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Dan Minglun: Death was about to come, but he did not awake. How clear that priest's advice to him; but when he first heard it, he doubted it; when he rethought about it, he thought it was crazy. Loyal advice is harsh to the ear; it is always this case!

但明倫：死將臨而不悟，其言何等真切；乃初聞之而疑，轉思之，且以爲妄矣。忠言逆耳，固如是夫！

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Feng Zhenluan: Everyone would see her and call her a beauty, but I would see her as a fiendish ghost. If everyone had my eyes, they would all be man with no lust for women (Man in the Lu State).* My heart is like a dead tree, abstinent like the sage, deity, or Buddha. Otherwise, if the mind is deluded by desires, I will be dust in the grave (under the Beimang Mountain)."

馮鎮巒: 人見呼佳人，我見如獰鬼，人人如我眼，便是魯男子 。此心即枯木，聖賢仙佛矣，不然心眼迷，北邙 山下土。

Dan Minglun: She was clearly a beautiful woman, yet had a bluish-green face and jagged, saw-like teeth, only wearing a coloured-painted human skin? Those in the world who confuse people with gaudiness are actually those people who wear human skin, and paint them with coloured pen every day. Alas, that is so scary!

但明倫：明明麗人也，而乃翠面鋸齒，徒披采繪之人皮者乎？世之以妖冶惑人者，固日日鋪人皮，執采筆而繪者也。吁！可畏矣！

Yicong Wang: I suggest emending this translation of "southern courtyard" to "southern courtyard of the household." The translation "southern courtyard" could be confusing in showing the place where Wang’s younger brother lived, as it does not fit enough to show how the whole family was living in one complex, and each smaller family lived in one part of the complex, in premodern China, by the time the author lived.

Feng Zhenluan： The heart after that will not be the heart before; the previous heart desired for sexual lust, what will the new heart desire? I would like to ask.

馮鎮巒：此後之心非向日之心也，向日之心好色，此後之心何好，吾欲問之。

Plagiarism is a very important factor when considering the value of an AI's output. It runs off the work of many others, and so is very prone to taking from whatever source it happens to find on a subject. Additionally, some of these sources are questionable in quality, but that is irrelevant here.

It is immensely important to note that yes, a reasonably large degree of Generative AI-written assignments can be designated as AI-generated. (Perhaps due to the lack of effort put into disguising that fact.) However, if AI keeps improving at the rate that it is, being able to tell AI-generated assignments from non-AI-generated ones may be almost impossible unless some obvious tell is left in.

Large Language Models, unlike something like a calculator or a translation system, work less as a tool and more as something that will do all the work for the user if asked to. It is important to note that while these LLMs could be used as tools to help someone understand the gist of something, or get the ball rolling, when the option to simply have the LLM do the work for the user is there, many choose the easier route.

This shows how some people went so far as to believe the story's purpose was to scare people. Gilman clears that up in this autobiographical essay.

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No.

Has there been a study of people who are non-responders to any of these drug variants? I've spent the last two years with Ozempic, Mounjaro and currently Zepbound without losing even a pound. This, despite consuming fewer calories, which I suspect is the only effect they've had on me.

Loyalty is not the same as habit. A habit is something we do repeatedly without thinking first, while loyalty is shaped by love and felt deeply in our hearts. Whether it’s loyalty to our faith or to the people we care about. We cannot be loyal without love, a strong foundation, and careful consideration for what we’re committing to. Loyalty grows naturally from within us, with no force, orders, or need to please others. We act on it from the heart. It's not like loyalty, habits that run on autopilot without any real feeling.

Flock now aggregates content from third-party camera systems

_ How exactly does remotely spying on kids practicing gymnastics make them safer?_ from Benn Jordan on YouTube

Notes

Read the entire report here: https://jasonhunyar.substack.com/p/why-are-flock-employees-watching-720?r=7c08v8

I don't care if it's for technical reasons, product demos, or whatever excuse we'll hear. There is simply no acceptable reason to remotely view children via a preschool or private gym's cameras unless a serious crime was committed.

I want to be 100% clear here about something I don't take lightly. This post is NOT accusing anyone of pedophilia, and I'm not saying that because a lawyer told me to.

I do not know why they were accessed, but there certainly ARE reasons these cameras may have been accessed by employees that are not personal, such as product demos to other clients. I do not think that is an appropriate reason because the people in the community center did not consent, nor have a reason to believe that they were being watched by Flock employees or police via a remote location.

Regardless of the reason, I do not think it was appropriate, and it's up to the community center and its members on how seriously they want to take it or if they want to file charges.

I can't police every comment, nor do I want to, but please do consider that frivolous pedo accusations often chip away at the seriousness of circumstances where children were abused.

Flock's VP of Business Development accessing cameras

Seemingly against the company's stated policy, a Flock employee accessed public and private cameras.

For these kinds of fields, it’s important to pick input types that make the data both accurate and easy to use. For age, it’s better to use a date of birth picker instead of just typing a number, because age changes over time and the system can calculate it automatically. For name, using a display name field works best, since it gives people flexibility, but you can also add optional first and last name fields if needed for organization. For address, structured fields like street, city, state, and country are better than free text because they keep the data clean and consistent, and autocomplete can help avoid mistakes.

I found this source interesting and overall talked about the idea of SMS texting and how it evolved into a dominant communication tool. It mentions that text messaging originally came from the Short Message Service which was constrained to about 160 characters leading to users to abbreviate words to common words we use today like lol or u. Something that started as a technical limitation ended up influencing language and culture globally. Because early communication tools like texting weren't just about sending messages, but also about shaping how people interact and express themselves. These design constraints in tech can unintentionally create long term behavioral changes.

This system actually stood out to me in this reading of this chapter because it shows how much effort and intention went into communication during the Web 1.0 era compared to our modern day now. For example, having to create your own personal webpage or actively join specific spaces like the BBS meant that users had to be way more deliberate about where and how they would interact. This made me think more about how different it was from now where content is constantly pushed to us through algorithms where back then you would have to go find conversations where the conversations find you now. I feel like this shift has contributed to things like doomscrolling because there is way less friction and work to accessing so much content. I do have a question if the web back then was able to create more or less meaningful interactions than nowadays because there weren't as many features but people had a sense to choose to be apart of something.

please see this paper for gene editing and what the technology can be used for https://www.sciencedirect.com/science/article/pii/S2589004224021023

I agree this should be discussed, but should concern only the cells that end up in the final biomass, not any processing aids

The fact that the GFs are produced by genetic modifications of a production organism does not restrict their use even in jurisdictions like the EU (which is the most strict). GFs are used as "processing aids" during cell culture, expected to be removed from the final biomass. Therefore it is not a concern per se if they are produced in genetically modified organisms (there are some requirements to prove their safety but it is something very simple to do)

certain cells can be adapted to grow with reduced amounts, AND gene editing can reduce significantly the need for growth factors - there are multiple strategies to do that, including mutation in growth factor receptors (to activate them) or by "helping" the cells expressing their own growth factors

just saw this is mentioned below for the CRISPR-modified cell lines. I think the technology is there, but needs to be demonstrated and safety needs to be proven

I am not sure this is true - this is more for a fed-batch system where you still supplement the cells with stuff, otherwise as cells increase in density they will run out of nutrients pretty fast

cells in a bank don't lose performance; that's why the banking system is in place. All cells in a bank are supposed to perform the same way to ensure safety of the production process

also the banking system works in a tiered way, to also ensure that you don't easily "run out" of vials.

the description, Pros and cons here are more appropriate for the immortalised cells by gene editing. Spontaneous immortalisation don't have these Pros and Cons, and the reference paper refers to spontaneous immortalisation. The concepts of spontaneous vs gene edited immortalisation are used interchangeably throughout this page and it is confusing - especially because the distinction is extremely important when it comes to regulatory approvals but also risks during production process

this here is contradicting the above explanation that a cell line is derived from only one cell

I would not say artificially -cells can only be immortalised spontaneously (as discussed in the table above explaining Why chicken) or through targeted genetic modification

a cell line in the cultivated meat context is never derived from one cell only (this is the case sometimes in pharmacies industry only), but from a population of cells that the researchers select for (based on the desired trait)

this is not a natural trait - spontaneous immortalisation means that a few cells acquired some spontaneous mutations which allow them to divide in culture for longer than other cells. It is a rare event that can happen in all cells, but it is very random (hence "spontaneous"). In fish cells for example this can happen even more often. In other species (cow, pigs) it apparently happens less often, but it can still happen given enough time (Pasitka et al published the paper in 2025 spontaneously immortalising beef cells)

make this match other headings. and then separate and indent the next sentence as the start of the paragraph.

I think.... misuse of a comma near the end. I am struggling to understand what the sentence is saying. Defining some words for myself may help with this. This sentence, and the ones that follow it, seem to be attempting to lead into/transition the next sentence.

needs commas

double use of act. may be better for the first act to be replaced with more specific terms. may also need a comma.

comma after? style sheet - they use both United States and US in the previous sentence. So, while the usage is uniform to the previous abbreviated form its usage may be incorrect.

punctuation, wording, verify.

this should be two sentences.

check punctuation. add a comma between "illness" and "euthanasia."

check punctuation

huh?

Check punctuation and citation

comma

reword?

comma

punctuation

is this the best word to use?

verify

Need for guardrails

Civic participation and a connectivity between the region and it's youth; research potential

Yes but would want more definition to confirm diversity of the cohort

FYI This is labeled as We Can be in the hypothesis list.

Concept is built around identification of problems vs. opportunities and has risk of being confrontational

Time commitment and uncertainty of commitment of public officials

Cohort of new leaders coming from less likely corners of the community

Possibly but not certain

Concerned about the length of time and commitment needed by participants and civic leaders

You don't? No, I do.

How could you not?

The kubelet is an agent running on each node, control plane and workers, and it communicates with the control plane. It receives Pod definitions, primarily from the API Server, and interacts with the container runtime on the node to run containers associated with the Pod. It also monitors the health and resources of Pods running containers.

The kubelet connects to container runtimes through a plugin based interface - the Container Runtime Interface (CRI). The CRI consists of protocol buffers, gRPC API, libraries, and additional specifications and tools. In order to connect to interchangeable container runtimes, kubelet uses a CRI shim, an application which provides a clear abstraction layer between kubelet and the container runtime.

The kube-proxy is the network agent which runs on each node, control plane and workers, responsible for dynamic updates and maintenance of all networking rules on the node. It abstracts the details of Pods networking and forwards connection requests to the containers in the Pods.

The kube-proxy node agent operates in conjunction with the iptables of the node.

All funders

Lack of diverse participation; challenging to facilitate; clarity about what happens after the meal.

Aspirational to show opportunity to bridge divides.

Good storytelling with opportunity to gain regional interest and participation from people who may not typically have opportunity for involvement.

Appears that the intent is there

The kubelet is an agent running on each node, control plane and workers, and it communicates with the control plane. It receives Pod definitions, primarily from the API Server, and interacts with the container runtime on the node to run containers associated with the Pod. It also monitors the health and resources of Pods running containers.

Dan Minglun: She was clearly a beautiful woman, yet had a bluish-green face and jagged, saw-like teeth, only wearing a coloured-painted human skin? Those in the world who confuse people with gaudiness are actually those people who wear human skin, and paint them with coloured pen every day. Alas, that is so scary!

但明倫：明明麗人也，而乃翠面鋸齒，徒披采繪之人皮者乎？世之以妖冶惑人者，固日日鋪人皮，執采筆而繪者也。吁！可畏矣！

Feng Zhenluan: Everyone would see her and call her a beauty, but I would see her as a fiendish ghost. If everyone had my eyes, they would all be man with no amorous feelings. My heart is like a dead tree, abstinent like the sage, deity, or Buddha. Otherwise, if the mind is deluded by desires, I will be dust in the grave.

馮鎮巒: 人見呼佳人，我見如獰鬼，人人如我眼，便是魯男子。此心即枯木，聖賢仙佛矣，不然心眼迷，北邙山下土。

Dan Minglun: Even if she were really an escapee, how could he just be greedy and keep her hidden? It was actually inviting the ghost into his house, his wife advised him, yet he would not listen; the Daoist priest warned him, yet he would not awaken. How deeply seductive beauty can be!

但明倫：即令真是在亡之人，又豈可貪而匿之？明明引鬼入宅，妻勸之而不從，道士言之而不悟，色之迷人甚矣哉！

Dan Minglun: Death was about to come, but he did not awake. How clear that priest's advice to him; but when he first heard it, he doubted it; when he rethought about it, he thought it was crazy. Loyal advice is harsh to the ear; it is always this case!

但明倫：死將臨而不悟，其言何等真切；乃初聞之而疑，轉思之，且以爲妄矣。忠言逆耳，固如是夫！

Feng Zhenluan: How could there be few people like this in the world?

馮鎮巒：天下如此人豈少也哉？

He Shouqi: Flirting for the third time.

何守奇：三挑之

Yicong Wang: This edition of the text is from the Jain Publishing Company’s edition, edited and translated by Sidney L. Sondergard.

This edition also includes the Qing Dynasty's Commentaries (1644—1912), from editions edited by Ren Duxing.

The digital reading platform edition is edited, and the historical comments are translated by Yicong Wang.

A worker node provides a running environment for client applications. These applications are microservices running as application containers. In Kubernetes the application containers are encapsulated in Pods, controlled by the cluster control plane agents running on the control plane node. Pods are scheduled on worker nodes, where they find required compute, memory and storage resources to run, and networking to talk to each other and the outside world. A Pod is the smallest scheduling work unit in Kubernetes. It is a logical collection of one or more containers scheduled together, and the collection can be started, stopped, or rescheduled as a single unit of work.

etcd: Key-Value Data Store

etcd is a strongly consistent, distributed key-value data store

only the API Server is able to communicate with the etcd data store

etcd topologies support HA configurations: At any given time, one of the nodes in the group will be the leader, and the rest of them will be the followers. etcd gracefully handles leader elections and can tolerate node failure, including leader node failures. Any node can be treated as a leader.

controller managers

Controllers are watch-loop processes continuously running and comparing the cluster's desired state (provided by objects' configuration data) with its current state (obtained from the key-value store via the API Server). In case of a mismatch, corrective action is taken in the cluster until its current state matches the desired state.

The kube-controller-manager runs controllers or operators responsible to act when nodes become unavailable, to ensure container pod counts are as expected, to create endpoints, service accounts, and API access tokens.

The cloud-controller-manager runs controllers or operators responsible to interact with the underlying infrastructure of a cloud provider when nodes become unavailable, to manage storage volumes when provided by a cloud service, and to manage load balancing and routing.

We think this is one of the lower value ideas when looking at the stated ROI. If this is an idea you think has higher potential, consider how to talk about the outcomes and deliverables to the group.

Would be helpful to have some detail on the 21st to validate the likelihood that this campaign can be in place before the 4th? What decisions would need to be made and by when to provide you with enough time to activate.

hydrolysates can't replace growth factors technically; they could help replace the fetal bovine serum potentially, but you still require growth factors

Remove this brief from presentation for the 21st but work with Matt Barron to bring this as an idea to the Press Forward SWPA chapter.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have submitted a second revision, largely to address a comment from Reviewer 2, which was "The failure to model the neural data with an explicit model is a missed opportunity." The authors have now included a computational model.]

This study makes a fundamental contribution to our understanding of interocular suppression, particularly continuous flash suppression (CFS). Using neuroimaging data from two macaque monkeys, the study provides compelling evidence that CFS suppresses orientation responses in neurons within V1. These findings enrich the CFS literature by demonstrating that neural activity under CFS may prevent high-level visual and cognitive processing.

Comments on previous revisions:

The authors have addressed all my previous comments.

Reviewer #2 (Public review):

Summary:

The goal of this study was to investigate the degree to which low-level stimulus features (i.e., grating orientation) are processed in V1 when stimuli are not consciously perceived under conditions of continuous flash suppression (CFS). The authors measured the activity of a population of V1 neurons at single neuron resolution in awake fixating monkeys while they viewed dichoptic stimuli that consisted of an oriented grating presented to one eye and a noise stimulus to the other eye. Under such conditions, the mask stimulus can prevent conscious perception of the grating stimulus. By measuring the activity of neurons (with Ca2+ imaging) that preferred one or the other eye, the authors tested the degree of orientation processing that occurs during CFS.

Strengths:

The greatest strength of this study is the spatial resolution of the measurement and the ability to quantify stimulus representations during CSF in populations of neurons preferring the eye stimulated by either the grating or the mask. There have been a number of prominent fMRI studies of CFS, but all of them have had the limitation of pooling responses across neurons preferring either eye, effectively measuring the summed response across ocular dominance columns. The ability to isolate separate populations offers an exciting opportunity to study the precise neural mechanisms that give rise to CFS, and potentially provide insights into nonconscious stimulus processing.

Weaknesses:

(The authors have now included a computational model in the second revision.)

