Normal response for animals toward climate changing, toward higher mountains, these animal patterns are more and widespread due to the climate change that's accelerating currently.

Why scientists believe that it's irreversible, why can't we cover it?What make them impossible to reverse back?

How severe the issue is, remind us the impact, saying half facing water insecurity highlights and emphasizes the issue, makes it fell urgent to solve and real.

There is global impact, although it is just 1.1degree, but it's making severe effect and we need to focus and mind this danger. Earth is sensitive and need to be protected, these small changes matter alot.

as before written by the aithor, small countries will low feasibility to solve issues them selves are the main target of climate change __> how is this feasible if countries for places like africa/ LAC???

As much as the Earth might still hold beauty like ecosystems, there’s also harsh injustice. The “hard limits” described in the report parallels with the story’s sense of inevitability

How realistic will that funding level be mobilized?

Even adaptation has limits here. The phrase “hard limits” evokes loss. For most communities, damage can’t be reversed, no matter what we do now.

Water insecurity is something affecting billions of people. It highlights climate change as an issue for human beings, not just an abstract phenomenon.

It is surprising that a small global temperature rise can trigger disruption globally. This undermines any ideas like “we still have time”, and things are really happening NOW.

very important to keep in mind because sometimes we think that our personal experiences aren't relatable.

This is really important to remember because in music classes often group work isn't a thing so its important to try to implement it into the classroom setting so gifted students and even students who may be struggling could help each other out and challenge each other.

I think this is really important because it can often be perceived as rude when gifted students are questioning or answering questions and its really important to keep the space welcoming and be ready to either redirect or be understanding even if it could be frustrating or interrupt a lesson It is important to keep the students needs and interests in mind and that they mean well.

This is really interesting statement because the gifted students are-often left with no services even though they may still need them and this could be a really isolating experience as they are just sign through his alone and not getting the services they truly need and deserve.

this specific point is really important because in the beginning of the chapter the vignette had a teacher saying she was happy that the student couldn't do the ensemble because it didn't work schedule wise because she didn't know how to challenge or didn't really want to and i think it is really important to still allow them to be in your room and succeed.

Teacher and parent input is always really important because they know the student the best and spend more time with students especially the parents. they know every little thing about their child and can be the best advocate for them and what they need.

Its really important they have moved on to multiple points of assessing gifted students and this really shows it because it was previously stated that students in marginalized groups often struggle to get what they need or even to be classified as gifted and the idea of it being expensive furthers this idea. A lot of special educations services tend to be more expensive and less accessible for all students and families.

I think it is really great that they've developed a new system that allows for the student to be tested in more than one way because the students may not show things in one area and its not a one size fits all thing and its really important to be able to distinguish things on student doesn't get misplaced so they can be the most successful version of themselves.

I think this is a really important point because as discussed with other students with disabilities they talk about not defining student by their disability and I think especially with gifted students people sort of throw this idea to the side and I think think its really important to keep in mind that they are still a person and student with a lot of potential and things to learn.

I find this point really interesting because I feel like in the classes we've taken for special education it is commonly highlighting students who may be developmentally behind and its never really ever talking about more gifted students and how to challenge them and not leave them behind just because they are gifted and don't seem to be struggling.

Connecting citizen's attitudes and feeling towards refugees from different countries, western nations responded with more sympathy to Ukraine refugees than other regions' refugees, selecting sympathy to use.

What effort should be made or changes to change public's and society's thoughts for refugees?

Are and how are journalists aware of these biases?

Interesting because of dehumanization being used, for justifying harsh policies, connects public's condition and thoughts to political decisions and motives.

Shows that war is normal elsewhere, and reflects that fact that in certain places of the planet, war is considered as normal to happen and expected, while some other places aren't, they are shocking and unexpected.

Suggests that the medias were shocked about wars in Europe, and shows how journalists were surprised, that war could happen here, revealing an interesting thing where violence should belong to.

Justified harsh policies to immigrants

There was initially strict laws against Ukraine refugees

Many Western journalists write that the Ukraine conflict as more shocking because its victims are white people, Christian, European. Contrasting them with refugees from Iraq, Afghanistan, or Syria, showing a bias toward different type pf people.

This quote challenges the idea that certain regions are “naturally” war-torn. When war happens in Europe or other Western nations, it is very normal, while it’s considered a norm in other regions. This may be saying how easily we normalize violence in certain parts of the world, and how this view shapes global empathy.

This quote is interesting because the quote asserts that anti-refugee seeking for settlements are not fact-based, but through the constructed narratives. This quote justify the harsh policies by dehumanizing the people they affect

This quotes is interesting because the author directly refuse the prejudice that refugees only appear in remote countries. Criticism to the prejudice leading to consequences.

Why does the author use "sticks and carrots" to emphasize that the peace in Europe can't be maintain

This is interesting because this quotes shows the prejudice from the people to the refugees from poor country, which they couldn't believe the refugees were from Ukraine.

This quotes have the similarities to the article "Wretch and Beauty" which both article illustrate the reporters as a character who reversing the fact. This similarities demonstrate the reporter spread he unverified rumors as "news" and media serve as the amplifier of rumors.

What hidden unfair view is in the official’s comment?

It can be connected to appearance based moral judgment in The Wretched and the Beautiful. This is because people consider the appearance of the aliens to react, and the speech from the journalist is also considering Ukraine's"appearance" to make judgement and speech.

This is interesting because it shows that the journalist is showing a bias towards Iraq, Afghanistan, Ukraine. I think it is how inequality stays alive because we’re taught some lives are more worth saving. ( journalist's word suggests Ukraine is better)

This is interesting because Bayoumi challenge how we think kindness is when people only choose the help people like them. Bayoumi also made me think who we sympathize with, who we support aren’t small.

feel empathy due to the simmilarity--> if they were simmilar/ people with black hair and yellow skin--> what will be the reaction???

The writer explains that helping Ukrainians is right, but the reasons must not be racist.

Shows racism racial and class prejudice.

People reacted strongly, and the journalist apologized, but the author argues that many other people have said similar things.

The author criticizes him, saying the word “civilized” means Ukrainians deserve more sympathy than Middle Eastern people.

Questions are important but you have to know what you are asking and where.

Don't cheat your way out of work

Make sure that you check everywhere on the syllabus before making demanding emails of questions that you may have.

Let's me know the people used personal experiences to develop not just a theory of practice.

Sugeriría que este párrafo vaya después y que el caso chileno fuera de entrada en la sección. De lo contrario, se pierde un poco la presencia del contexto. Asimismo, se podría agregar un párrafo para hacer ese puente.

En esta seccion de bivariados solo se muestran asociaciones entre merito y mjp, y clase? Sugiero que:

i) se parta por clase, mostrando ese grafico que hicimos en el html de analysis ii) luego merito, eligiendo entre el scatter o la matriz de correlaciones iii) clase es fija, por lo que con un grafico de medias está bueno, pero merito no, por ende, podriamos incorporar el rol tiempo en lo bivariado

Pienso que si bien se observan varios flows, lo central en cuanto tendencia es que, por un lado, la gran mayoria está en contra de esta idea, pero por otro lado, hay un creciente grupo que si lo está (reflejado en el crecimiento del agree+strongly agree desde el 2018 al 2023 por ejemplo). Por eso creo que lo central de este dato es eso, mostrar que aunque la mayoría lo rechaza, hay un crecimiento en el acuerdo y en consecuencia una dismincion en el desacuerdo. Creo que sería bueno nombrar esas diferencias de numero en el parrfao, como está en el paper ya publicado

Esta parte la moveria como parrafo final, cosa de que conecte mejor con el que ya existe que explica la interacción.

Esta idea está como no conectada con la que le sigue. Y la idea que le sigue (clase y actitudes) es más del parrafo anterior

In the context of pension systems based on individual contributions, such as the Chilean system, these distinctions are particularly relevant, as they allow us to differentiate preferences and perceptions regarding a system from which some people benefit. In contrast, others remain vulnerable, according to their class position and the consequent earnings and capacity to contribute.

Thomas Steinbeck on The Steinbeck Pencil Length<br /> by [[Blackwing]] on YouTube<br /> accessed on 2025-12-10T08:53:22

eLife Assessment

The authors present an important approach to identify imported P. falciparum malaria cases, combining genetic and epidemiological/travel data. This tool has the potential to be expanded to other contexts. The data was analyzed using convincing methods, including a novel statistical model. This study may be of interest to researchers in public health and infectious diseases beyond malaria.

