure S4: RNA translocation hypothesis illustrated on RNA004 data

· Supplementary Figure S5: Illustration of Nanopolish raw signal segmentation with eventalign results

· Supplementary Figure S6: Running time of SegPore on datasets of varying sizes

Below, we provide a point-by-point response to your comments.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors describe a new computational method (SegPore), which segments the raw signal from nanopore-direct RNA-Seq data to improve the identification of RNA modifications. In addition to signal segmentation, SegPore includes a Gaussian Mixture Model approach to differentiate modified and unmodified bases. SegPore uses Nanopolish to define a first segmentation, which is then refined into base and transition blocks. SegPore also includes a modification prediction model that is included in the output. The authors evaluate the segmentation in comparison to Nanopolish and Tombo, and they evaluate the impact on m6A RNA modification detection using data with known m6A sites. In comparison to existing methods, SegPore appears to improve the ability to detect m6A, suggesting that this approach could be used to improve the analysis of direct RNA-Seq data.

Strengths:

SegPore addresses an important problem (signal data segmentation). By refining the signal into transition and base blocks, noise appears to be reduced, leading to improved m6A identification at the site level as well as for single-read predictions. The authors provide a fully documented implementation, including a GPU version that reduces run time. The authors provide a detailed methods description, and the approach to refine segments appears to be new.

Weaknesses:

In addition to Nanopolish and Tombo, f5c and Uncalled4 can also be used for segmentation, however, the comparison to these methods is not shown.

The method was only applied to data from the RNA002 direct RNA-Sequencing version, which is not available anymore, currently, it remains unclear if the methods still work on RNA004.

Thank you for your comments.

To clarify the background, there are two kits for Nanopore direct RNA sequencing: RNA002 (the older version) and RNA004 (the newer version). Oxford Nanopore Technologies (ONT) introduced the RNA004 kit in early 2024 and has since discontinued RNA002. Consequently, most public datasets are based on RNA002, with relatively few available for RNA004 (as of 30 June 2025).

Nanopolish and Tombo were developed for raw signal segmentation and alignment using RNA002 data, whereas f5c and Uncalled4are the only two software supporting RNA004 data.  Since the development of SegPore began in January 2022, we initially focused on RNA002 due to its data availability. Accordingly, our original comparisons were made against Nanopolish and Tombo using RNA002 data.

We have now updated SegPore to support RNA004 and compared its performance against f5c and Uncalled4 on three public RNA004 datasets.

As shown in Table 2 of the revised manuscript, SegPore outperforms both f5c and Uncalled4 in raw signal segmentation. Moreover, the jiggling translocation hypothesis underlying SegPore is further supported, as shown in Supplementary Figure S4.

The overall improvement in accuracy appears to be relatively small.

Thank you for the comment.

We understand that the improvements shown in Tables 1 and 2 may appear modest at first glance due to the small differences in the reported standard deviation (std) values. However, even small absolute changes in std can correspond to substantial relative reductions in noise, especially when the total variance is low.

To better quantify the improvement, we assume that approximately 20% of the std for Nanopolish, Tombo, f5c, and Uncalled4 arises from noise. Using this assumption, we calculate the relative noise reduction rate of SegPore as follows:

Noise reduction rate = (baseline std − SegPore std) / (0.2 × baseline std) ​​

Based on this formula, the average noise reduction rates across all datasets are:

- SegPore vs Nanopolish: 49.52%

- SegPore vs Tombo: 167.80%

- SegPore vs f5c: 9.44%

- SegPore vs Uncalled4: 136.70%

These results demonstrate that SegPore can reduce the noise level by at least 9% given a noise level of 20%, which we consider a meaningful improvement for downstream tasks, such as base modification detection and signal interpretation. The high noise reduction rates observed in Tombo and Uncalled4 (over 100%) suggest that their actual noise proportion may be higher than our 20% assumption.

We acknowledge that this 20% noise level assumption is an approximation. Our intention is to illustrate that SegPore provides measurable improvements in relative terms, even when absolute differences appear small.

The run time and resources that are required to run SegPore are not shown, however, it appears that the GPU version is essential, which could limit the application of this method in practice.

Thank you for your comment.

Detailed instructions for running SegPore are provided in github (https://github.com/guangzhaocs/SegPore). Regarding computational resources, SegPore currently requires one CPU core and one Nvidia GPU to perform the segmentation task efficiently.

We present SegPore’s runtime for typical datasets in Supplementary Figure S6 in the revised manuscript.  For a typical 1 GB fast5 file, the segmentation takes approximately 9.4 hours using a single NVIDIA DGX‑1 V100 GPU and one CPU core.

Currently, GPU acceleration is essential to achieve practical runtimes with SegPore. We acknowledge that this requirement may limit accessibility in some environments. To address this, we are actively working on a full C++ implementation of SegPore that will support CPU-only execution. While development is ongoing, we aim to release this version in a future update.

Reviewer #2 (Public review):

Summary:

The work seeks to improve the detection of RNA m6A modifications using Nanopore sequencing through improvements in raw data analysis. These improvements are said to be in the segmentation of the raw data, although the work appears to position the alignment of raw data to the reference sequence and some further processing as part of the segmentation, and result statistics are mostly shown on the 'data-assigned-to-kmer' level.

As such, the title, abstract, and introduction stating the improvement of just the 'segmentation' does not seem to match the work the manuscript actually presents, as the wording seems a bit too limited for the work involved.

The work itself shows minor improvements in m6Anet when replacing Nanopolish eventalign with this new approach, but clear improvements in the distributions of data assigned per kmer. However, these assignments were improved well enough to enable m6A calling from them directly, both at site-level and at read-level.

Strengths:

A large part of the improvements shown appear to stem from the addition of extra, non-base/kmer specific, states in the segmentation/assignment of the raw data, removing a significant portion of what can be considered technical noise for further analysis. Previous methods enforced the assignment of all raw data, forcing a technically optimal alignment that may lead to suboptimal results in downstream processing as data points could be assigned to neighbouring kmers instead, while random noise that is assigned to the correct kmer may also lead to errors in modification detection.

For an optimal alignment between the raw signal and the reference sequence, this approach may yield improvements for downstream processing using other tools.<br /> Additionally, the GMM used for calling the m6A modifications provides a useful, simple, and understandable logic to explain the reason a modification was called, as opposed to the black models that are nowadays often employed for these types of tasks.

Weaknesses:

The work seems limited in applicability largely due to the focus on the R9's 5mer models. The R9 flow cells are phased out and not available to buy anymore. Instead, the R10 flow cells with larger kmer models are the new standard, and the applicability of this tool on such data is not shown. We may expect similar behaviour from the raw sequencing data where the noise and transition states are still helpful, but the increased kmer size introduces a large amount of extra computing required to process data and without knowledge of how SegPore scales, it is difficult to tell how useful it will really be. The discussion suggests possible accuracy improvements moving to 7mers or 9mers, but no reason why this was not attempted.

Thank you for pointing out this important limitation. Please refer to our response to Point 1 of Reviewer 1 for SegPore’s performance on RNA004 data. Notably, the jiggling behavior is also observed in RNA004 data, and SegPore achieves better performance than both f5c and Uncalled4.

The increased k-mer size in RNA004 affects only the training phase of SegPore (refer to Supplementary Note 1, Figure 5 for details on the training and testing phases). Once the baseline means and standard deviations for each k-mer are established, applying SegPore to RNA004 data proceeds similarly to RNA002. This is because each k-mer in the reference sequence has, at most, two states (modified and unmodified). While the larger k-mer size increases the size of the parameter table, it does not increase the computational complexity during segmentation. Although estimating the initial k-mer parameter table requires significant time and effort on our part, it does not affect the runtime for end users applying SegPore to RNA004 data.

Extending SegPore from 5-mers to 7-mers or 9-mers for RNA002 data would require substantial effort to retrain the model and generate sufficient training data. Additionally, such an extension would make SegPore’s output incompatible with widely used upstream and downstream tools such as Nanopolish and m6Anet, complicating integration and comparison. For these reasons, we leave this extension for future work.

The manuscript suggests the eventalign results are improved compared to Nanopolish. While this is believably shown to be true (Table 1), the effect on the use case presented, downstream differentiation between modified and unmodified status on a base/kmer, is likely limited as during actual modification calling the noisy distributions are usually 'good enough', and not skewed significantly in one direction to really affect the results too terribly.

Thank you for your comment. While current state-of-the-art (SOTA) methods perform well on benchmark datasets, there remains significant room for improvement. Most SOTA evaluations are based on limited datasets, primarily covering DRACH motifs in human and mouse transcriptomes. However, m6A modifications can also occur in non-DRACH motifs, where current models may underperform. Additionally, other RNA modifications—such as pseudouridine, inosine, and m5C—are less studied, and their detection may benefit from improved signal modeling.

We would also like to emphasize that raw signal segmentation and RNA modification detection are distinct tasks. SegPore focuses on the former, providing a cleaner, more interpretable signal that can serve as a foundation for downstream tasks. Improved segmentation may facilitate the development of more accurate RNA modification detection algorithms by the community.

Scientific progress often builds incrementally through targeted improvements to foundational components. We believe that enhancing signal segmentation, as SegPore does, contributes meaningfully to the broader field—the full impact will become clearer as the tool is adopted into more complex workflows.

Furthermore, looking at alternative approaches where this kind of segmentation could be applied, Nanopolish uses the main segmentation+alignment for a first alignment and follows up with a form of targeted local realignment/HMM test for modification calling (and for training too), decreasing the need for the near-perfect segmentation+alignment this work attempts to provide. Any tool applying a similar strategy probably largely negates the problems this manuscript aims to improve upon.

We thank the reviewer for this insightful comment.

To clarify, Nanopolish provides three independent commands: polya, eventalign, and call-methylation.

- The polya command identifies the adapter, poly(A) tail, and transcript region in the raw signal.

- The eventalign command aligns the raw signal to a reference sequence, assigning a signal segment to individual k-mers in the reference.

- The call-methylation command detects methylated bases from DNA sequencing data.

The eventalign command corresponds to “the main segmentation+alignment for a first alignment,” while call-methylation corresponds to “a form of targeted local realignment/HMM test for modification calling,” as mentioned in the reviewer’s comment. SegPore’s segmentation is similar in purpose to Nanopolish’s eventalign, while its RNA modification estimation component is similar in concept to Nanopolish’s call-methylation.

We agree the general idea may appear similar, but the implementations are entirely different. Importantly, Nanopolish’s call-methylation is designed for DNA sequencing data, and its models are not trained to recognize RNA modifications. This means they address distinct research questions and cannot be directly compared on the same RNA modification estimation task. However, it is valid to compare them on the segmentation task, where SegPore exhibits better performance (Table 1).

We infer the reviewer may suggest that because m6Anet is a deep neural network capable of learning from noisy input, the benefit of more accurate segmentation (such as that provided by SegPore) might be limited. This concern may arise from the limited improvement of SegPore+m6Anet over Nanopolish+m6Anet in bulk analysis (Figure 3). Several factors may contribute to this observation:

(i) For reads aligned to the same gene in the in vivo data, alignment may be inaccurate due to pseudogenes or transcript isoforms.

(ii) The in vivo benchmark data are inherently more complex than in vitro datasets and may contain additional modifications (e.g., m5C, m7G), which can confound m6A calling by altering the signal baselines of k-mers.

(iii) m6Anet is trained on events produced by Nanopolish and may not be optimal for SegPore-derived events.

(iv) The benchmark dataset lacks a modification-free (IVT) control sample, making it difficult to establish a true baseline for each k-mer.

In the IVT data (Figure 4), SegPore shows a clear improvement in single-molecule m6A identification, with a 3~4% gain in both ROC-AUC and PR-AUC. This demonstrates SegPore’s practical benefit for applications requiring higher sensitivity at the molecule level.

As noted earlier, SegPore’s contribution lies in denoising and improving the accuracy of raw signal segmentation, which is a foundational step in many downstream analyses. While it may not yet lead to a dramatic improvement in all applications, it already provides valuable insights into the sequencing process (e.g., cleaner signal profiles in Figure 4) and enables measurable gains in modification detection at the single-read level. We believe SegPore lays the groundwork for developing more accurate and generalizable RNA modification detection tools beyond m6A.

We have also added the following sentence in the discussion to highlight SegPore’s limited performance in bulk analysis:

“The limited improvement of SegPore combined with m6Anet over Nanopolish+m6Anet in bulk in vivo analysis (Figure 3) may be explained by several factors: potential alignment inaccuracies due to pseudogenes or transcript isoforms, the complexity of in vivo datasets containing additional RNA modifications (e.g., m5C, m7G) affecting signal baselines, and the fact that m6Anet is specifically trained on events produced by Nanopolish rather than SegPore. Additionally, the lack of a modification-free control (in vitro transcribed) sample in the benchmark dataset makes it difficult to establish true baselines for each k-mer. Despite these limitations, SegPore demonstrates clear improvement in single-molecule m6A identification in IVT data (Figure 4), suggesting it is particularly well suited for in vitro transcription data analysis.”

Finally, in the segmentation/alignment comparison to Nanopolish, the latter was not fitted(/trained) on the same data but appears to use the pre-trained model it comes with. For the sake of comparing segmentation/alignment quality directly, fitting Nanopolish on the same data used for SegPore could remove the influences of using different training datasets and focus on differences stemming from the algorithm itself.

In the segmentation benchmark (Table 1), SegPore uses the fixed 5-mer parameter table provided by ONT. The hyperparameters of the HHMM are also fixed and not estimated from the raw signal data being segmented. Only in the m6A modification task,  SegPore does perform re-estimation of the baselines for the modified and unmodified states of k-mers. Therefore, the comparison with Nanopolish is fair, as both tools rely on pre-defined models during segmentation.

Appraisal:

The authors have shown their method's ability to identify noise in the raw signal and remove their values from the segmentation and alignment, reducing its influences for further analyses. Figures directly comparing the values per kmer do show a visibly improved assignment of raw data per kmer. As a replacement for Nanopolish eventalign it seems to have a rather limited, but improved effect, on m6Anet results. At the single read level modification modification calling this work does appear to improve upon CHEUI.

Impact:

With the current developments for Nanopore-based modification largely focusing on Artificial Intelligence, Neural Networks, and the like, improvements made in interpretable approaches provide an important alternative that enables a deeper understanding of the data rather than providing a tool that plainly answers the question of whether a base is modified or not, without further explanation. The work presented is best viewed in the context of a workflow where one aims to get an optimal alignment between raw signal data and the reference base sequence for further processing. For example, as presented, as a possible replacement for Nanopolish eventalign. Here it might enable data exploration and downstream modification calling without the need for local realignments or other approaches that re-consider the distribution of raw data around the target motif, such as a 'local' Hidden Markov Model or Neural Networks. These possibilities are useful for a deeper understanding of the data and further tool development for modification detection works beyond m6A calling.

Reviewer #3 (Public review):

Summary:

Nucleotide modifications are important regulators of biological function, however, until recently, their study has been limited by the availability of appropriate analytical methods. Oxford Nanopore direct RNA sequencing preserves nucleotide modifications, permitting their study, however, many different nucleotide modifications lack an available base-caller to accurately identify them. Furthermore, existing tools are computationally intensive, and their results can be difficult to interpret.

Cheng et al. present SegPore, a method designed to improve the segmentation of direct RNA sequencing data and boost the accuracy of modified base detection.

Strengths:

This method is well-described and has been benchmarked against a range of publicly available base callers that have been designed to detect modified nucleotides.

Weaknesses:

However, the manuscript has a significant drawback in its current version. The most recent nanopore RNA base callers can distinguish between different ribonucleotide modifications, however, SegPore has not been benchmarked against these models.

I recommend that re-submission of the manuscript that includes benchmarking against the rna004_130bps_hac@v5.1.0 and rna004_130bps_sup@v5.1.0 dorado models, which are reported to detect m5C, m6A_DRACH, inosine_m6A and PseU.<br /> A clear demonstration that SegPore also outperforms the newer RNA base caller models will confirm the utility of this method.

Thank you for highlighting this important limitation. While Dorado, the new ONT basecaller, is publicly available and supports modification-aware basecalling, suitable public datasets for benchmarking m5C, inosine, m6A, and PseU detection on RNA004 are currently lacking. Dorado’s modification-aware models are trained on ONT’s internal data, which is not publicly released. Therefore, it is not currently feasible to evaluate or directly compare SegPore’s performance against Dorado for m5C, inosine, m6A, and PseU detection.

We would also like to emphasize that SegPore’s main contribution lies in raw signal segmentation, which is an upstream task in the RNA modification detection pipeline. To assess its performance in this context, we benchmarked SegPore against f5c and Uncalled4 on public RNA004 datasets for segmentation quality. Please refer to our response to Point 1 of Reviewer 1 for details.

Our results show that the characteristic “jiggling” behavior is also observed in RNA004 data (Supplementary Figure S4), and SegPore achieves better segmentation performance than both f5c and Uncalled4 (Table 2).

Recommendations for the authors:

Reviewing Editor:

Please note that we also received the following comments on the submission, which we encourage you to take into account:

took a look at the work and for what I saw it only mentions/uses RNA002 chemistry, which is deprecated, effectively making this software unusable by anyone any more, as RNA002 is not commercially available. While the results seem promising, the authors need to show that it would work for RNA004. Notably, there is an alternative software for resquiggling for RNA004 (not Tombo or Nanopolish, but the GPU-accelerated version of Nanopolish (f5C), which does support RNA004. Therefore, they need to show that SegPore works for RNA004, because otherwise it is pointless to see that this method works better than others if it does not support current sequencing chemistries and only works for deprecated chemistries, and people will keep using f5C because its the only one that currently works for RNA004. Alternatively, if there would be biological insights won from the method, one could justify not implementing it in RNA004, but in this case, RNA002 is deprecated since March 2024, and the paper is purely methodological.

Thank you for the comment. We agree that support for current sequencing chemistries is essential for practical utility. While SegPore was initially developed and benchmarked on RNA002 due to the availability of public data, we have now extended SegPore to support RNA004 chemistry.

To address this concern, we performed a benchmark comparison using public RNA004 datasets against tools specifically designed for RNA004, including f5c and Uncalled4. Please refer to our response to Point 1 of Reviewer 1 for details. The results show that SegPore consistently outperforms f5c and Uncalled4 in segmentation accuracy on RNA004 data.

Reviewer #2 (Recommendations for the authors):

Various statements are made throughout the text that require further explanation, which might actually be defined in more detail elsewhere sometimes but are simply hard to find in the current form.

(1) Page 2, “In this technique, five nucleotides (5mers) reside in the nanopore at a time, and each 5mer generates a characteristic current signal based on its unique sequence and chemical properties (16).”

5mer? Still on R9 or just ignoring longer range influences, relevant? It is indeed a R9.4 model from ONT.

Thank you for the observation. We apologize for the confusion and have clarified the relevant paragraph to indicate that the method is developed for RNA002 data by default. Specifically, we have added the following sentence:

“Two versions of the direct RNA sequencing (DRS) kits are available: RNA002 and RNA004. Unless otherwise specified, this study focuses on RNA002 data.”

(2) Page 3, “Employ models like Hidden Markov Models (HMM) to segment the signal, but they are prone to noise and inaccuracies.”

That's the alignment/calling part, not the segmentation?

Thank you for the comment. We apologize for the confusion. To clarify the distinction between segmentation and alignment, we added a new paragraph before the one in question to explain the general workflow of Nanopore DRS data analysis and to clearly define the task of segmentation. The added text reads:

“The general workflow of Nanopore direct RNA sequencing (DRS) data analysis is as follows. First, the raw electrical signal from a read is basecalled using tools such as Guppy or Dorado, which produce the nucleotide sequence of the RNA molecule. However, these basecalled sequences do not include the precise start and end positions of each ribonucleotide (or k-mer) in the signal. Because basecalling errors are common, the sequences are typically mapped to a reference genome or transcriptome using minimap2 to recover the correct reference sequence. Next, tools such as Nanopolish and Tombo align the raw signal to the reference sequence to determine which portion of the signal corresponds to each k-mer. We define this process as the segmentation task, referred to as "eventalign" in Nanopolish. Based on this alignment, Nanopolish extracts various features—such as the start and end positions, mean, and standard deviation of the signal segment corresponding to a k-mer. This signal segment or its derived features is referred to as an "event" in Nanopolish.”

We also revised the following paragraph describing SegPore to more clearly contrast its approach:

“In SegPore, we first segment the raw signal into small fragments using a Hierarchical Hidden Markov Model (HHMM), where each fragment corresponds to a sub-state of a k-mer. Unlike Nanopolish and Tombo, which directly align the raw signal to the reference sequence, SegPore aligns the mean values of these small fragments to the reference. After alignment, we concatenate all fragments that map to the same k-mer into a larger segment, analogous to the "eventalign" output in Nanopolish. For RNA modification estimation, we use only the mean signal value of each reconstructed event.”

We hope this revision clarifies the difference between segmentation and alignment in the context of our method and resolves the reviewer’s concern.

(3) Page 4, Figure 1, “These segments are then aligned with the 5mer list of the reference sequence fragment using a full/partial alignment algorithm, based on a 5mer parameter table. For example, 𝐴𝑗 denotes the base "A" at the j-th position on the reference.”

I think I do understand the meaning, but I do not understand the relevance of the Aj bit in the last sentence. What is it used for?

When aligning the segments (output from Step 2) to the reference sequence in Step 3, it is possible for multiple segments to align to the same k-mer. This can occur particularly when the reference contains consecutive identical bases, such as multiple adenines (A). For example, as shown in Fig. 1A, Step 3, the first two segments (μ₁ and μ₂) are aligned to the first 'A' in the reference sequence, while the third segment is aligned to the second 'A'. In this case, the reference sequence AACTGGTTTC...GTC, which contains exactly two consecutive 'A's at the start. This notation helps to disambiguate segment alignment in regions with repeated bases.