Reviewer #3 (Public review):

Summary:

In this study, Tang, Yu & colleagues investigate the impact of continuous flash suppression (CFS) on the responses of V1 neurons using 2-photon calcium imaging. The report that CFS substantially suppressed V1 orientation responses. This suppression happens in a graded fashion depending on the binocular preference of the neuron: neurons preferring the eye that was presented with the marker stimuli were most suppressed, while the neurons preferring the eye to which the grating stimuli were presented were least suppressed. Binocular neuron exhibited an intermediate level of suppression.

Strengths:

The imaging techniques are cutting-edge.

Weaknesses:

The strength of CFS suppression varies across animals, but the authors attribute this to comparable heterogeneity in the human psychophysics literature.

Comments on previous revisions:

The authors have addressed my comments from the previous round of review, and I have no further comments.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

This study makes a fundamental contribution to our understanding of interocular suppression, particularly continuous flash suppression (CFS). Using neuroimaging data from two macaque monkeys, the study provides compelling evidence that CFS suppresses orientation responses in neurons within V1. These findings enrich the CFS literature by demonstrating that neural activity under CFS may prevent high-level visual and cognitive processing.

Comments on revisions:

The authors have addressed all my previous comments.

Thanks for the very warm comments!

Reviewer #2 (Public review):

Summary:

The goal of this study was to investigate the degree to which low-level stimulus features (i.e., grating orientation) are processed in V1 when stimuli are not consciously perceived under conditions of continuous flash suppression (CFS). The authors measured the activity of a population of V1 neurons at single neuron resolution in awake fixating monkeys while they viewed dichoptic stimuli that consisted of an oriented grating presented to one eye and a noise stimulus to the other eye. Under such conditions, the mask stimulus can prevent conscious perception of the grating stimulus. By measuring the activity of neurons (with Ca2+ imaging) that preferred one or the other eye, the authors tested the degree of orientation processing that occurs during CFS.

Strengths:

The greatest strength of this study is the spatial resolution of the measurement and the ability to quantify stimulus representations during CSF in populations of neurons preferring the eye stimulated by either the grating or the mask. There have been a number of prominent fMRI studies of CFS, but all of them have had the limitation of pooling responses across neurons preferring either eye, effectively measuring the summed response across ocular dominance columns. The ability to isolate separate populations offers an exciting opportunity to study the precise neural mechanisms that give rise to CFS, and potentially provide insights into nonconscious stimulus processing.

Weaknesses:

However, while this is an impressive experimental setup, the major weakness of this study is that the experiments don't advance any theoretical account of why CFS occurs or what CFS implies for conscious visual perception. There are two broad camps of thinking with regard to CFS. On the one hand, Watanabe et al., 2011 reported that V1 activity remained intact during

CFS, implying that CFS interrupts stimulus processing downstream of V1. On the other hand, Yuval-Greenberg and Heeger (2013) showed that V1 activity is in fact reduced during CFS. By using a parametric experimental design, they measured the impact of the mask on the stimulus response as a function of contrast, and concluded that the mask reduces the gain of neural responses to the grating stimulus. They presented a theoretical model in which the mask effectively reduced the SNR of the grating, making it invisible in the same way that reducing contrast makes a stimulus invisible.

In the first submission of the manuscript, the authors incorrectly described the Yuval-Greenberg & Heeger (2013) paper and Watanabe et al. (2011) papers, suggesting that they had observed the same or similar effects of CFS on V1 activity, when in fact they had described opposite results. Reviewer 1 also observed that the authors appeared to be confused in their reading of these highly relevant papers. In the revision, the authors have reworked this paragraph, now correctly describing these sets of opposing results. However, I still do not understand what the authors are trying to argue: "...these studies were not designed to quantify the pure effect of CFS on stimulus-evoked V1 responses." I do not understand what is meant by "pure" in this case.

This is clarified as: “Nevertheless, these studies contrasted monocular and dichoptic masking conditions to equate stimulus input while manipulating perceptual visibility, which were not designed to quantify the pure effect of CFS on stimulus-evoked V1 responses, that is, the difference of BOLD signals between binocular masking and stimulus alone conditions.” (line 63)

Regardless, it is clear that the measurements in the present study strongly support the interpretation of Yuval-Greenberg & Heeger (i.e., that V1 activity is degraded by CFS, 'akin' to a loss in the contrast-to-noise ratio of neural activity). It would be appropriate for the authors to communicate this clearly.

We agree and added the following sentence in the text: “These results support the conclusion of Yuval-Greenberg and Heeger (2013) that V1 activity is degraded by CFS, ‘akin’ to a loss in the contrast-to-noise ratio of neural activity” (line 122)

I continue to be of the opinion that this study is lacking an adequate model of interocular interactions that might explain the Ca2+ imaging. The machine learning results are not terribly surprising - multivariate methods, such as SVMs, are more sensitive than univariate approaches. So it is plausible that an SVM can support decoding of the coarse orientation information, even when no tuning is evident in the univariate analyses. However, the link between this result and the underlying neurophysiology is opaque. The failure to model the neural data with an explicit model is a missed opportunity.

We agree and put “An ocular-dominance-dependent gain control model” back to the text. Fig. 2D now shows the results of model fitting.

(line 167)

An ocular-dominance-dependent gain control model

We developed an ocular dominance-dependent gain control model to account for the impact of CFS on V1 population orientation tuning. The model development followed two steps.

Step I. Population orientation tuning functions before CFS

The population orientation tuning functions due to monocular stimulation exhibited different amplitudes among OD groups (Fig. 2D, red curves), which could be simulated with Equation 1, an OD-weighted Gaussian basis function:

where parameters A, σ, and B corresponded to the amplitude, standard deviation, and minimal response of the Gaussian basis function, respectively, and θ represented the preferred orientation of a bin of neurons relative to the actual orientation of the grating stimulus. The weight parameter w was the mean of linearly transformed ODIs of neurons in a neuronal group, which equated to (ODI +1)/2 or 1 - (ODI + 1)/2, depending on contralateral or ipsilateral eye grating stimulation, and ranged from 0-1. Thus, a smaller w would indicate a higher preference for the eye seeing the grating, and a larger w would indicate a higher preference for the unstimulated eye (or the eye seeing the flashing masker under CFS). The w equated to 0.33, 0.50, and 0.67 in Monkey A, and 0.32, 0.5, and 0.68 in Monkey B, for the grating eye-preferring group, binocular group, and the masker eye-preferring group, respectively. The exponent s represented a nonlinear transformation.

Equation 1 fitted the baseline data well (Fig. 2D, red curves), resulting in goodness-of-fit (R²) values at 0.94 and 0.95 for the two monkeys, respectively. This indicated that the equation captured the OD-dependent population orientation tuning characteristics of V1 neurons with monocular stimulation before CFS.

Step II. The impacts of CFS

In step II, the model introduced several binocular combination factors to account for population orientation tuning functions under CFS.

To account for the OD-dependent changes of orientation tuning bandwidths under CFS, a w-dependent inhibition factor wt was introduced, which scaled the σ of the tuning functions, changing the monocular tunings R into R’:

This allowed different groups of neurons to exhibit various degrees of orientation tuning function broadening, capturing the pattern in which neurons preferring the eye seeing the grating displayed a sharper population orientation tuning curve under CFS than those preferring the eye seeing the masker.

Previous studies have shown that binocular neuronal responses can be modeled by incorporating interocular suppression and summation processes (Kato et al., 1981; Dougherty, Cox, Westerberg, & Maier, 2019; Zhang et al., 2024). Therefore, R’ was further normalized by the neural response to the flashing masker to simulate interocular suppression, which was the first component of Equation 3. Additionally, the neural response to the flashing masker was summed to simulate binocular summation, which was the second component of Equation 3. These two components when summed, determining the final neural responses under CFS:

where N was the empirical neural response to the monocularly presented flashing masker stimulation, a and b were scaling parameters, and k and m were nonlinearity parameters. The interocular normalization by masker response led to amplitude reduction of population orientation tuning functions for different groups of neurons, while the binocular summation with masker response elevated the minimal responses of tuning functions to their corresponding heights.

During the step II model fitting, the parameters A, σ, and s were inherited from the monocular tuning fits derived from Equation 1 and served as inputs, while the parameters a, k, b, m, and t were optimized. The model captured the CFS modulation on population orientation tuning curves well, with R2 = 0.99 and 0.98 for Monkeys A and B, respectively (Fig. 2D, red curves).

Reviewer #3 (Public review):

Summary:

In this study, Tang, Yu & colleagues investigate the impact of continuous flash suppression (CFS) on the responses of V1 neurons using 2-photon calcium imaging. The report that CFS substantially suppressed V1 orientation responses. This suppression happens in a graded fashion depending on the binocular preference of the neuron: neurons preferring the eye that was presented with the marker stimuli were most suppressed, while the neurons preferring the eye to which the grating stimuli were presented were least suppressed. Binocular neuron exhibited an intermediate level of suppression.

Strengths:

The imaging techniques are cutting-edge.

Weaknesses:

The strength of CFS suppression varies across animals, but the authors attribute this to comparable heterogeneity in the human psychophysics literature.

Comments on revisions:

The authors have addressed my comments from the previous round of review, and I have no further comments

Thanks!

are these really the only options?

this isn't true though — Lil Miquela has been around for years and there haven't been significant AI influencers who've taken it to the same level as her

where is the citation for this, I wonder?

banger

Case#: Patient 2 is a 24-year-old Japanese man. Birth weight was 1900 g (~4.2 lbs) and mental and motor development were both normal. He had graduated from high school. Physical examination demonstrated a height of 157.0 cm, body weight of 45.3 kg.

DiseaseAssertion: MPS1-S

FamilyInfo: He was born from nonconsanguineous, young and healthy parents. He had a healthy elder brother and an affected twin brother (Patient 1).

CasePresentingHPOs: Inguinal hernia, bronchial asthma, systolic ejection heart murmur, umbilical hernia, joint contractures, spastic gait, hypoesthesia, positive Romberg sign, Babinski signs, mild aortic valve stenosis (HP:0000023, HP:0002099, HP:0031664, HP:0001537, HP:0002828, HP:0002064, HP:0033748, HP:0002403, HP:0003487, HP:0001650)

CaseHPOFreeText: Admitted to the hospital due to a 3-month history of progressive gait disturbance, onset was 6 months after the development of gait disturbance in Patient 1. Exaggeration of deep tendon reflexes was slight in the upper extremities, and marked in the lower extremities. Radiographies of chest and cervical spine showed similar findings to those in Patient 1.

CaseNotHPOs: N/A

CaseNotHPOFreeText: N/A

CaseEnzymeAssay: N/A

CaseUrineGAGs: Urine chemistry examination demonstrated increased excretion of uronic acid (51.7 mg/g creatinine).

CaseERT: N/A

CaseBMT: N/A

Variant1: c.164dup (p.Leu56AlafsTer7) (c.252insC - in paper but nomenclature is not current)

Variant1ClinVarID: 855487

Variant1CAID: CA355945969

Variant2: c.1121C>A (p.Thr374Asn) (c.1209C>A - in paper but nomenclature is not current)

Variant2ClinVarID: 4078984

Variant2CAID: CA355963378

AdditionalVariants: N/A

ParentalGenotype: N/A

PreviouslyPublished N/A

Case#: Patient 1 is a 24-year-old Japanese man. His birth weight was 2300 g (~5 lbs) and he had graduated from a vocational school. Physical examination demonstrated a height of 156.6 cm (mean height of Japanese male at age 24 is 170.9 ± 6.0 (SD) cm, body weight of 45.8 kg (mean body weight of Japanese male at age 24 is 62.6 ± 9.8 (SD) kg according to the National Health and Nutrition Survey in Japan, 2006). He demonstrated overall intelligence quotient (IQ) of 101, verbal IQ of 93 and performance IQ of 112.

DiseaseAssertion: MPS1-S

FamilyInfo: He was born from nonconsanguineous, young and healthy parents. He had a healthy elder brother and an affected twin brother (Patient 2).

CasePresentingHPOs: Inguinal hernia (treated by surgical repair in childhood and again at age 20), systolic ejection heart murmur, umbilical hernia, scissor gait, Babinski sign, mild aortic valve stenosis, severe cervical cord compression (HP:0000023, HP:0031664, HP:0001537, HP:0012407, HP:0003487, HP:0001650, HP:0002341)

CaseHPOFreeText: Admitted to the hospital due to a 6-month history of progressive gait disturbance. Mental and motor development were normal. Past medical histories included Kawasaki disease at age 6 months. Deep tendon reflexes were mildly exaggerated in the upper extremities, and markedly exaggerated in the lower extremities with bilateral Babinski signs. Radiography of the chest demonstrated mild thoracic deformity and that of cervical spine demonstrated hypoplasia of vertebral body and spinous process. Brain MRI demonstrated enlarged perivascular space and small hyperintense lesions on fluid attenuated inversion recovery image. Cerebrospinal fluid (CSF) examinations demonstrated an elevated protein level (440 mg/dL; normal range 10–40 mg/dL), which would be resulted from CSF circulatory disturbance caused by severe spinal canal stenosis, without pleocytosis.

CaseNotHPOs: N/A

CaseNotHPOFreeText: N/A

CaseEnzymeAssay: N/A

CaseUrineGAGs: Urine chemistry examination demonstrated increased excretion of uronic acid (63.1 mg/g creatinine; normal range 8.3–12.3 mg/g creatinine).

CaseERT: N/A

CaseBMT: N/A

Variant1: c.164dup (p.Leu56AlafsTer7) (c.252insC - in paper but nomenclature is not current)

Variant1ClinVarID: 855487

Variant1CAID: CA355945969

Variant2: c.1121C>A (p.Thr374Asn) (c.1209C>A - in paper but nomenclature is not current)

Variant2ClinVarID: 4078984

Variant2CAID: CA355963378

AdditionalVariants: N/A

ParentalGenotype: N/A

PreviouslyPublished N/A

I find the golden rule very interesting because it’s a good representation of how human nature can be. It shows that people naturally understand fairness and care about how others feel. It also suggests that empathy is something we are capable of without needing strict rules. I think it’s a simple idea, but it says a lot about how humans can connect and treat each other well.

Americans seemed to believe that human rights were necessary everywhere and that America should help with it worldwide.

Despite how people saw the importance of human rights and such topics, they still had issues with subtopics within human rights, example right here with economic rights.

Belief that the United States still had more work to do on the topic of human rights.

So the early 1990s saw a lot of foreign aid for democracy.

May be a sign that promoting human rights began to become a big part of American culture during the 90s.

Shows how people In Americas viewed democracy as essential, promoting freedom.

Possibly an event worth researching in relation to the topic of freedom.

Taoism makes me think about how I try too hard to control everything in my life, like forcing outcomes instead of letting things happen naturally. The idea of going with the flow, like water, reminds me that sometimes doing less actually works better than doing more. For me, this means I should relax more and not stress over things I can’t control. If I follow this, I think I could feel calmer and make better decisions.

Time allowing it would be worth cross-checking your approach against a simple but-for model

Is this stable with respect to the impact of the Waldo Rule implementation? Meaning is it possible that getting hit by such a Waldo rule could cause an employer to kill the job entirely?

I get the intuition here, but is this stable? Meaning if another overlapping waldo rule were removed then a given job woudl be hit by Rule 244435 wouldn't it?

eLife Assessment

The authors adapt sequencing of nascent DNA (DNA linked to an RNA primer, "SNS-Seq") to map DNA replication origins in Trypanosoma brucei. The main impact of this work is reporting a new set of putative origins, which do not overlap with previously reported origins, but which appear to overlap with previously mapped DNA-RNA hybrid (R-loops). Thus, these valuable findings open up new avenues for further investigation into the mechanistic basis for firing of replication forks in this organism. However, the supporting evidence remains incomplete and would benefit from orthogonal validation. This work will be of interest to those studying DNA replication and epigenetic regulation of fork origins.

Reviewer #1 (Public review):

In this paper, Stanojcic and colleagues attempt to map sites of DNA replication initiation in the genome of the African trypanosome, Trypanosoma brucei. Their approach to this mapping is to isolate 'short-nascent strands' (SNSs), a strategy adopted previously in other eukaryotes (including in the related parasite Leishmania major), which involves isolation of DNA molecules whose termini contain replication-priming RNA. By mapping the isolated and sequenced SNSs to the genome (SNS-seq), the authors suggest that they have identified origins, which they localise to intergenic (strictly, inter-CDS) regions within polycistronic transcription units and suggest display very extensive overlap with previously mapped R-loops in the same loci. Finally, having defined locations of SNS-seq mapping, they suggest they have identified G4 and nucleosome features of origins, again using previously generated data. Though there is merit in applying a new approach to understand DNA replication initiation in T. brucei, where previous work has used MFA-seq and ChIP of a subunit of the Origin Replication Complex (ORC), there are two significant deficiencies in the study that must be addressed to ensure rigour and accuracy.