Reviewer #1 (Public review):

Summary:

This study presents a new Bayesian approach to estimate importation probabilities of malaria combining epidemiological data, travel history, and genetic data through pairwise IBD estimates. Importation is an important factor challenging malaria elimination, especially in low transmission settings. This paper focus on Magude and Matutuine, two districts in south Mozambique with very low malaria transmission. The results show isolation-by-distance in Mozambique, with genetic relatedness decreasing with distances larger than 100 km, and no spatial correlation for distances between 10 and 100 km. But again strong spatial correlation in distances smaller than 10 km. They report high genetic relatedness between Matutuine and Inhambane, higher than between Matutuine and Magude. Inhambane is the main source of importation in Matutuine, accounting for 63.5% of imported cases. Magude, on the other hand, shows smaller importation and travel rates than Matutuine, as it is a rural area with less mobility. Additionally, they report higher levels of importation and travel in the dry season, when transmission is lower. Also, no association with importation was found for occupation, sex and other factors. These data have practical implications for public health strategies aiming malaria elimination, for example, testing and treating travelers from Matutuine in the dry season.

Strengths:

The strength of this study relies in the combination of different sources of data - epidemiological, travel and genetic data - to estimate importation probabilities, the statistical analyses.

Weaknesses:

The authors recognize the limitations related to sample size and the biases of travel reports.

Reviewer #2 (Public review):

Summary:

Based on a detailed dataset, the authors present a novel Bayesian approach to classify malaria cases as either imported or locally acquired.

Strengths:

The proposed Bayesian approach for case classification is simple, well justified, and allows the integration of parasite genomics, travel history, and epidemiological data.

Weakness:

While the authors aim to classify cases as imported or locally acquired, the work lacks a quantification of the contribution of each case type to overall transmission.

Comments on revisions:

All my questions and concerns were satisfactorily addressed.

Reviewer #3 (Public review):

This work provides a novel statistical model to identify imported malaria cases, which are an important challenge for elimination, particularly in low-transmission areas. This tool was applied in Plasmodium falciparum populations in Mozambique and determined differences in importation rates in 2 low-transmission districts in the South.

Strengths:

The study has several strengths, mainly the development of a novel Bayesian model that integrates genomic, epidemiological, and travel data to estimate importation probabilities. The results showed insights into malaria transmission dynamics, particularly identifying importation sources and differences in importation rates in Mozambique. Finally, the relevance of the findings is to suggest interventions focusing on the traveler population to support efforts for malaria elimination.

Weaknesses:

The study also has some limitations, although the authors have plans to address them. The sample collection was not representative of some provinces, and not all samples had sufficient metadata for the risk factor analysis. Additionally, the authors used a proxy for transmission intensity and assumed some other conditions to calculate the importation probability for specific scenarios. They plan to conduct a new sample collection and include monthly malaria incidence estimates in the future.

Comments on revisions:

- Delete "We added this text to the discussion" in line 302 (Discussion)<br /> - I recommend adding the plans to address limitations indicated in the Response to Reviewers document in the Discussion. This would really strengthen the limitation section.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study presents a new Bayesian approach to estimate importation probabilities of malaria, combining epidemiological data, travel history, and genetic data through pairwise IBD estimates. Importation is an important factor challenging malaria elimination, especially in low-transmission settings. This paper focuses on Magude and Matutuine, two districts in southern Mozambique with very low malaria transmission. The results show isolation-by-distance in Mozambique, with genetic relatedness decreasing with distances larger than 100 km, and no spatial correlation for distances between 10 and 100 km. But again, strong spatial correlation in distances smaller than 10 km. They report high genetic relatedness between Matutuine and Inhambane, higher than between Matutuine and Magude. Inhambane is the main source of importation in Matutuine, accounting for 63.5% of imported cases. Magude, on the other hand, shows smaller importation and travel rates than Matutuine, as it is a rural area with less mobility. Additionally, they report higher levels of importation and travel in the dry season, when transmission is lower. Also, no association with importation was found for occupation, sex, and other factors. These data have practical implications for public health strategies aiming for malaria elimination, for example, testing and treating travelers from Matutuine in the dry season.

Strengths:

The strength of this study lies in the combination of different sources of data - epidemiological, travel, and genetic data - to estimate importation probabilities, and the statistical analyses.

Weaknesses:

The authors recognize the limitations related to sample size and the biases of travel reports.

We appreciate the review and comment about the manuscript.

Reviewer #2 (Public review):

Summary:

Based on a detailed dataset, the authors present a novel Bayesian approach to classify malaria cases as either imported or locally acquired.

Strengths:

The proposed Bayesian approach for case classification is simple, well justified, and allows the integration of parasite genomics, travel history, and epidemiological data. The work is well-written, very organized, and brings important contributions both to malaria control efforts in Mozambique and to the scientific community. Understanding the origin of cases is essential for designing more effective control measures and elimination strategies.

Weakness:

While the authors aim to classify cases as imported or locally acquired, the work lacks a quantification of the contribution of each case type to overall transmission.

The method presented here allows for classifying individual cases according to whether the infection occurred locally or was imported during a trip. By definition, it does not look to secondary infections after an importation event. Our next step is to conduct outbreak investigation to quantify the impact of importation events on the overall transmission, but this activity goes beyond the scope of this manuscript. We clarify this in the discussion section.

The Bayesian rationale is sound and well justified; however, the formulation appears to present an inconsistency that is replicated in both the main text and the Supplementary Material.

Thank you for pointing out the inconsistency in the final formula. In fact, the final formula corresponds to P(IA | G), instead of P(IA), so:

instead of

We have now corrected this error in the new version of the manuscript.

Reviewer #3 (Public review):

The authors present an important approach to identify imported P. falciparum malaria cases, combining genetic and epidemiological/travel data. This tool has the potential to be expanded to other contexts. The data was analyzed using convincing methods, including a novel statistical model; although some recognized limitations can be improved. This study will be of interest to researchers in public health and infectious diseases.

Strengths:

The study has several strengths, mainly the development of a novel Bayesian model that integrates genomic, epidemiological, and travel data to estimate importation probabilities. The results showed insights into malaria transmission dynamics, particularly identifying importation sources and differences in importation rates in Mozambique. Finally, the relevance of the findings is to suggest interventions focusing on the traveler population to help efforts for malaria elimination.

Weaknesses:

The study also has some limitations. The sample collection was not representative of some provinces, and not all samples had sufficient metadata for risk factor analysis, which can also be affected by travel recall bias. Additionally, the authors used a proxy for transmission intensity and assumed some conditions for the genetic variable when calculating the importation probability for specific scenarios. The weaknesses were assessed by the authors.

We acknowledge the limitations commented by the reviewer. We have the following plans to address the limitations. We will repeat the study for our data collected in 2023, which this time contains a good representation of all the provinces of Mozambique, and completeness of the metadata collection was ensured by implementing a new protocol in January 2023. Regarding the proxy for transmission intensity, we will refine the model by integrating monthly estimates of malaria incidence (previously calibrated to address testing and reporting rates) from the DHIS2 data, taking also into account the date of the reported cases in the analysis.

Reviewing Editor Comments:

The reviewers have made specific suggestions that could improve the clarity and accuracy of this report.

Reviewer #1 (Recommendations for the authors):

(1) Abstract, lines 36, 37 and 38: "Spatial genetic structure and connectivity were assessed using microhaplotype-based genetic relatedness (identity-by-descent) from 1605 P. falciparum samples collected (...)", but only 540 samples were successfully sequenced, therefore used in spatial genetic structure and connectivity analysis.

The 540 samples refer to those from Maputo province and are described in Fig. 1. The Spatial and connectivity analyses also included the samples from the rest of the provinces from the multi-cluster sampling scheme. Sample sizes from these provinces are described in Suppl. Table 2, and the total between them and the 540 samples from Maputo are the 1605 samples mentioned in the abstract. We specify this number in the caption of Sup. Fig. 4, and add it now into Fig. 3

(2) In the Introduction, some epidemiological context about Magude and Matutuine could be added. It is only mentioned in the Discussion section (lines 265-269).

We have added some context about both districts in the introduction now.

(3) In the Discussion, lines 241-244, could the lack of structure mean no barriers for gene flow due to high mobility in short distances? Maybe it could only be resolved with a large number of samples.

This could be an explanation (we mention it in the new version), although it is not something we can prove, or at least in this study.

Reviewer #2 (Recommendations for the authors):

The work is well written, very organized, and brings important contributions both to malaria control efforts in Mozambique and to the scientific community. Based on detailed datasets from Mozambique, the authors present a novel Bayesian approach to classify malaria cases as either imported or locally acquired. Understanding the origin of cases is essential for designing more effective control measures and elimination strategies. My review focuses on the Bayesian approach as well as on a few aspects of the presentation of results.