Additionally, this figure and its subscript include mapping with Guppy and Minimap2 but do not mention Nanopolish at all, while that seems an equally important step in the preprocessing (pg5). As such it is difficult to understand the role Nanopolish exactly plays. It's also not mentioned explicitly in the SegPore Workflow on pg15, perhaps it's part of step 1 there?

We thank the reviewer for pointing this out. We apologize for the confusion. As mentioned in the public response to point 3 of Reviewer 2, SegPore uses Nanopolish to identify the poly(A) tail and transcript regions from the raw signal. SegPore then performs segmentation and alignment on the transcript portion only. This step is indeed part of Step 1 in the preprocessing workflow, as described in Supplementary Note 1, Section 3.

To clarify this in the main text, we have updated the preprocessing paragraph on page 6 to explicitly describe the role of Nanopolish:

“We begin by performing basecalling on the input fast5 file using Guppy, which converts the raw signal data into ribonucleotide sequences. Next, we align the basecalled sequences to the reference genome using Minimap2, generating a mapping between the reads and the reference sequences. Nanopolish provides two independent commands: "polya" and "eventalign".
The "polya" command identifies the adapter, poly(A) tail, and transcript region in the raw signal, which we refer to as the poly(A) detection results. The raw signal segment corresponding to the poly(A) tail is used to standardize the raw signal for each read. The "eventalign" command aligns the raw signal to a reference sequence, assigning a signal segment to individual k-mers in the reference. It also computes summary statistics (e.g., mean, standard deviation) from the signal segment for each k-mer. Each k-mer together with its corresponding signal features is termed an event. These event features are then passed into downstream tools such as m6Anet and CHEUI for RNA modification detection. For full transcriptome analysis (Figure 3), we extract the aligned raw signal segment and reference sequence segment from Nanopolish's events for each read by using the first and last events as start and end points. For in vitro transcription (IVT) data with a known reference sequence (Figure 4), we extract the raw signal segment corresponding to the transcript region for each input read based on Nanopolish’s poly(A) detection results.”

Additionally, we revised the legend of Figure 1A to explicitly include Nanopolish in step 1 as follows:

“The raw current signal fragments are paired with the corresponding reference RNA sequence fragments using Nanopolish.”

(4) Page 5, “The output of Step 3 is the "eventalign," which is analogous to the output generated by the Nanopolish "eventalign" command.”

Naming the function of Nanopolish, the output file, and later on (pg9) the alignment of the newly introduced methods the exact same "eventalign" is very confusing.

Thank you for the helpful comment. We acknowledge the potential confusion caused by using the term “eventalign” in multiple contexts. To improve clarity, we now consistently use the term “events” to refer to the output of both Nanopolish and SegPore, rather than using "eventalign" as a noun. We also added the following sentence to Step 3 (page 6) to clearly define what an “event” refers to in our manuscript:

“An "event" refers to a segment of the raw signal that is aligned to a specific k-mer on a read, along with its associated features such as start and end positions, mean current, standard deviation, and other relevant statistics.”

We have revised the text throughout the manuscript accordingly to reduce ambiguity and ensure consistent terminology.

(5) Page 5, “Once aligned, we use Nanopolish's eventalign to obtain paired raw current signal segments and the corresponding fragments of the reference sequence, providing a precise association between the raw signals and the nucleotide sequence.”

I thought the new method's HHMM was supposed to output an 'eventalign' formatted file. As this is not clearly mentioned elsewhere, is this a mistake in writing? Is this workflow dependent on Nanopolish 'eventalign' function and output or not?

We apologize for the confusion. To clarify, SegPore is not dependent on Nanopolish’s eventalign function for generating the final segmentation results. As described in our response to your comment point 2 and elaborated in the revised text on page 4, SegPore uses its own HHMM-based segmentation model to divide the raw signal into small fragments, each corresponding to a sub-state of a k-mer. These fragments are then aligned to the reference sequence based on their mean current values.

As explained in the revised manuscript:

“In SegPore, we first segment the raw signal into small fragments using a Hierarchical Hidden Markov Model (HHMM), where each fragment corresponds to a sub-state of a k-mer. Unlike Nanopolish and Tombo, which directly align the raw signal to the reference sequence, SegPore aligns the mean values of these small fragments to the reference. After alignment, we concatenate all fragments that map to the same k-mer into a larger segment, analogous to the "eventalign" output in Nanopolish. For RNA modification estimation, we use only the mean signal value of each reconstructed event.”

To avoid ambiguity, we have also revised the sentence on page 5 to more clearly distinguish the roles of Nanopolish and SegPore in the workflow. The updated sentence now reads:

“Nanopolish provides two independent commands: "polya" and "eventalign".
The "polya" command identifies the adapter, poly(A) tail, and transcript region in the raw signal, which we refer to as the poly(A) detection results. The raw signal segment corresponding to the poly(A) tail is used to standardize the raw signal for each read. The "eventalign" command aligns the raw signal to a reference sequence, assigning a signal segment to individual k-mers in the reference. It also computes summary statistics (e.g., mean, standard deviation) from the signal segment for each k-mer. Each k-mer together with its corresponding signal features is termed an event. These event features are then passed into downstream tools such as m6Anet and CHEUI for RNA modification detection. For full transcriptome analysis (Figure 3), we extract the aligned raw signal segment and reference sequence segment from Nanopolish's events for each read by using the first and last events as start and end points. For in vitro transcription (IVT) data with a known reference sequence (Figure 4), we extract the raw signal segment corresponding to the transcript region for each input read based on Nanopolish’s poly(A) detection results.”

(6) Page 5, “Since the polyA tail provides a stable reference, we normalize the raw current signals across reads, ensuring that the mean and standard deviation of the polyA tail are consistent across all reads.”

Perhaps I misread this statement: I interpret it as using the PolyA tail to do the normalization, rather than using the rest of the signal to do the normalization, and that results in consistent PolyA tails across all reads.

If it's the latter, this should be clarified, and a little detail on how the normalization is done should be added, but if my first interpretation is correct:

I'm not sure if its standard deviation is consistent across reads. The (true) value spread in this section of a read should be fairly limited compared to the rest of the signal in the read, so the noise would influence the scale quite quickly, and such noise might be introduced to pores wearing down and other technical influences. Is this really better than using the non-PolyA tail part of the reads signal, using Median Absolute Deviation to scale for a first alignment round, then re-fitting the signal scaling using Theil Sen on the resulting alignments (assigned read signal vs reference expected signal), as Tombo/Nanopolish (can) do?

Additionally, this kind of normalization should have been part of the Nanopolish eventalign already, can this not be re-used? If it's done differently it may result in different distributions than the ONT kmer table obtained for the next step.

Thank you for this detailed and thoughtful comment. We apologize for the confusion. The poly(A) tail–based normalization is indeed explained in Supplementary Note 1, Section 3, but we agree that the motivation needed to be clarified in the main text.

We have now added the following sentence in the revised manuscript (before the original statement on page 5 to provide clearer context:

“Due to inherent variability between nanopores in the sequencing device, the baseline levels and standard deviations of k-mer signals can differ across reads, even for the same transcript. To standardize the signal for downstream analyses, we extract the raw current signal segments corresponding to the poly(A) tail of each read. Since the poly(A) tail provides a stable reference, we normalize the raw current signals across reads, ensuring that the mean and standard deviation of the poly(A) tail are consistent across all reads. This step is crucial for reducing…..”

We chose to use the poly(A) tail for normalization because it is sequence-invariant—i.e., all poly(A) tails consist of identical k-mers, unlike transcript sequences which vary in composition. In contrast, using the transcript region for normalization can introduce biases: for instance, reads with more diverse k-mers (having inherently broader signal distributions) would be forced to match the variance of reads with more uniform k-mers, potentially distorting the baseline across k-mers.

In our newly added RNA004 benchmark experiment, we used the default normalization provided by f5c, which does not include poly(A) tail normalization. Despite this, SegPore was still able to mask out noise and outperform both f5c and Uncalled4, demonstrating that our segmentation method is robust to different normalization strategies.

(7) Page 7, “The initialization of the 5mer parameter table is a critical step in SegPore's workflow. By leveraging ONT's established kmer models, we ensure that the initial estimates for unmodified 5mers are grounded in empirical data.”

It looks like the method uses Nanopolish for a first alignment, then improves the segmentation matching the reference sequence/expected 5mer values. I thought the Nanopolish model/tables are based on the same data, or similarly obtained. If they are different, then why the switch of kmer model? Now the original alignment may have been based on other values, and thus the alignment may seem off with the expected kmer values of this table.

Thank you for this insightful question. To clarify, SegPore uses Nanopolish only to identify the poly(A) tail and transcript regions from the raw signal. In the bulk in vivo data analysis, we use Nanopolish’s first event as the start and the last event as the end to extract the aligned raw signal chunk and its corresponding reference sequence. Since SegPore relies on Nanopolish solely to delineate the transcript region for each read, it independently aligns the raw signals to the reference sequence without refining or adjusting Nanopolish’s segmentation results.

While SegPore's 5-mer parameter table is initially seeded using ONT’s published unmodified k-mer models, we acknowledge that empirical signal values may deviate from these reference models due to run-specific technical variation and the presence of RNA modifications. For this reason, SegPore includes a parameter re-estimation step to refine the mean and standard deviation values of each k-mer based on the current dataset.

The re-estimation process consists of two layers. In the outer layer, we select a set of 5mers that exhibit both modified and unmodified states based on the GMM results (Section 6 of Supplementary Note 1), while the remaining 5mers are assumed to have only unmodified states. In the inner layer, we align the raw signals to the reference sequences using the 5mer parameter table estimated in the outer layer (Section 5 of Supplementary Note 1). Based on the alignment results, we update the 5mer parameter table in the outer layer. This two-layer process is generally repeated for 3~5 iterations until the 5mer parameter table converges.This re-estimation ensures that:

(1) The adjusted 5mer signal baselines remain close to the ONT reference (for consistency);

(2) The alignment score between the observed signal and the reference sequence is optimized (as detailed in Equation 11, Section 5 of Supplementary Note 1);

(3) Only 5mers that show a clear difference between the modified and unmodified components in the GMM are considered subject to modification.

By doing so, SegPore achieves more accurate signal alignment independent of Nanopolish’s models, and the alignment is directly tuned to the data under analysis.

(8) Page 9, “The output of the alignment algorithm is an eventalign, which pairs the base blocks with the 5mers from the reference sequence for each read (Fig. 1C).”

“Modification prediction

After obtaining the eventalign results, we estimate the modification state of each motif using the 5mer parameter table.”

This wording seems to have been introduced on page 5 but (also there) reads a bit confusingly as the name of the output format, file, and function are now named the exact same "eventalign". I assume the obtained eventalign results now refer to the output of your HHMM, and not the original Nanopolish eventalign results, based on context only, but I'd rather have a clear naming that enables more differentiation.

We apologize for the confusion. We have revised the sentence as follows for clarity:

“A detailed description of both alignment algorithms is provided in Supplementary Note 1. The output of the alignment algorithm is an alignment that pairs the base blocks with the 5mers from the reference sequence for each read (Fig. 1C). Base blocks aligned to the same 5-mer are concatenated into a single raw signal segment (referred to as an “event”), from which various features—such as start and end positions, mean current, and standard deviation—are extracted. Detailed derivation of the mean and standard deviation is provided in Section 5.3 in Supplementary Note 1. In the remainder of this paper, we refer to these resulting events as the output of eventalign analysis or the segmentation task. ”

(9) Page 9, “Since a single 5mer can be aligned with multiple base blocks, we merge all aligned base blocks by calculating a weighted mean. This weighted mean represents the single base block mean aligned with the given 5mer, allowing us to estimate the modification state for each site of a read.”

I assume the weights depend on the length of the segment but I don't think it is explicitly stated while it should be.

Thank you for the helpful observation. To improve clarity, we have moved this explanation to the last paragraph of the previous section (see response to point 8), where we describe the segmentation process in more detail.

Additionally, a complete explanation of how the weighted mean is computed is provided in Section 5.3 of Supplementary Note 1. It is derived from signal points that are assigned to a given 5mer.

(10) Page 10, “Afterward, we manually adjust the 5mer parameter table using heuristics to ensure that the modified 5mer distribution is significantly distinct from the unmodified distribution.”

Using what heuristics? If this is explained in the supplementary notes then please refer to the exact section.

Thank you for pointing this out. The heuristics used to manually adjust the 5mer parameter table are indeed explained in detail in Section 7 of Supplementary Note 1.

To clarify this in the manuscript, we have revised the sentence as follows:

“Afterward, we manually adjust the 5mer parameter table using heuristics to ensure that the modified 5mer distribution is significantly distinct from the unmodified distribution (see details in Section 7 of Supplementary Note 1).”

(11) Page 10, “Once the table is fixed, it is used for RNA modification estimation in the test data without further updates.”

By what tool/algorithm? Perhaps it is your own implementation, but with the next section going into segmentation benchmarking and using Nanopolish before this seems undefined.

Thank you for pointing this out. We use our own implementation. See Algorithm 3 in Section 6 of Supplementary Note 1.

We have revised the sentence for clarity:

“Once a stabilized 5mer parameter table is estimated from the training data, it is used for RNA modification estimation in the test data without further updates. A more detailed description of the GMM re-estimation process is provided in Section 6 of Supplementary Note 1.”

(12) Page 11, “A 5mer was considered significantly modified if its read coverage exceeded 1,500 and the distance between the means of the two Gaussian components in the GMM was greater than 5.”

Considering the scaling done before also not being very detailed in what range to expect, this cutoff doesn't provide any useful information. Is this a pA value?

Thank you for the observation. Yes, the value refers to the current difference measured in picoamperes (pA). To clarify this, we have revised the sentence in the manuscript to include the unit explicitly:

“A 5mer was considered significantly modified if its read coverage exceeded 1,500 and the distance between the means of the two Gaussian components in the GMM was greater than 5 picoamperes (pA).”

(13) Page 13, “The raw current signals, as shown in Figure 1B.”

Wrong figure? Figure 2B seems logical.

Thank you for catching this. You are correct—the reference should be to Figure 2B, not Figure 1B. We have corrected this in the revised manuscript.

(14) Page 14, Figure 2A, these figures supposedly support the jiggle hypothesis but the examples seem to match only half the explanation. Any of these jiggles seem to be followed shortly by another in the opposite direction, and the amplitude seems to match better within each such pair than the next or previous segments. Perhaps there is a better explanation still, and this behaviour can be modelled as such instead.

Thank you for your comment. We acknowledge that the observed signal patterns may appear ambiguous and could potentially suggest alternative explanations. However, as shown in Figure 2A, the red dots tend to align closely with the baseline of the previous state, while the blue dots align more closely with the baseline of the next state. We interpret this as evidence for the "jiggling" hypothesis, where k-mer temporarily oscillates between adjacent states during translocation.

That said, we agree that more sophisticated models could be explored to better capture this behavior, and we welcome suggestions or references to alternative models. We will consider this direction in future work.

(15) Page 15, “This occurs because subtle transitions within a base block may be mistaken for transitions between blocks, leading to inflated transition counts.”

Is it really a "subtle transition" if it happens within a base block? It seems this is not a transition and thus shouldn't be named as such.

Thank you for pointing this out. We agree that the term “subtle transition” may be misleading in this context. We revised the sentence to clarify the potential underlying cause of the inflated transition counts:

“This may be due to a base block actually corresponding to a sub-state of a single 5mer, rather than each base block corresponding to a full 5mer, leading to inflated transition counts. To address this issue, SegPore’s alignment algorithm was refined to merge multiple base blocks (which may represent sub-states of the same 5mer) into a single 5mer, thereby facilitating further analysis.”

(16) Page 15, “The SegPore "eventalign" output is similar to Nanopolish's "eventalign" command.”

To the output of that command, I presume, not to the command itself.

Thank you for pointing out the ambiguity. We have revised the sentence for clarity:

“The final outputs of SegPore are the events and modification state predictions. SegPore’s events are similar to the outputs of Nanopolish’s "eventalign" command, in that they pair raw current signal segments with the corresponding RNA reference 5-mers. Each 5-mer is associated with various features — such as start and end positions, mean current, and standard deviation — derived from the paired signal segment.”

(17) Page 15, “For selected 5mers, SegPore also provides the modification rate for each site and the modification state of that site on individual reads.”

What selection? Just all kmers with a possible modified base or a more specific subset?

We revised the sentence to clarify the selection criteria:

“For selected 5mers that exhibit both a clearly unmodified and a clearly modified signal component, SegPore reports the modification rate at each site, as well as the modification state of that site on individual reads.”

(18) Page 16, “A key component of SegPore is the 5mer parameter table, which specifies the mean and standard deviation for each 5mer in both modified and unmodified states (Figure 2A).”

Wrong figure?

Thank you for pointing this out. You are correct—it should be Figure 1A, not Figure 2A. We intended to visually illustrate the structure of the 5mer parameter table in Figure 1A, and we have corrected this reference in the revised manuscript.

(19) Page 16, Table 1, I can't quite tell but I assume this is based on all kmers in the table, not just a m6A modified subset. A short added statement to make this clearer would help.

Yes, you are right—it is averaged over all 5mers. We have revised the sentence for clarity as follows:

" As shown in Table 1, SegPore consistently achieved the best performance averaged on all 5mers across all datasets..…."

(20) Page 16, “Since the peaks (representing modified and unmodified states) are separable for only a subset of 5mers, SegPore can provide modification parameters for these specific 5mers. For other 5mers, modification state predictions are unavailable.”

Can this be improved using some heuristics rather than the 'distance of 5' cutoff as described before? How small or big is this subset, compared to how many there should be to cover all cases?

We agree that more sophisticated strategies could potentially improve performance. In this study, we adopted a relatively conservative approach to minimize false positives by using a heuristic cutoff of 5 picoamperes. This value was selected empirically and we did not explore alternative cutoffs. Future work could investigate more refined or data-driven thresholding strategies.

(21) Page 16, “Tombo used the "resquiggle" method to segment the raw signals, and we standardized the segments using the polyA tail to ensure a fair comparison.”

I don't know what or how something is "standardized" here.

Standardized’ refers to the poly(A) tail–based signal normalization described in our response to point 6. We applied this normalization to Tombo’s output to ensure a fair comparison across methods. Without this standardization, Tombo’s performance was notably worse. We revised the sentence as follows:

“Tombo used the "resquiggle" method to segment the raw signals, and we standardized the segments using the poly(A) tail to ensure a fair comparison (See preprocessing section in Materials and Methods).”

(22) Page 16, “To benchmark segmentation performance, we used two key metrics: (1) the log-likelihood of the segment mean, which measures how closely the segment matches ONT's 5mer parameter table (used as ground truth), and (2) the standard deviation (std) of the segment, where a lower std indicates reduced noise and better segmentation quality. If the raw signal segment aligns correctly with the corresponding 5mer, its mean should closely match ONT's reference, yielding a high log-likelihood. A lower std of the segment reflects less noise and better performance overall.”

Here the segmentation part becomes a bit odd:

A: Low std can be/is achieved by dropping any noisy bits, making segments really small (partly what happens here with the transition segments). This may be 'true' here, in the sense that the transition is not really part of the segment, but the comparison table is a bit meaningless as the other tools forcibly assign all data to kmers, instead of ignoring parts as transition states. In other words, it is a benchmark that is easy to cheat by assigning more data to noise/transition states.

B: The values shown are influenced by the alignment made between the read and expected reference signal. Especially Tombo tends to forcibly assign data to whatever looks the most similar nearby rather than providing the correct alignment. So the "benchmark of the segmentation performance" is more of an "overall benchmark of the raw signal alignment". Which is still a good, useful thing, but the text seems to suggest something else.

Thank you for raising these important concerns regarding the segmentation benchmarking.

Regarding point A, the base blocks aligned to the same 5mer are concatenated into a single segment, including the short transition blocks between them. These transition blocks are typically very short (4~10 signal points, average 6 points), while a typical 5mer segment contains around 20~60 signal points. To assess whether SegPore’s performance is inflated by excluding transition segments, we conducted an additional comparison: we removed 6 boundary signal points (3 from the start and 3 from the end) from each 5mer segment in Nanopolish and Tombo’s results to reduce potential noise. The new comparison table is shown in the following:

SegPore consistently demonstrates superior performance. Its key contribution lies in its ability to recognize structured noise in the raw signal and to derive more accurate mean and standard deviation values that more faithfully represent the true state of the k-mer in the pore. The improved mean estimates are evidenced by the clearly separated peaks of modified and unmodified 5mers in Figures 3A and 4B, while the improved standard deviation is reflected in the segmentation benchmark experiments.

Regarding point B, we apologize for the confusion. We have added a new paragraph to the introduction to clarify that the segmentation task indeed includes the alignment step.

“The general workflow of Nanopore direct RNA sequencing (DRS) data analysis is as follows. First, the raw electrical signal from a read is basecalled using tools such as Guppy or Dorado, which produce the nucleotide sequence of the RNA molecule. However, these basecalled sequences do not include the precise start and end positions of each ribonucleotide (or k-mer) in the signal. Because basecalling errors are common, the sequences are typically mapped to a reference genome or transcriptome using minimap2 to recover the correct reference sequence. Next, tools such as Nanopolish and Tombo align the raw signal to the reference sequence to determine which portion of the signal corresponds to each k-mer. We define this process as the segmentation task, referred to as "eventalign" in Nanopolish. Based on this alignment, Nanopolish extracts various features—such as the start and end positions, mean, and standard deviation of the signal segment corresponding to a k-mer. This signal segment or its derived features is referred to as an "event" in Nanopolish. The resulting events serve as input for downstream RNA modification detection tools such as m6Anet and CHEUI.”

(23) Page 17 “Given the comparable methods and input data requirements, we benchmarked SegPore against several baseline tools, including Tombo, MINES (26), Nanom6A (27), m6Anet, Epinano (28), and CHEUI (29).”