(i) The suggestion that the SNS-seq data is mapping DNA replication origins that are present in inter-CDS regions of the polycistronic transcription units of T. brucei is novel and does not agree with existing data on the localisation of ORC1/CDC6, and it is very unclear if it agrees with previous mapping of DNA replication by MFA-seq due to the way the authors have presented this correlation. For these reasons, the findings essentially rely on a single experimental approach, which must be further tested to ensure SNS-seq is truly detecting origins. Indeed, in this regard, the very extensive overlap of SNS-seq signal with RNA-DNA hybrids should be tested further to rule out the possibility that the approach is mapping these structures and not origins.

(ii) The authors' presentation of their SNS-seq data is too limited and therefore potentially provides a misleading view of DNA replication in the genome of T. brucei. The work is presented through a narrow focus on SNS-seq signal in the inter-CDS regions within polycistronic transcription units, which constitute only part of the genome, ignoring both the transcription start and stop sites at the ends of the units and the large subtelomeres, which are mainly transcriptionally silent. The authors must present a fuller and more balanced view of SNS-seq mapping, across the whole genome, to ensure full understanding and clarity.

In the revised manuscript, the authors have improved the presentation and analysis of their data, expanding the description of SNS-seq mapping across the genome, and more clearly assessing to what extent there is correlation between SNS-seq signal and previous mapping approaches to predict origins (by MFA-seq and ChiP-chip of ORC1/CDC6). With regard the correlation between SNS-seq and ORC/1CDC6 ChIP-chip, it should be noted that two datasets were generated in distinct strains of T. brucei (Lister 427 and TREU927, respectively), and it is unclear if the latter dataset can be accurately mapped to the strain used here. Notwithstanding this concern, these improvements clarify a number of aspects of the SNS-seq mapping: (1) the signal is more prevalent in the transcribed core of the genome than in the largely transcriptionally silent subtelomeres; and (2) whereas previous work revealed strong correlation between ORC1/CDC6 localisation and MFA-seq peaks at the ends of multigene transcription units, neither of these data show significant overlap with SNS-seq signal, which is not seen at transcription start or stop sites ('SSRs'; supplementary Fig.8D) and shows marked depletion at predicted ORC1/CDC6 sites (supplementary Fig.8C). To the authors' credit, they acknowledge this lack of correlation in the discussion.

The authors have not provided any new data to substantiate their assertion that SNS-seq accurately detects origins in T. brucei, and therefore the work rests on a single experimental approach, without validation. As a result, the suggestion of abundant, previously undetected origins in the intergenic regions of multigene transcription remains a prediction. One key untested limitation of the work lies in the observation that the very large majority of SNS-seq signal overlaps with previously RNA-DNA hybrids; without an experimental test, the suggestion that the authors have 'disclosed for the first time a strong link between RNA:DNA hybrid formation and DNA replication initiation' remains conjecture.

Reviewer #2 (Public review):

Summary:

Stanojcic et al. investigate the origins of DNA replication in the unicellular parasite Trypanosoma brucei. They perform two experiments, stranded SNS-seq and DNA molecular combing. Further, they integrate various publicly available datasets, such as G4-seq and DRIP-seq, into their extensive analysis. Using this data, they elucidate the structure of origins of replications. In particular, they find various properties located at or around origins, such as polynucleotide stretches, G-quadruplex structures, regions of low and high nucleosome occupancy, R-loops, and that origins are mostly present in intergenic regions. Combining their population-level SNS-seq and their single-molecule DNA molecular combing data, they elucidate the total number of origins as well as the number of origins active in a single cell.

Between the initial submission and this revision, the raised major concerns have not been resolved, and no additional validation has been provided.

Strengths:

(1) A very strong part of this manuscript is that the authors integrate several other datasets and investigate a large number of properties around origins of replication. Data analysis clearly shows the enrichment of various properties at the origins, and the manuscript is concluded with a very well-presented model that clearly explains the authors' understanding and interpretation of the data.

(2) The DNA combing experiment is an excellent orthogonal approach to the SNS-seq data. The authors used the different properties of the two experiments (one giving location information, one giving single-molecule information) well to extract information and contrast the experiments.

(3) The discussion is exemplary, as the authors openly discuss the strengths and weaknesses of the approaches used. Further, the discussion serves its purpose of putting the results in both an evolutionary and a trypanosome-focused context.

Weaknesses:

I have major concerns about the origin of replication sites determined from the SNS-seq data. As a caveat, I want to state that, before reading this manuscript, SNS-seq was unknown to me; hence, some of my concerns might be misplaced.

(1) There are substantial discrepancies between the origins identified here and those reported in previous studies. Given that the other studies precede this manuscript, it is the authors' duty to investigate these differences. A conclusion should be reached on why the results are different, e.g., by orthogonally validating origins absent in the previous studies.

(2) I am concerned that up to 96% percent of all SNS-seq peaks are filtered away. If there is so much noise in the data, how can one be sure that the peaks that remain are real? Upon request, the authors have performed a control, where randomly placed peaks were run through the same filtering process. Only approximately twice as many experimental peaks passed filtering compared to random peaks. While the authors emphasize reproducibility between replicates, technical artifacts from the protocol would also be reproducible. Moreover, in other SNS-seq studies, for example, Pratto et al. Cell 2021, Fig. 1B, + and − strand peaks always appear closely paired. This pattern contrasts strongly with Fig. 2A in this manuscript.

Further, I have some minor concerns that do not affect the main conclusions of the manuscript:

- Fig 2C: The regions shown in the heatmap have different sizes, and I presume that the regions are ordered by size on the y-axis? If so, does the cone-shaped pattern, which is origin-less for genic regions and origin-enriched for intergenic regions, arise from the size of the regions? (I.e., for each genic region, the region itself is origin-less and the flanking intergenic regions contain origins.) If this is the case, then the peaks/valleys, centered exactly on the center of the regions on the mean frequency plots, arise from the different sizes of the analyzed regions, not from the fact that origins are mostly found at the center of intergenic regions. This data would be better presented with all regions stretched to the same size. This has not been addressed in the revision.

- Line 123, "and the average length of origins was found to be approximately 150 bp.": To determine origins, the authors filter away overlapping peaks and peaks that are too far from each other. Both restrict the minimal and maximal length of origins that can be observed, and this, in turn, affects the average length. This has not been addressed in the revision.

Are claims well substantiated?:<br /> The identification of origins via SNS-seq appears to be incompletely supported to me.<br /> All downstream analyses depend on the reliability of origin identification.

Impact:<br /> This study has the potential to be valuable for two fields: In research focused on T. brucei as a disease agent, where essential processes that function differently than in mammals are excellent drug targets. Further, this study would impact basic research analyzing DNA replication over the evolutionary tree, where T. brucei can be used as an early-divergent eucaryotic model organism.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

In this paper, Stanojcic and colleagues attempt to map sites of DNA replication initiation in the genome of the African trypanosome, Trypanosoma brucei. Their approach to this mapping is to isolate 'short-nascent strands' (SNSs), a strategy adopted previously in other eukaryotes (including in the related parasite Leishmania major), which involves isolation of DNA molecules whose termini contain replication-priming RNA. By mapping the isolated and sequenced SNSs to the genome (SNS-seq), the authors suggest that they have identified origins, which they localise to intergenic (strictly, inter-CDS) regions within polycistronic transcription units and suggest display very extensive overlap with previously mapped R-loops in the same loci. Finally, having defined locations of SNS-seq mapping, they suggest they have identified G4 and nucleosome features of origins, again using previously generated data. Though there is merit in applying a new approach to understand DNA replication initiation in T. brucei, where previous work has used MFA-seq and ChIP of a subunit of the Origin Replication Complex (ORC), there are two significant deficiencies in the study that must be addressed to ensure rigour and accuracy.

(i) The suggestion that the SNS-seq data is mapping DNA replication origins that are present in inter-CDS regions of the polycistronic transcription units of T. brucei is novel and does not agree with existing data on the localisation of ORC1/CDC6, and it is very unclear if it agrees with previous mapping of DNA replication by MFA-seq due to the way the authors have presented this correlation. For these reasons, the findings essentially rely on a single experimental approach, which must be further tested to ensure SNS-seq is truly detecting origins. Indeed, in this regard, the very extensive overlap of SNS-seq signal with RNA-DNA hybrids should be tested further to rule out the possibility that the approach is mapping these structures and not origins.

(ii) The authors' presentation of their SNS-seq data is too limited and therefore potentially provides a misleading view of DNA replication in the genome of T. brucei. The work is presented through a narrow focus on SNS-seq signal in the inter-CDS regions within polycistronic transcription units, which constitute only part of the genome, ignoring both the transcription start and stop sites at the ends of the units and the large subtelomeres, which are mainly transcriptionally silent. The authors must present a fuller and more balanced view of SNS-seq mapping, across the whole genome, to ensure full understanding and clarity.

In the revised manuscript, the authors have improved the presentation and analysis of their data, expanding the description of SNS-seq mapping across the genome, and more clearly assessing to what extent there is correlation between SNS-seq signal and previous mapping approaches to predict origins (by MFA-seq and ChiP-chip of ORC1/CDC6). With regard the correlation between SNS-seq and ORC/1CDC6 ChIP-chip, it should be noted that two datasets were generated in distinct strains of T. brucei (Lister 427 and TREU927, respectively), and it is unclear if the latter dataset can be accurately mapped to the strain used here. Notwithstanding this concern, these improvements clarify a number of aspects of the SNS-seq mapping: (1) the signal is more prevalent in the transcribed core of the genome than in the largely transcriptionally silent subtelomeres; and (2) whereas previous work revealed strong correlation between ORC1/CDC6 localisation and MFA-seq peaks at the ends of multigene transcription units, neither of these data show significant overlap with SNS-seq signal, which is not seen at transcription start or stop sites ('SSRs'; supplementary Fig.8D) and shows marked depletion at predicted ORC1/CDC6 sites (supplementary Fig.8C). To the authors' credit, they acknowledge this lack of correlation in the discussion.

The authors have not provided any new data to substantiate their assertion that SNS-seq accurately detects origins in T. brucei, and therefore the work rests on a single experimental approach, without validation. As a result, the suggestion of abundant, previously undetected origins in the intergenic regions of multigene transcription remains a prediction. One key untested limitation of the work lies in the observation that the very large majority of SNS-seq signal overlaps with previously RNA-DNA hybrids; without an experimental test, the suggestion that the authors have 'disclosed for the first time a strong link between RNANA hybrid formation and DNA replication initiation' remains conjecture.

Reviewer #2 (Public review):

Summary:

Stanojcic et al. investigate the origins of DNA replication in the unicellular parasite Trypanosoma brucei. They perform two experiments, stranded SNS-seq and DNA molecular combing. Further, they integrate various publicly available datasets, such as G4-seq and DRIP-seq, into their extensive analysis. Using this data, they elucidate the structure of origins of replications. In particular, they find various properties located at or around origins, such as polynucleotide stretches, G-quadruplex structures, regions of low and high nucleosome occupancy, R-loops, and that origins are mostly present in intergenic regions. Combining their population-level SNS-seq and their single-molecule DNA molecular combing data, they elucidate the total number of origins as well as the number of origins active in a single cell.

Between the initial submission and this revision, the raised major concerns have not been resolved, and no additional validation has been provided.

Strengths:

(1) A very strong part of this manuscript is that the authors integrate several other datasets and investigate a large number of properties around origins of replication. Data analysis clearly shows the enrichment of various properties at the origins, and the manuscript is concluded with a very well-presented model that clearly explains the authors' understanding and interpretation of the data.

(2) The DNA combing experiment is an excellent orthogonal approach to the SNS-seq data. The authors used the different properties of the two experiments (one giving location information, one giving single-molecule information) well to extract information and contrast the experiments.

(3) The discussion is exemplary, as the authors openly discuss the strengths and weaknesses of the approaches used. Further, the discussion serves its purpose of putting the results in both an evolutionary and a trypanosome-focused context.

Weaknesses:

I have major concerns about the origin of replication sites determined from the SNS-seq data. As a caveat, I want to state that, before reading this manuscript, SNS-seq was unknown to me; hence, some of my concerns might be misplaced.

(1) There are substantial discrepancies between the origins identified here and those reported in previous studies. Given that the other studies precede this manuscript, it is the authors' duty to investigate these differences. A conclusion should be reached on why the results are different, e.g., by orthogonally validating origins absent in the previous studies.

We agree that orthogonally validation of origins detected by stranded SNS-seq is necessary and we are working on it.

(2) I am concerned that up to 96% percent of all SNS-seq peaks are filtered away. If there is so much noise in the data, how can one be sure that the peaks that remain are real? Upon request, the authors have performed a control, where randomly placed peaks were run through the same filtering process. Only approximately twice as many experimental peaks passed filtering compared to random peaks. While the authors emphasize reproducibility between replicates, technical artifacts from the protocol would also be reproducible. Moreover, in other SNS-seq studies, for example, Pratto et al. Cell 2021, Fig. 1B, + and − strand peaks always appear closely paired. This pattern contrasts strongly with Fig. 2A in this manuscript.

The size and overlap of peaks depend on the length of the SNS. In our study, the width of the peaks corresponds to the size of the short nascent strands (0.5–2.5 kb) chosen as the starting material, whereas the width of the peaks in Pratto et al., Cell, 2021 are much larger (few kb). This could be due to the longer SNS used in the Pratto et al. study. Consequently, the overlap of the longer SNS is more pronounced since the SNS fibres elongate in both directions: at the 3′ end by DNA polymerase and at the 5′ end by ligation of Okazaki fragments. Additionally, the genomic regions displayed in our Figure 2A and in Pratto et al, Figure 1B are presented at substantially different resolutions, with a roughly ten‑fold difference in scale.

Further, I have some minor concerns that do not affect the main conclusions of the manuscript:

- Fig 2C: The regions shown in the heatmap have different sizes, and I presume that the regions are ordered by size on the y-axis? If so, does the cone-shaped pattern, which is origin-less for genic regions and origin-enriched for intergenic regions, arise from the size of the regions? (I.e., for each genic region, the region itself is origin-less and the flanking intergenic regions contain origins.) If this is the case, then the peaks/valleys, centered exactly on the center of the regions on the mean frequency plots, arise from the different sizes of the analyzed regions, not from the fact that origins are mostly found at the center of intergenic regions. This data would be better presented with all regions stretched to the same size. This has not been addressed in the revision.

As the reviewer suggested, we have produced scaled plots of the stranded SNS-seq origins over genic and intergenic regions (see Figure 3, which is attached along with the Reviewer #2 (Recommendations for the authors)). However, we would prefer to keep the unscaled versions in the manuscript and add a note in the text as part of the Version of Record, explaining that the origins are evenly distributed throughout intergenic regions rather than being centred within them.

- Line 123, "and the average length of origins was found to be approximately 150 bp.": To determine origins, the authors filter away overlapping peaks and peaks that are too far from each other. Both restrict the minimal and maximal length of origins that can be observed, and this, in turn, affects the average length. This has not been addressed in the revision.

This observation is correct. By applying filtering and setting the maximum distance between the positive and negative peaks, we are most likely affecting the average length by excluding potentially wider origins.

We'll modify the text as part of the Version of Record.

Are claims well substantiated?:

The identification of origins via SNS-seq appears to be incompletely supported to me.<br /> All downstream analyses depend on the reliability of origin identification.<br /> Impact:

This study has the potential to be valuable for two fields: In research focused on T. brucei as a disease agent, where essential processes that function differently than in mammals are excellent drug targets. Further, this study would impact basic research analyzing DNA replication over the evolutionary tree, where T. brucei can be used as an early-divergent eucaryotic model organism.

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

eLife Assessment

The authors use sequencing of nascent DNA (DNA linked to an RNA primer, "SNS-Seq") to localise DNA replication origins in Trypanosoma brucei, so this work will be of interest to those studying either Kinetoplastids or DNA replication. The paper presents the SNS-seq results for only part of the genome, and there are significant discrepancies between the SNS-Seq results and those from other, previously-published results obtained using other origin mapping methods. The reasons for the differences are unknown and from the data available, it is not possible to assess which origin-mapping method is most suitable for origin mapping in T. brucei. Thus at present, the evidence that origins are distributed as the authors claim - and not where previously mapped - is inadequate.

We would like to clarify a few points regarding our study. Our primary objective was to characterise the topology and genome-wide distribution of short nascent-strand (SNS) enrichments. The stranded SNS-seq approach provides the high strand-specific resolution required to analyse origins. The observation that SNS-seq peaks (potential origins) are most frequently found in intergenic regions is not an artefact of analysing only part of the genome; rather, it is a result of analysing the entire genome.

We agree that orthogonal validation is necessary. However, neither MFA-seq nor TbORC1/CDC6 ChIP-on-chip has yet been experimentally validated as definitive markers of origin activity in T. brucei, nor do they validate each other.