The authors combine travel history, parasite genetic relatedness, and transmission intensity from different areas to compute the probability of infection occurring in the study area, given the P. falciparum genome. The Bayesian rationale is sound and well justified; however, the formulation appears to present an inconsistency that is replicated in both the main text and the Supplementary Material. According to Bayes' Rule:

P(I_A |G) = (P(I_A) ∙ P(G|I_A)) / (P(G)),

with

P(I_A) = K ∙ T_A ∙ PR_A,

P(G│I_A) = R'_A,

and assuming

P(I_A│G) + P(I_B│G) = 1,

the expression,

(T_A ∙ PR_A ∙ R'_A) / (T_A ∙ PR_A ∙ R'_A + T_B ∙ PR_B ∙ R'_B)

appears to refer to P(I_A│G), not to P(I_A) (as indicated in the main text and Supplementary Material).

P(I_A│G) + P(I_B│G) = (P(I_A) ∙ P(G|I_A) + P(I_B) ∙ P(G|I_B)) / P(G) = 1

⇒P(G) = P(I_A) ∙ P(G|I_A) + P(I_B) ∙ P(G|I_B)

⇒P(G) = K ∙ T_A ∙ PR_A ∙ R'_A + K ∙ T_B ∙ PR_B ∙ R'_B

⇒P(I_A│G) = (T_A ∙ PR_A ∙ R'_A) / (T_A ∙ PR_A ∙ R'_A + T_B ∙ PR_B ∙ R'_B)

Please clarify this.

As mentioned in a previous comment, we acknowledge this point from the reviewer.  In fact, the final formula corresponds to P(IA | G), instead of P(IA), so:

instead of

We have now corrected this error in the new version of the manuscript and in the supplementary information.

Additional comments:

(1) Figure 3A has a scale that includes negative values, which is not reasonable for R.

We agree that R estimates are not compatible with negative values. The intention of this scale was to show the overall mean R in the centre, in white, so that blue colours represented values below the average and red values above the average. However, we proceeded to update the figures according to your recommendations.

(2) I suggest using a common scale from 0 to 0.12 (maximum values among panels) across panels A, C, and D, as well as in Sup Fig 3, to facilitate comparison.

We updated the figures according to the recommendations.

(3) The x-axis labels in Figure 3A and Supplementary Figure 2A are not aligned with the x-axis ticks.

We updated the figures so that the alignment in the x-axis is clear.

(4) Supplementary Figure 5 would be better presented if the data were divided into four separate panels.

We have divided the figure into four separate panels.

(6) Figure 5D is not referenced in the main text.

We missed the mention, which is now fixed in the new version.

(7) The authors state: "No significant differences in R were found comparing parasite samples from Magude and the rest of the districts." However, Supplementary Figure 3 shows statistically significant relatedness between parasites from Magude and Matutuine. Please clarify this.

Answer: we added clarity to this sentence which was indeed confusing.

Reviewer #3 (Recommendations for the authors):

(1) Introduction: More background info about malaria in Mozambique would be appreciated.

We included some contextualisation about malaria in Mozambique and our study districts.

(2) Why were most of the samples collected from children? Is malaria most prevalent in that group? Information could be added in the introduction.

Children are usually considered an appropriate sentinel group for malaria surveillance for several reasons. First, most malaria cases reported from symptomatic outpatient visits are children, especially in areas with moderate to high burden. Second (and probably the cause for the first reason), their lower immunity levels, due to lower time of exposure, and their immature system, provides a cleaner scenario of the effects of malaria, since the body response is less adapted from past exposures. Finally, as a vulnerable population, they deserve a stronger focus in surveillance systems. We added a comment in the introduction referring to them as a common sentinel group for surveillance.

(3) Minor: Check spaces in the text (for example, line 333 and the start of the Discussion).

Thank you for noticing, we fixed in in the new version

(4) Minor: In my case, the micro (u) symbol can be observed in Word, but not in PDF.

One of the symbols produced an error, we hope that the new version is correct now.

(5) Were COI calculations with MOIRE performed across provinces and regions, or taking all samples as one population?

Wwe took all samples as one population. However, we validated that the same results (reaching equivalent numbers and the same conclusions) were obtained when run across different populations (regions or provinces). We mention this in the manuscript now.

(6) Have you tested lower values than 0.04 for PR in Maputo?

This would not have had any impact in the classification. Only two individuals reported a trip to Maputo city (where we assumed PR=0.04), and none of them were classified as imported. If lower values of PR were assumed, their probabilities of importation would have reduced, so that we would still obtain no imported cases.

(7) Map (Supplementary Figure 1): Please, improve the resolution (like in the zoom in) and add a scale and a compass rose.

We improved the resolution of the map. We did not add a scale and a compass rose, but labelled the coordinates as longitude and latitude to clarify the scale and orientation of the map. We added this in the rest of the maps of the manuscript as well.

(8) In this work, Pimp values were bimodal to 0 or 1, making the classification easy. I wonder in other scenarios, where Pimp values are more intermediate (0.4-0.6), is the threshold at 0.5 still useful? Is there another way, like having a confidence interval of Pimp, to ensure the final classification? A discussion on this topic may be appreciated.

In this case, we would recommend doing probabilistic analyses, keeping the probability of being imported as the final outcome, and quantifying the importation rates from the weighted sum of probabilities across individuals. We added this clarification in the Methods section: “ In case of obtaining a higher fraction of intermediate values (0.4-0.6), weighted sums of individual probabilities would be more appropriate to better quantify importation rates.”

(9) Results: More details per panel, not as the whole figure (Figure 2B, Figure 3A, etc) in the manuscript would be appreciated.

We appreciate the comment and added more details

(10) Figure 3: Please, add a color legend in panel B (not only in the caption, but in the panel, such as in A, C, D).

We added a color legend in panel B.

(11) Do the authors recommend routine surveillance to detect importation in Mozambique, or are these results solid enough to propose strategies? How possible is it that importation rates vary in the future in the south? If so, how feasible is it to implement all this process (including the amplicon sequencing) routinely?

We added the following text in the discussion: “While these results propose programmatic strategies for the two study districts, routine surveillance to detect importation in Mozambique would allow for identifying new strategies in other districts aiming for elimination, as well as monitoring changes in importation rates in Magude and Matutuine in the future. If scaling molecular surveillance is not feasible, travel reports could be integrated in the routing surveillance to extrapolate the case classification based on the results of this study. “

(12) Which other proxies of transmission intensity could have been used?

Better proxies of transmission intensity could be malaria incidence at the monthly level from national surveillance systems, or estimates of force of infection, for example from the use of molecular longitudinal data if available. We added this text in the discussion.

(13) Can this strategy be applied to P. vivax-endemic areas outside Africa?

This new method can also be applied to P. vivax-endemic areas outside Africa. Symptomatic P. vivax cases are not necessarily reflecting recent infections, so that travel reports might need to cover longer time periods, which does not require any essential adaptation to the method. We added this text to the discussion.

Hi!

Getting straight to the point when writing will make the process go a lot smoother

Do factors like age or cultural affect how the diffusion of responsibility impacts a person’s willingness to help?

would the witness inaction rate have been lower if there is a emergency channel for people to report things existed at that time?

The bystander effect reverses in dangerous situations，people seen other bystanders as potential support instead of reducing their own responsibility.

The bystander effect reverses in dangerous situations，people view other bystanders as potential support instead of reducing their own responsibility.

Diffusion of responsibility directly affects everyday tasks like group projects

Introduce to the core topic

This quote make connection to the article "Wretch and Beauty", nearly no people want to support alien might because their responsibility decrease, which

This quotes is interesting because it shocked me by emphasizing the bystander effect and state the change of responsibility when there are people around

Gives their study background

Group presence crushes individual action.

85% helped when alone, 31% helped in groups

replaced, by a one time(!) eval after 6 years.

superseded by Art 2 of the new proposal, similar provisions

changed to Member States may limit public access to spatial data sets and services where such access could adversly affect any of the following:

Replaced by 4. The Commission is empowered to adopt implementing acts laying down rules for the application of this Article, taking account of relevant, existing international standards and user requirements, in particular with relation to validation metadata. Those implementing acts shall be adopted in accordance with the procedure referred to in Article 22b(2)’

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deleted

deleted

Replaced by The Commission is empowered to adopt delegated acts in accordance with Article 22a in order to amend Annexes I, II and III by adapting the description of the existing data themes in the light of technological and economic developments

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"village worthy" persone che sono popolari e rispettate all'interno della propria comunità, ma peccano di reali qualità morali.

[https://ntu-sp.primo.exlibrisgroup.com/discovery/fulldisplay/alma991016713204505146/65NTU_INST:65NTU_INST]

this again mixes use and re-use as interchangeable. Not all spatial data is actually open by default, so we get some return to pre-2007. Who will notice when govs stop providing access?

themes to be redefined?

themes again, not data sets. And is it really 33 of 34 themes. Not all themes fully.