It seems m6Anet is actually Nanopolish+m6Anet in Figure 3C, this needs a minor clarification here.

m6Anet uses Nanopolish’s estimated events as input by default.

(24) Page 18, Figure 3, A and B are figures without any indication of what is on the axis and from the text I believe the position next to each other on the x-axis rather than overlapping is meaningless, while their spread is relevant, as we're looking at the distribution of raw values for this 5mer. The figure as is is rather confusing.

Thanks for pointing out the confusion. We have added concrete values to the axes in Figures 3A and 3B and revised the figure legend as follows in the manuscript:

“(A) Histogram of the estimated mean from current signals mapped to an example m6A-modified genomic location (chr10:128548315, GGACT) across all reads in the training data, comparing Nanopolish (left) and SegPore (right). The x-axis represents current in picoamperes (pA).

(B) Histogram of the estimated mean from current signals mapped to the GGACT motif at all annotated m6A-modified genomic locations in the training data, again comparing Nanopolish (left) and SegPore (right). The x-axis represents current in picoamperes (pA).”

(25) Page 18 “SegPore's results show a more pronounced bimodal distribution in the raw signal segment mean, indicating clearer separation of modified and unmodified signals.”

Without knowing the correct values around the target kmer (like Figure 4B), just the more defined bimodal distribution could also indicate the (wrongful) assignment of neighbouring kmer values to this kmer instead, hence this statement lacks some needed support, this is just one interpretation of the possible reasons.

Thank you for the comment. We have added concrete values to Figures 3A and 3B to support this point. Both peaks fall within a reasonable range: the unmodified peak (125 pA) is approximately 1.17 pA away from its reference value of 123.83 pA, and the modified peak (118 pA) is around 7 pA away from the unmodified peak. This shift is consistent with expected signal changes due to RNA modifications (usually less than 10 pA), and the magnitude of the difference suggests that the observed bimodality is more likely caused by true modification events rather than misalignment.

(26) Page 18 “Furthermore, when pooling all reads mapped to m6A-modified locations at the GGACT motif, SegPore showed prominent peaks (Fig. 3B), suggesting reduced noise and improved modification detection.”

I don't think the prominent peaks directly suggest improved detection, this statement is a tad overreaching.

We revised the sentense to the following:

“SegPore exhibited more distinct peaks (Fig. 3B), indicating reduced noise and potentially enabling more reliable modification detection”.

(27) Page18 “(2) direct m6A predictions from SegPore's Gaussian Mixture Model (GMM), which is limited to the six selected 5mers.”

The 'six selected' refers to what exactly? Also, 'why' this is limited to them is also unclear as it is, and it probably would become clearer if it is clearly defined what this refers to.

It is explained the page 16 in the SegPore’s workflow in the original manuscript as follows:

“A key component of SegPore is the 5mer parameter table, which specifies the mean and standard deviation for each 5mer in both modified and unmodified states (Fig. 2A1A). Since the peaks (representing modified and unmodified states) are separable for only a subset of 5mers, SegPore can provide modification parameters for these specific 5mers. For other 5mers, modification state predictions are unavailable.”

e select a small set of 5mers that show clear peaks (modified and unmodified 5mers) in GMM in the m6A site-level data analysis. These 5mers are provided in Supplementary Fig. S2C, as explained in the section “m6A site level benchmark” in the Material and Methods (page 12 in the original manuscript).

“…transcript locations into genomic coordinates. It is important to note that the 5mer parameter table was not re-estimated for the test data. Instead, modification states for each read were directly estimated using the fixed 5mer parameter table. Due to the differences between human (Supplementary Fig. S2A) and mouse (Supplementary Fig. S2B), only six 5mers were found to have m6A annotations in the test data’s ground truth (Supplementary Fig. S2C). For a genomic location to be identified as a true m6A modification site, it had to correspond to one of these six common 5mers and have a read coverage of greater than 20. SegPore derived the ROC and PR curves for benchmarking based on the modification rate at each genomic location….”

We have updated the sentence as follows to increase clarity:

“which is limited to the six selected 5mers that exhibit clearly separable modified and unmodified components in the GMM (see Materials and Methods for details).”

(28) Page 19, Figure 4C, the blue 'Unmapped' needs further explanation. If this means the segmentation+alignment resulted in simply not assigning any segment to a kmer, this would indicate issues in the resulting mapping between raw data and kmers as the data that probably belonged to this kmer is likely mapped to a neighbouring kmer, possibly introducing a bimodal distribution there.

This is due to deletion event in the full alignment algorithm. See Page 8 of SupplementaryNote1:

During the traceback step of the dynamic programming matrix, not every 5mer in the reference sequence is assigned a corresponding raw signal fragment—particularly when the signal’s mean deviates substantially from the expected mean of that 5mer. In such cases, the algorithm considers the segment to be generated by an unknown 5mer, and the corresponding reference 5mer is marked as unmapped.

(29) Page 19, “For six selected m6A motifs, SegPore achieved an ROC AUC of 82.7% and a PR AUC of 38.7%, earning the third-best performance compared with deep leaning methods m6Anet and CHEUI (Fig. 3D).”

How was this selection of motifs made, are these related to the six 5mers in the middle of Supplementary Figure S2? Are these the same six as on page 18? This is not clear to me.

It is the same, see the response to point 27.

(30) Page 21 “Biclustering reveals that modifications at the 6th, 7th, and 8th genomic locations are specific to certain clusters of reads (clusters 4, 5, and 6), while the first five genomic locations show similar modification patterns across all reads.”

This reads rather confusingly. Both the '6th, 7th, and 8th genomic locations' and 'clusters 4,5,6' should be referred to in clearer terms. Either mark them in the figure as such or name them in the text by something that directly matches the text in the figure.

We have added labels to the clusters and genomic locations Figure 4C, and revised the sentence as follows:

“Biclustering reveals that modifications at g6 are specific to cluster C4, g7 to cluster C5, and g8 to cluster C6, while the first five genomic locations (g1 to g5) show similar modification patterns across all reads.”

(31) Page 21, “We developed a segmentation algorithm that leverages the jiggling property in the physical process of DRS, resulting in cleaner current signals for m6A identification at both the site and single-molecule levels.”

Leverages, or just 'takes into account'?

We designed our HHMM specifically based on the jiggling hypothesis, so we believe that using the term “leverage” is appropriate.

(32) Page 21, “Our results show that m6Anet achieves superior performance, driven by SegPore's enhanced segmentation.”

Superior in what way? It barely improves over Nanopolish in Figure 3C and is outperformed by other methods in Figure 3D. The segmentation may have improved but this statement says something is 'superior' driven by that 'enhanced segmentation', so that cannot refer to the segmentation itself.

We revise it as follows in the revised manuscript:

”Our results demonstrate that SegPore’s segmentation enables clear differentiation between m6A-modified and unmodified adenosines.”

(33) Page 21, “In SegPore, we assume a drastic change between two consecutive 5mers, which may hold for 5mers with large difference in their current baselines but may not hold for those with small difference.”

The implications of this assumption don't seem highlighted enough in the work itself and may be cause for falsely discovering bi-modal distributions. What happens if such a 5mer isn't properly split, is there no recovery algorithm later on to resolve these cases?

We agree that there is a risk of misalignment, which can result in a falsely observed bimodal distribution. This is a known and largely unavoidable issue across all methods, including deep neural network–based methods. For example, many of these models rely on a CTC (Connectionist Temporal Classification) layer, which implicitly performs alignment and may also suffer from similar issues.

Misalignment is more likely when the current baselines of neighboring k-mers are close. In such cases, the model may struggle to confidently distinguish between adjacent k-mers, increasing the chance that signals from neighboring k-mers are incorrectly assigned. Accurate baseline estimation for each k-mer is therefore critical—when baselines are accurate, the correct alignment typically corresponds to the maximum likelihood.

We have added the following sentence to the discussion to acknowledge this limitation:

“As with other RNA modification estimation methods, SegPore can be affected by misalignment errors, particularly when the baseline signals of adjacent k-mers are similar. These cases may lead to spurious bimodal signal distributions and require careful interpretation.”

(34) Page 21, “Currently, SegPore models only the modification state of the central nucleotide within the 5mer. However, modifications at other positions may also affect the signal, as shown in Figure 4B. Therefore, introducing multiple states to the 5mer could help to improve the performance of the model.”

The meaning of this statement is unclear to me. Is SegPore unable to combine the information of overlapping kmers around a possibly modified base (central nucleotide), or is this referring to having multiple possible modifications in a single kmer (multiple states)?

We mean there can be modifications at multiple positions of a single 5mer, e.g. C m5C m6A m7G T. We have revised the sentence to:

“Therefore, introducing multiple states for a 5mer to accout for modifications at mutliple positions within the same 5mer could help to improve the performance of the model.”

(35) Page 22, “This causes a problem when apply DNN-based methods to new dataset without short read sequencing-based ground truth. Human could not confidently judge if a predicted m6A modification is a real m6A modification.”

Grammatical errors in both these sentences. For the 'Human could not' part, is this referring to a single person's attempt or more extensively tested?

Thanks for the comment. We have revised the sentence as follows:

“This poses a challenge when applying DNN-based methods to new datasets without short-read sequencing-based ground truth. In such cases, it is difficult for researchers to confidently determine whether a predicted m6A modification is genuine (see Supplmentary Figure S5).”

(36) Page 22, “…which is easier for human to interpret if a predicted m6A site is real.”

"a" human, but also this probably meant to say 'whether' instead of 'if', or 'makes it easier'.

Thanks for the advice. We have revise the sentence as follows:

“One can generally observe a clear difference in the intensity levels between 5mers with an m6A and those with a normal adenosine, which makes it easier for a researcher to interpret whether a predicted m6A site is genuine.”

(37) Page 22, “…and noise reduction through its GMM-based approach…”

Is the GMM providing noise reduction or segmentation?

Yes, we agree that it is not relevant. We have removed the sentence in the revised manuscript as follows:

“Although SegPore provides clear interpretability and noise reduction through its GMM-based approach, there is potential to explore DNN-based models that can directly leverage SegPore's segmentation results.”

(38) Page 23, “SegPore effectively reduces noise in the raw signal, leading to improved m6A identification at both site and single-molecule levels…”

Without further explanation in what sense this is meant, 'reduces noise' seems to overreach the abilities, and looks more like 'masking out'.

Following the reviewer’s suggestion, we change it to ‘mask out'’ in the revised manuscript.

“SegPore effectively masks out noise in the raw signal, leading to improved m6A identification at both site and single-molecule levels.”

Reviewer #3 (Recommendations for the authors):

I recommend the publication of this manuscript, provided that the following comments (and the comments above) are addressed.

In general, the authors state that SegPore represents an improvement on existing software. These statements are largely unquantified, which erodes their credibility. I have specified several of these in the Minor comments section.

Page 5, Preprocessing: The authors comment that the poly(A) tail provides a stable reference that is crucial for the normalisation of all reads. How would this step handle reads that have variable poly(A) tail lengths? Or have interrupted poly(A) tails (e.g. in the case of mRNA vaccines that employ a linker sequence)?

We apologize for the confusion. The poly(A) tail–based normalization is explained in Supplementary Note 1, Section 3.

As shown in Author response image 1 below, the poly(A) tail produces a characteristic signal pattern—a relatively flat, squiggly horizontal line. Due to variability between nanopores, raw current signals often exhibit baseline shifts and scaling of standard deviations. This means that the signal may be shifted up or down along the y-axis and stretched or compressed in scale.

Author response image 1.

The normalization remains robust with variable poly(A) tail lengths, as long as the poly(A) region is sufficiently long. The linker sequence will be assigned to the adapter part rather than the poly(A) part.

To improve clarity in the revised manuscript, we have added the following explanation:

“Due to inherent variability between nanopores in the sequencing device, the baseline levels and standard deviations of k-mer signals can differ across reads, even for the same transcript. To standardize the signal for downstream analyses, we extract the raw current signal segments corresponding to the poly(A) tail of each read. Since the poly(A) tail provides a stable reference, we normalize the raw current signals across reads, ensuring that the mean and standard deviation of the poly(A) tail are consistent across all reads. This step is crucial for reducing…..”

We chose to use the poly(A) tail for normalization because it is sequence-invariant—i.e., all poly(A) tails consist of identical k-mers, unlike transcript sequences which vary in composition. In contrast, using the transcript region for normalization can introduce biases: for instance, reads with more diverse k-mers (having inherently broader signal distributions) would be forced to match the variance of reads with more uniform k-mers, potentially distorting the baseline across k-mers.

Page 7, 5mer parameter table: r9.4_180mv_70bps_5mer_RNA is an older kmer model (>2 years). How does your method perform with the newer RNA kmer models that do permit the detection of multiple ribonucleotide modifications? Addressing this comment is crucial because it is feasible that SegPore will underperform in comparison to the newer RNA base caller models (requiring the use of RNA004 datasets).

Thank you for highlighting this important point. For RNA004, we have updated SegPore to ensure compatibility with the latest kit. In our revised manuscript, we demonstrate that the translocation-based segmentation hypothesis remains valid for RNA004, as supported by new analyses presented in the supplementary Figure S4.

Additionally, we performed a new benchmark with f5c and Uncalled4 in RNA004 data in the revised manuscript (Table 2), where SegPore exhibit a better performance than f5c and Uncalled4.

We agree that benchmarking against the latest Dorado models—specifically rna004_130bps_hac@v5.1.0 and rna004_130bps_sup@v5.1.0, which include built-in modification detection capabilities—would provide valuable context for evaluating the utility of SegPore. However, generating a comprehensive k-mer parameter table for RNA004 requires a large, well-characterized dataset. At present, such data are limited in the public domain. Additionally, Dorado is developed by ONT and its internal training data have not been released, making direct comparisons difficult.

Our current focus is on improving raw signal segmentation quality, which are upstream tasks critical to many downstream analyses, including RNA modification detection. Future work may include benchmarking SegPore against models like Dorado once appropriate data become available.

The Methods and Results sections contain redundant information - please streamline the information in these sections and reduce the redundancy. For example, the benchmarking section may be better situated in the Results section.

Following your advice, we have removed redundant texts about the Segmentation benchmark from Materials and Methods in the revised manuscript.

Minor comments

(1) Introduction

Page 3: "By incorporating these dynamics into its segmentation algorithm...". Please provide an example of how motor protein dynamics can impact RNA translocation. In particular, please elaborate on why motor protein dynamics would impact the translocation of modified ribonucleotides differently to canonical ribonucleotides. This is provided in the results, but please also include details in the Introduction.

Following your advice, we added one sentence to explain how the motor protein affect the translocation of the DNA/RNA molecule in the revised manuscript.

“This observation is also supported by previous reports, in which the helicase (the motor protein) translocates the DNA strand through the nanopore in a back-and-forth manner. Depending on ATP or ADP binding, the motor protein may translocate the DNA/RNA forward or backward by 0.5-1 nucleotides.”

As far as we understand, this translocation mechanism is not specific to modified or unmodified nucleotides. For further details, we refer the reviewer to the original studies cited.

Page 3: "This lack of interpretability can be problematic when applying these methods to new datasets, as researchers may struggle to trust the predictions without a clear understanding of how the results were generated." Please provide details and citations as to why researchers would struggle to trust the predictions of m6Anet. Is it due to a lack of understanding of how the method works, or an empirically demonstrated lack of reliability?

Thank you for pointing this out. The lack of interpretability in deep learning models such as m6Anet stems primarily from their “black-box” nature—they provide binary predictions (modified or unmodified) without offering clear reasoning or evidence for each call.

When we examined the corresponding raw signals, we found it difficult to visually distinguish whether a signal segment originated from a modified or unmodified ribonucleotide. The difference is often too subtle to be judged reliably by a human observer. This is illustrated in the newly added Supplementary Figure S5, which shows Nanopolish-aligned raw signals for the central 5mer GGACT in Figure 4B, displayed both uncolored and colored by modification state (according to the ground truth).

Although deep neural networks can learn subtle, high-dimensional patterns in the signal that may not be readily interpretable, this opacity makes it difficult for researchers to trust the predictions—especially in new datasets where no ground truth is available. The issue is not necessarily an empirically demonstrated lack of reliability, but rather a lack of transparency and interpretability.

We have updated the manuscript accordingly and included Supplementary Figure S5 to illustrate the difficulty in interpreting signal differences between modified and unmodified states.

Page 3: "Instead of relying on complex, opaque features...". Please provide evidence that the research community finds the figures generated by m6Anet to be difficult to interpret, or delete the sections relating to its perceived lack of usability.

See the figure provided in the response to the previous point. We added a reference to this figure in the revised manuscript.

“Instead of relying on complex, opaque features (see Supplementary Figure S5), SegPore leverages baseline current levels to distinguish between…..”

(2) Materials and Methods

Page 5, Preprocessing: "We begin by performing basecalling on the input fast5 file using Guppy, which converts the raw signal data into base sequences.". Please change "base" to ribonucleotide.

Revised as requested.

Page 5 and throughout, please refer to poly(A) tail, rather than polyA tail throughout.

Revised as requested.

Page 5, Signal segmentation via hierarchical Hidden Markov model: "...providing more precise estimates of the mean and variance for each base block, which are crucial for downstream analyses such as RNA modification prediction." Please specify which method your HHMM method improves upon.

Thank you for the suggestion. Since this section does not include a direct comparison, we revised the sentence to avoid unsupported claims. The updated sentence now reads:

"...providing more precise estimates of the mean and variance for each base block, which are crucial for downstream analyses such as RNA modification prediction."

Page 10, GMM for 5mer parameter table re-estimation: "Typically, the process is repeated three to five times until the 5mer parameter table stabilizes." How is the stabilisation of the 5mer parameter table quantified? What is a reasonable cut-off that would demonstrate adequate stabilisation of the 5mer parameter table?

Thank you for the comment. We assess the stabilization of the 5mer parameter table by monitoring the change in baseline values across iterations. If the absolute change in baseline values for all 5mers is less than 1e-5 between two consecutive iterations, we consider the estimation to have stabilized.

Page 11, M6A site level benchmark: why were these datasets selected? Specifically, why compare human and mouse ribonuclotide modification profiles? Please provide a justification and a brief description of the experiments that these data were derived from, and why they are appropriate for benchmarking SegPore.

Thank you for the comment. These data are taken from a previous benchmark studie about m6A estimation from RNA002 data in the literature (https://doi.org/10.1038/s41467-023-37596-5). We think the data are appropreciate here.

Thank you for the comment. The datasets used were taken from a previous benchmark study on m6A estimation using RNA002 data (https://doi.org/10.1038/s41467-023-37596-5). These datasets include human and mouse transcriptomes and have been widely used to evaluate the performance of RNA modification detection tools. We selected them because (i) they are based on RNA002 chemistry, which matches the primary focus of our study, and (ii) they provide a well-characterized and consistent benchmark for assessing m6A detection performance. Therefore, we believe they are appropriate for validating SegPore.

(3) Results

Page 13, RNA translocation hypothesis: "The raw current signals, as shown in Fig. 1B...". Please check/correct figure reference - Figure 1B does not show raw current signals.

Thank you for pointing this out. The correct reference should be Figure 2B. We have updated the figure citation accordingly in the revised manuscript.

Page 19, m6A identification at the site level: "For six selected m6A motifs, SegPore achieved an ROC AUC of 82.7% and a PR AUC of 38.7%, earning the third best performance compared with deep leaning methods m6Anet and CHEUI (Fig. 3D)." SegPore performs third best of all deep learning methods. Do the authors recommend its use in conjunction with m6Anet for m6A detection? Please clarify in the text.

This sentence aims to convey that SegPore alone can already achieve good performance. If interpretability is the primary goal, we recommend using SegPore on its own. However, if the objective is to identify more potential m6A sites, we suggest using the combined approach of SegPore and m6Anet. That said, we have chosen not to make explicit recommendations in the main text to avoid oversimplifying the decision or potentially misleading readers.

Page 19, m6A identification at the single molecule level: "one transcribed with m6A and the other with normal adenosine". I assume that this should be adenine? Please replace adenosine with adenine throughout.

Thank you for pointing this out. We have revised the sentence to use "adenine" where appropriate. In other instances, we retain "adenosine" when referring specifically to adenine bound to a ribose sugar, which we believe is suitable in those contexts.

Page 19, m6A identification at the single molecule level: "We used 60% of the data for training and 40% for testing". How many reads were used for training and how many for testing? Please comment on why these are appropriate sizes for training and testing datasets.

In total, there are 1.9 million reads, with 1.14 million used for training and 0.76 million  for testing (60% and 40%, respectively). We chose this split to ensure that the training set is sufficiently large to reliably estimate model parameters, while the test set remains substantial enough to robustly evaluate model performance. Although the ratio was selected somewhat arbitrarily, it balances the need for effective training with rigorous validation.

(4) Discussion

Page 21: "We believe that the de-noised current signals will be beneficial for other downstream tasks." Which tasks? Please list an example.

We have revised the text for clarity as follows:

“We believe that the de-noised current signals will be beneficial for other downstream tasks, such as the estimation of m5C, pseudouridine, and other RNA modifications.”

Page 22: "One can generally observe a clear difference in the intensity levels between 5mers with a m6A and normal adenosine, which is easier for human to interpret if a predicted m6A site is real." This statement is vague and requires qualification. Please reference a study that demonstrates the human ability to interpret two similar graphs, and demonstrate how it relates to the differences observed in your data.

We apologize for the confusion. We have revised the sentence as follows:

“One can generally observe a clear difference in the intensity levels between 5mers with an m6A and those with a normal adenosine, which makes it easier for a researcher to interpret whether a predicted m6A site is genuine.”

We believe that Figures 3A, 3B, and 4B effectively illustrate this concept.

Page 23: How long does SegPore take for its analyses compared to other similar tools? How long would it take to analyse a typical dataset?