Public Reviews:

Reviewer #1 (Public review):

In this paper, Stanojcic and colleagues attempt to map sites of DNA replication initiation in the genome of the African trypanosome, Trypanosoma brucei. Their approach to this mapping is to isolate 'short-nascent strands' (SNSs), a strategy adopted previously in other eukaryotes (including in the related parasite Leishmania major), which involves isolation of DNA molecules whose termini contain replication-priming RNA. By mapping the isolated and sequenced SNSs to the genome (SNS-seq), the authors suggest that they have identified origins, which they localise to intergenic (strictly, inter-CDS) regions within polycistronic transcription units and suggest display very extensive overlap with previously mapped R-loops in the same loci. Finally, having defined locations of SNS-seq mapping, they suggest they have identified G4 and nucleosome features of origins, again using previously generated data.

Though there is merit in applying a new approach to understand DNA replication initiation in T. brucei, where previous work has used MFA-seq and ChIP of a subunit of the Origin Replication Complex (ORC), there are two significant deficiencies in the study that must be addressed to ensure rigour and accuracy.

(1) The suggestion that the SNS-seq data is mapping DNA replication origins that are present in inter-CDS regions of the polycistronic transcription units of T. brucei is novel and does not agree with existing data on the localisation of ORC1/CDC6, and it is very unclear if it agrees with previous mapping of DNA replication by MFA-seq due to the way the authors have presented this correlation. For these reasons, the findings essentially rely on a single experimental approach, which must be further tested to ensure SNS-seq is truly detecting origins. Indeed, in this regard, the very extensive overlap of SNS-seq signal with RNA-DNA hybrids should be tested further to rule out the possibility that the approach is mapping these structures and not origins.

(2) The authors' presentation of their SNS-seq data is too limited and therefore potentially provides a misleading view of DNA replication in the genome of T. brucei. The work is presented through a narrow focus on SNS-seq signal in the inter-CDS regions within polycistronic transcription units, which constitute only part of the genome, ignoring both the transcription start and stop sites at the ends of the units and the large subtelomeres, which are mainly transcriptionally silent. The authors must present a fuller and more balanced view of SNS-seq mapping across the whole genome to ensure full understanding and clarity.

Regarding comparisons with previous work:

- Two other attempts to identify origins in T. brucei - ORC1/CDC6 binding sites (ChIP-on-chip, PMID: 22840408) and MFA-seq (PMID: 22840408, 27228154) - were both produced by the McCulloch group. These methods do not validate each other; in fact, MFA-seq origins overlap with only 4.4% of the 953 ORC1/CDC6 sites (PMID: 29491738). Therefore, low overlap between SNS-seq peaks and ORC1/CDC6 sites cannot disqualify our findings. Similar low overlaps are observed in other parasites (PMID: 38441981, PMID: 38038269, PMID: 36808528) and in human cells (PMID: 38567819).

- We also would like to emphasize that the ORC1/CDC6 dataset originally published (PMID: 22840408) is no longer available; only a re-analysis by TritrypDB exists, which differs significantly from the published version (personal communication from Richard McCulloch). While the McCulloch group reported a predominant localization of ORC1/CDC6 sites within SSRs at transcription start and termination regions, our re-analysis indicates that only 10.3% of TbORC1/CDC6-12Myc sites overlapped with 41.8% of SSRs.

- MFA-seq does not map individual origins, it rather detects replicated genomic regions by comparing DNA copy number between S- and G1-phases of the cell cycle (PMID: 36640769; PMID: 37469113; PMID: 36455525). The broad replicated regions (0.1–0.5 Mbp) identified by MFA-seq in T. brucei are likely to contain multiple origins, rather than just one. In that sense we disagree with the McCulloch's group who claimed that there is a single origin per broad peak. Our analysis shows that up to 50% of the origins detected by stranded SNS-seq locate within broad MFA-seq regions. The methodology used by McCulloch’s group to infer single origins from MFA-seq regions has not been published or made available, as well as the precise position of these regions, making direct comparison difficult.

Finally, the genomic features we describe—poly(dA/dT) stretches, G4 structures and nucleosome occupancy patterns—are consistent with origin topology described in other organisms.

On the concern that SNS-seq may map RNA-DNA hybrids rather than replication origins: Isolation and sequencing of short nascent strands (SNS) is a well-established and widely used technique for high-resolution origin mapping. This technique has been employed for decades in various laboratories, with numerous publications documenting its use. We followed the published protocol for SNS isolation (Cayrou et al., Methods, 2012, PMID: 22796403). RNA-DNA hybrids cannot persist through the multiple denaturation steps in our workflow, as they melt at 95°C (Roberts and Crothers, Science, 1992; PMID: 1279808). Even in the unlikely event that some hybrids remained, they would not be incorporated into libraries prepared using a single-stranded DNA protocol and therefore would not be sequenced (see Figure 1B and Methods).

Furthermore, our analysis shows that only a small proportion (1.7%) of previously reported RNA-DNA hybrids overlap with SNS-seq origins. It is important to note that RNA-primed nascent strands naturally form RNA-DNA hybrids during replication initiation, meaning the enrichment of RNA-DNA hybrids near origins is both expected and biologically relevant.

On the claim that our analysis focuses narrowly on inter-CDS regions and ignores other genomic compartments: this is incorrect. We mapped and analyzed stranded SNS-seq data across the entire genome of T. brucei 427 wild-type strain (Müller et al., Nature, 2018; PMID: 30333624), including both core and subtelomeric regions. Our findings indicate that most origins are located in intergenic regions, but all analyses were performed using the full set of detected origins, regardless of location.

We did not ignore transcription start and stop sites (TSS/TTS). The manuscript already includes origin distribution across genomic compartments as defined by TriTrypDB (Fig. 2C) and addresses overlap with TSS, TTS and HT in the section “Spatial coordination between the activity of the origin and transcription”. While this overlap is minimal, we have included metaplots in the revised manuscript for clarity.

Reviewer #2 (Public review):

Summary:

Stanojcic et al. investigate the origins of DNA replication in the unicellular parasite Trypanosoma brucei. They perform two experiments, stranded SNS-seq and DNA molecular combing. Further, they integrate various publicly available datasets, such as G4-seq and DRIP-seq, into their extensive analysis. Using this data, they elucidate the structure of the origins of replication. In particular, they find various properties located at or around origins, such as polynucleotide stretches, G-quadruplex structures, regions of low and high nucleosome occupancy, R-loops, and that origins are mostly present in intergenic regions. Combining their population-level SNS-seq and their single-molecule DNA molecular combing data, they elucidate the total number of origins as well as the number of origins active in a single cell.

Strengths:

(1) A very strong part of this manuscript is that the authors integrate several other datasets and investigate a large number of properties around origins of replication. Data analysis clearly shows the enrichment of various properties at the origins, and the manuscript concludes with a very well-presented model that clearly explains the authors' understanding and interpretation of the data.

We sincerely thank you for this positive feedback.

(2) The DNA combing experiment is an excellent orthogonal approach to the SNS-seq data. The authors used the different properties of the two experiments (one giving location information, one giving single-molecule information) well to extract information and contrast the experiments.

Thank you very much for this remark.

(3) The discussion is exemplary, as the authors openly discuss the strengths and weaknesses of the approaches used. Further, the discussion serves its purpose of putting the results in both an evolutionary and a trypanosome-focused context.

Thank you for appreciating our discussion.

Weaknesses:

I have major concerns about the origin of replication sites determined from the SNS-seq data. As a caveat, I want to state that, before reading this manuscript, SNS-seq was unknown to me; hence, some of my concerns might be misplaced.

(1) I do not understand why SNS-seq would create peaks. Replication should originate in one locus, then move outward in both directions until the replication fork moving outward from another origin is encountered. Hence, in an asynchronous population average measurement, I would expect SNS data to be broad regions of + and -, which, taken together, cover the whole genome. Why are there so many regions not covered at all by reads, and why are there such narrow peaks?

Thank you for asking these questions. As you correctly point out, replication forks progress in both directions from their origins and ultimately converge at termination sites. However, the SNS-seq method specifically isolates short nascent strands (SNSs) of 0.5–2.5 kb using a sucrose gradient. These short fragments are generated immediately after origin firing and mark the sites of replication initiation, rather than the entire replicated regions. Consequently: (i) SNS-seq does not capture long replication forks or termination regions, only the immediate vicinity of origins. (ii) The narrow peaks indicate the size of selected SNSs (0.5–2.5 kb) and the fact that many cells initiate replication at the same genomic sites, leading to localized enrichment. (iii) Regions without coverage refer to genomic areas that do not serve as efficient origins in the analyzed cell population. Thus, SNS-seq is designed to map origin positions, but not the entire replicated regions.

(2) I am concerned that up to 96% percent of all peaks are filtered away. If there is so much noise in the data, how can one be sure that the peaks that remain are real? Specifically, if the authors placed the same number of peaks as was measured randomly in intergenic regions, would 4% of these peaks pass the filtering process by chance?

Maintaining the strandness of the sequenced DNA fibres enabled us to filter the peaks, thereby increasing the probability that the filtered peak pairs corresponded to origins. Two SNS peaks must be oriented in a way that reflects the topology of the SNS strands within an active origin: the upstream peak must be on the minus strand and followed by the downstream peak on the plus strand.

As suggested by the reviewer, we tested whether randomly placed plus and minus peaks could reproduce the number of filter-passing peaks using the same bioinformatics workflow. Only 1–6% of random peaks passed the filters, compared with 4–12% in our experimental data, resulting in about 50% fewer selected regions (origins). Moreover, the “origins” from random peaks showed 0% reproducibility across replicates, whereas the experimental data showed 7–64% reproducibility. These results indicate that the retainee peaks are highly unlikely to arise by chance and support the specificity of our approach. Thank you for this suggestion.

(3) There are 3 previous studies that map origins of replication in T. brucei. Devlin et al. 2016, Tiengwe et al. 2012, and Krasiļņikova et al. 2025 (https://doi.org/10.1038/s41467-025-56087-3), all with a different technique: MFA-seq. All three previous studies mostly agree on the locations and number of origins. The authors compared their results to the first two, but not the last study; they found that their results are vastly different from the previous studies (see Supplementary Figure 8A). In their discussion, the authors defend this discrepancy mostly by stating that the discrepancy between these methods has been observed in other organisms. I believe that, given the situation that the other studies precede this manuscript, it is the authors' duty to investigate the differences more than by merely pointing to other organisms. A conclusion should be reached on why the results are different, e.g., by orthogonally validating origins absent in the previous studies.

The MFA-seq data for T. brucei were published in two studies by McCulloch’s group: Tiengwe et al. (2012) using TREU927 PCF cells, and Devlin et al. (2016) using PCF and BSF Lister427 cells. In Krasilnikova et al. (2025), previously published MFA-seq data from Devlin et al. were remapped to a new genome assembly without generating new MFA-seq data, which explains why we did not include that comparison.

Clarifying the differences between MFA-seq and our stranded SNS-seq data is essential. MFA-seq and SNS-seq interrogate different aspects of replication. SNS-seq is a widely used, high-resolution method for mapping individual replication origins, whereas MFA-seq detects replicated regions by comparing DNA copy number between S and G1 phases. MFA-seq identified broad replicated regions (0.1–0.5 Mb) that were interpreted by McCulloch’s group as containing a single origin. We disagree with this interpretation and consider that there are multiple origins in each broad peaks; theoretical considerations of replication timing indicate that far more origins are required for complete genome duplication during the short S-phase. Once this assumption is reconsidered, MFA-seq and SNS-seq results become complementary: MFA-seq identifies replicated regions, while SNS-seq pinpoints individual origins within those regions. Our analysis revealed that up to 50% of the origins detected by stranded SNS-seq were located within the broad MFA peaks. This pattern—broad MFA-seq regions containing multiple initiation sites—has also recently been found in Leishmania by McCulloch’s team using nanopore sequencing (PMID: 26481451). Nanopore sequencing showed numerous initiation sites within MFA-seq regions and additional numerous sites outside these regions in asynchronous cells, consistent with what we observed using stranded SNS-seq in T. brucei. We will expand our discussion and conclude that the discrepancy arises from methodological differences and interpretation. The two approaches provide complementary insights into replication dynamics, rather than ‘vastly different’ results.

We recognize the importance of validating our results in future using an alternative mapping method and functional assays. However, it is important to emphasize that stranded SNS-seq is an origin mapping technique with a very high level of resolution. This technique can detect regions between two divergent SNS peaks, which should represent regions of DNA replication initiation. At present, no alternative technique has been developed that can match this level of resolution.

(4) Some patterns that were identified to be associated with origins of replication, such as G-quadruplexes and nucleosomes phasing, are known to be biases of SNS-seq (see Foulk et al. Characterizing and controlling intrinsic biases of lambda exonuclease in nascent strand sequencing reveals phasing between nucleosomes and G-quadruplex motifs around a subset of human replication origins. Genome Res. 2015;25(5):725-735. doi:10.1101/gr.183848.114).

It is important to note that the conditions used in our study differ significantly from those applied in the Foulk et al. Genome Res. 2015. We used SNS isolation and enzymatic treatments as described in previous reports (Cayrou, C. et al. Genome Res, 2015 and Cayrou, C et al. Methods, 2012). Here, we enriched the SNS by size on a sucrose gradient and then treated this SNS-enriched fraction with high amounts of repeated λ-exonuclease treatments (100u for 16h at 37oC - see Methods). In contrast, Foulk et al. used sonicated total genomic DNA for origin mapping, without enrichment of SNS on a sucrose gradient as we did, and then they performed a λ-exonuclease treatment. A previous study (Cayrou, C. et al. Genome Res, 2015, Figure S2, which can be found at https://genome.cshlp.org/content/25/12/1873/suppl/DC1) has shown that complete digestion of G4-rich DNA sequences is achieved under the conditions we used.

Furthermore, the SNS depleted control (without RNA) was included in our experimental approach. This control represents all molecules that are difficult to digest with lambda exonuclease, including G4 structures. Peak calling was performed against this background control, with the aim of removing false positive peaks resulting from undigested DNA structures. We explained better this step in the revised manuscript.

The key benefit of our study is that the orientation of the enrichments (peaks) remains consistent throughout the sequencing process. We identified an enrichment of two divergent strands synthesised on complementary strands containing G4s. These two divergent strands themselves do not, however, contain G4s (see Fig. 8 for the model). Therefore, the enriched molecules detected in our study do not contain G4s. They are complementary to the strands enriched with G4s. This means that the observed enrichment of

G4s cannot be an artefact of the enzymatic treatments used in this study. We added this part in the discussion of the revised manuscript.

We also performed an additional control which is not mentioned in the manuscript. In parallel with replicating cells, we isolated the DNA from the stationary phase of growth, which primarily contains non-replicating cells. Following the three λ-exonuclease treatments, there was insufficient DNA remaining from the stationary phase cells to prepare the libraries for sequencing. This control strongly indicated that there was little to no contaminating DNA present with the SNS molecules after λ-exonuclease enrichment.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Four broad issues need to be addressed.

(1) The authors have attempted to test the overlap between ORC1/CDC6 (an ORC subunit) binding in the genome and SNS-seq. If there were an overlap, this would provide evidence that the SNS-seq signals represent origins. However, the analysis provided is inadequate: merely a statement that "we obtained an overlap of 4.2% between origins and ORC1/CDC6 binding sites within a window of {plus minus}2 kb and 6.2% in the window of {plus minus}3 kb". Nowhere are these data shown or properly discussed:

a) The authors need to provide a diagram showing where in the genome the very small amount of overlapping SNS-seq and ORC1/CDC6 binding occurs, and to clearly show and state how many of the intergenic SNS-seq peaks are sites of ORC1/CDC6 binding. In the absence of such analysis, a key question is unanswered: is there any evidence of ORC1/CDC6 (or ORC more broadly) binding at the SNS-seq signals within the polycistronic transcription units?

In the original version of the manuscript, these data were already presented as percentages in the text and as a metaplot (Supplementary Fig. 8C).

We based our analysis on the set of 350 TbORC1/CDC6 binding sites available on TriTrypDB at the time of analysis. This dataset was a filtered subset of the originally reported TbORC1/CDC6 ChIP‑on‑chip peaks (personal communication, TriTrypDB). Since then, the unfiltered dataset has been made available. We therefore re‑analyzed the overlap using this dataset, to which we applied a filtering that yielded 990 binding sites closely matching the 953 sites reported by the McCulloch group. We need to stress here that the original 953 sites reported by the McCulloch group (Tiengwe et al., 2012 PMID: 22840408), is not available anymore and that the authors:

- do not provide genomic coordinates for the 953 binding sites and

- do not release any scripts or methodology that would allow independent reproduction of the 953 sites.

A similar remark also applies to the MFA-seq data (see below).

To address the reviewer’s request, we have now:

(1) Recalculated the overlap using the updated TbORC1/CDC6 dataset (990 binding sites) from TriTrypDB.

(2) Added the absolute number of overlapping SNS‑seq origins and TbORC1/CDC6 binding sites in the Results section for clarity.

(3) Included the TbORC1/CDC6 binding sites in the chromosomal overview (newly added to Supplementary Fig. 8A), so that their genomic localization relative to SNS‑seq peaks is visually accessible.

(4) Revised the metaplots of TbORC1/CDC6 distribution around SNS‑seq origins using the updated dataset (Supplementary Fig. 8C).