INSPIRE charging provisions replace by ODD charging provisions (and those are imo potentially eroded in the digital omnibus)

Wrt standards this makes usage (g2g) the same as re-use (INSPIRE was not only for re-use imo) but not explicitly?

non psbs / third parties removed from scope INSPIRE.

agreed wrt rigid tech reqs. Stil, I wonder if HVD doesn't actually build on them

I read this as taking out all INSPIRE obligations, whereas the HVD reg builds on these pre-existing obligations. (Stating that sharing data / services must be open)- [ ] Crosscheck if HVD states an explicit independent mandate, without reference to INSPIRE mandates. #geonovumtb #10mins #belangrijkeerst

reporting obligation folded into HVD reporting (already combined in practice per 2026)

Remains a Directive that amends INSPIRE directive. While ODD / HVD are regulations.

ODD and HVD

as announced, the inspire portal will move into the eu data portal. Has consequences for the HVD monitoring (now part of inspire portal) too and shifts resp from #jrc to #eupb

FIWARE. These MIMS are hardly usable though.

a yes then.

Interoperable Europe Act. I wonder. Are we going to see INSPIRE components adopted as interoperability solutions by the interoperability board? INSPIRE is not just a gov thing either.

strikes harmonisation, deletes the rabbithole.

List of regs where INSPIRE is referred to

on the alignment of reporting obligations in the field of legislation related to the environment

although definitions will shift in the omnibus and that carries uncertain effects.

only partly true. Not all INSPIRE themes covered in HVD, and not all themes covered are fully covered (e.g. transport networks is limited to inland waterways).

Ask #dgenv #jrc about foreseen support actions and tools

Tied to GDDS

Tied to DA (incl DGA,ODD), HVD, and Interoperable Europe Act here. Note: #dgdigit assumes no connection w Interop act until its review.

this proposal is fulfilling the announced greendata4all initiative.

interoperability impacted how by removing harmonisation?

removes tech reqs --> harmonisation?

connect to horizontal legislation --> ODD(DA) / HVD

Venezuela’s crisis shows how authoritarian rule and economic collapse can push entire populations into exile.

Unlike traditional wars, the displacement here caused from social collapse and environmental pressure

This statistic shows how civil war can completely destabilize a nation’s social structure and create a humanitarian emergency that goes beyond national borders.

eLife Assessment

This study presents an important finding that has identified 27 differentially methylated regions as a signature for non-invasive early cancer detection and predicting prognosis for colorectal cancer. The findings demonstrate promising clinical potential, particularly for improving cancer screening and patient monitoring. In general, the evidence supporting the claims of the authors is solid. A larger sample size will be key to further improving this work in the future. The work will be of interest to researchers interested in cancer diagnosis or colorectal cancer monitoring.

Reviewer #1 (Public review):

Summary:

Colorectal cancer (CRC) is the third most common cancer globally and the second leading cause of cancer-related deaths. Colonoscopy and fecal immunohistochemical testing are among the early diagnostic tools that have significantly enhanced patient survival rates in CRC. Methylation dysregulation has been identified in the earliest stages of CRC, offering a promising avenue for screening, prediction, and diagnosis. The manuscript entitled "Early Diagnosis and Prognostic Prediction of Colorectal Cancer through Plasma Methylation Regions" by Zhu et al. presents that a panel of genes with methylation pattern derived from cfDNA (27 DMRs), serving as a noninvasive detection method for CRC early diagnosis and prognosis.

Strengths:

The authors provided evidence that the 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stages III-IV. Additionally, compared with the traditional tumor marker CEA, 27 DMRs prediction showed a superior sensitivity, highlighting the potential clinical application.

Weaknesses:

The major concerns are the design of DMRs screening, the relatively low sensitivity of this DMRs' pattern in detecting early-stage of CRC, the limited size of the cohorts, and the lack of comparison with the traditional diagnosis test.

Comments on revisions:

All my concerns have been cleared, and I have no further questions.

Reviewer #2 (Public review):

In this study, the authors aimed to develop cfDNA markers for comprehensive diagnosis, metastatic assessment, and prognostic prediction of colorectal cancer (CRC). Through integrative analysis of public 450K DNA methylation datasets and in-house targeted bisulfite sequencing (BS-seq) data from CRC and paired normal tissues, as well as plasma samples, they identified a signature comprising 27 differentially methylated regions (DMRs). This signature was subsequently validated for three clinical applications: cancer detection, metastasis prediction, and prognosis assessment.

Strengths:

The 27-DMR signature demonstrates value for both diagnosis and prognosis of CRC. Additionally, the datasets generated in this study serve as a valuable resource for the research community.

Weaknesses:

The validation cohorts for cancer detection and metastasis prediction were relatively small, which may limit the generalizability of the findings. The cancer detection model's performance does not surpass some published methods or commercial products.

Author response:

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

Reviewer #1 (Public review):

Summary:

Colorectal cancer (CRC) is the third most common cancer globally and the second leading cause of cancer-related deaths. Colonoscopy and fecal immunohistochemical testing are among the early diagnostic tools that have significantly enhanced patient survival rates in CRC. Methylation dysregulation has been identified in the earliest stages of CRC, offering a promising avenue for screening, prediction, and diagnosis. The manuscript entitled "Early Diagnosis and Prognostic Prediction of Colorectal Cancer through Plasma Methylation Regions" by Zhu et al. presents that a panel of genes with methylation pattern derived from cfDNA (27 DMRs), serving as a noninvasive detection method for CRC early diagnosis and prognosis.

Strengths:

The authors provided evidence that the 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stage III-IV.

Weaknesses:

The major concerns are the design of DMR screening, the relatively low sensitivity of this DMR pattern in detecting early-stage CRC, the limited size of the cohorts, and the lack of comparison with the traditional diagnosis test.

We sincerely thank the reviewer for their thorough evaluation and constructive feedback on our manuscript. We are encouraged that the reviewer found our 27-DMR panel promising for predicting distant metastasis and for its performance in late-stage CRC. We have carefully considered the weaknesses pointed out and have made revisions to address these concerns, which we believe have significantly strengthened our paper.

We agree with the reviewer that achieving high sensitivity for early-stage disease is the ultimate goal for any noninvasive screening test. Detecting the minute quantities of cfDNA shed from early-stage tumors is a well-recognized challenge in the field. Although the sensitivity of our current panel for early-stage CRC is modest, its core strengths, lie in its capability to also detect advanced adenomas and its excellent performance in assessing CRC metastasis and prognosis. Furthermore, we have now added a direct comparative analysis of our 27-DMR panel against the most widely used clinical serum biomarker for CRC, carcinoembryonic antigen (CEA), using samples from the same patient cohorts. Our results demonstrate that 27-DMR methylation score significantly outperforms CEA in diagnostic accuracy for early-stage CRC (64% vs. 18%) (Table s7). And in the Discussion section, we have also acknowledged our limitations and suggest that future studies are warranted to combine the cfDNA methylation model with commonly used clinical markers, such as CEA and CA19-9, with the aim of improving the sensitivity for early diagnosis.

We acknowledge the reviewer's concern regarding the cohort size and validation in larger, prospective, multi-center cohorts is essential before this panel can be considered for clinical application. We have explicitly stated this as a limitation of our study in the Discussion section and have highlighted the need for future large-scale validation studies (Page 18, Lines 367-373). We once again thank the reviewer for their insightful comments, which have allowed us to substantially improve our manuscript. We hope that the revised version is now suitable for publication.

Reviewer #2 (Public review):

This work presents a 27-region DMR model for early diagnosis and prognostic prediction of colorectal cancer using plasma methylation markers. While this non-invasive diagnostic and prognostic tool could interest a broad readership, several critical issues require attention.

Major Concerns:

(1) Inconsistencies and clarity issues in data presentation

(a) Sample size discrepancies

The abstract mentions screening 119 CRC tissue samples, while Figure 1 shows 136 tissues. Please clarify if this represents 119 CRC and 17 normal samples.

We sincerely thank the reviewer for this careful observation and for pointing out the inconsistency. We apologize for the error and the confusion it caused. Regarding Figure 1: The reviewer is correct. The number 136 in the original Figure 1 was an error. This was due to an inadvertent double-counting of the tumor samples that were used in the differential analysis against adjacent normal tissues. The actual number of tissue samples used in this analysis is 89. We have now corrected this value in the revised Figure 1.

Regarding the Abstract: The 119 CRC tissue samples mentioned in the abstract represents the total number of unique tumor samples analyzed across all stages of our study. This number is composed of two cohorts: the initial 15 pairs of tissues used for preliminary screening, and the subsequent 89 tissue samples used for validation, totaling 119 samples. We have ensured all sample numbers are now consistent throughout the revised manuscript.

The plasma sample numbers vary across sections: the abstract cites 161 samples, Figure 1 shows 116 samples, and the Supplementary Methods mentions 77 samples (13 Normal, 15 NAA, 12 AA, 37 CRC).