We have added run-time statistics for datasets of varying sizes in the revised manuscript (see Supplementary Figure S6). This figure illustrates SegPore’s performance across different data volumes to help estimate typical processing times.

(5) Figures

Figure 4C. Please number the hierachical clusters and genomic locations in this figure. They are referenced in the text.

Following your suggestion, we have labeled the hierarchical clusters and genomic locations in Figure 4C in the revised manuscript.

In addition, we revised the corresponding sentence in the main text as follows: “Biclustering reveals that modifications at g6 are specific to cluster C4, g7 to cluster C5, and g8 to cluster C6, while the first five genomic locations (g1 to g5) show similar modification patterns across all reads.”

DOI: 10.1016/j.isci.2025.113610

Resource: (IMSR Cat# JAX_003831,RRID:IMSR_JAX:003831)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:003831

What is this?

DOI: 10.1016/j.isci.2025.113610

Resource: (IMSR Cat# JAX_008766,RRID:IMSR_JAX:008766)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:008766

What is this?

Ocoom describes a deep sense he has to educate his community on Christianity and literacy as a pathway to Indigenous self-help and survival.

eLife assessment

This is a valuable study that combines a wide range of approaches to provide a biophysical and evolutionary mechanism that could explain why some particular mutations in the SARS-CoV-2 protein N arose during the COVID-19 pandemic. The evidence is solid and relies on multiple experimental approaches. However, some of the results were dependent on extremely high protein concentrations, which may affect certain conclusions.

Reviewer #1 (Public review):

Summary:

The authors attempted to clarify the impact of N protein mutations on ribonucleoprotein (RNP) assembly and stability using analytical ultracentrifugation (AUC) and mass photometry (MP). These complementary approaches provide a more comprehensive understanding of the underlying processes. Both SV-AUC and MP results consistently showed enhanced RNP assembly and stability due to N protein mutations.

The overall research design appears well planned, and the experiments were carefully executed.

Strengths:

SV-AUC, performed at higher concentrations (3 µM), captured the hydrodynamic properties of bulk assembled complexes, while MP provided crucial information on dissociation rates and complex lifetimes at nanomolar concentrations. Together, the methods offered detailed insights into association states and dissociation kinetics across a broad concentration range. This represents a thorough application of solution physicochemistry.

Weaknesses:

Unlike AUC, MP observes only a part of the solution. In MP, bound molecules are accumulated on the glass surface (not dissociated), thus the concentration in solution should change as time develops. How does such concentration change impact the result shown here?

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors apply a variety of biophysical and computational techniques to characterize the effects of mutations in the SARS-CoV-2 N protein on the formation of ribonucleoprotein particles (RNPs). They find convergent evolution in multiple repeated independent mutations strengthening binding interfaces, compensating for other mutations that reduce RNP stability but which enhance viral replication.

Strengths:

The authors assay the effects of a variety of mutations found in SARS-CoV-2 variants of concern using a variety of approaches, including biophysical characterization of assembly properties of RNPs, combined with computational prediction of the effects of mutations on molecular structures and interactions. The findings of the paper contribute to our increasing understanding of the principles driving viral self-assembly, and increase the foundation for potential future design of therapeutics such as assembly inhibitors.

Weaknesses:

For the most part, the paper is well-written, the data presented support the claims made, and the arguments are easy to follow. However, I believe that parts of the presentation could be substantially improved. I found portions of the text to be overly long and verbose and likely could be substantially edited; the use of acronyms and initialisms is pervasive, making parts of the exposition laborious to follow; and portions of the figures are too small and difficult to read/understand.

Reviewer #3 (Public review):

Summary:

This manuscript investigates how mutations in the SARS-CoV-2 nucleocapsid protein (N) alter ribonucleoprotein (RNP) assembly, stability, and viral fitness. The authors focus on mutations such as P13L, G214C, and G215C, combining biophysical assays (SV-AUC, mass photometry, CD spectroscopy, EM), VLP formation, and reverse genetics. They propose that SARS-CoV-2 exploits "fuzzy complex" principles, where distributed weak interfaces in disordered regions allow both stability and plasticity, with measurable consequences for viral replication.

Strengths:

(1) The paper demonstrates a comprehensive integration of structural biophysics, peptide/protein assays, VLP systems, and reverse genetics.

(2) Identification of both de novo (P13L) and stabilizing (G214C/G215C) interfaces provides a mechanistic insight into RNP formation.

(3) Strong application of the "fuzzy complex" framework to viral assembly, showing how weak/disordered interactions support evolvability, is a significant conceptual advance in viral capsid assembly.

(4) Overall, the study provides a mechanistic context for mutations that have arisen in major SARS-CoV-2 variants (Omicron, Delta, Lambda) and a mechanistic basis for how mutations influence phenotype via altered biomolecular interactions.

Weaknesses:

(1) The arrangement of N dimers around LRS helices is presented in Figure 1C, but the text concedes that "the arrangement sketched in Figure 1C is not unique" (lines 144-146) and that AF3 modeling attempts yielded "only inconsistent results" (line 149).<br /> The authors should therefore present the models more cautiously as hypotheses instead. Additional alternative arrangements should be included in the Supplementary Information, so the readers do not over-interpret a single schematic model.

(2) Negative-stained EM fibrils (Figure 2A) and CD spectra (Figure 2B) are presented to argue that P13L promotes β-sheet self-association. However, the claim could benefit from more orthogonal validation of β-sheet self-association. Additional confirmation via FTIR spectra or ThT fluorescence could be used to further distinguish structured β-sheets from amorphous aggregation.

(3) In the main text, the authors alternate between emphasizing non-covalent effects ("a major effect of the cysteines already arises in reduced conditions without any covalent bonds," line 576) and highlighting "oxidized tetrameric N-proteins of N:G214C and N:G215C can be incorporated into RNPs". Therefore, the biological relevance of disulfide redox chemistry in viral assembly in vivo remains unclear. Discussing cellular redox plausibility and whether the authors' oxidizing conditions are meant as a mechanistic stress test rather than physiological mimicry could improve the interpretation of these results.

The paper could benefit if the authors provide a summary figure or table contrasting reduced vs. oxidized conditions for G214C/G215C mutants (self-association, oligomerization state, RNP stability). Explicitly discuss whether disulfides are likely to form in infected cells.

(4) VLP assays (Figure 7) show little enhancement for P13L or G215C alone, whereas Figure 8 shows that P13L provides clear fitness advantages. This discrepancy is acknowledged but not reconciled with any mechanistic or systematic rationale. The authors should consider emphasizing the limitations of VLP assays and the sources of the discrepancy with respect to Figure 8.

(5) Figures 5 and 6 are dense, and the several overlays make it hard to read. The authors should consider picking the most extreme results to make a point in the main Figure 5 and move the other overlays to the Supplementary. Additionally, annotating MP peaks directly with "2×, 4×, 6× subunits" can help non-experts.

(6) The paper has several names and shorthand notations for the mutants, making it hard to keep up. The authors could include a table that contains mutation keys, with each shorthand (Ancestral, Nο/No, Nλ, etc.) mapped onto exact N mutations (P13L, Δ31-33, R203K/G204R, G214C/G215C, etc.). They could then use the same glyphs (Latin vs Greek) consistently in text and figure labels.

(7) The EM fibrils (Figure 2A) and CD spectra (Figure 2B) were collected at mM peptide concentrations. These are far above physiological levels and may encourage non-specific aggregation. Similarly, the authors mention" ultra-weak binding energies that require mM concentrations to significantly populate oligomers". On the other hand, the experiments with full-length protein were performed at concentrations closer to biologically relevant concentrations in the micromolar range. While I appreciate the need to work at high concentrations to detect weak interactions, this raises questions about physiological relevance. Specifically:

a) Could some of the fibril/β-sheet features attributed to P13L (Figure 2A-C) reflect non-specific aggregation at high concentrations rather than bona fide self-association motifs that could play out in biologically relevant scenarios?

b) How do the authors justify extrapolating from the mM-range peptide behaviors to the crowded but far lower effective concentrations in cells?

The authors should consider adding a dedicated section (either in Methods or Discussion) justifying the use of high concentrations, with estimation of local concentrations in RNPs and how they compare to the in vitro ranges used here. For concentration-dependent phenomena discussed here, it is vital to ensure that the findings are not artefacts of non-physiological peptide aggregation..

Author response:

We thank the Reviewers and Editors for their time and insightful comments. We are encouraged by their positive assessment and we look forward to addressing the points raised. Areas of primary concern include (1) the use of high concentrations in peptide experiments; (2) improvement of the presentation and discussion of the results; and (3) clarification of the impact of surface adsorption on the mass photometry analyses.

Regarding (1), we will better explain why some experiments with isolated disordered N-terminal extension were necessarily carried out at high concentrations, in order to demonstrate the potential for these peptides to weakly self-associate. While much lower nucleocapsid protein concentrations are present in the cytosol on average, and are used in our ribonucleoprotein assembly experiments, there are two important physiologically relevant cases where high local concentrations do occur: First, high effective concentrations of tethered disordered N-terminal extensions exist locally in the volume sampled by individual ribonucleoprotein complexes, and, second, high nucleocapsid concentrations are prevalent in its macromolecular condensates. Thus, weak interactions of N-terminal extensions can play a critical role strengthening fuzzy ribonucleoprotein complexes and also altering condensate properties, both of which were confirmed in our experiments. Nonetheless, we do not expect the observed fibrillar state of the concentrated isolated N-terminal peptide to be physiologically relevant, since physiologically they will always remain tethered to the full-length protein impeding fibrillar superstructures.

(2) We are grateful for the Reviewers’ suggestions to enhance the clarity and accessibility of our findings and to streamline the presentation. We intend to tighten up the text and improve figures throughout, and add discussion points, as proposed.

(3) We plan to add an analysis of the extent that irreversible surface adsorption decreases solute concentration in mass photometry, and discuss why this has negligible impact on the conclusions drawn under our experimental conditions.In summary, we agree these points all provide opportunities to strengthen the manuscript further and we are glad to revise our manuscript accordingly.

Author response:

The following is the authors’ response to the original reviews

Recommendations for the Authors:

Reviewer #1:

We think that this manuscript brings an important contribution that will be of interest in the areas of statistical physicists, (microbiota) ecology, and (biological) data science. The evidence of their results is solid and the work improves the state-of-the-art in terms of methods. We have a few concerns that, in our opinion, the authors should address.

Major concerns:

(1) While the paper could be of interest for the broad audience of e-Life, the way it is written is accessible mainly to physicists. We encourage the authors to take the broad audience into account by i) explaining better the essence of what is being done at each step, ii) highlighting the relevance of the method compared to other methods, iii) discussing the ecological implications of the results.

Examples on how to approach i) include: Modify or expand Figure 1 so that non-familiar readers can understand the summary of the work (e.g. with cartoons representing communities, diseased states and bacterial interactions and their relationship with the inference method); in each section, summarize at the beginning the purpose of what is going to be addressed in this section, and summarize at the end what the section has achieved; in Figure 2, replace symbols by their meaning as much as possible-the same for Figure 1, at the very least in the figure caption.

Example on how to approach ii): Since the authors aim to establish a bridge between disordered systems and microbiome ecology, it could be useful to expand a bit the introduction on disordered systems for biologists/biophysicists. This could be done with an additional text box, which could also highlight the advantages of this approach in comparison to other techniques (e.g. model-free approaches can also classify healthy and diseased states).

Example on how to approach iii): The authors could discuss with more depth the ecological implications of their results. For example, do they have a hypothesis on why demographic and neutral effects could dominate in healthy patients?

We thank the reviewer for the observations. Following the suggestion in the revised version, each section outlines the goal of what will be addressed in that section, and summarizes what we have achieved at the end; We also updated Figure 1 and Figure 2.

(i) For figure 1, we expanded and hopefully made more clear how we conceptualize the problem, use the data, andestablish our method. In Figure 2, we enriched the y labels of each panel with the name associated with the order parameter.

(ii) We thank the reviewer for helping us improve the readability of the introductory part, thus providing moreinsights into disordered systems techniques for a broader audience. We have added a few explanations at the end of page 2 – to explain the advantages of such methodology compared to other strategies and models.

(iii) We thank the reviewer for raising the need for a more in-depth ecological discussion of our results. A simple wayto understand why neutral effects may dominate in healthy patients is the following. Neutrality implies that species differences are mainly shaped by stochastic processes such as demographic noise, with species treated as different realizations of the same underlying stochastic ecological dynamics. In our analysis, we observe that healthy individuals tend to exhibit highly similar microbial communities, suggesting that the compositional variability among their microbiomes is compatible—at least in part—with the fluctuations expected from demographic stochasticity alone. In contrast, patients with the disease display significantly more heterogeneous microbial compositions. The diversity and structure of their gut communities cannot be satisfactorily explained by neutral demographic fluctuations alone.

This discrepancy implies that additional deterministic forces—such as altered ecological interactions—are driving the divergence observed in dysbiotic states. In diseased individuals, the breakdown of such interactions leads to a structurally distinct regime that may correspond to a phase of marginal stability, as indicated by our theoretical modeling. This shift marks a transition from a community governed by neutrality and demographic noise to one dominated by non-neutral ecological forces (as depicted in Figure 4). We added these comments in the discussion section of the revised manuscript.

(2) Taking into account the broader audience, we invite the authors to edit the abstract, as it seems to jump from one ecological concept to another without explicitly communicating what is the link between these concepts. From the first two sentences, the motivation seems to be species diversity, but no mention of diversity comes after the second sentence. There is no proper introduction/definition of what macroecological states are. After that, the authors switch to healthy and unhealthy states, without previously introducing any link between gut microbiota states and the host’s health (which perhaps could be good in the first or second sentence, although other framings can be as valid). After that, interactions appear in the text and are related to instability, but the reader might not know whether this is surprising or if healthy/unhealthy states are generally related to stability.

We pointed out a few examples, but the authors could extend their revision on i), ii) and iii) beyond such specific comments. In our opinion, this would really benefit the paper.

In response to the reviewer’s concern about conceptual clarity and structure, we substantially revised the abstract to improve its accessibility and logical flow. In the revised abstract, we now clearly link species diversity to microbiome structure and function from the outset, addressing initial confusion. We provide a concise definition of ”macroecological states,” framing them as reproducible statistical patterns reflecting community-level properties. Additionally, the revised version explicitly connects gut microbiome states to host health earlier, resolving the previous abrupt shift in focus. Finally, we conclude by highlighting how disordered systems theory advances our understanding of microbiome stability and functioning, reinforcing the novelty and broader significance of our approach. Overall, the revised abstract better serves a broad interdisciplinary audience, including readers unfamiliar with the technicalities of disordered systems or microbial ecology, while preserving the scientific depth and accuracy of our work

(3) The connection with consumer-resource (CR) models is quite unusual. In Equation (12), why do the authors assume that the consumption term does not depend on R? This should be addressed, since this term is usually dependent on R in microbial ecology models.

In case this is helpful, it is known that the symmetric Lotka-Volterra model emerges from time-scale separation in the MacArthur model, where resources reproduce logistically and are consumed by other species (e.g., plants eaten by herbivores). Consumer-resource models form a broad category, while the MacArthur model is a specific case featuring logistic resource growth. For microbes, a more meaningful justification of the generalized Lotka-Volterra (GLV) model from a consumer-resource perspective involves the consumer-resource dynamics in a chemostat, where time-scale separation is assumed and higher-order interactions are neglected. See, for example: a) The classic paper by MacArthur: R. MacArthur. Species packing and competitive equilibrium for many species. Theoretical Population Biology, 1(1):1-11, 1970. b) Recent works on time-scale separation in chemostat consumer-resource models: Anna Posfai et al., PRL, 2017 Sireci et al., PNAS, 2023 Akshit Goyal et al., PRX-Life, 2025

We thank the reviewer for the observation. We apologize for the typo that appeared in the main text and that we promptly corrected. The Consumers-Resources model we had in mind is the classical case proposed by MacArthur, where resources are self-regulated according to a logistic growth mechanism, which leads to the generalized LotkaVolterra model we employ in our work.

Minor concerns:

(1) The title has a nice pun for statistical physicists, but we wonder if it can be a bit confusing for the broader audience of e-Life. Although we leave this to the author’s decision, we’d recommend considering changing the title, making it more explicit in communicating the main contribution/result of the work.

Following the reviewer’s suggestion, we have introduced an explanatory subtitle: “Linking Species Interactions to Dysbiosis through a Disordered Lotka-Volterra Framework”.

(2) Review the references - some preprints might have already been published: Pasqualini J. 2023, Sireci 2022, Wu 2021.

We thank the reviewer for pointing our attention to this inaccuracy. We updated the references to Pasqualini and Sireci papers. To our knowledge, Wu’s paper has appeared as an arXiv preprint only.

(3) Species do not generally exhibit identical carrying capacities (see Grilli, Nat. Commun., 2020; some taxa are generally more abundant than others. The authors could discuss whether the model, with the inferred parameters, can accurately reproduce the distribution of species’ mean abundances.

We thank the reviewer for this insightful comment. As discussed in the revised manuscript (lines 294–299), our current model does not accurately reproduce the empirical species abundance distribution (SAD). This limitation stems from the assumption of constant carrying capacities across species. While empirical observations (e.g., Grilli et al., Nat. Commun., 2020 [1]) show heterogeneous mean abundances often following power-law or log-normal distributions. However, our model assumes constant carrying capacity, resulting in SADs devoid of fat tails, which diverge from empirical data.

This simplification is implemented to maintain the analytical tractability of the disordered generalized Lotka-Volterra (dGLV) framework, a common approach also found in prior works such as Bunin (2017) and Barbier et al. (2018) [2, 3]. Introducing heterogeneity in carrying capacities, such as drawing them from a log-normal distribution, or switching to multiplicative (rather than demographic) noise, could indeed produce SADs that better align with empirical data. Nevertheless, implementing changes would significantly complicate the analytical treatment.

We acknowledge these directions as promising avenues for future research. They could help enhance the empirical realism of the model and its capacity to capture observed macroecological patterns while posing new theoretical challenges for disordered systems analysis

(4) A substantial number of cited works (Grilli, Nat. Commun., 2020; Zaoli & Grilli, Science Advances, 2021; Sireci et al., PNAS, 2023; Po-Yi Ho et al., eLife, 2022) suggest that environmental fluctuations play a crucial role in shaping microbiome composition and dynamics. Is the authors’ analysis consistent with this perspective? Do they expect their conclusions to remain robust if environmental fluctuations are introduced?

We thank the reviewer for stressing this point. The introduction of environmental fluctuations in the model formally violates detailed balance, thereby preventing the definition of an energy function. To date, no study has integrated random interactions together with both demographic and environmental noise within a unified analytical framework. This is certainly a highly promising direction that some of the authors are already exploring. However, given the inherently out-of-equilibrium nature of the system and the absence of a free energy, we would need to adopt a Dynamical Mean-Field Theory formalism and eventually analyze the corresponding stationary equations to be solved self-consistently. We added, however, a brief note in the Discussion section.

(5) The term “order parameters“ may not be intuitive for a biological audience. In any case, the authors should explicitly define each order parameter when first introduced.

We thank the reviewer for the comment. We introduced the names of the order parameters as soon as they are introduced, along with a brief explanation of their meaning that may be accessible to an audience with biological background.

(6) Line 242: Should ψU be ψD?

We thank the reviewer for the observation. We corrected the typo.

(7) Given that the authors are discussing healthy and diseased states and to avoid confusion, the authors could perhaps use another word for ’pathological’ when they refer to dynamical regimes (e.g., in Appendix 2: ’letting the system enter the pathological regime of unbounded growth’).

We thank the reviewer for the helpful comment. As suggested, we used the term “unphysical” instead of “pathological” where needed.

Reviewer #2:

(1) A technical point that I could not understand is how the authors deal with compositional data. One reason for my confusion is that the order parameters h and q0 are fixed n data to 1/S and 1/S2, and thus I do not see how they can be informative. Same for carrying capacity, why is it not 1 if considering relative abundance?

We thank the reviewer for raising this point. We acknowledge that the treatment of compositional data and the interpretation of order parameters h and q0 were not sufficiently clarified in the manuscript. Additionally, there was an imprecision in the text regarding the interpretation of these parameters.

As defined in revised Eq. (4) of the manuscript, h and q0 are to be averaged over the entire dataset, summing across samples α. Specifically, and , where S_α is the number of species present in sample α and is the average over samples. These parameters are therefore informative, as they encapsulate sample-level ecological diversity, and their variation reflects biological differences between healthy and diseased states. For instance, Pasqualini et al., 2024 [4] reported significant differences in these metrics between health conditions, thereby supporting their ecological relevance.

Regarding carrying capacities, we clarify that although we work with relative abundance data (i.e., compositional data), we do not fix the carrying capacity K to 1. Instead, we set K to the maximum value of xi (relative abundance) within each sample, to preserve compatibility with empirical data and allow for coexistence. While this remains a modeling assumption, it ensures better ecological realism within the constraints of the disordered GLV framework.

(2) Obviously I’m missing something, so it would be nice to clarify in simple terms the logic of the argument. I understand that Lagrange multipliers are going to be used in the model analysis, and there are a lot of technical arguments presented in the paper, but I would like a much more intuitive explanation about the way the data can be used to infer order parameters if those are fixed by definition in compositional data.

We thank the reviewer for the observation. The order parameters can be measured directly from the data, even in the presence of compositionality, as explained above. We can connect those parameters with the theory even for compositional data, because the only effect of adding the compositionality constraint is to shift the linear coefficient in the Hamiltonian, which corresponds to shifting the average interaction µ. However, the resulting phase diagram is mostly affected by the variance of the interactions σ2 (as µ is such that we are in the bounded phase).