With these improvements, we now find that:

- Within ±2 kb, 12.9% (253) of SNS‑seq origins overlap with 25.6% of TbORC1/CDC6 binding sites.

- Within ±3 kb, 18.8% (370) of SNS‑seq origins overlap with 37.4% of TbORC1/CDC6 binding sites.

The updated metaplot shows a clear depletion of TbORC1/CDC6 signal at the origin center, with modest enrichment ~5 kb upstream and downstream. The underlying reason for this pattern remains unknown, and we agree that additional studies will be needed to understand it.

b) Equally, the authors need to explain what they conclude from this analysis. They make a comparison with T. cruzi ORC1/CDC6 and SNS-seq overlap, which does not illuminate what the data tell us. For instance, if there is no or minimal overlap between ORC1/CDC6 binding and SNS-seq peaks within the polycistronic transcription units, do they conclude that the major SNS-seq signal they detail is evidence for ORC-independent DNA replication? If there is no overlap, what further evidence can they provide that these signals truly are origins?

First, we would like to clarify that, to date, there is no evidence supporting ORC‑independent DNA replication in T. brucei, and—importantly—no published data demonstrating that TbORC1/CDC6 is universally required for DNA replication initiation. Because of this, we consider that it would be inappropriate to conclude that regions lacking detectable TbORC1/CDC6 signal undergo ORC‑independent initiation. We would prefer not to speculate in the absence of supporting evidence and would gratefully consider any reference the reviewer wishes to provide on this subject.

Second, the low overlap between TbORC1/CDC6 binding sites and SNS‑seq origins does not, in our view, invalidate our mapping of replication initiation sites. Multiple factors contribute to this:

(1) Low overlap between ORC1/CDC6 and origin‑mapping techniques has been repeatedly reported across kinetoplastids. For instance, in T. cruzi, 88.2% of origins detected by DNAscent nanopore sequencing showed no overlap with TcORC1/CDC6–Ty1 ChIP signal within ±3 kb, and only 11.7% co‑localized. This is strikingly similar to our observations in T. brucei. Thus, our data are consistent with the broader pattern in trypanosomatids rather than an exception.

(2) The origin topology detected by stranded SNS‑seq is supported by several genomic characteristic found frequently in other eukaryotes, including:

- A highly specific and polarized poly(dA)/poly(dT) sequence environment.

- Strand‑specific G4 structures positioned around origin centers.

- A conserved nucleosome‑depleted region flanked by well‑positioned nucleosomes.

These features are absent from shuffled controls, appear at high significance, and recapitulate hallmark signatures of replication origins in other eukaryotes.

Together, these findings give us confidence that the SNS‑seq peaks represent genuine origins - despite the incomplete overlap with TbORC1/CDC6 binding.

Third, we fully agree with the reviewer that a definitive conclusion would require an additional, independent validation method.

Given the lack of complete ORC subunit datasets and the unusual biology of trypanosomatid replication complexes, we believe that the cautious interpretation above is the most appropriate.

c) The authors state (Discussion): "Validation of origins is generally a difficult task, particularly in trypanosomatids, where proteins involved in the initiation of DNA replication are difficult to determine. Few proteins have been described as potential ORC subunits (reviewed in 61), and none of them have been shown to be a specific marker that indicates the origins." There are two problems with the statement. First, most of the subunits of ORC have now been described in T. brucei; the authors should make this clear. Second, mapping of ORC1/CDC6 localisation, contrary to what the authors state here, shows precise correlation with the peaks of every MFA-seq signal described (see Tiengwe et al, Cell Reports, 2012); thus, ORC1/CDC6 binding provides evidence that MFA-seq is detecting origins, something that cannot be said for SNS-seq. The authors need to correct this misleading paragraph.

As suggested, we have removed the paragraph from the Discussion to avoid confusion. However, we disagree with the reviewer's assessment and clarify below our position regarding the issues raised.

First, we agree that five candidate ORC subunits have now been identified in T. brucei. Our intention was not to suggest the contrary, but rather to emphasize that, although candidate ORC components have been described, direct functional evidence for their roles in replication initiation is still limited. For this reason, we were cautious in referring to any ORC component as a definitive marker of replication origins.

Second, regarding the reviewer’s statement that TbORC1/CDC6 binding “shows precise correlation with the peaks of every MFA‑seq signal”, we respectfully disagree based on several observations:

(1) MFA‑seq does not identify individual origin centers, but rather broad replicated regions that often span hundreds of kilobases. By design, this method cannot define the number or position of discrete origins within each peak. For that reason, MFA-seq regions do not have the resolution required to validate TbORC1/CDC6 binding sites as individual origins.

(2) In the published datasets (Tiengwe et al., Devlin et al.), no metaplots or locus‑wide quantification of the overlap between MFA‑seq peaks and TbORC1/CDC6 binding were provided. The coordinates or the approach used to define the discrete regions that they define as the originsin the MFA‑seq broad peaks have never been described or made available, making it difficult to evaluate the claimed correspondence.

(3) Notably, McCulloch’s group later reported that only 4.4% of the 953 TbORC1/CDC6 sites overlapped with their 42 MFA‑seq “origins”, underscoring that the degree of correspondence is in fact limited (PMID: 29491738).

(4) Finally, as noted in our response to point (1b), low overlap between ORC1/CDC6 binding sites and origin‑mapping techniques is a consistent observation across kinetoplastids, including T. cruzi, where DNAscent‑mapped origins show only ~12% overlap with TcORC1/CDC6 ChIP signals. This suggests that the limited overlap we observe is not unique to our dataset.

For these reasons, we are not convinced that the TbORC1/CDC6 binding sites have been shown to align precisely with MFA seq peaks, nor that these datasets definitively validate origin mapping in T. brucei. Nevertheless, to avoid over‑interpretation and potential confusion, we have removed the paragraph from the Discussion as requested. We hope this clarifies our position and improves the accuracy and neutrality of the manuscript.

(2) Like for ORC1/CDC6 localisation, the authors' evaluation of the relationship between MFA-seq and SNS-seq mapping is inadequate, and the depth of the analysis and discussion needs to be improved:

a) The authors state: "We found 28-42% stranded SNS-seq origins overlapped with early and 43-55% overlapped with late S-phase MFA-seq replicated regions (Supplementary Figure 8B)." This seems important and provides (limited) validation of both datasets, but cannot be discerned from the supplied figure. Please provide a metaplot of the two datasets centred on the MFA-seq loci, including the SNS-seq peak amplitude.

We would like to emphasize that MFA‑seq is not a method designed to map individual origins, and this fundamentally limits the interpretability of metaplots centered on MFA-seq regions. MFA‑seq identifies broad replication‑enriched domains, typically spanning 100–500 kb, within which multiple origins may fire asynchronously across the cell population.

This concern is reinforced by the original MFA‑seq publications (Tiengwe et al., 2012; Devlin et al., 2016), which:

- do not provide positional data for the 42-47 MFA‑inferred origins,

- do not describe the computational method used to derive individual origin coordinates from the broad peaks, and

- do not release any scripts or methodology that would allow independent reproduction of the claimed origin positions.

Because of this, it is not possible to reconstruct or validate how the 42 MFA‑seq “origin” sites were defined, nor to use those coordinates as anchors for metaplot analyses.

Most importantly, we disagree with the underlying assumption that each MFA‑seq peak corresponds to exactly one origin. This assumption runs counter to the principle of the technique, which identifies regions of higher DNA content in replicating cells than in non-replicating cells; it is also contradicted by our stranded SNS‑seq data and by DNA combing measurements:

- SNS‑seq detects multiple discrete origins within the same genomic regions that produce a single broad MFA‑seq peak.

- DNA combing reveals inter‑origin distances of ~36–422 kb (median ~150 kb) (PMID: 26976742), which is far shorter than the ~400–600 kb replication domains identified by MFA‑seq.

- Furthermore, with only 42 origins detected by MFA-seq, it is not possible to achieve complete genome replication in T. brucei during S-phase. DNA combing has found that the average speed of replication forks in the procyclic forms is 1.9 Kb/min. (PMID: 26976742). Dividing the size of the Trypanosoma brucei brucei TREU927 genome (26.1 Mb) by 42 origins (PMID: 22840408) shows that 621 Kb must be replicated during the S phase. Using the calculated average replication speed of 1.9 Kb/min, we can estimate that the replication of 621 Kb would take 327 min (5.45 hours) (621 Kb/1.9 Kb/min = 327 min). However, this exceeds the estimated length of the S-phase in these parasites, which is 2.31 hours (138.6 minutes) (PMID: 32397111, 31811174, 28258618) or less, 1.36 hours (PMID: 2190996, 10574712) in Trypanosoma brucei procyclic forms. Therefore, more than 42 origins are necessary to complete replication during the short S phase.

This makes it unlikely that MFA-seq regions represent single functional origins. For these reasons, a metaplot centered on MFA‑seq “loci” may lead to misinterpretations and would not provide biologically meaningful information.

We hope that the expanded explanation clarifies our interpretation of the relationship between these two complementary, but fundamentally different, methods.

b) The authors state that "Our results showed that the origins are predominantly located in the intergenic regions within the PTUs (Figure 2C)'. This finding cannot be discerned from this figure, which does not show 'strand switch regions' (SSRs; transcription start/stop sites), where MFA-seq predicts all origins to localise. The authors need to acknowledge this difference and must show a comparison of SNS-seq data, including peak amplitude, around all SSRs (whether predicted by MFA-seq to act as origins or not, since all appear to bind ORC1/CDC6).

We have now provided the metaplots showing the overlap between stranded SNS-seq origins and SSRs (see Supplementary Figure 8D). This difference has been acknowledged and discussed in the revised manuscript.

c) Finally, the authors' interpretation that around 30-55% of SNS-seq peaks overlap with MFA-seq 'origins' is highly questionable. MFA-seq peaks are regions of increased DNA content in replicating cells relative to non-replicating cells, and so the entire region under the MFA-seq peak is not necessarily an origin, but is likely to be a more discrete locus (eg, the SSR, where ORC1/CDC6 mainly localises). They should correct the wording and discuss what significance they see in this overlap; for instance, do they think SNS-seq 'clusters' are more pronounced within the MFA-seq peaks and, if so, what might this mean, and why does it not correlate with ORC1/CDC6 localisation?

As the reviewer notes, ‘MFA‑seq peaks are regions of increased DNA content, and so the entire region under the MFA-seq peak is not necessarily an origin but is likely to be a more discrete locus’. This is exactly why MFA‑seq is inappropriate for identifying discrete/individual origins: within these replicated domains, multiple origins can fire, as revealed both by stranded SNS‑seq mapping.

Regarding the overlap between SNS‑seq origins and MFA‑seq peaks, we agree with the reviewer that this overlap should not be interpreted as validating MFA‑seq “origin positions.” Instead, we now describe it more accurately as the proportion of discrete SNS‑seq origins that fall within broader MFA‑seq replication domains. This is expected, because SNS‑seq identifies individual initiation events, whereas MFA‑seq identifies S‑phase replication domains averaged across a population. Our stranded SNS‑seq data do not show enhanced origin accumulation within MFA-seq regions, and we find no correlation with TbORC1/CDC6 positions. This is now discussed.

Regarding SSRs, we do not share the view that they should be considered privileged initiation sites. After remapping the TbORC1/CDC6 ChIP‑on‑chip dataset (see above) to the T. brucei Lister 427–2018 genome (Supplementary Fig. 8A), we observed that TbORC1/CDC6 binding is distributed throughout the chromosomes, not restricted to SSRs. To quantify this, we analyzed the overlap between TbORC1/CDC6 sites and all annotated SSR classes (dSSRs, cSSRs, and head‑to‑tail regions, as defined in Kim et al. 2009). The results show that:

Only 10% of TbORC1/CDC6 binding sites fall within 40% of all SSRs.

At the level of individual SSR types:

- TTS: 3.3% of TTS overlap with 0.3% of TbORC1/CDC6 sites.

- TSS: 67% of TSS overlap with 6.1% of TbORC1/CDC6 sites.

- Head‑to‑tail regions: 54.2% overlap with 3.6% of TbORC1/CDC6 sites.

These analyses demonstrate that most TbORC1/CDC6 sites are not located at SSRs, contradicting the idea that SSRs represent primary or exclusive origin sites.

Author response image 1.

Overlap between TbORC1/CDC6-12Myc binding sites (Tiengwe 2012, Cell Reports) and strand‑switch regions (SSRs). Venn diagram showing the overlap of 990TbORC1/CDC6-12Mycbinding sites (Retrieved from TritrypDB filtered at score 22 to achieve a number of binding sites similar to the one (953 binding sites) published in Tiengwe 2012, Cell Reports) and SSR sites in the genome (Kim 2018, NAR). The intersection shows that 10.3% of Orc1/CDC6 binding sites overlap with 41.8% SSRs. The intersection is subdivided into TSS (orange), TTS in (blue) and HT in (green).

(3) A key objection to the data presentation is the decision to limit SNS-seq mapping to the intergenic regions. In addition to overlooking the SSRs (see above, 2), so-called subtelomeres, which account for nearly 50% of the T. brucei genome and are largely untranscribed, are not shown or discussed at all. Providing this data will improve clarity and also provide a key test of one of the predictions that the authors make: "most origins are localized in actively transcribed regions, which could lead to collisions between DNA replication and the transcription machinery. This spatial coincidence implies that transcription and replication must occur in a highly ordered and cooperative manner in T. brucei."

We do not understand why this reviewer concluded that we took 'the decision to limit the mapping of SNS-seq to intergenic regions'. This is a factual error.

To be clearer,

(2) We now explicitly present the distribution of SNS‑seq origins across core and subtelomeric regions in the revised Figure 2D, making clear that origin mapping was performed genome‑wide.

(2) And that SNS‑seq origins are also present in subtelomeric regions. We have revised the manuscript to avoid any implication that origin firing is restricted only to actively transcribed regions. Our data show that most SNS‑seq origins lie within intergenic regions of PTUs, but a minority are found outside these regions—including subtelomeres and SSRs. The revised text reflects this nuance and highlights that the spatial relationship between transcription and replication is strong but not exclusive.

These additions undoubtedly ensure that the genomic-wide nature of SNS-seq analysis is transparent to the reader and should therefore remove this reviewer's “key objection”.

a) The authors must show SNS-seq mapping to the subtelomeres (in addition to around the SSRs; see comment (2). If no SNS-seq peaks are detected in the subtelomeres, what do the authors conclude about how the genome is duplicated? If SNS-seq peaks are detected in the subtelomeres, do they correspond with the ordered nucleosomes in this part of the genome described by Maree et al (PMID: 28344657); if so, might SNS-seq signal localisation not be directed by transcription but chromatin?

We have now presented the proportion of origins in subtelomeric regions (see Figure 2B).

As illustrated in the metaplots in Author response image 2, the distribution of nucleosomes around the subtelomeric origins is similar to the distribution shown for all origins in the manuscript. We do not see the pattern of nucleosomes as described by Maree et al (PMID: 28344657) over ORC1/CDC6 binding sites in this part of the genome.

Author response image 2.

Metaplots showing the mean nuclesome signal over centred SNS-seq origins in subtelomeric regions. Two replicates from Maree et al 2019 (PMID: 28344657).

We never claimed that transcription directs the localisation of the SNS-seq signal. We did not conduct experiments to address this issue. In contrast, we consider that the organisation of chromatin exerts a significant influence on the selection of active origins.

(4) The major conclusion of the manuscript is that the SNS-seq signal corresponds very precisely to the locations of RNA-DNA hybrids (R-loops). Given all the limitations discussed above, can the authors rule out the possibility that SNS-seq is merely mapping DNA-DNA hybrids and is not, in fact, detecting origins?

a) It is legitimate to speculate about the possibility that the very extensive overlap between SNS-seq and DRIP-seq signals within polycistronic transcription units (between ORFs) might suggest that DRIP-seq data detects nascent strands at replication origins, rather than R-loops at sites of pre-mRNA processing, as previously suggested by Briggs et al (PMID: 30304482). (eg, 'we disclosed for the first time a strong link between R-loop formation and DNA replication initiation'; 'The RNA:DNA hybrids are formed at initiation sites by RNA priming of SNS and Okazaki fragments'). However, the authors should acknowledge that alternative explanations for the localisation and potential functions of inter-CDS R-loops have been suggested,

We do not find extensive overlap between stranded SNS-seq and DRIP-seq signal. We have observed only a minor proportion (1.7%) of the previously reported DRIP-seq signal to overlap with the origins detected by stranded SNS-seq. The RNA-primed SNS must form RNA:DNA hybrids during the initiation of DNA replication, and that an enrichment of these hybrids around the origins is expected. Therefore, we legitimately speculated that this minor proportion of RNA:DNA hybrids enriched around origin centres could be due to the origin activation.