We sincerely thank the reviewer for their meticulous review and for identifying these inconsistencies in the plasma sample numbers. We apologize for this oversight and the lack of clarity.

Figure 1 & Supplementary Methods (77 samples): The number 116 in the original Figure 1 was a clerical error. The correct number is 77, which is the cohort used for our differential methylation analysis. This number is now consistent with the Supplementary Methods. This cohort is composed of 13 Normal, 15 NAA, 12 AA, and 37 CRC samples. The figure has been revised accordingly.

Abstract (161 samples): The total of 161 plasma samples mentioned in the abstract is the sum of two distinct sample sets used for different stages of our analysis: The 77 samples (13 Normal, 15 NAA, 12 AA, 37 CRC) used for the differential analysis.  An additional 84 samples (33 Normal, 51 CRC) which served as the training set for the LASSO regression model. We have now clarified these distinctions in the text and ensured consistency across the abstract, figures, and methods sections.

(b) Methodological inconsistencies

The Supplementary Material reports 477 hypermethylated sites from TCGA data analysis (Δβ>0.20, FDR<0.05), but Figure 1 indicates 499 sites.

The manuscript states that analyzing TCGA data across six cancer types identified 499 CRC-specific methylation sites, yet Figure 1 shows 477. Please also explain the rationale for selecting these specific cancer types from TCGA.

We sincerely thank the reviewer for their sharp observation and for highlighting these inconsistencies. We apologize for this clerical error, which occurred when labeling the figure. The numbers 477 and 499 in Figure 1 were inadvertently swapped and the text in Supplementary Material is correct. We have now corrected this error throughout the manuscript to ensure clarity and consistency. We deeply regret the confusion this has caused.

Regarding the rationale for selecting the cancer types:

The selection of colorectal, esophageal, gastric, lung, liver, and breast cancers was based on the following strategic criteria to ensure the stringent identification of CRC-specific markers. Firstly, esophageal, gastric, liver, and colorectal cancers all originate from the gastrointestinal tract and share developmental and functional similarities. Comparing CRC against these closely related cancers allowed us to filter out general GI-tract-related methylation patterns and isolate those that are truly unique to colorectal tissue. Secondly, we included lung and breast cancer as they are two of the most common non-GI malignancies worldwide with distinct tissue origins. This helps ensure our identified markers are not just pan-cancer methylation events but are specific to CRC, even when compared against highly prevalent cancers from different lineages. Finally, these six cancer types have some of the largest and most complete datasets available in the TCGA database, including high-quality methylation data. This provided a robust statistical foundation for a reliable cross-cancer comparison. We hope this explanation clarifies our methodology. Thank you again for your valuable feedback.

"404 CRC-specific DMRs" mentioned in the main text while "404 MCBs" in Figure 1, the authors need to clarify if these terms are interchangeable or how MCBs are defined.

We sincerely thank the reviewer for pointing out this important inconsistency in terminology. We apologize for the confusion this has caused and for the error in Figure 1. The two terms are closely related in our study. The final 404 markers are technically DMRs that were identified through an analysis of MCBs. To avoid confusion, we have decided to unify the terminology. The manuscript has now been revised to consistently use "DMRs", which is the most accurate final descriptor. The label in Figure 1 has been corrected accordingly.

(2) Methodological documentation

The Results section requires a more detailed description of marker identification procedures and justification of methodological choices.

Figure 3 panels need reordering for sequential citation.

We thank the reviewer for this valuable suggestion. We agree that the original Results section lacked sufficient detail regarding the marker identification procedures and the justification for our methodological choices. To address this, we have substantially rewritten the "Methylation markers selection" subsection. This revised section provides a clear, step-by-step narrative of our marker discovery. The revised text now integrates the specific methodological details and statistical criteria. For instance, we now explicitly describe the three-pronged approach for the initial TCGA data mining and the specific criteria (Δβ, FDR, log2FC) for each, and the analysis methodology such as Wilcoxon test and LASSO regression analysis. We believe this detailed narrative now provides the necessary description and justification for our methodological choices directly within the results, significantly improving the clarity and logical flow of our manuscript. This revision can be found on (Page 9-11, Lines 180-195, 202-213). We hope these changes fully address the reviewer's concerns.

We thank the reviewer for pointing out the citation order of the panels in Figure 3. This was a helpful suggestion for improving the clarity of our manuscript. We have now reordered the panels in Figure 3 to ensure they are cited sequentially within the text. These adjustments have been made in the "Development and validation of the CRC diagnosis model" subsection of the Results (Page 11, lines 224-230). We appreciate the reviewer's attention to detail.

(3) Quality control and data transparency

No quality control metrics are presented for the in-house sequencing data (e.g., sequencing quality, alignment rate, BS conversion rate, coverage, PCA plots for each cohort).

The analysis code should be publicly available through GitHub or Zenodo.

At a minimum, processed data should be made publicly accessible to ensure reproducibility.

We sincerely thank the reviewer for their valuable and constructive feedback regarding quality control and data transparency. We fully agree that these elements are crucial for ensuring the robustness and reproducibility of our research. As the reviewer suggested, we have made all processed data and the key quality control metrics for each sample including sequencing quality scores, bisulfite (BS) conversion rates, and sequencing coverage publicly available to ensure the reproducibility of our findings. The analysis was performed using standard algorithms as detailed in the Methods section. While we are unable to host the code in a public repository at this time, all analysis scripts are available from the corresponding author upon reasonable request. The data has been deposited in the National Genomics Data Center (NGDC) and is accessible under the accession number OMIX009128. This information is now clearly stated in the "Data and Code Availability" section of the manuscript. We thank the reviewer again for pushing us to improve our manuscript in this critical aspect.

Reviewer #3 (Public review):

Summary:

This article provides a model for early diagnosis and prognostic prediction of Colorectal Cancer and demonstrates its accuracy and usability. However, there are still some minor issues that need to be revised and paid attention to.

Strengths:

A large amount of external datasets were used for verification, thus demonstrating robustness and accuracy. Meanwhile, various influencing factors of multiple samples were taken into account, providing usability.

Weaknesses:

There are notable language issues that hinder readability, as well as a lack of some key conclusions provided.

We are very grateful to the reviewer for their positive assessment of our study and for the constructive feedback provided. We are particularly encouraged that the reviewer recognized the strengths of our work, especially the robustness demonstrated through extensive external validation and the practical usability of our model. Regarding the weaknesses, we have taken the comments very seriously and have thoroughly revised the manuscript. We sincerely apologize for the language issues that hindered readability in our initial submission. To address this, the entire manuscript has undergone a comprehensive round of professional language polishing and editing. We have carefully reviewed and revised the text to improve clarity, flow, and grammatical accuracy. Besides, we agree that the conclusions could be stated more explicitly. To rectify this, we have substantially revised the final paragraph of the Discussion and the Conclusion section (Page 14-18, lines 279-305, 319-334, 346-348, 358-360, 367-379). We now more clearly summarize the main findings of our study, emphasize the clinical significance and potential applications of our model, and provide clear take-home messages. We thank you again for your time and insightful comments, which have been invaluable in improving the quality of our paper. We hope the revised manuscript now meets the standards for publication.

Reviewer #1 (Recommendations for the authors):

Detail comments are outlined below:

(1) In this study, the authors have highlighted methylated cfDNA as a noninvasive approach for CRC early diagnosis. However, the small size of cohorts for plasma screening, particularly the sample number of NAA and AA , may cause bias in the selection of DMRs. This bias may lead to inappropriate DMRs for early diagnosis. Furthermore, the similar issues for the training set with a high percentage of late-stage CRC, no AA or NAA samples were included. This absence may be the key factor in screening changed methylated cfDNA that can predict the early stages of CRC.

We are very grateful to the reviewer for this insightful methodological critique. We agree that cohort composition and sample size are critical factors in the development of robust biomarkers, and we appreciate the opportunity to clarify our study design and the interpretation of our results.

We agree with the reviewer that the number of precancerous lesion samples (NAA and AA) in our initial plasma screening cohort was limited. This is a valid point. However, it is important to contextualize the role of this step within our overall multi-stage marker selection funnel. The markers evaluated in this plasma cohort were not discovered from this small sample set alone. They were the result of a rigorous pre-selection process based on large-scale public TCGA data and our own tissue-level sequencing. This robust, tissue-based validation ensured that only the most promising CRC-specific markers were advanced for plasma testing. Therefore, while the plasma cohort was modest in size, its purpose was to confirm the circulatory detectability of markers already known to have a strong tissue-of-origin signal, thereby mitigating the potential bias from a smaller discovery set.