(3) Another point that I did not understand comes from the fact that the authors claim that interaction variance is smaller in unhealthy microbiomes. Yet they also find that those are closer to instability, and are more driven by niche processes. I would have expected the opposite to be true, more variance in the interactions leading to instability (as in May’s original paper for instance). Is this apparent paradox explained by covariations in demographic stochasticity (T) and immigration rate (lambda)? If so, I think it would be very useful to comment on that.

As Altieri and coworkers showed in their PRL (2021) [5], the phase diagram of our model differs fundamentally from that of Biroli et al. (2018) [6]. In the latter, the intuitive rule – greater interaction variance yields greater instability – indeed holds. For the sake of clarity, we have attached below the resulting phase diagram obtained by Altieri et al.

The apparent paradox arises because the two phase diagrams are tuned by different parameters. Consequently, even at low temperature and with weak interaction variance, our system may sit nearer to the replica-symmetrybreaking (RSB) line.

Fig. 3 in the main text it is not a (σ,T) phase diagram where all other parameters are kept constant. Rather, it is a plot of the inferred σ and T parameters from the data (without showing the corresponding µ).

To capture the full, non-trivial influence of all parameters on stability, we studied the so-called “replicon eigenvalue” in the RS (i.e. single equilibrium) approximation. This leading eigenvalue measures how close a given set of inferred parameters – and hence a microbiome – is to the RSB threshold. For a visual representation of these findings, refer to Figure 4.

Author response image 1.

(4) What do the empirical SAD look like? It would be nice to see the actual data and how the theoretical SADs compare.

The empirical species abundance distributions (SADs) analyzed in our study are presented and discussed in detail in Pasqualini et al., 2024 [4]. Given the overlap in content, we chose not to reproduce these figures in the current manuscript to avoid redundancy.

As we also clarify in the revised text, the theoretical SAD is derived from the disordered generalized Lotka-Volterra (dGLV) model in the unique fixed point phase typically exhibit exponential tails. These distributions do not match the heavier-tailed patterns (e.g., log-normal or power-law-like) observed in empirical microbiome data. This discrepancy stems from the simplifying assumptions of the dGLV framework, including the use of constant carrying capacities and demographic noise.

In the revised manuscript, we have added a brief discussion in the revised manuscript to explicitly acknowledge this limitation and emphasize it as a direction for future refinement of the model, such as incorporating heterogeneous carrying capacities or exploring alternative noise structures.

(5) Some typos: often “niche” is written “nice”.

We thank the reviewer for this suggestion. After inspecting the text, we corrected the reported typos.

Reviewer #3:

Major comments:

(1) In the S3 text, the authors say that filtered metagenomic reads were processed using the software Kaiju. The description of the pipeline does not mention how core genes were selected, which is often a crucial step in determining the abundance of a species in a metagenomic sample. In addition, the senior author of this manuscript has published a version of Kaiju that leverages marker genes classification methods (deemed Core-Kaiju), but it was not used for either this manuscript or Pasqualini et al. (2014; Tovo et al., 2020). I am not suggesting that the data necessarily needs to be reprocessed, but it would be useful to know how core genes were chosen in Pasqualini et al. and why Core-Kaiju was not used (2014).

Prior to the current manuscript and the PLOS Computational Biology paper by Pasqualini et al. [4], we applied the core-Kaiju protocol to the same dataset used in both studies. However, this tool was originally developed and validated using general catalogs of culturable organisms, not specifically tuned for gut microbiomes. As a result, we have realized that in many samples Core Kajiu would filter only very few species (in some samples, the number of identified species was as low as 5–10), undermining the reliability of the analysis. Due to these limitations, we opted to use the standard Kaiju version in our work. We are actively developing an improved version of the core-Kaiju protocol that will overcome the discussed limitations and preliminary results (not shown here) indicate the robustness of the obtained patterns also in this case.

(2) My understanding of Pasqualini et al. was that diseased patients experienced larger fluctuations in abundance, while in this study, they had smaller fluctuations (Figure 3a; 2024). Is this a discrepancy between the two models or is there a more nuanced interpretation?

We thank the reviewer for the observation. This is only an apparent discrepancy, as the term fluctuation has different meanings in the two contexts. The fluctuations referred to by the reviewer correspond to a parameter of our theory—namely, noise in the interactions. Conversely, in Pasqualini et al. σ indicates environmental fluctuations. Nevertheless, there is no conceptual discrepancy in our results: in both studies, unhealthy microbiomes were found to be less stable. In fact, also in this study, notably Fig. 4, shows that unhealthy microbiomes lie closer to the RSB line, a phenomenon that is also associated with enhanced fluctuations.

(3) Line 38-41: It would be helpful to explicitly state what “interaction patterns” are being referenced here. The final sentence could also be clarified. Do microbiomes “host“ interactions or are they better described as a property (“have”, “harbor”). The word “host” may confuse some readers since it is often used to refer to the human host. I am also not sure what point is being made by “expected to govern natural ones”. There are interactions between members of a microbiome; experimental studies have characterized some of these interactions, which we expect to relate in some way to interactions in nature. Is this what the authors are saying?

Thanks. We agree that this sentence was not clear. Indeed, we are referring to pairwise species interactions and not to host-microbiome interactions. We have rewritten this part in the following way: In fact, recent work shows that the network-level properties of species-species interactions —for example, the sign balance, average strength, and connectivity of the inferred interaction matrix— shift systematically between healthy and dysbiotic gut communities (see for instance, [7, 8]). Pairwise species interactions have been quantified in simplified in-vitro consortia [9, 10]; we assume that the same classes of interactions also operate—albeit in a more complex form—in the native gut microbiome.

(4) Line 43: I appreciate that the authors separated neutral vs. logistic models here.

(5) Lines 51-75: The framing here is well-written and convincing. Network inference is an ongoing, active subject in ecology, and there is an unfortunate focus on inferring every individual interaction because ecologists with biology backgrounds are not trained to think about the problem in the language of statistical physics.

We thank the reviewer for these positive comments.

(6) Line 87: Perhaps I’m missing something obvious, but I don’t see how ρi sets the intrinsic timescale of the dynamics when its units are 1/(time*individuals), assuming the dimensions of ri are inverse time.

We thank the reviewer for the observation. We corrected this phrase in the main text.

(7) Lines 189-190: “as close as possible to the data” it would aid the reader if you specified the criteria meant by this statement.

We thank the reviewer for the observation. We removed the sentence, as it introduced some redundancy in our argument. In the subsequent text, the proposed method is exposed in details.

(8) Line 198: It would aid the reader if you provided some context for what the T - σ plane represents.

We thank the referee for the helpful indication. Indeed, we have better clarified the mutual role of the demographic noise amplitude and strength of the random interaction matrix, as theoretically predicted in the PRL (2021) by Altieri and coworkers [5]. Please, find an additional paragraph on page 6 of the resubmitted version.

(9) Line 217: Specifying what is meant by “internal modes“ would aid the typical life science reader.

We thank the reviewer for the suggestion. Recognizing that referring to “internal modes” to describe the SAD shape in that context might cause confusion, we replaced “internal modes“ with “peaks”.

(10) Line 219: Some additional justification and clarification are needed here, as some may think of “m“ as being biomass.

We added a sentence to better explain this concept. “In classical and quantum field theory, the particle-particle interaction embedded in the quadratic term is typically referred to as a mass source. In the context of this study, captures quadratic fluctuations of species abundances, as also appearing in the expression of the leading eigenvalue of the stability matrix.”

Minor comments:

(1) I commend the authors for removing metagenomic reads that mapped to the human genome in the preprocessing stage of their pipeline. This may seem like an obvious pre-processing step, but it is unfortunately not always implemented.

We thank the referee for pointing this potential issue. The data used in this work, as well as the bioinformatic workflow used to generate them has been described in detail in Pasqualini et al., 2024 [4]. As one of the main steps for preprocessing, we remove reads mapping to the human genome.

(2) Line 13: “Bacterial“ excludes archaea, and while you may not have many high-abundance archaea in your human gut data, this sentence does not specify the human gut. Usually, this exclusion is averted via the term “microbial“, though sometimes researchers raise objections to the term when the data does not include fungal members (e.g., all 16S studies).

We thank the reviewer for this suggestion. As to include archaeal organisms, we adopt the term “microbial“ instead of “bacterial“.

(3) Line 18: This manuscript is being submitted under the “Physics of Living Systems“ tract, but it may be useful to explicitly state in the Abstract that disordered systems are a useful approach for understanding large, complex communities for the benefit of life science researchers coming from a biology background.

Thank. We have modified the abstract following this suggestion.

(4) Line 68: Consider using “adapted“ or something similar instead of “mutated“ if there is no specific reason for that word choice.

We thank the reviewer for this suggestion, which was implemented in the text.

(5) Line 111: It would be useful to define annealed and quenched for a general life science audience.

We thank the reviewer for this suggestion. In the “Results” section, we have opted for “time-dependent disordered interactions” to reach a broader audience and avoid any jargon. Moreover, in the Discussion we added a detailed footnote: “In contrast to the quenched approximation, the annealed version assumes that the random couplings are not fixed but instead fluctuate over time, with their covariance governed by independent Ornstein–Uhlenbeck processes.”

(6) Line 124: Likewise for the replicon sector.

We thank the reviewer for the suggestion. We added a footnote on page 4, after the formula, to highlight the physical intuition behind the introduction of the replicon mode.

“The replicon eigenvalue refers to a particular type of fluctuation around the saddle-point (mean-field) solution within the replica framework. When the Hessian matrix of the replicated free energy is diagonalized, fluctuations are divided into three sectors: longitudinal, anomalous, and replicon. The replicon mode is the most sensitive to criticality signaling – by its vanishing trend – the emergence of many nearly-degenerate states. It essentially describes how ‘soft’ the system is to microscopic rearrangements in configuration space.”

(7) Figure 2: It would be helpful to include y-axis labels for each order parameter alongside the mathematical notation.

We thank the reviewer for this suggestion. Now the y-axis of Figure 2 includes, along the mathmetical symbol, the label of the represented quantities.

(8) Line 242: Subscript “U” is used to denote “Unhealthy” microbiomes, but “D” is used to denote “Diseased” in Figs. 2 and 3 (perhaps elsewhere as well).

We thank the reviewer for this observation. After checking the various subscripts in the text, coherently with figure 2 and 3, we homogenized our notation, adopting the subscript “D“ for symbols related to the diseased/unhealthy condition.

(9) Line 283: “not to“ should be “not due to“

We thank the reviewer for this suggestion. After inspecting the text, we corrected the reported error.

(10) Equations 23, 34: Extra “=“ on the RHS of the first line.

We consistently follow the same formatting across all the line breaks in the equations throughout the text.

We are thus resubmitting our paper, hoping to have satisfactorily addressed all referees’ concerns.

References

(1) Jacopo Grilli. Macroecological laws describe variation and diversity in microbial communities. Nature communications, 11(1):4743, 2020.

(2) Guy Bunin. Ecological communities with lotka-volterra dynamics. Physical Review E, 95(4):042414, 2017.

(3) Matthieu Barbier, Jean-Franc¸ois Arnoldi, Guy Bunin, and Michel Loreau. Generic assembly patterns in complex ecological communities. Proceedings of the National Academy of Sciences, 115(9):2156–2161, 2018.

(4) Jacopo Pasqualini, Sonia Facchin, Andrea Rinaldo, Amos Maritan, Edoardo Savarino, and Samir Suweis. Emergent ecological patterns and modelling of gut microbiomes in health and in disease. PLOS Computational Biology, 20(9):e1012482, 2024.

(5) Ada Altieri, Felix Roy, Chiara Cammarota, and Giulio Biroli. Properties of equilibria and glassy phases of the random lotka-volterra model with demographic noise. Physical Review Letters, 126(25):258301, 2021.

(6) Giulio Biroli, Guy Bunin, and Chiara Cammarota. Marginally stable equilibria in critical ecosystems. New Journal of Physics, 20(8):083051, 2018.

(7) Amir Bashan, Travis E Gibson, Jonathan Friedman, Vincent J Carey, Scott T Weiss, Elizabeth L Hohmann, and Yang-Yu Liu. Universality of human microbial dynamics. Nature, 534(7606):259–262, 2016.

(8) Marcello Seppi, Jacopo Pasqualini, Sonia Facchin, Edoardo Vincenzo Savarino, and Samir Suweis. Emergent functional organization of gut microbiomes in health and diseases. Biomolecules, 14(1):5, 2023.

(9) Jared Kehe, Anthony Ortiz, Anthony Kulesa, Jeff Gore, Paul C Blainey, and Jonathan Friedman. Positive interactions are common among culturable bacteria. Science advances, 7(45):eabi7159, 2021.

(10) Ophelia S Venturelli, Alex V Carr, Garth Fisher, Ryan H Hsu, Rebecca Lau, Benjamin P Bowen, Susan Hromada, Trent Northen, and Adam P Arkin. Deciphering microbial interactions in synthetic human gut microbiome communities. Molecular systems biology, 14(6):e8157, 2018.

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(d) the claimant has been recognized as a Convention refugee by a country other than Canada and can be sent or returned to that country;

(e) the claimant came directly or indirectly to Canada from a country designated by the regulations, other than a country of their nationality or their former habitual residence; or

(f) the claimant has been determined to be inadmissible on grounds of security, violating human or international rights, serious criminality or organized criminality.

(e) the claimant came directly or indirectly to Canada from a country designated by the regulations, other than a country of their nationality or their former habitual residence

1. No State Party shall expel, return ("refouler") or extradite a person to another State where there are substantial grounds for believing that he would be in danger of being subjected to torture.

2. For the purpose of determining whether there are such grounds, the competent authorities shall take into account all relevant considerations including, where applicable, the existence in the State concerned of a consistent pattern of gross, flagrant or mass violations of human rights.

1. No Contracting State shall expel or return (“refouler”) a refugee in any manner whatsoever to the frontiers of territories where his life or freedom would be threatened on account of his race, religion, nationality, member- ship of a particular social group or political opinion.
2. The benefit of the present provision may not, however, be claimed by a refugee whom there are reasonable grounds for regarding as a danger to the security of the country in which he is, or who, having been convicted by a final judgment of a particularly serious crime, constitutes a danger to the community of that country

95 (1) Refugee protection is conferred on a person when

(a) the person has been determined to be a Convention refugee or a person in similar circumstances under a visa application and becomes a permanent resident under the visa or a temporary resident under a temporary resident permit for protection reasons;

**(b) the Board determines the person to be a Convention refugee or a person in need of protection; or

(c) except in the case of a person described in subsection 112(3), the Minister allows an application for protection.**

E. This Convention shall not apply to a person who is recognized by the competent authorities of the country in which he has taken residence as having the rights and obligations which are attached to the possession of the nationality of that country.

F. The provisions of this Convention shall not apply to any person with respect to whom there are serious reasons for considering that: (a) he has committed a crime against peace, a war crime, or a crime against humanity, as defined in the international instruments drawn up to make provision in respect of such crimes; (b) he has committed a serious non-political crime outside the country of refuge prior to his admission to that country as a refugee; (c) he has been guilty of acts contrary to the purposes and principles of the United Nations.

The attitude towards the natives from Sarah.

eLife Assessment

The study reports a potential pathway for isoleucine biosynthesis mediated by the underground activity of AHASII, which converts glyoxylate and pyruvate to 2-ketobutyrate. While the findings are valuable in revealing a possible alternative route for isoleucine production, the evidence presented remains incomplete. More comprehensive biochemical experiments are required to substantiate the physiological feasibility of this pathway.

Reviewer #1 (Public review):

As presented in this short report, the focus is to only establish that acetohydroxyacid synthase II can have underground activity to generate 2-ketobutyrate (from glyoxylate and pyruvate). Additionally, the gene that encodes this protein has an inactivating point mutation in the lab strain of E. coli. In strains lacking the conventional Ile biosynthesis pathway, this enzyme gets reactivated (after short-term laboratory evolution) and putatively can contribute to producing sufficient 2-ketobutyrate, which can feed into Ile production. This is clearly a very interesting observation and finding, and the paper focuses on this single point.

However, the manuscript as it currently stands is 'minimal', and just barely shows that this reaction/pathway is feasible. There is no characterization of the restored enzyme's activity, rate, or specificity. Additionally, there is no data presented on how much isoleucine can be produced, even at saturating concentrations of glyoxylate or pyruvate. This would greatly benefit from more rigorous characterization of this enzyme's activity and function, as well as better demonstration of how effective this pathway is in generating 2-ketobutyrate (and then its subsequent condensation with pyruvate).

Reviewer #2 (Public review):

Summary:

The manuscript by Rainaldi et al. reports a new sub-pathway for isoleucine biosynthesis by demonstrating the promiscuous activity of the native enzyme acetohydroxyacid synthase II (AHAS II). AHAS-II is primarily known to catalyze the condensation of 2-ketobutyrate (2KB) with pyruvate to form a further downstream intermediate, AHB, in the isoleucine biosynthesis pathway. However, the catalysis of pyruvate and glyoxylate condensation to produce 2KB via the ilvG encoded AHAS II is reported in this manuscript for the first time.

Using an isoleucine/2KB auxotrophic E. coli strain, the authors report (i) repair of the inactivating frameshift mutation in the ilvG gene, which encodes AHAS-II, supports growth in glyoxylate-supplemented media, (ii) the promiscuity of AHAS-II in glyoxylate and pyruvate condensation, resulting in the formation of isoleucin precursors (2-KB), aiding the biosynthesis of isoleucine, and (iii) comparable efficiency of the recursive AHAS-II route to the canonical routes of isoleucin biosynthesis via computational Flux-based analysis.

Strengths:

The authors have used laboratory evolution to uncover a non-canonical metabolic route. The metabolomics and FBA have been used to strengthen the claim.

Weaknesses:

While the manuscript proposes an interesting metabolic route for the isoleucine biosynthesis, the data lack key controls, biological replicates, and consistency. The figures and methods are presented inadequately. In the current state, the data fails to support the claims made in the manuscript.

Author response:

We gratefully acknowledge the comments on our manuscript and the time you took to read and understand our work. Nevertheless, it is the opinion of these authors that the evidence provided in the submitted paper is strong and we performed multiple replicates of the experiments. In particular, gene deletion and complementation is the accepted gold standard for studies in physiology. In the isoleucine auxotroph (IMaux) strain carrying an ilvG deletion, growth is only possible if ilvG is reintroduced on a plasmid and induced. Additionally, isotopic labeling clearly demonstrates the activity of the proposed pathway. Regardless, we agree with the reviewers that the paper and the scientific community would benefit from an in vitro characterization of the promiscuity of IlvG, so we will perform this experiment and resubmit the paper for further revision, and in this revision also provide more detail on the replicates performed.

I strongly agree with the impact this paragraph mentioned, it is very important to know that grading can be overwhelming and could make one loses confidence while learning. So it is indeed possible to have negative impact.

This article basically talks about the definition of troll, and how would people define troll online and in real life. I personally dislike this behavior because it would harm people intentionally. Trolls did not actually consider others feelings, and they also want to show how smart they are. This will make the trolls feel good by showing how much knowledge they know.

this shows that research isn`t just one way, its different questions for every part.

every field has its own way if doing research. its important to understand the methods used in your discipline.

I frequently run news sources through Media Bias Fact-Check as a "background check" on most news publications I stumble across. This resource was especially useful when I was working on the Fact Checking assignment. Sometimes it's fairly easy to notice conspiratorial and click-baiting content (such as the scenario with the faux meat article), but not always. When biases and a source's interests are unclear, MBFC helps me decide if I should keep reading or drop the source all together.

Finding an answer to these three questions before I set off to do research has made it easier for me to avoid wasting time on rabbit holes. Even if I'm just scrawling out bulleted terms and loose concepts, it's proven to be helpful in redirecting myself when I stray from the true goals of any given information search.

Most of trolls like the feeling of being smart, and sometimes it is easy to troll something when there are lots of resources on the internet. The trolls have enough time to get prepared, and this is the mean reason why trolls know everything. Being smart make the trolls feel good, and once they have this feeling, they will continue to troll.

I'm just marking this typo for review later in the semester!

I'm once again highlighting the precursory sentence to the instructions, lest the entire page be marked up. At last, I will not be as daunted by the towering shelves of Steely Library.

To avoid highlighting this entire page, I shall leave my comment here. This may be the first time it's been plainly laid out to me how citations are used to trace sources. Throughout our schooling in K-12, it was drilled into my head how important citations are and how to write them. Perhaps I simply have a goldfish brain, but this chapter finally made the process of actually using citations "click" in my head.

In the past, world politics mainly focused on how countries interacted with each other (that’s “international”). Now, the focus is on the global level — meaning that what happens in the world is shaped not just by governments, but also by international organizations, corporations, social movements, and even individuals across borders.

The group of actors who make decisions and influence global issues isn’t limited to a few powerful Western nations anymore. It includes many different countries, regions, and people from all over the world.

Power is now spread out a more players, and the pushback against Western control goes far beyond just China’s rise.

This article looks at longterm changes that have taken place due to a more connected and global world.

for an argument ask about thesis and main idea. for a story, include main events or idea

for specific word use Italic, and for direct quot use quotation marks

search.-hyp.is?user=gyuri&any=formulative

self

The Strange Loop is closed and becomes a spiral

Spiral Loop

I've been writing to think about

formulative writing-to-think

in /hyperpost/🌐/🎭/gyuri/📓/2025/10/2/💾/snarf/ as the way to augment individual's capactity for formulative thinking per se

a long term perennial focus of my thinking

as demonstrated by this hypothes.is faceted annotation search

https://jonudell.info/h/facet/?user=gyuri&any=formulative&max=100&exactTagSearch=true&addQuoteContext=true&expanded=true

and the result that it is annotated hwere

In /💾/snarf/index.html hyperpost.peergos.me

i added the Clue

.see - formulative thinking

Really? More?