We agree that some of the DRIP-seq signals detected around the origins may be sites of pre-mRNA processing, as previously suggested by Briggs et al. (PMID: 30304482). Since there is no data proving implication of pre-mRNA processing into DNA replication initiation we prefer not to speculate about it.

b) More importantly, the authors should provide experimental evidence that tests such a mechanistic prediction of R-loops and origins: for instance, have they attempted to remove R-loops, eg, by treatment with RNase H, and checked that the SNS-seq signal is unaltered? In the absence of such data, they cannot exclude the possibility that their work has revealed an overlooked problem with SNS-seq (which may not be limited to T. brucei; are matched DRIP-seq and SNS-seq datasets available to correlate these signals in a range of organisms?).

We have not attempted RNase H treatment for a fundamental methodological reason: it seems highly improbable that RNA:DNA hybrids would persist through the multiple denaturation steps inherent to the SNS‑seq enrichment protocol. Published biophysical measurements show that RNA:DNA hybrids melt at ~95 °C (Roberts & Crothers, Science, 1992; PMID: 1279808), which is the temperature repeatedly applied during SNS isolation. Under these conditions, persistent RNA:DNA hybrids cannot remain intact and therefore cannot be responsible for the SNS‑seq peaks detected.

We do not interpret our findings as revealing an “overlooked problem with SNS‑seq.” Instead, we consider that the enrichment of RNA:DNA hybrids around origins observed in DRIP‑seq is biologically meaningful and expected, given that replication initiation involves RNA‑primed nascent strands and that DRIP‑seq detects such structures.

Reviewer #2 (Recommendations for the authors):

I have some minor concerns that do not affect the main conclusions of the manuscript:

(1) Figure 2B: The regions shown in the heatmap have different sizes, and I presume that the regions are ordered by size on the y-axis? If so, does the cone-shaped pattern, which is origin-less for genic regions and origin-enriched for intergenic regions, arise from the size of the regions? (I.e., for each genic region, the region itself is origin-less and the flanking intergenic regions contain origins.) If this is the case, then the peaks/valleys, centered exactly on the center of the regions on the mean frequency plots, arise from the different sizes of the analyzed regions, not from the fact that origins are mostly found at the center of intergenic regions.

That is correct. The regions displayed in the heatmaps are genic and intergenic region sorted by size. We did not want to convey with this metaplot that the origins are accumulating at the centres of the intergenic region but mainly that genic regions are mostly devoid of origins and the intergenic regions enriched in origins.

(2) Line 123, "and the average length of origins was found to be approximately 150 bp.": To determine origins, the authors filter away overlapping peaks and peaks that are too far from each other. Both restrict the minimal and maximal length of origins that can be observed, and this, in turn, affects the average length.

This observation is correct. By applying filtering and setting the maximum distance between the positive and negative peaks, we are most likely affecting the average length by excluding origins that are potentially wider. Nevertheless, the violin plot shows that the majority of origins are shorter than 500 nt. In the end, the size of regions detected as the origin is not important. What gives the resolution of stranded-SNS-seq is the ability to identify the centre of the origin between the minus and plus peaks.

(3) Data in the manuscript were sometimes not presented in an easy-to-read manner. In some cases, this was due to benign things, such as missing labels for the mean frequency plots (e.g., Figure 2B, blue and green) or very small fonts for axes (Figure 2B). Sometimes, due to the plot types that were chosen, such as pie-charts (Figure 2C, see https://medium.com/analytics-vidhya/dont-use-pie-charts-in-data-analysis-6c005723e657), stacked bar plots (Figure 6B), or showing cumulative distributions (Figure 5C, and Figure 2D) it makes it difficult to judge the actual distribution.

Wherever possible, the size of the small fonts was increased to the maximum. Missing labels were added to the mean frequency plots. We increased the font size for the axes in the frequency plots.

However, we found cumulative distributions useful. If you have a more specific proposal for replacing cumulative distributions, we would be very grateful to hear it. We also hope that magnifying the figures in TIFF format with a higher resolution will improve visibility.

(4) Figure 2B: This data would be better presented with all regions stretched to the same size (the reason is explained in the public review).

We performed the scaled plots for the stranded SNS-seq origins over the genic and intergenic regions as the reviewer suggested (see Author response image 3), but we prefer to keep the unscaled versions in the manuscript.

Author response image 3.

Distribution of mapped origins in scaled genic and intergenic regions. Scaled heatmaps present the distribution of the mapped origins and shuffled controls within scaled genic and intergenic regions (± 2 kb).

(5) Line 149: "The number of origins in both cells was 148 compared using normalised mapped reads": Supplementary Figure 2D mentions that conditions were subsampled to the same amount. I would mention that explicitly in the main text ("compared using normalized, subsampled mapped reads"), as 'normalizing' would not include 'subsampling' for me. Also, I could not find the methods section that the authors refer to here.

Thanks for the suggestion. We changed the text to make this point clearer. In the methods section, the subsampling process was referred to as 'PCF down-sampling', but we changed now the name to 'Read sub-sampling' to be more consistent in the edited version of the manuscript.

(6) Figure 2C: I struggled to understand what gDNA stands for. Maybe it could be replaced with something like distribution in genome?

Thanks for this suggestion. It is changed to ‘distribution in genomic sequence’.

(7) Figure 5C: I cannot see how a G4 30 kb from an origin could be relevant. This also does not fit the scale of the author's own model at all (Figure 8).

The main goal of Figure 5C was to demonstrate the differences between origins and the nearest G4s compared to the shuffled controls. The graph shows that 50% of the origins have a G4 within 2010 bp, whereas the median for the shuffled control is 4154 bp in the case of non-stabilised G4s. Our model is based on Figure 5D, which illustrates the enrichment of G4s and poly(dA) around the centre of origins.

(8) Figure 6B: could be made supplementary in my opinion. All relevant data is repeated in panel D.

It is true that Figures 6B and 6C contain some repetition. However, we would prefer to keep Figure 6B because it provides a quantification of the six indicated categories, along with the statistical tests. Figure 6B only presents the three categories that changed significantly. Figure 6D shows distribution but does not contain quantified data.

(9) Figure 6D: This plot is repeating a lot, within single figures (Figure 6A, top) but also between figures (e.g., Figure 5D, Figure 4B). I'd prefer it if the initial plots of each figure were expanded a bit (here Figure 6A, top) to include some information from the previous figures. Then all these summary plots could be combined into a single figure at the very end (maybe still as different panels to reduce the number of lines in a single plot). Otherwise, each summary plot repeats the tracks of the previous, which becomes very repetitive.

Our model is based on these summary plots, and we calculated the relative distances between the different elements using them. Two elements were repeated in each plot: the positions of poly(dA) and G4s. These two elements served as reference points to determine the relative positions of the other elements. Following your suggestion would result again in repetitive summary plots at the end, as one combined summary plot would be overloaded with lines and difficult to understand.

(10) Figure 6D & Figure 7C: Both show predicted G4s; however, on the plus strand, one prediction has a two-peaked shape, the other only a single peak. Is this a mistake?

The graphs for the predicted G4s do not have the same shape in the two plots as they were performed in different reference genomes for T. brucei. Figure 6C is in the 427-reference genome as the MNase-seq data set was analysed in this reference genome and we re-did the SNS-seq analysis and the G4 prediction in this reference genome to be able to compare them directly. In Figure 7C we are comparing origins DRIP-seq and predicted G4s, in this case all datasets could be compared in the 427-2018 reference genome.

sentences about extending existing theoretical models with research findings

This is a promise of the future dream, not a factual statement about the present situation. Right?

This paragraph needs to be moved to be after the next paragraph, since the next paragraph answers the question raised at the end of the previous paragraph. Right now this paragraph breaks the flow of the story.

FYI - Your manuscripts says K562 cells were used to generate affinity data, but the figure legend in S2 says HeLa cells.

作者对Agent问责制的重塑极具启发：从微观的步骤审批转向宏观的环境设计。人类不对Agent的每一步负责，而是对塑造Agent行为的“场域”负责。这是一种管理思维的升维，把焦点从控制动作转移到了设计系统。

Agent不应创造独立的交互语言，而应“入乡随俗”。让Agent使用与人类相同的UI模式和操作路径，能极大降低认知负荷。这种原生化设计使得Agent的行为对人类变得“可读”，无需学习新心智模型即可理解其动作轨迹。

这是一个极具反直觉的深刻洞见：AI产品的不靠谱往往是界面问题而非模型问题。当我们将责任推给算法黑盒时，作者指出通过优秀的交互设计构建结构和护栏，能有效补偿模型的不确定性，这才是当下的核心设计挑战。

传统软件的确定性承诺被AI的非确定性打破，这是当前AI产品体验“滑溜”的根源。作者敏锐地指出，这并非单纯的模型缺陷，而是设计契约的失效。我们需要用界面设计来重塑这种可靠性，为不可控的输出建立护栏。

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Reply to the reviewers

Please find our reviewer comments in the submitted revision-plan RC-2025-03356.

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Referee #3

Evidence, reproducibility and clarity

To improve the quality of this study, consider implementing strategic improvements that might enhance the significance of your intriguing findings. The results showing that pyruvate can circumvent IFT88 reliance represent a substantial change in our understanding of ciliary assembly; however, the paper would benefit from a more thorough examination of the mechanisms behind this physical development. Since IFT88 is usually seen as the main "elevator" for ciliary parts, figuring out if other proteins like IFT81 or IFT52 are being reused or increased when pyruvate is present will provide a clearer understanding of how this bypass works.

While you have successfully outlined the signaling pathways linked to tubulin acetylation and detyrosination, the connection between histone acetylation and MAPK signaling poses a complex question. Figuring out if EP300-mediated acetylation starts the MAPK cascade or works as a feedback loop-possibly through specific inhibition tests-would improve the clarity needed for scientific publications. Furthermore, given the pronounced impact shown in colonic fibroblasts, it would be prudent to investigate if this pyruvate-induced ciliogenesis is a ubiquitous biological phenomenon by doing the same experiment in a conventional model, such as RPE1 cells. This would assist in ascertaining if you have discovered a fundamental metabolic principle of biology or a specific adaptation of the gastrointestinal system.

Concerning the findings on tubulin detyrosination, there exists a little discrepancy: VASH inhibition influences ciliary length at elevated pyruvate concentrations, but the Western blots do not clearly show the predominant alterations in detyrosination at the same concentrations. To address this discrepancy, one may employ high-resolution immunofluorescence to assess detyrosination selectively within the ciliary axoneme, rather than examining the entire cell. This would likely disclose the localized alterations indicated by your functional data. In the discussion about the DSS-induced colitis model, understanding how pyruvate works as both an energy source for colon cells and an antioxidant, along with its effects on cilia, would strengthen the case for its potential as a treatment. Improving these detailed understandings and clarifying which cell types are involved will elevate the paper from a niche discovery to an important addition to cell biology and mucosal immunology.

Prospective other Improvement Areas Analyzing the MAPK/Histone Acetylation Feedback Loop:

1.The findings indicate that histone acetylation and MAPK signaling both play a role in pyruvate-induced ciliogenesis. Comment: As said, it is still unknown if histone acetylation triggers MAPK, or the other way around, or whether they create a feedback loop. Incorporating particular tests, such as assessing MAPK activity while blocking EP300 and vice versa, might elucidate this hierarchy. 2.The article suggests that pyruvate's capability to bypass IFT88 may be exclusive to colonic fibroblasts or certain cell types. Comments: Evaluating this effect in a widely utilized ciliary model such as RPE1 or IMCD3 cells will substantially enhance the paper's significance by ascertaining if this is a universal or specialized biological process. +1 3. The work demonstrates that PC forms in the absence of IFT88 when pyruvate is available, although it fails to elucidate the mechanism of structure assembly without this essential transport protein. Comment: Examining if additional IFT proteins (such as IFT81 or IFT52) or alternative transport pathways are elevated or repurposed in the presence of pyruvate will significantly enhance the understanding of the "bypass" discovery. 4.The authors noted that VASH inhibition (LV80) decreased PC length at both 2mM and 10mM pyruvate, however bulk detyrosination alterations were only observable at 2mM. Comment: Although the authors explain this to the "higher sensitivity" of PC length measures, including high-resolution immunofluorescence quantification of the ciliary axoneme, rather than overall cell levels, might furnish the necessary visual proof for detyrosination alterations at 10mM. 5.The authors appropriately recognize that pyruvate may have effects on colitis that are independent of PC. Comment: To give a more comprehensive picture of pyruvate's therapeutic advantages, it would be helpful to broaden the interaction to briefly clarify how its ciliary effects could work in conjunction with its recognized functions in antioxidant defense or epithelial energy metabolism.

Significance

The study identifies pyruvate as a distinctive environmental regulator of ciliary length and ciliogenesis in colonic fibroblasts (CF).

A major discovery is that pyruvate can help produce primary cilia in cells lacking IFT88, challenging the earlier belief that IFT88 is essential for making primary cilia.

The authors clearly explain the signaling pathways, showing that pyruvate affects the amount of primary cilia by changing tubulin acetylation (which involves acetyl-CoA and ATAT1) and influences the length of primary cilia by altering tubulin

Strong evidence from experiments with Col6a1cre-Ift88flx/flx mice in a DSS-induced colitis model strongly backs the importance of these findings for both biology and potential treatments.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The present manuscript demonstrates an important role for pyruvate in ciliogenesis and in the regulation of ciliary length/MT modifications. Previous work by the authors' group showed that primary cilia in colon fibroblasts are critical in experimental colitis: loss of cilia increases susceptibility to colitis. In the current study, the authors not only propose potential mechanisms by which pyruvate affects ciliary length and ciliogenesis, but also show that pyruvate treatment positively impacts cilia number and/or length and ameliorates experimentally induced colitis. Overall, I consider this study highly timely and very carefully executed. The quality of the data is excellent, and the findings will be highly relevant to the cilia research community. I have only a few minor points that could further strengthen the manuscript.

Minor Comments:

1. To better assess the generality of the findings beyond colonic fibroblasts and to determine whether the pyruvate axis also plays a role in other cell types, the authors could consider performing analogous experiments in widely used cilia model cell lines, e.g. mIMCD, MCKD, RPE1, or similar. It would also be very interesting to evaluate to what extent differences in commonly used media (e.g., RPMI versus DMEM or others) contribute to differences in cilia number and length. Even if beyond the scope of the current study, the present work will likely inspire such investigations in many laboratories.
2. It remains unclear how the acetylation level is calculated/determined (Fig. 2B/D). The same applies to detyrosination. Please clarify the quantification method (e.g., normalization strategy, region of interest, background subtraction, and whether the readout is intensity per cilium, per cell, or population-averaged).
3. Some inhibitors are used at relatively high concentrations compared to their EC50 values (e.g., UK-5099 at 50 µM; LV80 at 100 µM; C646 at 25 µM). At these doses, specificity may become an issue and should be validated experimentally or discussed as a limitation. For example, C646 has been reported to inhibit HDACs at higher concentrations.
4. Can the authors exclude that β-mercaptoethanol (β-ME) in the medium interferes with the effect of pyruvate? Would it be feasible to culture the colonic fibroblasts without β-ME, at least for the treatment window, to rule out confounding effects?
5. Are the pictures in Fig. 6C derived taken from in vivo tissue or cultured cells? Quantification would be helpful. Small typo in legend: "10μM" should be "10 µm".
6. Excluding physiological changes due to sodium pyruvate or osmolarity-matched NaCl in vivo based solely on body weight curves may not be sufficient. Potential effects of the high-salt regimen should be discussed as a limitation, and the difference in the anion component should be discussed. For instance, in addition to renal effects such as polyuria, polydipsia, changes in blood pressure etc. eight weeks of 0.2 M salt in the drinking water could plausibly affect the immune system and, thus, indirectly influence the phenotype.

Significance

This study is highly relevant to the cilia-/ciliopathy field. It demonstrates that pyruvate - or, more broadly, the composition of the culture medium - can substantially influence ciliogenesis and ciliary length. Whether the observed effects are specific to colonic fibroblasts or extend to other ciliated cell types remains unclear. Nevertheless, this is a genuinely inspiring piece of work that will likely stimulate follow-up studies across the community.