Our primary aim was to first build a model that could robustly and accurately identify a definitive cancer-specific methylation signal. By training the model on clear-cut invasive cancer cases versus healthy controls, we could isolate the most powerful and specific markers for established malignancy. Our working hypothesis was that these strong cancer-specific methylation patterns are initiated during the precursor stages and would therefore be detectable, albeit at lower levels, in precancerous lesions.  Unfortunately, the panel could only identify a limited proportion of precancerous lesions (48.4% in the NAA group and 52.2% in the AA group). We fully agree with the reviewer's sentiment that including a larger and more balanced set of precancerous lesions in future training cohorts could potentially optimize a model specifically for adenoma detection. We have now explicitly added this point to our Discussion section, highlighting it as an important direction for future research (Page 18, lines 367-373).

(2) The sensitivity of 27 DMRs in the external validation set (for NAA, AA and CRC 0-Ⅱare 48.4%. 52.2% and 66.7%, respectively) were much lower compared with previously published studies, like ColonES assay (DOI: 10.1016/j.eclinm.2022.101717) and ColonSecure test (DOI: 10.1186/s12943-023-01866-z). The 27 DMRs from the layered screening process did not show superior performance in a small population of an external validation cohort. Therefore, it is unlikely that this DMR pattern will be applicable to the general population in the future.

We sincerely thank the reviewer for their insightful comments and for providing a thorough comparison with the highly relevant ColonES and ColonSecure assays. This has given us an important opportunity to clarify the unique contributions and specific clinical applications of our 27-DMR panel.

We acknowledge the reviewer's point that the sensitivities of our panel for precancerous lesions (NAA: 48.4%, AA: 52.2%), while substantial, are numerically lower than those reported by the excellent ColonES assay (AA: 79.0%). However, it is important to clarify that while the ColonES and ColonSecure tests are outstanding benchmarks designed primarily for early detection and screening, the primary objective and contribution of our study were slightly different. Our model demonstrated an exceptional ability to predict distant metastasis with an AUC of 0.955 and a strong capacity for predicting overall prognosis with an AUC of 0.867. Our goal was to develop a multi-functional, biologically-rooted biomarker panel that not only contributes to early detection but, more importantly, provides crucial information for post-diagnosis patient management, including staging, risk stratification, and prognostication, from a single preoperative sample. We believe this ability to preoperatively identify high-risk patients who may require more aggressive treatment or intensive surveillance is the key contribution of our work. It provides a distinct clinical utility that complements, rather than directly competes with, pure screening assays.

We agree with the reviewer that our external validation was performed on a limited cohort, and we have acknowledged this as a limitation in our Discussion section. However, the purpose of this validation was to provide a proof-of-concept for the panel's performance across its multiple functions. The promising and exceptionally high-performing results in the prognostic domain strongly warrant further validation in larger, prospective, multi-center cohorts.

(3) The 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stage III-IV. In contrast, the increase of AA and 0-II groups was very mild in the validation cohort. This observation raises concerns regarding the study design, particularly in the context of the layered screening process and sample assigning.

We sincerely thank the reviewer for this insightful and critical comment. We agree with the reviewer's observation that the methylation score increased more remarkably in late-stage (III-IV) CRC compared to the milder increase in adenoma (AA) and early-stage (0-II) CRC in the validation cohort. However, the observed pattern is biologically plausible and consistent with the nature of colorectal cancer progression. Carcinogenesis is a multi-step process involving the gradual accumulation of genetic and epigenetic alterations. The methylation changes we identified are likely associated with tumor progression and metastasis. Therefore, it is expected that advanced, metastatic cancers (Stage III-IV), which have undergone significant biological changes, would exhibit a much stronger and more robust methylation signal compared to pre-cancerous lesions (adenomas) or early-stage, non-metastatic cancers (Stage 0-II). The "mild" increase in early stages reflects the initial, more subtle epigenetic alterations, while the "remarkable" increase in late stages reflects the extensive changes required for invasion and metastasis. We believe this graduated increase actually strengthens the validity of our methylation signature, as it mirrors the underlying biological progression of the disease. We hope this response and the corresponding revisions address the reviewer's comments.

(4) The authors did not provide the 27 DMRs prediction efficacy comparison with other noninvasive CRC assays, like a CEA and a FIT test.

Thank you for this valuable suggestion. We agree that comparing our model with established non-invasive assays is crucial for demonstrating its clinical potential. Following your advice, we have now included a direct comparison of the diagnostic performance between our model and the traditional tumor marker, carcinoembryonic antigen (CEA), using the external validation cohort. The results show that our model has a significantly higher sensitivity for detecting early-stage colorectal cancer and adenomas compared to CEA. This detailed comparison has been added as Table s7 in the supplementary materials, and the corresponding description has been incorporated into the Results section of our manuscript (Page 12, lines 234-236). Regarding the Fecal Immunochemical Test (FIT), we unfortunately could not perform a direct statistical comparison because very few individuals in our cohort had undergone FIT. A comparison based on such a small sample size would lack statistical power and might not yield meaningful conclusions. We have acknowledged this as a limitation of our study in the Discussion section.We believe these additions and clarifications have substantially strengthened our manuscript. Thank you again for your constructive feedback.

(5) The authors did not explicitly describe how they assigned the plasma samples to the distinct sets, nor did they specify the criteria for the plasma screen set, training set, and validation set. The detailed information for the patient grouping should be listed.

Responce: Thank you for this essential feedback. We agree that a transparent and detailed description of the sample allocation process is crucial for the manuscript. We apologize for the previous lack of clarity and have now revised the Methods section to address this. Our patient cohorts were assigned to the screening, training, and validation sets based on a chronological splitting strategy. Specifically, samples were allocated based on the date of collection in a consecutive manner. This approach was chosen to minimize selection bias and to provide a more realistic, forward-looking assessment of the model's performance, simulating a prospective validation scenario. The screening set comprised 89 tissue samples and 77 plasma samples collected between June to December 2020. The primary purpose of this set was for the initial discovery and screening of potential methylation markers. The training set and validation set included 165 plasma samples collected from December 2020 to July 2022. The external validation cohort comprised 166 plasma samples collected from from July 2022 to December 2022. The subsection titled "Study design and samples" within the Methods section of the revised manuscript, which now contains all of this detailed information (Page 6, lines 116-133). We believe this detailed explanation now makes our study design clear and transparent. Thank you again for helping us improve our manuscript.

Reviewer #2 (Recommendations for the authors):

The manuscript requires significant language editing to improve clarity and readability. We recommend that the authors seek professional editing services for revision.

Thank you for your constructive comments on the language of our manuscript. We apologize for any lack of clarity in the previous version. To address this, we have performed a thorough revision of the manuscript. The text has been carefully reviewed and edited by a native English-speaking colleague who is an expert in our research field. We have focused on correcting all grammatical errors, improving sentence structure, and refining the phrasing throughout the document to enhance readability. We are confident that these extensive revisions have significantly improved the clarity of the manuscript. We hope you will find the current version much easier to read and understand.

Reviewer #3 (Recommendations for the authors):

(1) However, I think the abstract part of the article is too detailed and should be more concise and shortened. It is not necessary to show detailed values but to summarize the results.

Thank you for this valuable suggestion. We agree that the previous version of the abstract was overly detailed and that a more concise summary would be more effective for the reader. Following your advice, we have substantially revised the abstract. We have removed the specific numerical values (such as detailed statistics) and have instead focused on summarizing the key findings and their broader implications (Page 3, lines 54-60, 64-66, 70-72). The revised abstract is now shorter and provides a clearer, high-level overview of our study's background, methods, main results, and conclusions. We believe these changes have significantly improved its readability and impact. We hope you will find the current version more appropriate.

(2) Figure 4, the color in the legend and plot are not the same, and should be revised.

Thank you for your careful attention to detail and for pointing out the color inconsistency in Figure 4. We apologize for this oversight. We have now corrected the figure as you suggested, ensuring that the colors in the legend perfectly match those in the plot. The revised Figure 4 has been updated in the manuscript. We appreciate your help in improving the quality of our figures.

(3) Please pay attention to the article format, such as the consistency of fonts and punctuation marks. (For example, Lines 75 and Line 230).

Thank you for your meticulous review and for pointing out the inconsistencies in our manuscript's formatting. We sincerely apologize for these oversights and any inconvenience they may have caused. Following your feedback, we have carefully corrected the specific issues you highlighted. Furthermore, we have conducted a thorough proofread of the entire manuscript to ensure consistency in all fonts, punctuation marks, and overall adherence to the journal's formatting guidelines. We appreciate your help in improving the presentation and professionalism of our paper.

Illustrates the physical and social impact of fear-based testimonies. People having fits were removed by authorities to stop the panic.

Burroughs supposedly appeared in spirit and made her sign a book. Demonstrates belief in invisible influence of the accused.

Physical presence interpreted as supernatural influence. People thought Burroughs could harm them just by looking. Reinforces fear and perception of hidden power.