There’s a strong contrast between haste and hesitation in this passage

The phrase “dogtom parts” refers to Polyneikes’s body being torn apart by dogs, symbolizing the dishonor of being left unburied. In ancient Greek culture, this was considered a terrible fate because an unburied soul could not rest in the afterlife

Need appliance repair in Sydney? Learn key factors to choose the right home appliance repair service. From certifications to warranties, make an informed choice with Best Repairs.

Reminds me of the saying that eyes are the window to the soul

The key and the significance of all this is really in the subtlety

So true. This is a hard truth to deal with.

Amazing how subconscious all this is and unconscious it is.

The signs are subtle, but they are there

Rewrite the information in your own words but keep all the details

Within starts of an introduction these are ways to catch the reader’s interest and make them want to keep reading. It helps draw people in and gets them thinking about the topic right from the start.

eLife Assessment

This valuable biomechanical analysis of kangaroo kinematics and kinetics across a range of hopping speeds and masses is a step towards understanding a long-standing problem in locomotion biomechanics: the mechanism for how kangaroos, unlike other mammals, can increase hopping speed without a concomitant increase in metabolic cost. The authors convincingly demonstrate that changes in kangaroo posture with speed increase tendon stress/strain and hence elastic energy storage/return. This greater tendon elastic energy storage/return may counteract the increased cost of generating muscular force at faster speeds and thus allows for the invariance in metabolic cost. This methodologically impressive study sets the stage for further work to investigate the relation of hopping speed to metabolic cost more definitively.

Reviewer #1 (Public review):

Summary:

The study explored the biomechanics of kangaroo hopping across both speed and animal size to try and explain the unique and remarkable energetics of kangaroo locomotion.

Strengths:

Brings kangaroo locomotion biomechanics into the 21st century. Remarkably difficult project to accomplish. Excellent attention to detail. Clear writing and figures.

General Comments

This is a very impressive tour de force by an all-star collaborative team of researchers. The study represents a tremendous leap forward (pun intended) in terms of our understanding of kangaroo locomotion. Some might wonder why such an unusual species is of much interest. But, in my opinion, the classic study by Dawson and Taylor in 1973 of kangaroos launched the modern era of running biomechanics/energetics and applies to varying degrees to all animals that use bouncing gaits (running, trotting, galloping and of course hopping). The puzzling metabolic energetics findings of Dawson & Taylor (little if any increase in metabolic power despite increasing forward speed) remain a giant unsolved problem in comparative locomotor biomechanics and energetics. It is our "dark matter problem".

This study is certainly a hop towards solving the problem. The study clearly shows that the ankle and to a lesser extent the mtp joint are where the action is. They show in great detail by how much and by what means the ankle joint tendons experience increased stress at faster forward speeds. Since these were zoo animals, direct measures were not feasible, but the conclusion that the tendons are storing and returning more elastic energy per hop at faster speeds is solid. The conclusion that net muscle work per hop changes little from slow to fast forward speeds is also solid. Doing less muscle work can only be good if one is trying to minimize metabolic energy consumption. However, to achieve the greater tendon stresses, there must be greater muscle forces. Unless one is willing to reject the premise of the cost of generating force hypothesis, that is an important issue to confront. Further, the present data support the Kram & Dawson finding of decreased contact times at faster forward speeds. Kram & Taylor and subsequent applications of (and challenges to) their approach support the idea that shorter contact times (tc) require recruiting more expensive muscle fibers and hence greater metabolic costs. The present authors have clarified that this study has still not tied up the metabolic energetics across speed problem and they now point out how the group is now uniquely and enviably poised to explore the problem more using a dynamic SIMM model that incorporates muscle energetics.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The study explored the biomechanics of kangaroo hopping across both speed and animal size to try and explain the unique and remarkable energetics of kangaroo locomotion.

Strengths:

The study brings kangaroo locomotion biomechanics into the 21st century. It is a remarkably difficult project to accomplish. There is excellent attention to detail, supported by clear writing and figures.

Weaknesses:

The authors oversell their findings, but the mystery still persists. 

The manuscript lacks a big-picture summary with pointers to how one might resolve the big question.

General Comments

This is a very impressive tour de force by an all-star collaborative team of researchers. The study represents a tremendous leap forward (pun intended) in terms of our understanding of kangaroo locomotion. Some might wonder why such an unusual species is of much interest. But, in my opinion, the classic study by Dawson and Taylor in 1973 of kangaroos launched the modern era of running biomechanics/energetics and applies to varying degrees to all animals that use bouncing gaits (running, trotting, galloping and of course hopping). The puzzling metabolic energetics findings of Dawson & Taylor (little if any increase in metabolic power despite increasing forward speed) remain a giant unsolved problem in comparative locomotor biomechanics and energetics. It is our "dark matter problem".

Thank you for the kind words.

This study is certainly a hop towards solving the problem. But, the title of the paper overpromises and the authors present little attempt to provide an overview of the remaining big issues. 

We have modified the title to reflect this comment.  “Postural adaptations may contribute to the unique locomotor energetics seen in hopping kangaroos”

The study clearly shows that the ankle and to a lesser extent the mtp joint are where the action is. They clearly show in great detail by how much and by what means the ankle joint tendons experience increased stress at faster forward speeds.

Since these were zoo animals, direct measures were not feasible, but the conclusion that the tendons are storing and returning more elastic energy per hop at faster speeds is solid. The conclusion that net muscle work per hop changes little from slow to fast forward speeds is also solid. 

Doing less muscle work can only be good if one is trying to minimize metabolic energy consumption. However, to achieve greater tendon stresses, there must be greater muscle forces. Unless one is willing to reject the premise of the cost of generating force hypothesis, that is an important issue to confront. Further, the present data support the Kram & Dawson finding of decreased contact times at faster forward speeds. Kram & Taylor and subsequent applications of (and challenges to) their approach supports the idea that shorter contact times (tc) require recruiting more expensive muscle fibers and hence greater metabolic costs. Therefore, I think that it is incumbent on the present authors to clarify that this study has still not tied up the metabolic energetics across speed problems and placed a bow atop the package. 

Fortunately, I am confident that the impressive collective brain power that comprises this author list can craft a paragraph or two that summarizes these ideas and points out how the group is now uniquely and enviably poised to explore the problem more using a dynamic SIMM model that incorporates muscle energetics (perhaps ala' Umberger et al.). Or perhaps they have other ideas about how they can really solve the problem.

You have raised important points, thank you for this feedback. We have added a limitations and considerations section to the discussion which highlights that there are still unanswered questions. Line 311-328

Considerations and limitations

“First, we believe it is more likely that the changes in moment arms and EMA can be attributed to speed rather than body mass, given the marked changes in joint angles and ankle height observed at faster hopping speeds. However, our sample included a relatively narrow range of body masses (13.7 to 26.6 kg) compared to the potential range (up to 80 kg), limiting our ability to entirely isolate the effects of speed from those of mass. Future work should examine a broader range of body sizes. Second, kangaroos studied here only hopped at relatively slow speeds, which bounds our estimates of EMA and tendon stress to a less critical region. As such, we were unable to assess tendon stress at fast speeds, where increased forces would reduce tendon safety factors closer to failure. A different experimental or modelling approach may be needed, as kangaroos in enclosures seem unwilling to hop faster over force plates. Finally, we did not determine whether the EMA of proximal hindlimb joints (which are more difficult to track via surface motion capture markers) remained constant with speed. Although the hip and knee contribute substantially less work than the ankle joint (Fig. 4), the majority of kangaroo skeletal muscle is located around these proximal joints. A change in EMA at the hip or knee could influence a larger muscle mass than at the ankle, potentially counteracting or enhancing energy savings in the ankle extensor muscle-tendon units. Further research is needed to understand how posture and muscles throughout the whole body contribute to kangaroo energetics.”

Additionally, we added a line “Peak GRF also naturally increased with speed together with shorter ground contact durations (Fig. 2b, Suppl. Fig 1b)” (line 238) to highlight that we are not proposing that changes in EMA alone explain the full increase in tendon stress. Both GRF and EMA contribute substantially (almost equally) to stress, and we now give more equal discussion to both. For instance, we now also evaluate how much each contributes: “If peak GRF were constant but EMA changed from the average value of a slow hop to a fast hop, then stress would increase 18%, whereas if EMA remained constant and GRF varied by the same principles, then stress would only increase by 12%. Thus, changing posture and decreasing ground contact duration both appear to influence tendon stress for kangaroos, at least for the range of speeds we examined” (Line 245-249)

We have added a paragraph in the discussion acknowledging that the cost of generating force problem is not resolved by our work, concluding that “This mechanism may help explain why hopping macropods do not follow the energetic trends observed in other species (Dawson and Taylor 1973, Baudinette et al. 1992, Kram and Dawson 1998), but it does not fully resolve the cost of generating force conundrum” Line 274-276.

I have a few issues with the other half of this study (i.e. animal size effects). I would enjoy reading a new paragraph by these authors in the Discussion that considers the evolutionary origins and implications of such small safety factors. Surely, it would need to be speculative, but that's OK.

We appreciate this comment from the reviewer, however could not extend the study to discuss animal size effects because, as we now note in the results: “The range of body masses may not be sufficient to detect an effect of mass on ankle moment in addition to the effect of speed.” Line 193

Reviewer #2 (Public Review):

Summary

This is a fascinating topic that has intrigued scientists for decades. I applaud the authors for trying to tackle this enigma. In this manuscript, the authors primarily measured hopping biomechanics data from kangaroos and performed inverse dynamics. 

While these biomechanical analyses were thorough and impressively incorporated collected anatomical data and an Opensim model, I'm afraid that they did not satisfactorily address how kangaroos can hop faster and not consume more metabolic energy, unique from other animals.  Noticeably, the authors did not collect metabolic data nor did they model metabolic rates using their modelling framework. Instead, they performed a somewhat traditional inverse dynamics analysis from multiple animals hopping at a self-selected speed.

In the current study, we aimed to provide a joint-level explanation for the increases of tendon stress that are likely linked to metabolic energy consumption.

We have now included a limitations section in the manuscript (See response to Rev 1). We plan to expand upon muscle level energetics in the future with a more detailed musculoskeletal model.

Within these analyses, the authors largely focused on ankle EMA, discussing its potential importance (because it affects tendon stress, which affects tendon strain energy, which affects muscle mechanics) on the metabolic cost of hopping. However, EMA was roughly estimated (CoP was fixed to the foot, not measured) and did not detectibly associate with hopping speed (see results Yet, the authors interpret their EMA findings as though it systematically related with speed to explain their theory on how metabolic cost is unique in kangaroos vs. other animals

As noted in our methods, EMA was not calculated from a fixed centre of pressure (CoP). We did fix the medial-lateral position, owing to the fact that both feet contacted the force plate together, but the anteroposterior movement of the CoP was recorded by the force plate and thus allowed to move. We report the movement (or lack of movement) in our results. The anterior-posterior axis is the most relevant to lengthening or shortening the distance of the ‘out-lever’ R, and thereby EMA. It is necessary to assume fixed medial-lateral position because a single force trace and CoP is recorded when two feet land on the force plate. The mediallateral forces on each foot cancel out so there is no overall medial-lateral movement if the forces are symmetrical (e.g. if the kangaroo is hopping in a straight path and one foot is not in front of the other). We only used symmetrical trials so that the anterior-posterior movement of the CoP would be reliable. We have now added additional details into the text to clarify this

Indeed, the relationship between R and speed (and therefore EMA and speed) was not significant. However, the significant change in ankle height with speed, combined with no systematic change in COP at midstance, demonstrates that R would be greater at faster speeds. If we consider the nonsignificant relationship between R and speed to indicate that there is no change in R, then these two results conflict. We could not find a flaw in our methods, so instead concluded that the nonsignificant relationship between R and speed may be due to a small change in R being undetectable in our data. Taking both results into account, we believe it is more likely that there is a non-detectable change in R, rather than no change in R with speed, but we presented both results for transparency. We have added an additional section into the results to make this clearer (Line 177-185) “If we consider the nonsignificant relationship between R (and EMA) and speed to indicate that there is no change in R, then it conflicts with the ankle height and CoP result. Taking both into account, we think it is more likely that there is a small, but important, change in R, rather than no change in R with speed. It may be undetectable because we expect small effect sizes compared to the measurement range and measurement error (Suppl. Fig. 3h), or be obscured by a similar change in R with body mass. R is highly dependent on the length of the metatarsal segment, which is longer in larger kangaroos (1 kg BM corresponded to ~1% longer segment, P<0.001, R²=0.449). If R does indeed increase with speed, both R and r will tend to decrease EMA at faster speeds.”

These speed vs. biomechanics relationships were limited by comparisons across different animals hopping at different speeds and could have been strengthened using repeated measures design

There is significant variation in speed within individuals, not just between individuals. The preferred speed of kangaroos is 2-4.5 m/s, but most individuals showed a wide speed range within this. Eight of our 16 kangaroos had a maximum speed that was 1-2m/s faster than their slowest trial. Repeated measures of these eight individuals comprises 78 out of the 100 trials.   It would be ideal to collect data across the full range of speeds for all individuals, but it is not feasible in this type of experimental setting. Interference with animals such as chasing is dangerous to kangaroos as they are prone to adverse reactions to stress. We have now added additional information about the chosen hopping speeds into the results and methods sections to clarify this “The kangaroos elected to hop between 1.99 and 4.48 m s^-1, with a range of speeds and number of trials for each individual (Suppl. Fig. 9).”  (Line 381-382)

There are also multiple inconsistencies between the authors' theory on how mechanics affect energetics and the cited literature, which leaves me somewhat confused and wanting more clarification and information on how mechanics and energetics relate

We thank the reviewer for this comment. Upon rereading we now understand the reviewers position, and have made substantial revisions to the introduction and discussion (See comments below) 

My apologies for the less-than-favorable review, I think that this is a neat biomechanics study - but am unsure if it adds much to the literature on the topic of kangaroo hopping energetics in its current form.

Again we thank the reviewer for their time and appreciate their efforts to strengthen our manuscript.

Reviewer #3 (Public Review):

Summary:

The goal of this study is to understand how, unlike other mammals, kangaroos are able to increase hopping speed without a concomitant increase in metabolic cost. They use a biomechanical analysis of kangaroo hopping data across a range of speeds to investigate how posture, effective mechanical advantage, and tendon stress vary with speed and mass. The main finding is that a change in posture leads to increasing effective mechanical advantage with speed, which ultimately increases tendon elastic energy storage and returns via greater tendon strain. Thus kangaroos may be able to conserve energy with increasing speed by flexing more, which increases tendon strain.

Strengths:

The approach and effort invested into collecting this valuable dataset of kangaroo locomotion is impressive. The dataset alone is a valuable contribution.

Thank you!

Weaknesses:

Despite these strengths, I have concerns regarding the strength of the results and the overall clarity of the paper and methods used (which likely influences how convincingly the main results come across).

(1) The paper seems to hinge on the finding that EMA decreases with increasing speed and that this contributes significantly to greater tendon strain estimated with increasing speed. It is very difficult to be convinced by this result for a number of reasons:

It appears that kangaroos hopped at their preferred speed. Thus the variability observed is across individuals not within. Is this large enough of a range (either within or across subjects) to make conclusions about the effect of speed, without results being susceptible to differences between subjects? 

Apologies, this was not clear in the manuscript. Kangaroos hopping at their preferred speed means we did not chase or startle them into high speeds to comply with ethics and enclosure limitations. Thus we did not record a wide range of speeds within the bounds of what kangaroos are capable of in the wild (up to 12 m/s), but for the range we did measure (~2-4.5 m/s), there is a large amount of variation in hopping speed within each individual kangaroo. Out of 16 individuals, eight individuals had a difference of 1-2m/s between their slowest and fastest trials, and these kangaroos accounted for 78 out of 100 trials. Of the remainder, six individuals had three for fewer trials each, and two individuals had highly repeatable speeds (3 out of 4, and 6 out of 7 trials were within 0.5 m/s). We have now removed the terminology “preferred speed” e.g line 115. We have added additional information about the chosen hopping speeds into the results and methods, including an appendix figure “The kangaroos elected to hop between 1.99 and 4.48 m s^-1, with a range of speeds and number of trials for each individual (Suppl. Fig. 9).” (Line 381-382)

In the literature cited, what was the range of speeds measured, and was it within or between subjects?

For other literature, to our knowledge the highest speed measured is ~9.5m/s (see supplementary Fig1b) and there were multiple measures for several individuals (see methods Kram & Dawson 1998). 

Assuming that there is a compelling relationship between EMA and velocity, how reasonable is it to extrapolate to the conclusion that this increases tendon strain and ultimately saves metabolic cost?  They correlate EMA with tendon strain, but this would still not suggest a causal relationship (incidentally the p-value for the correlation is not reported). 

The functions that underpin these results (e.g. moment = GRF*R) come from physical mechanics and geometry, rather than statistical correlations. Additionally, a p-value is not appropriate in the relationship between EMA and stress (rather than strain) because the relationship does not appear to be linear. We have made it clearer in the discussion that we are not proposing that entire change in stress is caused by changes in EMA, but that the increase in GRF that naturally occurs with speed will also explain some of the increase in stress, along with other potential mechanisms. The discussion has been extensively revised to reflect this. 

Tendon strain could be increasing with ground reaction force, independent of EMA. Even if there is a correlation between strain and EMA, is it not a mathematical necessity in their model that all else being equal, tendon stress will increase as ema decreases? I may be missing something, but nonetheless, it would be helpful for the authors to clarify the strength of the evidence supporting their conclusions.

Yes, GRF also contributes to the increase in tendon stress in the mechanism we propose (Suppl. Fig. 8), see the formulas in Fig 6, and we have made this clearer in the revised discussion (see above comment).  You are correct that mathematically stress is inversely proportional to EMA, which can be observed in Fig. 7a, and we did find that EMA decreases. 

The statistical approach is not well-described. It is not clear what the form of the statistical model used was and whether the analysis treated each trial individually or grouped trials by the kangaroo. There is also no mention of how many trials per kangaroo, or the range of speeds (or masses) tested. 

The methods include the statistical model with the variables that we used, as well as the kangaroo masses (13.7 to 26.6 kg, mean: 20.9 ± 3.4 kg). We did not have sufficient within individual sample size to use a linear mixed effect model including subject as a random factor, thus all trials were treated individually. We have included this information in the results section. 

We have now moved the range of speeds from the supplementary material to the results and figure captions. We have added information on the number of trials per kangaroo to the methods, and added Suppl. Fig. 9 showing the distribution of speeds per kangaroo.

We did not group the data e.g. by using an average speed per individual for all their trials, or by comparing fast to slow groups for statistical analysis (the latter was only for display purposes in our figures, which we have now made clearer in the methods statistics section). 

Related to this, there is no mention of how different speeds were obtained. It seems that kangaroos hopped at a self-selected pace, thus it appears that not much variation was observed. I appreciate the difficulty of conducting these experiments in a controlled manner, but this doesn’t exempt the authors from providing the details of their approach.

Apologies, this was not clear in the manuscript. Kangaroos hopping at their preferred speed means we did not chase or startle them into high speeds to comply with ethics and enclosure limitations. Thus we did not record a wide range of speeds within the bounds of what kangaroos are capable of in the wild (up to 12 m/s). We have now removed the terminology “preferred speed” e.g. line 115. We have added additional information about the chosen hopping speeds into the results and methods, including an appendix figure (see above comment). (Line 381-382)

Some figures (Figure 2 for example) present means for one of three speeds, yet the speeds are not reported (except in the legend) nor how these bins were determined, nor how many trials or kangaroos fit in each bin. A similar comment applies to the mass categories. It would be more convincing if the authors plotted the main metrics vs. speed to illustrate the significant trends they are reporting.

Thank you for this comment. The bins are used only for display purposes and not within the statistical analysis. We have clarified this in the revised manuscript: “The data was grouped into body mass (small 17.6±2.96 kg, medium 21.5±0.74 kg, large 24.0±1.46 kg) and speed (slow 2.52±0.25 m s^-1, medium 3.11±0.16 m s^-1, fast 3.79±0.27 m s^-1) subsets for display purposes only”. (Line 495-497)

(2) The significance of the effects of mass is not clear. The introduction and abstract suggest that the paper is focused on the effect of speed, yet the effects of mass are reported throughout as well, without a clear understanding of the significance. This weakness is further exaggerated by the fact that the details of the subject masses are not reported.

Indeed, the primary aim of our study was to explore the influence of speed, given the uncoupling of energy from hopping speed in kangaroos. We included mass to ensure that the effects of speed were not driven by body mass (i.e.: that larger kangaroos hopped faster). Subject masses were reported in the first paragraph of the methods, albeit some were estimated as outlined in the same paragraph.

(3) The paper needs to be significantly re-written to better incorporate the methods into the results section. Since the results come before the methods, some of the methods must necessarily be described such that the study can be understood at some level without turning to the dedicated methods section. As written, it is very difficult to understand the basis of the approach, analysis, and metrics without turning to the methods.

The methods after the discussion is a requirement of the journal. We have incorporated some methods in the results where necessary but not too repetitive or disruptive, e.g. Fig. 1 caption, and specifying we are only analysing EMA for the ankle joint

Reviewing Editor (Recommendations For The Authors):

Below is a list of specific recommendations that the authors could address to improve the eLife assessment:

(1) Based on the data presented and the fact that metabolic energy was not measured, the authors should temper their conclusions and statements throughout the manuscript regarding the link between speed and metabolic energy savings. We recommend adding text to the discussion summarizing the strengths and limitations of the evidence provided and suggesting future steps to more conclusively answer this mystery.

There is a significant body of work linking metabolic energy savings to measured increases in tendon stress in macropods. However, the purpose of this paper was to address the unanswered questions about why tendon stress increases. We found that stress did not only increase due to GRF increasing with speed as expected, but also due to novel postural changes which decreased EMA. In the revised manuscript, we have tempered our conclusions to make it clearer that it is not just EMA affecting stress, and added limitations throughout the manuscript (see response to Rev 1). 