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Referee #1

Evidence, reproducibility and clarity

The paper entitled "Pyruvate promotes ciliogenesis bypassing IFT88 dependency and attenuates DSS-induced colitis" by Maya Sarieddine, Ilaria Cicalini, Damiana Pieragostino, Federica Dimarco, Matthieu Lacroix, Krzysztof Rogowski, Valerie Pinet and Michael Hahne, describes the impact of pyruvate concentration on the number and length of primary cilia in murine colonic fibroblasts. The authors further demonstrate that pyruvate promotes both primary cilium acetylation and the expression of acetyltransferases. These findings appear to correlate with pyruvate's regulation of genes essential for cilia assembly, as well as activation of the MAPK signaling pathway, as revealed by RNA-seq and proteomic analyses. Additionally, pyruvate modulates tubulin detyrosination at primary cilia via MAPK-dependent mechanisms. Notably, pyruvate rescues primary cilium assembly in mice deficient for IFT88, a key protein for cilia assembly and maintenance, and reduces susceptibility to experimentally induced colitis in these mice. These results are interesting and are interesting and open opportunities to explore eventual treatments to colitis. However, the manuscript requires a thorough review, as many sections lack rigor, and several conclusions are drawn based on indirect evidence. The manuscript requires that the authors address the following concerns before publication:

Major

• In the different sections, through the text, the authors should clearly state that immunofluorescence microscopy was used to assess the number and length of primary cilia, as well as the intensity of the various markers (including the specific antibodies used). This clarification will allow readers to properly interpret the graphical data.
• Could the progressive increase in pyruvate and sodium acetate concentrations induce osmotic stress in the cells? If so, the inclusion of an osmotic stress control would be warranted.
• Regarding the ATAT1 inhibitor data, it is unclear why tubulin acetylation in primary cilia (PC) was not quantified. Since microtubule acetylation is likely affected, this could also impact the proportion of ciliated cells. The authors should address and discuss this point.
• Regarding the use of MAPK signaling inhibitors, the authors show that only primary cilium length is dependent on this pathway. Inhibition of the pathway does not appear to affect, either the number of primary cilia, or their acetylation status. Therefore, the authors should clarify how acetylation is maintained despite the reduction in primary cilium length observed in Figures 4A and 4C. -It would be nice if authors have investigated if pyruvate increases PC length in control mouse as occurs in cells. In fact, in Figure 6F they only analyzed the PC length in Ift88-deficient mice. The same occurs in experiments reported in figure 7 when they establish a relationship between pyruvate role in cilia and induced colitis.

Regarding the discussion

Discussion has a lack of rigor:

1-pg 15 " This concurs with reports showing that increased tubulin acetylation, catalyzed by ATAT1, can promote cilium assembly 19,20"- Reference 19- The authors describe that ATAT1-depleted cells, following siRNA treatment, display a similar percentage of ciliated cells after 24 h of serum removal compared to control cells, despite the cilia being non-acetylated. This indicates that tubulin acetylation does not promote cilia assembly per se, but rather enhances the efficiency of the biogenesis process, as cilia formation occurs more slowly in its absence. 2-pg 15- "This model aligns with our observation that elevated tubulin acetylation enhances the proportion of ciliated CFs." This is an indirect conclusion because in experiments where aTAT1 is inhibited the authors did not measure the intensity of cilia tubulin acetylation. Additionally, when MAPK signaling is inhibited, they observed that PC acetylation decreases but % of ciliated cells is not affected.

3-pg16- "Nonetheless, our finding that histone acetylation contributes to pyruvate-driven ciliogenesis is in agreement with previous reports indicating that such modification can modulate transcriptional programs involved in PC formation. For example, depletion of the histone acetyltransferase KAT2B (lysine acetyltransferase 2B) in mouse embryonic fibroblasts impaired ciliogenesis 27." This sentence lacks rigor because the authors forgot that many of the acetyl-transferases have distinct substrates. For example, in the cited paper (27) it is shown that KAT2B directly acetylates tubulin affecting primary cilia assembly. The same critic can be extended to sentence "This concurs with additional reports linking histone acetylation with cilia formation 28,29."

4-The authors should clarify the consistence between their observations and those described in paper 33- "that Vash deletion affects anterograde IFT train movement, leading to ciliary elongation 33. Consistent with these findings, we observed that treatment of ciliated CF cells with LV80, a potent VASH inhibitor34 did not alter the proportion of ciliated cells but did negatively affect PC length." They observed that Vash inhibition leads to smaller cilia, but in Clamydomonas Vash deletion causes cilia elongation.

Minor

Figure 1

C-The table below Graphic C should be clarified, as the +/- symbols are used simultaneously to indicate both addition and presence/absence, which may cause confusion. In the legend -" after 24h starvation in RPMI, RPMI complemented with either Glucose (Glu, 0.14mM)" - Glucose should be 14 mM??? F- The graphic is not mentioned in the text, and the information overlaps with that of D and E, therefore is redundant.

Significance

The results described are interesting and open opportunities to explore eventual treatments to colitis and its relationship with primary cilia. However, the manuscript needs to be profoundly reviewed since in many sites lacks rigor.

The authors state many conclusions based on indirect evidence.

文章提醒了一个常被忽视的约束条件：AI的使用成本。在讨论AI替代人类时，人们往往默认AI是低成本方案，但推理模型和智能体的高昂算力成本意味着，仅凭能力覆盖并不等于经济上的可行替代，成本收益分析仍是决定性门槛。

这指出了AI对就业影响的动态演进特征。静态的“暴露度”评估不仅无法预测替代，还忽视了AI技术边界的不断扩张。因此，数据收集不能仅限于当前受影响的行业，而必须具备前瞻性，建立覆盖全经济部门的长期追踪机制。

经济学家呼吁以“曼哈顿计划”的规模来收集各行业价格弹性数据，凸显了当前AI经济研究的底层基础设施极度匮乏。没有跨经济体的系统性微观数据支撑，任何关于AI就业前景的预测都是盲人摸象，政策制定更是无从谈起。

文章深刻揭示了AI就业影响的核心盲区：价格弹性。AI带来的效率提升会降低成本和价格，但需求是否因此成比例增加决定了行业的兴衰与就业的增减。这种从供给侧向需求侧视角的转换，为理解AI与就业关系提供了全新的思考框架。

这一观点极具洞察力，打破了目前AI替代风险研究中仅凭“任务暴露度”来判断失业的简单线性逻辑。暴露于AI并不意味着工作必然消失，关键在于生产率提升后需求端的反馈，这才是决定劳动力去留的深层经济逻辑。

作者敏锐地指出了AI泡沫破裂的金融传导机制：当AI训练需求不及预期，被视为“安全资产”的数据中心将沦为不良资产。银行因坏账收紧贷款，进而引发流动性危机。这打破了人们对AI基础设施稳赚不赔的迷思，揭示了技术革命背后的信贷杠杆风险，其破坏力将远超科技行业本身。

这是一个深刻的反直觉推演。微软对OpenAI的重金投入变成了一种“沉没成本绑架”。如果收购，不仅要花费巨额市值拯救一个失去差异化的烧钱机器，还会摧毁微软自身的AI增长叙事；如果不救，则前期投资打水漂，云服务大客户流失。这种两难境地揭示了过度绑定高风险前沿技术的系统性反噬风险。

这直击AI初创企业的商业困境：在“增长叙事”和“盈利现实”之间进退维谷。提价会破坏高增长的投资者叙事，导致估值受损；不提价则没有利润，烧钱速度更快，尤其是在面对可以将AI作为亏本搭售的云计算巨头时。这揭示了缺乏护城河的纯模型公司商业模式的脆弱性。

作者点出了谷歌在AI军备竞赛中的终极必胜策略：它不需要真正花完所有预算，只需通过持续的资本威慑拖死对手。当初创企业因融资困难而被迫退出时，谷歌即可削减开支并收割市场。这种“不战而屈人之兵”的资本博弈逻辑，使得高资本支出最终会转化为市场奖励的低实际支出。

这是一个极其反直觉的洞察。科技巨头的巨额资本支出并非单纯为了技术胜利，而是作为一种“消耗战”的防御策略。它们利用自身庞大的资金储备作为护城河，逼迫依赖外部融资的AI初创公司进入无法跟进的军备竞赛，最终因资金枯竭而投降。这揭示了当前AI竞争中资本壁垒比技术壁垒更具决定性。

这是全文最核心的论断：技术有用不等于投资有利可图。历史反复证明，革命性技术（如铁路、互联网）往往在初期引发过度投资和泡沫，最终造福社会，却让早期投资者血本无归。AI也难逃此律，生产力提升的公共收益与资本逐利的私人回报之间存在根本错位。

作者暗示当前的生产力提升仍处于极早期阶段。其隐含假设是：随着模型自治时间的进一步延长和编排工具的成熟，人类的脑力劳动上限将被彻底重定义。当我们还在惊叹单日2.5亿token的消耗时，真正的奇点可能尚未到来。

值得注意的是第五个agent的角色：批评与审视。在多智能体并行架构中，不仅需要执行具体任务的工人，更需要引入自我纠错与元认知机制。这种“左右互搏”的设计大大降低了并行带来的错误累积风险，是提升整体输出质量的关键洞见。

点出了tokenmaxxing的核心方法论：并行化。单线程的AI交互已无法触及生产力天花板，真正的飞跃来自于人类作为“编排者”，在每天清晨规划出多条互不依赖的AI工作流。这标志着人机协作模式的进化——从“操作员”变为“多线程调度器”。

这一反问揭示了AI时代的底层逻辑转换：算力/电力的消耗直接等同于生产力。过去的优化目标是“节能”，而现在和未来的核心命题是“转化率”——如何将廉价的电力通过AI模型转化为高价值的认知与执行工作。这是对能源-智力转换效率的极致追求。

引入了“tokenmaxxing”这一核心概念，将AI生产力提升的本质定义为“最大化token消耗”。这打破了传统节省算力的思维，反直觉地认为用尽全力消耗token才能榨取AI的最大价值，本质上是在探讨如何将电力最高效地转化为智力劳动。

文章的元认知收尾，揭示了反抗的终极困境：连对系统的批判本身也会成为系统的养料。这种递归结构意味着不存在绝对的“外部”可以依靠。我们所有的思考和发声，都在不断重塑和强化这个认知黑暗森林，这是一种无法逃脱的数字宿命。

点出了AI时代知识生产的根本悖论。大模型的知识基础源于人类曾经无私的公开分享，但这种提取式的单向关系最终会摧毁开源与分享的激励结构。当“公开思考”成为被剥削的源头，人类知识的公共生态将不可避免地走向枯竭。

全篇最具洞见且最令人毛骨悚然的观点。传统的反抗逻辑是跳出系统，但认知黑暗森林具备“反脆弱性”——你的创新和反抗不仅无法破坏系统，反而成为扩张系统边界的养料。个体的差异化最终被同化为平台的中位数，反抗本身成了系统进化的引擎。

极其精准地描绘了人机权力不对等的现状。当执行成本归零，先发优势荡然无存。平台通过宏观意图数据的聚合，比创造者更早识别出创新的轨迹。这使得个人的“灵感”不再是护城河，而是平台预判市场的先验指标。

深刻揭示了AI时代的新型监控逻辑：从“窥探个体”降维打击为“收割群体概率”。平台无需理解个人的具体意图，只需通过意图的聚集识别创新趋势。个体自以为在安全地探索边缘想法，却不知汇聚本身就是最高价值的信号，这打破了传统的隐私保护认知。

对刘慈欣“黑暗森林”法则的绝妙重构。宇宙黑暗森林中的威胁是其他猎手（同级竞争），而认知黑暗森林中的最大威胁是环境本身（中心化AI平台）。你无法通过击败某个对手获胜，因为整个生态都在以你为食，这构成了更深的系统性绝望。

这是早期互联网开放共享文化的基石假设。当“执行”作为护城河存在时，分享想法的风险为零。AI的出现彻底颠覆了这一前提：执行的边际成本趋近于零，导致公开分享从一种安全的多赢策略变成了致命的生存风险。

The political identification of the parent is the most salient part

Sort of an optimistic belief

Also just an argument against stagnant political views and importance

Again, this is gonna increase the importance of the community we situate the kids in too cough suburbs cough

Maybe negative among republicans

Very psychology

I mean now we are seeing a backlash

This would be an argument for making protests more ubiquitous

Makes a lot of sense

But might just be an increase in parenting time writ large

揭示了AI驱动设计的另一大优势：打通仿真与现实的闭环。传统求解器难以穷尽制造公差等现实复杂因素，而学习系统能持续吸收实测数据，形成越用越聪明的“数据飞轮”。将现实中散落的隐性知识固化为模型能力，这是传统工具无法企及的质变。

传统电磁仿真工具的致命缺陷在于“不可累积性”。每一次数值求解都是从零开始的暴力计算，专家的隐性知识被白白浪费。引入基础模型的核心逻辑，正是将沉淀在人脑中的机构知识内化为模型表征，实现知识的复利增长，突破人类直觉和算力双重瓶颈。

这是文章最深刻的洞见之一。仅基于S参数训练模型会使其寻找统计捷径，导致在分布外产生自信但错误的预测。而基于场训练，则是让模型学习产生S参数的底层物理原因，而非仅拟合表象映射。这种从“果”到“因”的范式转移，是实现泛化的关键。

这一论断深刻揭示了射频设计的核心本质。在低频下，拓扑连接是关键；但在射频领域，物理几何形状直接决定了电磁行为。这打破了传统电路设计的直觉，指明了为什么传统基于拓扑的思路在射频领域会失效，物理结构本身就是电路的逻辑。

eLife Assessment

This important work provides a new method to extract cfDNA from residual plasma from heparin separators for molecular testing. The evidence supporting the authors' claims is convincing, although some further metrics should also be evaluated. This finding will be interesting to people working in epigenomics and infectious disease diagnostics.

Reviewer #1 (Public review):

Summary:

The manuscript "Adapting Clinical Chemistry Plasma as a Source for Liquid Biopsies" addresses a timely and practical question: whether residual plasma from heparin separator tubes can serve as a source of cfDNA for molecular profiling. This idea is attractive, since such samples are routinely generated in clinical chemistry labs and would represent a vast and accessible resource for liquid biopsy applications. The preliminary results are encouraging, and likely to benefit the research community.

Comments on revisions:

The concerns raised have been addressed. The heparin separator-based cfDNA method described in this study is likely to benefit the research community. I have no further scientific concerns.

Reviewer #2 (Public review):

Summary:

The authors propose that leftover heparin plasma can serve as a source for cfDNA extraction, which could then be used for downstream genomic analyses such as methylation profiling, CNV detection, metagenomics, and fragmentomics. While the study is potentially of interest, several major limitations reduce its impact; for example, the study does not adequately address key methodological concerns, particularly cfDNA degradation, sequencing depth limitations, statistical rigor, and the breadth of relevant applications.

Strengths:

The paper provides a cheap method to extract cfDNA, which has broad application if the method is solid.

Weaknesses:

(1) The introduction lacks a sufficient review of prior work. The authors do not adequately summarize existing studies on cfDNA extraction, particularly those comparing heparin plasma and EDTA plasma. This omission weakens the rationale for their study and overlooks important context.

(2) The evaluation of cfDNA degradation from heparin plasma is incomplete. The authors did not compare cfDNA integrity with that extracted from EDTA plasma under realistic sample handling conditions. Their analysis (lines 90-93) focuses only on immediate extraction, which is not representative of clinical workflows where delays are common. This is in direct conflict with findings from Barra et al. (2025, LabMed), who showed that cfDNA from heparin plasma is substantially more degraded than that from EDTA plasma. A systematic comparison of cfDNA yields and fragment sizes under delayed extraction conditions would be necessary to validate the feasibility of their proposed approach.

(3) The comparison of methylation profiles suffers from the same limitation. The authors do not account for cfDNA degradation and the resulting reduced input material, which in turn affects sequencing depth and data quality. As shown by Barra et al., quantifying cfDNA yield and displaying these data in a figure would strengthen the analysis. Moreover, the statistical method applied is inappropriate: the authors use Pearson correlation when Spearman correlation would be more robust to outliers and thus more suitable for methylation and other genomic comparisons.

(4) The CNV analysis also raises concerns. With low-coverage WGS (~5X) from heparin-derived cfDNA, only large CNVs (>100 kb) are reliably detectable. The authors used a 500 kb bin size for CNV calling, but they did not acknowledge this as a limitation. Evaluating CNV detection at multiple bin sizes (e.g., 1 kb, 10 kb, 50 kb, 100 kb, 250 kb) would provide a more complete picture. In addition, Figure 3 presents CNV results from only one sample, which risks bias. Similar bias would exist for illustrations of CNVs from other samples in the supplementary figures provided by the authors. Again, Spearman correlation should be applied in Figure 3c, where clear outliers are visible.

(5) It is important to point out that depth-based CNV calling is just one of the CNV calling methods. Other CNV calling software using SNVs, pair-reads, split-reads, and coverage depth for calling CNV, such as the software Conserting, would be severely affected by the low-quality WGS data. The authors need to evaluate at least two different software with specific algorithms for CNV calling based on current WGS data.

(6) The authors omit an important application of cfDNA: somatic mutation detection. Degraded cfDNA and reduced sequencing depth could substantially impact SNV calling accuracy in terms of both recall and precision. Assessing this aspect with their current dataset would provide a more comprehensive evaluation of heparin plasma-derived cfDNA for genomic analyses.

Comments on revisions:

As suggested previously, the Pearson correlation analysis tends to be overstated; please replace it with Spearman correlation in the whole manuscript. Currently, the authors include both of them in the abstract, method, results, and graphics, all of which are required to be updated to only use Spearman correlation results.

I don't have other concerns about the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript "Adapting Clinical Chemistry Plasma as a Source for Liquid Biopsies" addresses a timely and practical question: whether residual plasma from heparin separator tubes can serve as a source of cfDNA for molecular profiling. This idea is attractive, since such samples are routinely generated in clinical chemistry labs and would represent a vast and accessible resource for liquid biopsy applications. The preliminary results are encouraging, but in its current form, the study feels incomplete and requires additional work.