Spectral evidence: some witnesses claim Burroughs is linked to the dead through supernatural phenomena. They said that Burroughs’ dead wives appeared.

Burroughs repeatedly denies supernatural involvement, the haunting of his house, and coercion of his family. His answers are factual and restrained, yet the community interprets them through a lens of suspicion.

Accusations extended to other women, showing how fear spreads in the community, and multiple people can be implicated. Accusing others could be a survival strategy: shifting blame to reduce her own risk, similar to Tituba.

Describes fits and afflictions of the children. Reflects the belief that witches could harm others through supernatural means. Their suffering increases suspicion and legitimizes the accusations.

Children accuse her directly. Fear and community pressure turn children into key witnesses, making denial almost impossible.

Sarah Good denies all accusations. Shows how accused witches often responded rationally.

Accusations quickly led to imprisonment. Shows real legal consequences of fear and suspicion.

The children’s fits continue even when she is not near them, suggesting spectral influence.

Describes fits and afflictions of the children. Reflects belief in supernatural influence.

This examination shows how fear of punishment influenced testimony, and how survival in Salem often meant “admitting” to witchcraft even if innocent. It also reveals the social dynamics of accusation: once one person is accused, others could quickly be implicated.

Tituba ends her testimony, emphasizing the subjective nature of spectral evidence. Much of her story is shaped by what she believes will protect her.

Description of the Devil as a threatening figure. Highlights how Puritan fears shaped the narrative.

Magical travel or teleportation, typical in witchcraft lore. Shows Tituba shaping her story to meet the interrogators’ expectations.

Animals acting as familiars reinforce the expectation that witches had helpers. Again, likely emphasized by Tituba to fit the narrative expected of her.

She describes threats from spirits, reinforcing coercion. Highlights how the accused were pressured to confess or participate in accusations to survive.

Tituba accuses other women of harming children. This was likely a strategy to protect herself, showing how accusations could be used to shift suspicion onto others.

Tituba claims the Devil appeared and forced her to serve him. By framing herself as under the Devil’s influence, she confirms the Puritan expectation of witchcraft, even though she may not be guilty.

Tituba initially denies causing any harm. Tituba initially denies harming anyone. This shows her rational defense, but it also highlights how the accused had to navigate extreme fear to survive. It highlights the gap between personal truth and communal belief.

equates the extremities of the physcial world with the extremities of his empire

due to the policy of immigrants, kids lost their parents, showing the huge hostility to immigrants.

why does the author speically mention "US citizens"?

By using strong military forces to capture two criminal, is it really useful, or it is only works as threaten?

Children never faced this type of situation before, using military forces directly to them is immoral, and the purpose was to seperate them from their parents.

What is CNN, what do they want to expose to the public?

It is ironic that even children are getting arrested, the police don't have any empathy or kindness.

what would the children also been arrested?

the word "invasion" describe the crucial of this scene, showing the harsh treatment act on the immigrants, illustrating the harsh conditions they faces.

When having a too big power to legalized to do something, people always forget with naturals or what are their orginal purposes.

This shows the environment that law enforcement officer with guns created, all those things were pulled out, which is a messy and scary place.

Improving the quality of schools is the most direct approach we can take to solve this problem. However, it is extremely difficult to do so.

To combat this problem, the government has implemented programs to increase low income families' buying power. However, as we've learned in this class, these programs haven't been the most effective, and they are opposed by many

Being in a academic environment as an average student in a low income family, according to studies, hinders kids' learning and academic performance. This can be attributed to teacher quality, the behaviors of peers, and the resources provided by the school the low income family student attends.

Educational gap has an especially crucial impact on skill attainment in the earlier stages of life, as kids in both low and high income families rely primarily on their families to have access to educational materials. Income directly impacts the educational materials these families can provide to their children, and to develop in the field of STEM, access to these materials is especially crucial.

Gap mentioned in the previous annotation is only increasing.

Gap impacts academic preparedness for college, preventing low income family students from having the same collegiate opportunities as high income family students. This also can go on to impact skill attainment, putting low income family students at a disadvantage in terms of the job market.

Residential segregation makes students in low income families subject to a lower quality education than higher income families within public schools.

Uno versus muchos. Debería muchos vs muchos, para que sea patrón.

Poner la figura aquí

No se entiende. Simplificar.

No es necesario entrar en este detalle. Pero si se va a decir, hay que usar citas para fundamentar.

even though there is no direct evidence, this man still think that this Taliban will hurt the country, showing the hostility of the native towards the foreigners.

connection to the allegory, matches to a teacher who wants to give the aliens a place to live, showing that not all of the people hates "aliens".

does this sentence only describe the view, or there is another meaning?

The contrast between sympathy and cruelty highlights the moral confusion inside the system.

why does the man emphasize the word "Obama"?

This is a connection to the allegory "The Wretched and the Beautiful". This is the idea of most of the people when they meet the first group of aliens. People doesn't want to pay for the "alien" .

dreamcatcher is a symbol of protection, showing that the recruiter have a potential to protect people.

This is a irony. The female recruiter is judging people and evaluate their ability through their appearance, showing that she value appearance more than other. However, she is describe as drinking bright-colored drink and full of tattoos, showing a contrast of what she thinks and what she actually show.

The author introduces Willow to show a softer side of law enforcement, but this gentle image contrasts with ICE’s harsh duties.

In this sentence, the author compares the common situation to a grand sense of mission. The author criticizes male safety fantasies and the construction of violent identities.

In that paragraph, the author describes the video as a strong visual contrast. This video is kind of propaganda, which connects the migration to terrorism.

What’s an example of how negative attitudes might lead someone to perceive a threat that isn’t supported by data?

How might bringing up a event that is so violent shape the readers openness to the what the study is going to say?

eLife Assessment

This study identifies the uncharacterised protein FAM53C as a novel, potential regulator of the G1/S cell cycle transition, linking its function to the DYRK1A kinase and the RB/p53 pathways. The work is valuable and of interest to the cell cycle field, leveraging a strong computational screen to identify a new candidate. The findings are solid, although confidence in the siRNA depletion phenotypes would have been higher with rescue experiments using an siRNA-resistant cDNA and more robust quantification of some immunoassay data.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #1 (Public review):

Summary:

Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle. They did so by screening publicly available data from the Cancer Dependency Map and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. They show in RPE1 cells that loss of FAMC53 leads to a DYRK1A + P53-dependent cell cycle arrest. Combined inactivation of FAM53C and DYRK1A in a TP53-null background caused S-phase entry with subsequent apoptosis. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.

The authors have revised the manuscript, and I respond here point-by-point to indicate which parts of the revision I found compelling, and which parts were less convincing. So the numbering is consistent with the numbering in my first review report.

(1) The p21 knockdowns are a valuable addition, and the claim that other p53 targets than p21 are involved in the FAMC53 RNAi-mediated arrest is now much more solid. Minor detail: if S4D is a quantification of S4C, it is hard to believe that the quantification was done properly (at least the DYRK1Ai conditions). Perhaps S4C is not the best representative example, or some error was made?

(2a) I appreciate the decision to remove the cyclin D1 phosphorylation data. A more nuanced model now emerges. It is not clear to me however why the Protein Simple immunoassay was used for experiments with RPE cells, and not the cortical organoids. Even though no direct claims are made based on the phospho-cyclin D data in Figure 5E+G, showing these data suggests that FAM53C deletion increases DYRK1A-mediated cyclin D1 phosphorylation. I find it tricky to show these data, while knowing now that this effect could not be shown in the RPE1 cells.<br /> (2b) The quantifications of the immunoassays are not convincing. In multiple experiments, the HSP90 levels vary wildly, which indicates big differences in protein loading if HSP90 is a proper loading control. This is for example problematic for the interpretation of figure 3F and S3I. The cyclin D1 "bands" look extremely similar between siCtrl and siFAM53C (Fig S3I), in fact the two series of 6 samples with different dosages of DYRK1Ai look seem an identical repetition of each other. I did not have to option to overlay them, but it would be important to check if a mistake was made here. The cyclin D1 signals aside, the change in cycD1/HSP90 ratios seems to be entirely caused by differences in HSP90 levels. Careful re-analysis of the raw data and more equal loading seem necessary. The same goes (to a lesser extent) for S3J+K.<br /> (2c) the new model in Fig S4L: what do the arrows at the right FAM53C and p53 that merge a point straight towards S-phase mean? They suggest that p53 (and FAM53C) directly promote S-phase progression, but most likely this is not what the authors intended with it.

(3) Clear; nicely addressed.

(4) Thank you for correcting.

(5) I appreciate that the authors are now more careful to call the IMPC analysis data preliminary. This is acceptable to me, but nevertheless, I suggest the authors to seriously consider taking this part entirely out. The risk of chance finding and the extremely skewed group sizes (as reviewer #2 had pointed out) hamper the credibility of this statistical analysis.