(2) To provide stronger evidence of a link between speed, mechanics, and metabolic savings the authors can consider estimating metabolic energy expenditure from their OpenSIM model. This is one suggestion, but the authors likely have other, possibly better ideas. Such a model should also be able to explain why the metabolic rate increases with speed during uphill hopping.

Extending the model to provide direct metabolic cost estimates will be the goal of a future paper, however the models does not have detailed muscle characteristics to do this in the formulation presented here. It would be a very large undertaking which is beyond the scope of the current manuscript. As per the comment above, the results of this paper are not reliant on metabolic performance. 

(3) The authors attempt to relate the newly quantified hopping biomechanics to previously published metabolic data. However, all reviewers agree that the logic in many instances is not clear or contradictory. Could one potential explanation be that at slow speeds, forces and tendon strain are small, and thus muscle fascicle work is high? Then, with faster speeds, even though the cost of generating isometric force increases, this is offset by the reduction in the metabolic cost of muscular work. The paper could provide stronger support for their hypotheses with a much clearer explanation of how the kinematics relate to the mechanics and ultimately energy savings.

In response to the reviewers comments, we have substantially modified the discussion to provide clearer rationale.

(4) The methods and the effort expended to collect these data are impressive, but there are a number of underlying assumptions made that undermine the conclusions. This is due partly to the methods used, but also the paper's incomplete description of their methods. We provide a few examples below:

It would be helpful if the authors could speak to the effect of the limited speeds tested and between-animal comparisons on the ability to draw strong conclusions from the present dataset. ·

Throughout the discussion, the authors highlight the relationship between EMA and speed. However, this is misleading since there was no significant effect of speed on EMA. Speed only affected the muscle moment arm, r. At minimum, this should be clarified and the effect on EMA not be overstated. Additionally, the resulting implications on their ability to confidently say something about the effect of speed on muscle stress should be discussed. 

We have now provided additional details, (see responses above) to these concerns. For instance, we added a supplementary figure showing the speed distribution per individual. The primary reviewer concern (that each kangaroo travelled at a single speed) was due to a miscommunication around the terminology “preferred” which has now been corrected. 

We now elaborate in the results why we are not very concerned that EMA is insignificant. The statistical insignificance of EMA is ultimately due to the insignificance of the direct measurement of R, however, we now better explain in the results why we believe that this statistical insignificance is due to error/noise of the measurement which is relatively large compared to the effect size. Indirect indications of how R may increase with speed (via ankle height from the ground) are statistically significant. Lines 177-185. 

We consider this worth reporting because, for instance, an 18% change in EMA will be undetectable by measurement, but corresponds to an 18% change in tendon stress which is measurable and physiologically significant (safety factor would decrease from 2 to 1.67).  We presented both significant and insignificant results for transparency. 

We have also discussed this within a revised limitations section of the manuscript (Line 311328). 

Reviewer #1 (Recommendations For The Authors):

Title: I would cut the first half of the title. At least hedge it a bit. "Clues" instead of "Unlocking the secrets".

We have revised the title to: “Postural adaptations may contribute to the unique locomotor energetics seen in hopping kangaroos”

In my comments, ... typically indicates a stylistic change suggested to the text.

Overall, the paper covers speed and size. Unfortunately, the authors were not 100% consistent in the order of presenting size then speed, or speed then size. Just choose one and stick with it.

We have attempted to keep the order of presenting size and speed consistent, however there are several cases where this would reduce the readability of the manuscript and so in some cases this may vary. 

One must admit that there is a lot of vertical scatter in almost all of the plots. I understand that these animals were not in a lab on a treadmill at a controlled speed and the animals wear fur coats so marker placements vary/move etc. But the spread is quite striking, e.g. Figure 5a the span at one speed is almost 10x. Can the authors address this somewhere? Limitations section?

The variation seen likely results from attempting to display data in a 2D format, when it is in fact the result of multiple variables, including speed, mass, stride frequency and subject specific lengths. Slight variations in these would be expected to produce some noise around the mean, and I think it’s important to consider this while showing the more dominant effects. 

In many locations in the manuscript, the term "work" is used, but rarely if ever specified that this is the work "per hop". The big question revolves around the rate of metabolic energy consumption (i.e. energy per time or average metabolic power), one must not forget that hop frequency changes somewhat across speed, so work per hop is not the final calculation.

Thank you for this comment. We have now explicitly stated work per hop in figure captions and in the results (line 208). The change in stride frequency at this range of speeds is very small, particularly compared to the variance in stride frequency (Suppl. Fig. 1d), which is consistent with other researchers who found that stride frequency was constant or near constant in macropods at analogous speeds (e.g. Dawson and Taylor 1973, Baudinette et al. 1987). 

Line 61 ....is likely related.

Added “likely” (line 59)

Line 86 I think the Allen reference is incomplete. Wasn't it in J Exp Biology?

Thank you. Changed. 

Line 122 ... at faster speeds and in larger individuals.

Changed: “We hypothesised that (i) the hindlimb would be more crouched at faster speeds, primarily due to the distal hindlimb joints (ankle and metatarsophalangeal), independent of changes with body mass” (Line 121-122).

Line 124 I found this confusing. Try to re-word so that you explain you mean more work done by the tendons and less by the ankle musculature.

Amended: “changes in moment arms resulting from the change in posture would contribute to the increase in tendon stress with speed, and may thereby contribute to energetic savings by increasing the amount of positive and negative work done by the ankle without requiring additional muscle work” (Line 123)

Line 129 hopefully "braking" not "breaking"!

Thank you. Fixed. (Line 130)

Line 129 specify fore-aft horizontal force.

Added "fore-aft" to "negative fore-aft horizontal component" (Line 130-131)

Line 130 add something like "of course" or "naturally" since if there is zero fore-aft force, the GRF vector of course must be vertical. 

Added "naturally" (Line 132)

Line 138 clarify that this section is all stance phase. I don't recall reading any swing phase data.

Changed to: "Kangaroo hindlimb stance phase kinematics varied…" (Line 141)

Line 143 and elsewhere. I found the use of dorsiflexion and plantarflexion confusing. In Figure 3, I see the ankle never flexing more than 90 degrees. So, the ankle joint is always in something of a flexed position, though of course it flexes and extends during contact. I urge the authors to simplify to flextion/extension and drop the plantar/dorsi.

We have edited this section to describe both movements as greater extension (plantarflexion). (Line 147). We have further clarified this in the figure caption for figure 3.  

Line 147 ...changes were…

Fixed, line 150

Line 155 I'm a bit confused here. Are the authors calculating some sort of overall EMA or are they saying all of the individual joint EMAs all decreased?

Thank you, we clarified that it is at the ankle. Line 158

Line 158 since kangaroos hop and are thus positioned high and low throughout the stance phase, try to avoid using "high" and "low" for describing variables, e.g. GRF or other variables. Just use "greater/greatest" etc.

Thanks for this suggestion. We have changed "higher" into "greater" where appropriate throughout the manuscript e.g. line 161

Lines 162 and 168 same comment here about "r" and "R". Do you mean ankle or all joints?

Clarified that it is the gastrocnemius and plantaris r, and the R to the ankle. (Lines 164-165)

Line 173 really, ankle height?

Added: ankle height is "vertical distance from the ground". Line 177

Line 177 is this just the ankle r?

Added "of the ankle" line 158 and “Achilles” line 187 

Line 183 same idea, which tendon/tendons are you talking about here?

Added "Achilles" to be more clear (Line 187)

Line 195 substitute "converted" for "transferred".

Done (Line 210)

Line 223 why so vague? i.e. why use "may"? Believe in your data. ...stress was also modulated by changes....

Changed "may" to "is"

Line 229 smaller ankle EMA (especially since you earlier talked about ankle "height").

Changed “lower” to “smaller” Line 254

Line 2236 ...and return elastic energy…

Added "elastic" line 262

Line 244 IMPORTANT: Need to explain this better! I think you are saying that the net work at the ankle is staying the same across speed, BUT it is the tendons that are storing and returning that work, it's not that the muscles are doing a lot of negative/positive work.

Changed: “The consistent net work observed among all speeds suggests the ankle extensor muscle-tendon units are performing similar amounts of ankle work independent of speed, which would predominantly be done by the tendon.” Line 270-272)

Line 258-261 I think here is where you are over-selling the data/story. Although you do say "a" mechanism (and not "the" mechanism, you still need to deal with the cost of generating more force and generating that force faster.

We removed this sentence and replaced it with a discussion of the cost of generating force hypothesis, and alternative scenarios for the how force and metabolics could be uncoupled. 

Line 278 "the" tendon? Which tendon?

Added "Achilles"

Line 289. I don't think one can project into the past.

Changed “projected” to "estimated"

Line 303 no problem, but I've never seen a paper in biology where the authors admit they don't know what species they were studying!

Can’t be helped unfortunately. It is an old dataset and there aren’t photos of every kangaroo. Fortunately, from the grey and red kangaroos we can distinguish between, we know there are no discernible species effects on the data. 

Lines 304-306 I'm not clear here. Did you use vertical impulse (and aerial time) to calculate body weight? Or did you somehow use the braking/propulsive impulse to calculate mass? I would have just put some apples on the force plate and waited for them to stop for a snack.

Stationary weights were recorded for some kangaroos which did stand on the force plate long enough, but unfortunately not all of them were willing to do so. In those cases, yes, we used impulse from steady-speed trials to estimate mass. We cross-checked by estimated mass from segment lengths (as size and mass are correlated). This is outlined in the first paragraph of the methods.

Lines 367 & 401 When you use the word "scaled" do you mean you assumed geometric similarity?

No, rather than geometric scaling, we allowed scaling to individual dimensions by using the markers at midstance for measurements. We have amended the paragraph to clarify that the shape of the kangaroo changes and that mass distribution was preserved during the shape change (line 441-446) 

Lines 381-82 specify "joint work"

Added "joint work"  (Line 457)

Figure 1 is gorgeous. Why not add the CF equation to the left panel of the caption?

We decided to keep the information in the figure caption. “Total leg length was calculated as the sum of the segment lengths (solid black lines) in the hindlimb and compared to the pelvisto-toe distance (dashed line) to calculate the crouch factor”

Figure 2 specify Horizontal fore-aft.

Done

Figure 3g I'd prefer the same Min. Max Flexion vertical axis labels as you use for hip & knee.

While we appreciate the reviewer trying to increase the clarity of this figure, we have left it as plantar/dorsi flexion since these are recognised biomechanical terms. To avoid confusion, we have further defined these in the figure caption “For (f-g), increased plantarflexion represents a decrease in joint flexion, while increased dorsiflexion represents increased flexion of the joint.”

Figure 4. I like it and I think that you scaled all panels the same, i.e. 400 W is represented by the same vertical distance in all panels. But if that's true, please state so in the Caption. It's remarkable how little work occurs at the hip and knee despite the relatively huge muscles there.

Is it true that the y axes are all at the same scale. We have added this to the caption. 

Figure 5 Caption should specify "work per hop".

Added

Figure 7 is another beauty.

Thank you!

Supplementary Figure 3 is this all ANKLE? Please specify.

Clarified that it is the gastrocnemius and plantaris r, and the R to the ankle.

Reviewer #2 (Recommendations For The Authors):

To 'unlock the secrets of kangaroo locomotor energetics' I expected the authors to measure the secretive outcome variable, metabolic rate using laboratory measures. Rather, the authors relied on reviewing historic metabolic data and collecting biomechanics data across different animals, which limits the conclusions of this manuscript.

We have revised to the title to make it clearer that we are investigating a subset of the energetics problem, specifically posture. “Postural adaptations may contribute to the unique locomotor energetics seen in hopping kangaroos.” We have also substantially modified the discussion to temper the conclusions from the paper. 

After reading the hypothesis, why do the authors hypothesize about joint flexion and not EMA? Because the following hypothesis discusses the implications of moment arms on tendon stress, EMA predictions are more relevant (and much more discussed throughout the manuscript).

Ankle and MTP angles are the primary drivers of changes in r, R & thus, EMA. We used a two part hypothesis to capture this. We have rephased the hypotheses: “We hypothesised that (i) the hindlimb would be more crouched at faster speeds, primarily due to the distal hindlimb joints (ankle and metatarsophalangeal), independent of changes with body mass, and (ii) changes in moment arms resulting from the change in posture would contribute to the increase in tendon stress with speed, and may thereby contribute to energetic savings by increasing the amount of positive and negative work done by the ankle without requiring additional muscle work.”

If there were no detectable effects of speed on EMA, are kangaroos mechanically like other animals (Biewener Science 89 & JAP 04) who don't vary EMA across speeds? Despite no detectible effects, the authors state [lines 228-229] "we found larger and faster kangaroos were more crouched, leading to lower ankle EMA". Can the authors explain this inconsistency? Lines 236 "Kangaroos appear to use changes in posture and EMA". I interpret the paper as EMA does not change across speed.

Apologies, we did not sufficiently explain this originally. We now explain in the results our reasoning behind our belief that EMA and R may change with speed. “If we consider the nonsignificant relationship between R (and EMA) and speed to indicate that there is no change in R, then it conflicts with the ankle height and CoP result. Taking both into account, we think it is more likely that there is a small, but important, change in R, rather than no change in R with speed. It may be undetectable because we expect small effect sizes compared to the measurement range and measurement error (Suppl. Fig. 3h), or be obscured by a similar change in R with body mass. R is highly dependent on the length of the metatarsal segment, which is longer in larger kangaroos (1 kg BM corresponded to ~1% longer segment, P<0.001, R²=0.449). If R does indeed increase with speed, both R and r will tend to decrease EMA at faster speeds.” (Line 177-185)

Lines 335-339: "We assumed the force was applied along phalanx IV and that there was no medial or lateral movement of the centre of pressure (CoP)". I'm confused, did the authors not measure CoP location with respect to the kangaroo limb? If not, this simple estimation undermines primary results (EMA analyses).

We have changed "The anterior or posterior movement of the CoP was recorded by the force plate" to read: "The fore-aft movement of the CoP was recorded by the force plate within the motion capture coordinate system" (Line 406-407) and added more justification for fixing the CoP movement in the other axis: “It was necessary to assume the CoP was fixed in the mediallateral axis because when two feet land on the force plate, the lateral forces on each foot are not recorded, and indeed cancel if the forces are symmetrical (i.e. if the kangaroo is hopping in a straight path and one foot is not in front of the other). We only used symmetrical trials to ensure reliable measures of the anterior-posterior movement of the CoP.” (Line 408-413)

The introduction makes many assertions about the generalities of locomotion and the relationship between mechanics and energetics. I'm afraid that the authors are selectively choosing references without thoroughly evaluating alternative theories. For example, Taylor, Kram, & others have multiple papers suggesting that decreasing EMA and increasing muscle force (and active muscle volume) increase metabolic costs during terrestrial locomotion. Rather, the authors suggest that decreasing EMA and increasingly high muscle force at faster speeds don't affect energetics unless muscle work increases substantially (paragraph 2)? If I am following correctly, does this theory conflict with active muscle volume ideas that are peppered throughout this manuscript?

Yes, as you point out, the same mechanism does lead to different results in kangaroos vs humans, for instance, but this is not a contradiction. In all species, decreasing EMA will result in an increase in muscle force due to less efficient leverage (i.e. lower EMA) of the muscles, and the muscle-tendon unit will be required to produce more force to balance the joint moment. As a consequence, human muscles activate a greater volume in order for the muscle-tendon unit to increase muscle work and produce enough force. We are proposing that in kangaroos, the increase in work is done by the achilles tendon rather than the muscles. Previous research suggests that macropod ankle muscles contract isometrically or that the fibres do not shorten more at faster speeds i.e. muscle work does not increase with speed. Instead, the additional force seems to come from the tendon storing and subsequently returning more strain energy (indicated by higher stress). We found that the increase in tendon stress comes from higher ground force at faster speeds, and from it adopting a more crouched posture which increases the tendons’ stresses compared to an upright posture for a given speed (think of this as increasing the tendon’s stress capacity). We have substantially revised the discussion to highlight this.

Similarly, does increased gross or net tendon mechanical energy storage & return improve hopping energetics? Would more tendon stress and strain energy storage with a given hysteresis value also dissipate more mechanical energy, requiring leg muscles to produce more net work? Does net or gross muscle work drive metabolic energy consumption?

Based on the cost of generating force hypothesis, we think that gross muscle work would be linked to driving metabolic energy consumption. Our idea here is that the total body work is a product of the work done by the tendon and the muscle combined. If the tendon has the potential to do more work, then the total work can increase without muscle work needing to increase.

The results interpret speed effects on biomechanics, but each kangaroo was only collected at 1 speed. Are inter-animal comparisons enough to satisfy this investigation?

We have added a figure (Suppl Fig 9) to demonstrate the distribution of speed and number of trials per kangaroo. We have also removed "preferred" from the manuscript as this seems to cause confusion. Most kangaroos travelled at a range of “casual” speeds.

Abstract: Can the authors more fully connect the concept of tendon stress and low metabolic rates during hopping across speeds? Surely, tendon mechanics don't directly drive the metabolic cost of hopping, but they affect muscle mechanics to affect energetics.

Amended to: " This phenomenon may be related to greater elastic energy savings due to increasing tendon stress; however, the mechanisms which enable the rise in stress, without additional muscle work remain poorly understood." (Lines 25-27).

The topic sentence in lines 61-63 may be misleading. The ensuing paragraph does not substantiate the topic sentence stating that ankle MTUs decouple speeds and energetics.

We added "likely" to soften the statement. (Line 59)

Lines 84-86: In humans, does more limb flexion and worse EMA necessitate greater active muscle volume? What about muscle contractile dynamics - See recent papers by Sawicki & colleagues that include Hill-type muscle mechanics in active muscle volume estimates.

Added: “Smaller EMA requires greater muscle force to produce a given force on the ground, thereby demanding a greater volume of active muscle, and presumably greater metabolic rates than larger EMA for the same physiology”. (Line 80-82)

Lines 106: can you give the context of what normal tendon safety factors are?

Good idea. Added: "far lower than the typical safety factor of four to eight for mammalian tendons (Ker et al. 1988)." Line 106-107

I thought EMA was relatively stable across speeds as per Biewener [Science & JAP '04]. However the authors gave an example of an elephant to suggest that it is typically inversely related to speed. Can the authors please explain the disconnect and the most appropriate explanation in this paragraph?

Knee EMA in particular changed with speed in Biewener 2004. What is “typical” probably depends on the group of animals studied; e.g., cursorial quadrupedal mammals generally seem to maintain constant EMA, but other groups do not.

These cases are presented to show a range of consequences for changing EMA (usually with mass, but sometimes with speed). We have made several adjustments to the paragraph to make this clearer. Lines 85-93.

The results depend on the modeled internal moment arm (r). How confident are the authors in their little r prediction? Considering complications of joint mechanics in vivo including muscle bulging. Holzer et al. '20 Sci Rep demonstrated that different models of the human Achilles tendon moment arm predict vastly different relationships between the moment arm and joint angle.

Our values for r and EMA closely align with previous papers which measured/calculate these values in kangaroos, such as Kram 1998, and thus we are confident in our interpretation.  

This is a misleading results sentence: Small decreases in EMA correspond to a nontrivial increase in tendon stress, for instance, reducing EMA from 0.242 (mean minimum EMA of the slow group) to 0.206 (mean minimum EMA of the fast group) was associated with an ~18% increase in tendon stress. The authors could alternatively say that a ~15% decrease in EMA was associated with an ~18% increase in tendon stress, which seems pretty comparable.

Thank you for pointing this out, it is important that it is made clearer. Although the change in relative magnitude is approximately the same (as it should be), this does not detract from the importance. The "small decrease in EMA" is referring to the absolute values, particularly in respect to the measurement error/noise. The difference is small enough to have been undetectable with other methods used in previous studies. We have amended the sentence to clarify this.

It now reads: “Subtle decreases in EMA which may have been undetected in previous studies correspond to discernible increases in tendon stress. For instance, reducing EMA from 0.242 (mean minimum EMA of the slow group) to 0.206 (mean minimum EMA of the fast group) was associated with an increase in tendon stress from ~50 MPa to ~60 MPa, decreasing safety factor from 2 to 1.67 (where 1 indicates failure), which is both measurable and physiologically significant.” (Line 195-200)

Lines 243-245: "The consistent net work observed among all speeds suggests the ankle extensors are performing similar amounts of ankle work independent of speed." If this is true, and presumably there is greater limb work performed on the center of mass at faster speeds (Donelan, Kram, Kuo), do more proximal leg joints increase work and energy consumption at faster speeds?

The skin over the proximal leg joints (knee and hip) moves too much to get reliable measures of EMA from the ratio of moment arms. This will be pursued in future work when all muscles are incorporated in the model so knee and hip EMA can be determined from muscle force.

We have added limitations and considerations paragraph to the manuscript: “Finally, we did not determine whether the EMA of proximal hindlimb joints (which are more difficult to track via surface motion capture markers) remained constant with speed. Although the hip and knee contribute substantially less work than the ankle joint (Fig. 4), the majority of kangaroo skeletal muscle is located around these proximal joints. A change in EMA at the hip or knee could influence a larger muscle mass than at the ankle, potentially counteracting or enhancing energy savings in the ankle extensor muscle-tendon units. Further research is needed to understand how posture and muscles throughout the whole body contribute to kangaroo energetics.” (Line 321-328)

Lines 245-246: "Previous studies using sonomicrometry have shown that the muscles of tammar wallabies do not shorten considerably during hops, but rather act near-isometrically as a strut" Which muscles? All muscles? Extensors at a single joint?