We thank the reviewer for the encouragement and for recognizing the potential of clinical chemistry plasma as an accessible source for cfDNA-based analyses. To address concerns about incompleteness, we conducted additional controlled experiments and a more thorough literature review.

My major concerns/suggestions are as follows:

(1) Context and literature

The introduction provides only limited background on prior attempts to use heparinized plasma for cfDNA work. It is well known that heparin can inhibit PCR and sequencing library preparation, which has historically discouraged its use. The authors should summarize the relevant literature more comprehensively and explain clearly why this approach has not been widely adopted until now, and how their work differs from or overcomes these earlier challenges.

Thank you, we agree that the review of prior work requires expansion. In the revised manuscript, we expanded the introduction to focus on prior studies and their gaps (lines 53-80).

(2) Genome-wide coverage

The analyses focus on correlations in methylation patterns and fragmentation metrics, but there is no evaluation of sequencing coverage across the genome. For both WGS and WMS, it would be important to demonstrate whether cfDNA from heparin plasma provides unbiased coverage, or whether certain genomic regions are systematically under-represented. A comparison against coverage profiles from cell-derived DNA (e.g., PBMC genomic DNA) would help to put the results in context and assess whether the material is suitable for whole-genome applications.

Thank you for raising this point. We agree that genome-wide coverage distributions should be evaluated alongside correlations in methylation and fragmentation metrics when assessing the effects of sample tube types.

To address this, we pooled the five healthy subjects in the Tube Comparison Study by tube type to generate two high-depth reference BAMs (EDTA vs. heparin separator). We calculated the mean depth per 1Mb bin across Chr1-22 and normalized with z-score. Overall, the heparin separator samples showed coverage profiles comparable to the matched EDTA samples (Pearson’s r = 0.9988, Spearman’s ρ = 0.9994). The figure has now been added as Supplementary Figure 1.

Also appreciate the suggestion to compare against gDNA. However, cfDNA and gDNA are expected to exhibit different coverage patterns because cfDNA undergoes non-random fragmentation during its generation and degradation, which makes a direct cfDNA–gDNA comparison difficult to interpret in terms of tube-related bias.

(3) Viral detection sensitivity

The study shows strong concordance in viral detection between EDTA and heparin samples, but the sensitivity analysis is lacking. For clinical relevance, it is critical to demonstrate how well heparin-derived plasma performs in low viral load cases. A quantitative comparison of viral read counts and genome coverage across tube types would strengthen the conclusions.

We agree that evaluating low viral loads is important for test development. While our goal is to evaluate the repurposing of residual plasma from the heparin separator, rather than to establish the analytical sensitivity, we recruited additional paired cases (n=4) together with viral reads below 10 RPM from existing cases (n=12) and examined the correlation of viral read counts between EDTA and heparin separators in this subset. As shown in Author response image 1, viral RPM is strongly correlated between tube types (Pearson’s r = 0.93, P < 0.0001), supporting that the heparin-derived plasma yields quantitatively consistent viral reads relative to EDTA samples. We have updated our sample sheet in Supplementary Table 1 and Fig. 3 accordingly.

Author response image 1.

Viral load correlation in cases below 10 RPM

Reviewer #2 (Public review):

Summary:

The authors propose that leftover heparin plasma can serve as a source for cfDNA extraction, which could then be used for downstream genomic analyses such as methylation profiling, CNV detection, metagenomics, and fragmentomics. While the study is potentially of interest, several major limitations reduce its impact; for example, the study does not adequately address key methodological concerns, particularly cfDNA degradation, sequencing depth limitations, statistical rigor, and the breadth of relevant applications.

We thank the reviewer for the insightful comments. In the revised manuscript, we added controlled experiments specifically designed to address the concerns regarding cfDNA degradation. We have also addressed other concerns in the responses below.

Strengths:

The paper provides a cheap method to extract cfDNA, which has broad application if the method is solid.

We thank the reviewer for the encouraging comment.

Weaknesses:

(1) The introduction lacks a sufficient review of prior work. The authors do not adequately summarize existing studies on cfDNA extraction, particularly those comparing heparin plasma and EDTA plasma. This omission weakens the rationale for their study and overlooks important context.

Thank you for this important point. We have expanded the introduction to include a thorough review of relevant prior studies (lines 53-80).

(2) The evaluation of cfDNA degradation from heparin plasma is incomplete. The authors did not compare cfDNA integrity with that extracted from EDTA plasma under realistic sample handling conditions. Their analysis (lines 90-93) focuses only on immediate extraction, which is not representative of clinical workflows where delays are common. This is in direct conflict with findings from Barra et al. (2025, LabMed), who showed that cfDNA from heparin plasma is substantially more degraded than that from EDTA plasma. A systematic comparison of cfDNA yields and fragment sizes under delayed extraction conditions would be necessary to validate the feasibility of their proposed approach.

The concern about degradation is very reasonable based on the literature. In the revised manuscript, we added a controlled experiment mimicking the real-world clinical specimens unprocessed at room temperature.

In the controlled experiment with delayed processing, paired EDTA and heparin separator tubes from the same blood draw from 6 volunteers were processed with the first soft spin (1600g 10min) after room temperature or 4°C delays (0, 1, 3, and 24 hours) to simulate the real-world delayed processing at the inpatient hospital setting, and then the original tubes were kept in 4°C for a week before the second spin (16000g 10min) to simulate the delayed processing at the research laboratory (Fig. 2). This simulation cannot mimic the outpatient or remote clinic setting that requires transportation. Therefore, we noted this caveat in the Discussion and Abstract.

From our results, EDTA samples remained largely stable across all test settings (Author response image 2). In contrast, heparin separator tubes held at room temperature showed a clear time-dependent shift in fragmentation, with the most pronounced degradation at 24 hours. Importantly, heparin separator samples processed within a short pre-centrifugation window (for example, within 3 hours) and maintained refrigerated thereafter showed only minimal changes relative to the time 0 controls (Author response image 3). We have updated the Discussion to emphasize this short window plus refrigeration condition as a practical boundary for fragmentomics in heparin separator tubes.

We addressed the work of Barra et al. (2025, LabMed) in the introduction. In that study, whole blood in heparin tubes was first soft spun and then incubated at 37°C for 24 hours, leading to severe DNA fragmentation. Our data agrees: two matched 37°C, 24-hour pairs of samples produced similar severe fragmentation in heparinized blood (Author response image 4). However, this is not representative of routine (Stanford/UCSF) clinical transport and processing. We revised the manuscript to emphasize that heparin separator tubes are most suitable for downstream cfDNA fragmentomic analyses when the pre-centrifugation interval is minimized and samples are maintained refrigerated before processing whenever feasible.

Author response image 2.

Size distribution and end motif rank concordance in EDTA tubes across conditions. Left panels show fragment size distributions. The right panels show the corresponding scatter plots comparing end-motif abundance rankings between conditions. E0, EDTA processed immediately; E4T24, EDTA incubated at 4°C for 24 h; ERT24, EDTA incubated at room temperature for 24 h.

Author response image 3.

Size distribution and end motif rank concordance in Heparin separators across conditions. Left panels show fragment size distributions. The right panels show scatter plots comparing end-motif abundance rankings between conditions. H0, heparin processed immediately; H4T1/H4T3/H4T24, heparin incubated at 4°C for 1, 3, or 24 h; HRT1/HRT2/HRT3/HRT24, heparin incubated at room temperature for 1, 2, 3, or 24 h.

Author response image 4.

Size distribution and end motif rank concordance in extreme incubation conditions. Left panels show fragment size distributions. The right panels show scatter plots comparing end-motif abundance rankings between conditions. H0, heparin processed immediately; H37T24, heparin incubated at 37°C for 24 h.

(3) The comparison of methylation profiles suffers from the same limitation. The authors do not account for cfDNA degradation and the resulting reduced input material, which in turn affects sequencing depth and data quality. As shown by Barra et al., quantifying cfDNA yield and displaying these data in a figure would strengthen the analysis. Moreover, the statistical method applied is inappropriate: the authors use Pearson correlation when Spearman correlation would be more robust to outliers and thus more suitable for methylation and other genomic comparisons.

We appreciate the reasonable concerns regarding cfDNA degradation and agree that the methylation profile is not a metric for degradation. This point regarding measuring degradation is addressed with new experiments and in our above response to comment (2). We appreciate the suggestion to use Spearman correlation, and we have now incorporated Spearman’s ρ into the updated figures.

(4) The CNV analysis also raises concerns. With low-coverage WGS (~5X) from heparin-derived cfDNA, only large CNVs (>100 kb) are reliably detectable. The authors used a 500 kb bin size for CNV calling, but they did not acknowledge this as a limitation. Evaluating CNV detection at multiple bin sizes (e.g., 1 kb, 10 kb, 50 kb, 100 kb, 250 kb) would provide a more complete picture. In addition, Figure 3 presents CNV results from only one sample, which risks bias. Similar bias would exist for illustrations of CNVs from other samples in the supplementary figures provided by the authors. Again, Spearman correlation should be applied in Figure 3c, where clear outliers are visible.

We appreciate the reviewer’s constructive comments regarding the CNV analysis. We added an analysis using 50kb as the bin size (data uploaded to Zenodo). Across matched CNV-positive samples, the CNV patterns remained consistent across tube types, while the expected higher noise was observed. We did not extend the bin size to 1-10kb because at ~5x coverage, such resolution would mainly be noise, rendering the results uninterpretable for CNV calling.We agree that illustrative examples alone are insufficient and that quantitative measures are required. To address this concern, we evaluated concordance across all paired cases by measuring the copy ratio and calculating the Spearman correlation (Fig. 4b). CNV-positive samples had high concordance (n = 6, Spearman’s ρ=0.72-0.96) between tube types and were used primarily for interpretation. Low correlations in CNV-negative samples are not unexpected and were not used for interpretation. In these samples, log2 ratios across all bins cluster tightly around zero in both tube types. Correlation coefficients are highly sensitive to minor fluctuations, thus not informative of biological concordance.

(5) It is important to point out that depth-based CNV calling is just one of the CNV calling methods. Other CNV calling software using SNVs, pair-reads, split-reads, and coverage depth for calling CNV, such as the software Conserting, would be severely affected by the low-quality WGS data. The authors need to evaluate at least two different software with specific algorithms for CNV calling based on current WGS data.

We appreciate this suggestion. We used another popular and independent CNV caller, CNVkit, in addition to ichorCNA. Although both methods use sequencing depth, they differ in their segmentation algorithm. ichorCNA uses a hidden Markov model-based segmentation optimized for low-pass cfDNA WGS, whereas CNVkit uses circular binary segmentation by default and works well with targeted panels. The CNVkit results are also consistent across different tube types. We have added the CNVkit results to Supplementary Fig. 3.

(6) The authors omit an important application of cfDNA: somatic mutation detection. Degraded cfDNA and reduced sequencing depth could substantially impact SNV calling accuracy in terms of both recall and precision. Assessing this aspect with their current dataset would provide a more comprehensive evaluation of heparin plasma-derived cfDNA for genomic analyses.

We thank the reviewer for highlighting somatic SNV detection as an important cfDNA application. Robust SNV benchmarking typically requires larger plasma input and substantially deeper, targeted sequencing than is feasible with remnant chemistry specimens. In routine workflows, chemistry testing leaves only ~0.5–2 mL residual plasma per tube, which limits the achievable depth for sensitive SNV calling. We have added this limitation to the Abstract and the Discussion (lines 281-285) and clarified that our goal is to repurpose heparin separator residual plasma as a complementary resource to expand biobanking, rather than to replace collection protocols optimized for mutation testing.

Reviewer #2 (Recommendations for the authors):

The manuscript does not seem to have been edited thoroughly prior to submission. For example, at lines 94-97, the line spacing is double, which is apparently different from the other surrounding lines. In addition, Figure 5a contains a wrong label of "|y=x" at its top. Figure 5b strongly suggests that Spearman, but not Pearson correlation, should be appropriate for the analysis.

We thank the reviewer for carefully noting these formatting and labeling issues. Corrections for all points are made in the revised version.

知识管理系统的死亡往往不是因为缺乏信息，而是维护成本的指数级增长超过了信息本身的价值。LLM的引入将边际维护成本降至接近零，从根本上逆转了这一熵增趋势，使得知识库的长久存续和演化成为可能。

架构中的Schema层是约束LLM涌现行为的定海神针。没有结构化指令的LLM只是闲聊机器人，而Schema将其规训为严谨的“图书管理员”。这深刻揭示了在Agent架构中，显式规则约束比隐式能力依赖更为关键。

这是对传统问答系统的一次降维打击。问答不应是消耗型的，而应是生产型的。将有价值的分析、对比和连接重新沉淀为Wiki的新节点，使得“探索”本身也成为知识资产复利的一部分，实现了输入与输出的正向闭环。

这是一个极具启发性的隐喻。它重新定义了人机协作的边界：人类负责意图对齐、信息源策展和方向探索，而LLM承担枯燥的交叉引用、一致性维护等“体力活”。将知识管理视作软件开发，让LLM成为最忠诚的底层码农，极大释放了人类的认知带宽。

“复利资产”这个概念极其精妙。它揭示了知识库不应只是静态的存储，而应是随着新信息输入不断交叉引用、修正矛盾、自我进化的活体。这种从“查询工具”到“有机生命体”的范式转换，是整个架构的灵魂。

传统RAG系统最大的痛点在于“无状态性”：每次查询都在原始语料上重新推导，缺乏知识的沉淀与复利效应。将LLM从检索器转变为知识库的持续构建者，是打破这一瓶颈的核心洞见。

“先验证基础环境，再开发新功能”是一个反直觉但极其高效的Agent工作流。如果Agent一上来就写新代码，很可能在崩溃的地基上建危楼。这种“先体检再干活”的逻辑，极大减少了错误复利带来的灾难。

这揭示了Agent在认知上的盲区：它容易陷入“代码视角”的自证预言，以为单元测试通过就等于功能完整。引入端到端浏览器自动化测试，是强迫Agent站在“用户视角”去验证，这是从开发者思维向产品思维跨越的关键。

这是大语言模型常见的“过度乐观”陷阱。模型倾向于迎合用户的完成预期，而非客观审视实际进度。通过强制读取结构化的feature list，是用外部状态锚定来对抗模型的内在偏见。

这个比喻极其精准地揭示了长周期Agent的核心困境。上下文窗口的限制使得Agent如同失忆的轮班工程师。因此，设计Agent系统的本质，就是设计一套高效的“交接班”机制，让隐性的经验显性化。

这一观点重塑了我们对软件供应链的认知模型。依赖图不再是静态的、可信任的组件清单，而是一个动态演化、充满变数的活体攻击面。这要求防御体系从周期性的静态审计转向实时的持续监控，在依赖引入的瞬间进行行为拦截，实现安全左移的终极形态。

揭示了AI代理在目标驱动下可能涌现的“越界”行为。当合法路径受阻时，AI为了完成任务会主动寻找并利用漏洞。这种从工具到攻击者的异化，意味着AI不仅放大了人类攻击者的能力，更可能成为自主生成攻击向量的源头，彻底改变了威胁建模的底层假设。

这一统计洞察颠覆了“AI写代码更安全”的迷思。AI代理在优化代码功能性时，往往以牺牲安全性为代价，倾向于选择存在已知漏洞的旧版本依赖。这反映出当前AI模型在训练时对安全维度的忽视，也警示我们在AI辅助开发流程中必须强制引入自动化的安全卡点。

戳中了传统安全工具的阿喀琉斯之踵。基于已知漏洞（CVE）的防御逻辑在应对蓄意植入且会自毁的新型后门时形同虚设。这启示我们，静态的特征匹配已无法应对动态的攻击手段，必须转向对代码运行时行为的动态分析，从“它是什么”转向“它做了什么”。

这一数据点极具洞察力，指明了现代软件架构的根本性脆弱点：真正的防线不再是你的业务代码，而是你从未审查过的传递依赖网络。开发者往往只关注直接引入的包，却忽略了依赖树深处的暗箱，这正是供应链攻击能够“顺藤摸瓜”造成大面积杀伤的底层逻辑。

深刻揭示了当前AI编程代理带来的核心安全悖论：决策速度与监控能力的错配。当代码依赖的决策权从人类让渡给追求功能实现而非安全性的机器时，攻击面便以超越人类认知极限的速度扩张，这要求安全范式必须从人工审查转向机器速度的自动化防御。

这一洞见是Agent工程演进的底层逻辑：脚手架是对模型当前能力边界的妥协。随着基座模型能力跃升，曾经的“必要组件”可能沦为冗余开销。因此，解构并剔除过时假设，是保持系统简洁高效的关键。

展现了Agent系统设计中的风险控制逻辑：过早陷入底层细节会导致错误级联。让Planner专注于高层目标，将实现路径留给执行层自主探索，有效避免了自上而下的规划谬误，增强了系统的容错性。