Added "gastrocnemius and plantaris" Line 164-165

Lines 249-254: "The cost of generating force hypothesis suggests that faster movement speeds require greater rates of muscle force development, and in turn greater cross-bridge cycling rates, driving up metabolic costs (Taylor et al. 1980, Kram and Taylor 1990). The ability for the ankle extensor muscle fibres to remain isometric and produce similar amounts of work at all speeds may help explain why hopping macropods do not follow the energetic trends observed in quadrupedal species." These sentences confuse me. Kram & Taylor's cost of force-generating hypothesis assumes that producing the same average force over shorter contact times increases metabolic rate. How does 'similar muscle work' across all speeds explain the ability of macropods to use unique energetic trends in the cost of force-generating hypothesis context?

Thank you for highlighting this confusion. We have substantially revised the discussion clarify where the mechanisms presented deviate from the cost of generating force hypothesis. Lines 270-309

Reviewer #3 (Recommendations For The Authors):

In addition to the points described in the public review, I have additional, related, specific comments:

(1) Results: Please refer to the hypotheses in the results, and relate the the findings back to the hypotheses.

We now relate the findings back to the hypotheses 

Line 142 “In partial support of hypothesis (i), greater masses and faster speeds were associated with more crouched hindlimb postures (Fig. 3a,c).”.

Lines 205-206: “The increase in tendon stress with speed, facilitated in part by the change in moment arms by the shift in posture, may explain changes in ankle work (c.f. Hypothesis (ii)).” 

(2) Results: please provide the main statistical results either in-line or in a table in the main text.

We (the co-authors) have discussed this at length, and have agreed that the manuscript is far more readable in the format whereby most statistics lie within the supplementary tables, otherwise a reader is met with a wall of statistics. We only include values in the main text when the magnitude is relevant to the arguments presented in the results and discussion.

(3) Line 140: Describe how 'crouched' was defined.

We have now added a brief definition of ‘Crouch factor’ after the figure caption. (Line 143) (Fig. 3a,c; where crouch factor is the ratio of total limb length to pelvis to toe distance).

(4) Line 162: This seems to be a main finding and should be a figure in the main text not supplemental. Additionally, Supplementary Figures 3a and b do not show this finding convincingly There should be a figure plotting r vs speed and r vs mass.

The combination of r and R are represented in the EMA plot in the main text. The r and R plots are relegated to the supplementary because the main text is already very crowded.  Thank you for the suggestion for the figure plotting r and R versus speed, this is now included as Suppl. Fig. 3h

(5) Line 166: Supplementary Figure 3g does not show the range of dorsiflexion angles as a function of speed. It shows r vs dorsiflexion angle. Please correct.

Thanks for noticing this, it was supposed to reference Fig 3g rather than Suppl Fig 3g in the sentence regarding speed. We have fixed this, Line 170. 

We had added a reference to Suppl Fig 3 on Line 169 as this shows where the peak in r with ankle angle occurs (114.4 degrees).

(6) Line 184: Where are the statistical results for this statement?

The relationship between stress and EMA does not appear to be linear, thus we only present R^{^}2 for the power relationship rather than a p-value. 

(7) Line 192: The authors should explain how joint work and power relate/support the overall hypotheses. This section also refers to Figures 4 and 5 even though Figures 6 and 7 have already been described. Please reorganize.

We have added a sentence at the end of the work and power section to mention hypothesis (ii) and lead into the discussion where it is elaborated upon. 

“The increase in positive and negative ankle work may be due to the increase in tendon stress rather than additional muscle work.” Line 219-220 We have rearranged the figure order.

(8) The statistics are not reported in the main text, but in the supplementary tables. If a result is reported in the main text, please report either in-line or with a table in the main text.

We leave most statistics in the supplementary tables to preserve the readability of the manuscript. We only include values in the main text when the magnitude is relevant to the arguments raised in the results and discussion.

eLife Assessment

This important paper employs multiple experimental approaches and presents evidence that changes in membrane voltage directly affect ERK signaling to regulate cell division. This result is relevant because it supports an ion channel-independent pathway by which changes in membrane voltage can affect cell growth. The evidence now presented is solid and the data support the conclusions. This paper should be of interest to a broad readershp in the areas of cell and developemental biology and electrophysiology.

Reviewer #1 (Public review):

This is a contribution to the field of developmental bioelectricity. How do changes of resting potential at the cell membrane affect downstream processes? Zhou et al. reported in 2015 that phosphatidylserine and K-Ras cluster upon plasma membrane depolarization and that voltage-dependent ERK activation occurs when constitutively active K-RasG12V mutants are overexpressed. In this paper, the authors advance the knowledge of this phenomenon by showing that membrane depolarization up-regulates mitosis and that this process is dependent on voltage-dependent activation of ERK. ERK activity's voltage-dependence is derived from changes in the dynamics of phosphatidylserine in the plasma membrane and not by extracellular calcium dynamics. This paper reports an interesting and important finding. It is somewhat derivative of Zhou et al., 2015 (https://www.science.org/doi/full/10.1126/science.aaa5619). The main novelty seems to be that they find quantitatively different conclusions upon conducting similar experiments, albeit with a different cell line (U2OS) than those used by Zhou et al. Sasaki et al. do show that increased K+ levels increase proliferation, which Zhou et al. did not look at. The data presented in this paper are a useful contribution to a field often lacking such data.

Reviewer #2 (Public review):

Sasaki et al. use a combination of live-cell biosensors and patch-clamp electrophysiology to investigate the effect of membrane potential on the ERK MAPK signaling pathway, and probe associated effects on proliferation. This is an effect that has long been proposed, but a convincing demonstration has remained elusive, because it is difficult to perturb membrane potential without disturbing other aspects of cell physiology in complex ways. The time-resolved measurements here are a nice contribution to this question, and the perforated patch clamp experiments with an ERK biosensor are fantastic - they come closer to addressing the above difficulty of perturbing voltage than any prior work. It would have been difficult to obtain these observations with any other combination of tools.

Comments on previous revisions:

The authors have done a good job addressing the comments on the previous submission.

Reviewer #3 (Public review):

Summary:

This paper demonstrates that membrane depolarization induces a small increase in cell entry into mitosis. Based on previous work from another lab, the authors propose that ERK activation might be involved. They show convincingly using a combination of assays that ERK is activated by membrane depolarization. They show this is Ca2+ independent and is a result of activation of the whole K-Ras/ERK cascade which results from changed dynamics of phosphatidylserine in the plasma membrane that activates K-Ras. Although the activation of the Ras/ERK pathway by membrane depolarization is not new, linking it to an increase in cell proliferation is novel.

Strengths:

A major strength of the study is the use of different techniques - live imaging with ERK reporters, as well as Western blotting to demonstrate ERK activation as well as different methods for inducing membrane depolarization. They also use a number of different cell lines. Via Western blotting the authors are also able to show that the whole MAPK cascade is activated.

Weaknesses:

In the previous round of revisions, the authors addressed the issues with Figure 1, and the data presented are much clearer. The authors did also attempt to pinpoint when in the cell cycle ERK is having its activity, but unfortunately, this was not conclusive.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

This is a contribution to the field of developmental bioelectricity. How do changes of resting potential at the cell membrane affect downstream processes? Zhou et al. reported in 2015 that phosphatidylserine and K-Ras cluster upon plasma membrane depolarization and that voltage-dependent ERK activation occurs when constitutively active K-RasG12V mutants are overexpressed. In this paper, the authors advance the knowledge of this phenomenon by showing that membrane depolarization up-regulates mitosis and that this process is dependent on voltage-dependent activation of ERK. ERK activity's voltage-dependence is derived from changes in the dynamics of phosphatidylserine in the plasma membrane and not by extracellular calcium dynamics. This paper reports an interesting and important finding. It is somewhat derivative of Zhou et al., 2015. (https://www.science.org/doi/full/10.1126/science.aaa5619). The main novelty seems to be that they find quantitatively different conclusions upon conducting similar experiments, albeit with a different cell line (U2OS) than those used by Zhou et al. Sasaki et al. do show that increased K+ levels increase proliferation, which Zhou et al. did not look at. The data presented in this paper are a useful contribution to a field often lacking such data.

Strengths:

Bioelectricity is an important field for areas of cell, developmental, and evolutionary biology, as well as for biomedicine. Confirmation of ERK as a transduction mechanism and a characterization of the molecular details involved in the control of cell proliferation are interesting and impactful.

Weaknesses:

The authors lean heavily on the assumption that the Nernst equation is an accurate predictor of membrane potential based on K+ level. This is a large oversimplification that undermines the author's conclusions, most glaringly in Figure 2C. The author's conclusions should be weakened to reflect that the activity of voltage gated ion channels and homeostatic compensation are unaccounted for.

We appreciate the reviewer’s thoughtful comment regarding our reliance on the Nernst equation to estimate membrane potential. We agree that the Nernst equation is a simplification and does not account for the activity of other ions, voltage-gated channels, or homeostatic compensation mechanisms. To address this concern, we conducted electrophysiological experiments in which the membrane potential was directly controlled using the perforated patch-clamp technique (Fig. 3). Under these conditions, we also monitored the membrane potential and confirmed that there was negligible drift within 20 minutes of perfusion with 145 mM K^⁺ (only a 1–5 mV change). These results suggest that the influence of voltage-gated channels and homeostatic compensation is minimal in our experimental setup. We revised the manuscript to clarify these limitations and to present our conclusions more cautiously in light of this point.

“A potential limitation of extracellular K^⁺-based approaches is their reliance on the Nernst equation to estimate membrane potential, which oversimplifies the actual situation by neglecting voltage-gated ion channel activity and compensatory mechanisms. To directly address this concern, we measured membrane potential using the perforated patch-clamp technique and confirmed that the potential was stable during perfusion with 145 mM K^⁺ (only a 1–5 mV drift within 20 min). Moreover, we used a voltage clamp to precisely control the membrane potential and demonstrated that ERK activity was directly regulated by the voltage itself, excluding the influence of other secondary factors. An additional strength of electrophysiology is its ability to examine the effects of repolarization, which is difficult to assess with conventional perfusion-based methods owing to slow solution exchange.”

There are grammatical tense errors are made throughout the paper (ex line 99 "This kinetics should be these kinetics")

We thank the reviewer for pointing out the grammatical errors. We carefully revised the entire manuscript.

Line 71: Zhou et al. use BHK, N2A, PSA-3 cells, this paper uses U2OS (osteosarcoma) cells. Could that explain the differences in bioelectric properties that they describe? In general, there should be more discussion of the choice of cell line. Why were U2OS cells chosen? What are the implications of the fact that these are cancer cells, and bone cancer cells in particular? Does this paper provide specific insights for bone cancers? And crucially, how applicable are findings from these cells to other contexts?

We thank the reviewer for this valuable comment regarding the choice of cell line. We selected U2OS cells primarily because they are well suited for live-cell FRET imaging. We did not use BHK, N2A, or PSA-3 cells, and therefore it is difficult for us to provide a clear comparison with the specific bioelectric properties reported in Zhou et al. Nevertheless, we agree that cancer cell lines, including U2OS, may exhibit bioelectric properties that differ from those of non-cancerous cells. While this could be a potential limitation, we are inclined to consider voltage-dependent ERK activation to be a fundamental and generalizable phenomenon, not restricted to osteosarcoma cells. The key components of this pathway—phosphatidylserine, Ras, MAPK (including ERK)—are expressed in essentially all mammalian cells. In support of our view, we observed voltage-dependent ERK activation not only in U2OS cells but also in HeLa, HEK293, and A431 cells. These results strongly suggest that the mechanism we describe is not cell-type specific but rather a universal feature of mammalian cells. In the revised Discussion, we expanded our rationale to choose U2OS cells, while addressing the potential implications of using a cancer-derived cell line. 

“In this study, we primarily used U2OS cells because their flat morphology makes them suitable for live-cell FRET imaging. Although cancer cell lines, including U2OS, may display bioelectric properties that differ from those of noncancerous cells, our findings raise the possibility that voltage-dependent ERK activation is a fundamental and broadly applicable phenomenon rather than a feature specific to osteosarcoma cells. This conclusion is supported by the fact that essential components of this pathway, namely phosphatidylserine, Ras, and MAPK (including ERK), are ubiquitously expressed in mammalian cells. Consistent with this finding, we observed voltage-dependent ERK activation across multiple cell lines: U2OS, HeLa, HEK293, and A431 cells (Fig.S2). These observations indicate that the mechanism we describe is not cell-type-restricted, but rather a universal property of mammalian cells.”

Line 115: The authors use EGF to calibrate 'maximal' ERK stimulation. Is this level near saturation? Either way is fine, but it would be useful to clarify.

We thank the reviewer for raising this important point. The YFP/CFP ratio obtained after EGF stimulation is generally considered to represent saturation levels detectable by EKAREV imaging. However, we acknowledge that it remains uncertain whether 10 ng/mL EGF induces the absolute maximal ERK activity in all contexts. To clarify this point, we revised the manuscript (result) text as follows:

“To normalize variation among cells, cells were stimulated with EGF (10 ng/mL) at the end of the experiment, which presumably yielded a near-saturated YFP/CFP value (ERK activity). This value was used to determine the maximum ERK activity in each cell”

Line 121: Starting line 121 the authors say "Of note, U2OS cells expressed wild-type K-Ras but not an active mutant of K-Ras, which means voltage dependent ERK activation occurs not only in tumor cells but also in normal cells". Given that U2OS cells are bone sarcoma cells, is it appropriate to refer to these as 'normal' cells in contrast to 'tumor' cells?

We thank the reviewer for pointing this out. We agree that it is not appropriate to contrast U2OS cells with “normal” cells, since they are sarcoma-derived. To address this point, we revised the sentence to weaken the claim and avoid the misleading terminology.

“Importantly, as U2OS cells express wild-type K-Ras rather than an oncogenic mutant (16), our results raise the possibility that voltage-dependent ERK activation may also occur in non-transformed cells.”

Line 101: These normalizations seem reasonable, the conclusions sufficiently supported and the requisite assumptions clearly presented. Because the dish-to-dish and cell-to-cell variation may reflect biologically relevant phenomena it would be ideal if non-normalized data could be added in supplemental data where feasible.

We thank the reviewer for this helpful suggestion. As recommended, we added representative non-normalized data in the Supplemental Figure S1, which illustrates the non-normalized variation across cells and dishes.

Figure 2C is listed as Figure 2D in the text

There is no Figure 2F (Referenced in line 148)

We thank the reviewer for pointing out these errors. The incorrect figure citations were corrected.

Reviewer #2 (Public review):

Sasaki et al. use a combination of live-cell biosensors and patch-clamp electrophysiology to investigate the effect of membrane potential on the ERK MAPK signaling pathway, and probe associated effects on proliferation. This is an effect that has long been proposed, but a convincing demonstration has remained elusive, because it is difficult to perturb membrane potential without disturbing other aspects of cell physiology in complex ways. The time-resolved measurements here are a nice contribution to this question, and the perforated patch clamp experiments with an ERK biosensor are fantastic - they come closer to addressing the above difficulty of perturbing voltage than any prior work. It would have been difficult to obtain these observations with any other combination of tools.

However, there are still some concerns as detailed in specific comments below:

Specific comments:

(1) All the observations of ERK activation, by both high extracellular K+ and voltage clamp, could be explained by cell volume increase (more discussion in subsequent comments). There is a substantial literature on ERK activation by hypotonic cell swelling (e.g. https://doi.org/10.1042/bj3090013, https://doi.org/10.1002/j.1460-2075.1996.tb00938.x, among others). Here are some possible observations that could demonstrate that ERK activation by volume change is distinct from the effects reported here:

(i) Does hypotonic shock activate ERK in U2OS cells?

(ii) Can hypotonic shock activate ERK even after PS depletion, whereas extracellular K+ cannot?

(iii) Does high extracellular K+ change cell volume in U2OS cells, measured via an accurate method such as fluorescence exclusion microscopy?

(iv) It would be helpful to check the osmolality of all the extracellular solutions, even though they were nominally targeted to be iso-osmotic.

(2) Some more details about the experimental design and the results are needed from Figure 1:

(i) For how long are the cells serum-starved? From the Methods section, it seems like the G1 release in different K+ concentration is done without serum, is this correct? Is the prior thymidine treatment also performed in the absence of serum?

(ii) There is a question of whether depolarization constitutes a physiologically relevant mechanism to regulate proliferation, and how depolarization interacts with other extracellular signals that might be present in an in vivo context. Does depolarization only promote proliferation after extended serum starvation (in what is presumably a stressed cell state)? What fraction of total cells are observed to be mitotic (without normalization), and how does this compare to the proliferation of these cells growing in serum-supplemented media? Can K+ concentration tune proliferation rate even in serum-supplemented media?

(3) In Figure 2, there are some possible concerns with the perfusion experiment:

(i) Is the buffer static in the period before perfusion with high K+, or is it perfused? This is not clear from the Methods. If it is static, how does the ERK activity change when perfused with 5 mM K+? In other words, how much of the response is due to flow/media exchange versus change in K+ concentration?

(ii) Why do there appear to be population-average decreases in ERK activity in the period before perfusion with high K+ (especially in contrast to Fig. 3)? The imaging period does not seem frequent enough for photobleaching to be significant.

(4) Figure 3 contains important results on couplings between membrane potential and MAPK signaling. However, there are a few concerns:

(i) Does cell volume change upon voltage clamping? Previous authors have shown that depolarizing voltage clamp can cause cells to swell, at least in the whole-cell configuration: https://www.cell.com/biophysj/fulltext/S0006-3495(18)30441-7 . Could it be possible that the clamping protocol induces changes in ERK signaling due to changes in cell volume, and not by an independent mechanism?

(ii) Does the -80 mV clamp begin at time 0 minutes? If so, one might expect a transient decrease in sensor FRET ratio, depending on the original resting potential of the cells. Typical estimates for resting potential in HEK293 cells range from -40 mV to -15 mV, which would reach the range that induces an ERK response by depolarizing clamp in Fig. 3B. What are the resting potentials of the cells before they are clamped to -80 mV, and why do we not see this downward transient?

(5) The activation of ERK by perforated voltage clamp and by high extracellular K+ are each convincing, but it is unclear whether they need to act purely through the same mechanism - while additional extracellular K+ does depolarize the cell, it could also be affecting function of voltage-independent transporters and cell volume regulatory mechanisms on the timescales studied. To more strongly show this, the following should be done with the HEK cells where there is already voltage clamp data:

(i) Measure resting potential using the perforated patch in zero-current configuration in the high K+ medium. Ideally this should be done in the time window after high K+ addition where ERK activation is observed (10-20 minutes) to minimize the possibility of drift due to changes in transporter and channel activity due to post-translational regulation.

(ii) Measure YFP/CFP ratio of the HEK cells in the high K+ medium (in contrast to the U2OS cells from Fig. 2 where there is no patch data).

(iii) The assertion that high K+ is equivalent to changes in Vmem for ERK signaling would be supported if the YFP/CFP change from K+ addition is comparable to that induced by voltage clamp to the same potential. This would be particularly convincing if the experiment could be done with each of the 15 mM, 30 mM, and 145 mM conditions.

(6) Line 170: "ERK activity was reduced with a fast time course (within 1 minute) after repolarization to -80 mV." I don't see this in the data: in Fig. 3C, it looks like ERK remains elevated for > 10 min after the electrical stimulus has returned to -80 mV

Comments on revisions:

The authors have done a good job addressing the comments on the previous submission.

Reviewer #3 (Public review):

Summary:

This paper demonstrates that membrane depolarization induces a small increase in cell entry into mitosis. Based on previous work from another lab, the authors propose that ERK activation might be involved. They show convincingly using a combination of assays that ERK is activated by membrane depolarization. They show this is Ca2+ independent and is a result of activation of the whole K-Ras/ERK cascade which results from changed dynamics of phosphatidylserine in the plasma membrane that activates K-Ras. Although the activation of the Ras/ERK pathway by membrane depolarization is not new, linking it to an increase in cell proliferation is novel.

Strengths

A major strength of the study is the use of different techniques - live imaging with ERK reporters, as well as Western blotting to demonstrate ERK activation as well as different methods for inducing membrane depolarization. They also use a number of different cell lines. Via Western blotting the authors are also able to show that the whole MAPK cascade is activated.

Weaknesses

A weakness of the study is the data in Figure 1 showing that membrane depolarization results in an increase of cells entering mitosis. There are very few cells entering mitosis in their sample in any condition. This should be done with many more cells to increase the confidence in the results. The study also lacks a mechanistic link between ERK activation by membrane depolarization and increased cell proliferation.

The authors did achieve their aims with the caveat that the cell proliferation results could be strengthened. The results, for the most par,t support the conclusions.

This work suggests that alterations in membrane potential may have more physiological functions than action potential in the neural system as it has an effect on intracellular signalling and potentially cell proliferation.

In the revised manuscript, the authors have now addressed the issues with Figure 1, and the data presented are much clearer. They did also attempt to pinpoint when in the cell cycle ERK is having its activity, but unfortunately, this was not conclusive.

Reviewer #2 (Recommendations for the authors):

Small issues:

Fig. 1A. Please add a mark on the timeline showing when the K+ concentration is changed. Also, please add a time axis that matches the time axis in (C), so readers can know when in C the medium was changed.

1B caption: unclear what "the images were 20 min before and after cytokinesis" means, given that the images go from -30 min to +20 min. Maybe the authors mean, "the indicated times are measured relative to cytokinesis."

Thank you for bringing these points to our attention that can confuse readers. We revised the figure legend.

Line 214: nonoclusters --> nanoclusters

Line 475: 10 mm -> 10 ¥mum

Corrected.

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I consider trolling to be an extremely unhealthy online behavior. I strongly oppose any posts engaging in malicious discussions, regardless of the reasons or emotions behind them. Looking back at past news, I recall numerous instances where innocent individuals suffered negative consequences like depression or withdrawal due to public opinion, while those participating in malicious discussions never felt remorse.

I believe this is true. When children know teachers care about them, they feel happy and want to try new things in the classroom.

He is trying to convince her to undergo the procedure, and if she does, their relationship will be fine.