Reviewer #2 (Public review):

The authors sought to identify new regulators of the G1/S transition by mining the Cancer Dependency Map (DepMap) co-dependency dataset. This analysis successfully identified FAM53C, a poorly characterized protein, as a candidate. The strength of the paper lies in this initial discovery and the subsequent biochemical work convincingly showing that FAM53C can directly interact with the kinase DYRK1A, a known cell cycle regulator.

The authors then present evidence, primarily from acute siRNA knockdown in RPE-1 cells, that loss of FAM53C induces a strong G1 cell cycle arrest. Their follow-up investigation proposes a model where FAM53C normally inhibits DYRK1A, thereby protecting Cyclin D from degradation and preventing p53 activation, to allow for G1/S progression. The authors have commendably addressed some concerns from the initial review: they have now demonstrated the G1 arrest using two independent siRNAs (an improvement over the initial pool), shown the effect in several additional cancer cell lines (U2OS, A549, HCT-116), and developed a more nuanced model that incorporates p53 activation, which helps to explain some of the complex data.

However, a central and critical weakness persists. The entire functional model is built upon the very strong G1 arrest phenotype observed in vitro following acute knockdown. This finding is in stark contrast to data from other contexts. As the authors note, the knockout of Fam53c in mice results in minimal phenotypes, and the DepMap data itself suggests the gene is largely non-essential in most cancer cell lines.

This major discrepancy creates two competing interpretations:

As the authors suggest, FAM53C has a critical role in the cell cycle, but its loss is rapidly masked by compensatory mechanisms in long-term knockout models (like iPSCs and mice) or in established cancer cell lines.

The strong acute G1 arrest is an experimental artifact of the siRNA-mediated knockdown, and not a true reflection of FAM53C's primary function.

The authors' new controls (using two individual siRNAs and showing the arrest is RB-dependent) make an off-target effect less likely, but they do not definitively rule it out. The gold-standard experiment to distinguish between these two possibilities-a rescue of the phenotype using an siRNA-resistant cDNA-has not been performed.

Because this key control is missing, the foundation of the paper's functional claims is not as solid as it needs to be. While the study provides an interesting and valuable new candidate for the cell cycle field to investigate, readers should be cautious in accepting the strength of FAM53C's role in the G1/S transition until this central discrepancy is definitively resolved.

Reviewer #3 (Public review):

Summary:

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53C-depleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off-target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.

Major comments:

(1) Whole study is based on one siRNA to Fam53C and its specificity was not validated. Level of the knock down was shown only in the first figure and not in the other experiments. The observed phenotypes in the cell cycle progression may be affected by variable knock-down efficiency and/or potential off target effects.

(2) Experiments focusing on the cell cycle progression were done in a single cell line RPE1 that showed a strong sensitivity to FAM53C depletion. In contrast, phenotypes in IPSCs and in mice were only mild suggesting that there might be large differences across various cell types in the expression and function of FAM53C. Therefore, it is important to reproduce the observations in other cell types.

(3) Authors state that FAM53C is a direct inhibitor of DYRK1A kinase activity (Line 203), however this model is not supported by the data in Fig 4A. FAM53C seems to be a good substrate of DYRK1 even at high concentrations when phosphorylations of cyclin D is reduced. It rather suggests that DYRK1 is not inhibited by FAM53C but perhaps FAM53C competes with cyclin D. Further, authors should address if the phosphorylation of cyclin D is responsible for the observed cell cycle phenotype. Is this Cyclin D-Thr286 phosphorylation, or are there other sites involved?

(4) At many places, information on statistical tests is missing and SDs are not shown in the plots. For instance, what statistics was used in Fig 4C? Impact of FAM53C on cyclin D phosphorylation does not seem to be significant. IN the same experiment, does DYRK1 inhibitor prevent modification of cyclin D?

(5) Validation of SM13797 compound in terms of specificity to DYRK1 was not performed.

(6) A fraction of cells in G1 is a very easy readout but it does not measure progression through the G1 phase. Extension of the S phase or G2 delay would indirectly also result in reduction of the G1 fraction. Instead, authors could measure the dynamics of entry to S phase in cells released from a G1 block or from mitotic shake off.

Comments to the revised manuscript:

In the revised version of the manuscript, authors addressed most of the critical points. They now include new data with depletion of FAM53C using single siRNAs that show small but significant enrichment of population of the G1 cells. This G1 arrest is likely caused by a combined effects on induction of p21 expression and decreased levels of cyclin D1. Authors observed that inhibition of DYRK1 rescued cyclin D1 levels in FAM53 depleted cells suggesting that FAM53C may inhibit DYRK1. This possibility is also supported by in vitro experiments. On the other hand, inhibition of DYRK1 did not rescue the G1 arrest upon depletion of FAM53C, suggesting that FAM53C may have also DYRK1-independent role in G1. Functional rescue experiments with cyclin D1 mutants and detection of DYRK1 activity in cells would be necessary to conclusively explain the function of FAM53C in progression through G1 phase but unfortunately these experiments were technically not possible. Knock out of FAM53C in iPSCs and in mice suggest that FAM53C may have additional functions besides the cell cycle control and/or that adaptation may have occurred in these model systems. Overall, the study implicated FAM53C in fine tuning DYRK1 activity in cells that may to some extent influence the progression through G1 phase. In addition, FAM53C may also have DYRK1 and cell cycle independent functions that remain to be addressed by future studies.

Author response:

(1) General Statements

We thank the Reviewers for a fair review of our work and helpful suggestions. We have significantly revised the manuscript in response to these suggestions. We provide a point-by-point response to the Reviewers below but wanted to highlight in our response a recurring concern related to the strong cell cycle arrest observed upon the acute FAM53C knock-down being different than the limited phenotypes in other contexts, including the knockout mice and DepMap data.

First, we now show that we can recapitulate the strong G1 arrest resulting from the FAM53C knock-down using two independent siRNAs in RPE-1 cells, supporting the specificity of the effects.

Second, the G1 arrest that results from the FAM53C knock-down is also observed in cells with inactive p53, suggesting it is not due to a non-specific stress response due to “toxic” siRNAs. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype.

Third, we have performed experiments in other human cells, including cancer cell lines. As would be expected for cancer cells, the G1 arrest is less pronounced but is still significant, indicating that the G1 arrest is not unique to RPE-1 cells.

Fourth, it is not unexpected that compensatory mechanisms would be activated upon loss of FAM53C during development or in cancer – which may explain the lack of phenotypes in vivo or upon long-term knockout. This has been true for many cell cycle regulators, either because of compensation by other family members that have overlapping functions, or by a larger scale rewiring of signaling pathways. 

(2) Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity): 

Summary: 

Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle.

They did so by screening public available data from the Cancer Dependency Map, and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. DYRK1A in its is known to induce degradation of cyclin D, leading the authors to propose a model in which DYRK1Adependent cyclin D degradation is inhibited by FAM53C to permit S-phase entry. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.  

Major comments: 

The authors show convincing evidence that FAM53C loss can reduce S-phase entry in cell cultures, and that it can bind to DYRK1A. However, FAM53 has multiple other binding partners and I am not entirely convinced that negative regulation of DYRK1A is the predominant mechanism to explain its effects on S-phase entry. Some of the claims that are made based on the biochemical assays, and on the physiological effects of FAM53C are overstated. In addition, some choices made methodology and data representation need further attention. 

(1) The authors do note that P21 levels increase upon FAM53C. They show convincing evidence that this is not a P53-dependent response. But the claim that " p21 upregulation alone cannot explain the G1 arrest in FAM53C-deficient cells (line 138-139) is misleading. A p53-independent p21 response could still be highly relevant. The authors could test if FAM53C knockdown inhibits proliferation after p21 knockdown or p21 deletion in RPE1 cells. 

The Reviewer raises a great point. Our initial statement needed to be clarified and also need more experimental support. We have performed experiments where we knocked down FAM53C and p21 individually, as well as in combination, in RPE-1 cells. These experiment show that p21 knock-down is not sufficient to negate the cell cycle arrest resulting from the FAM53C knockdown in RPE-1 cells (Figure 4B,C and Figure S4C,D).

We now extended these experiments to conditions where we inhibited DYRK1A, and we also compared these data to experiments in p53-null RPE-1 cells. Altogether, these experiments point to activation of p53 downstream of DYRK1A activation upon FAM53C knock-down, and indicate that p21 is not the only critical p53 target in the cell cycle arrest observed in FAM53C knock-down cells (Figure 4 and Figure S4).

(2) The authors do not convincingly show that FAM53C acts as a DYRK1A inhibitor in cells. Figures 4B+C and S4B+C show extremely faint P-CycD1 bands, and tiny differences in ratios. The P values are hovering around the 0.05, so n=3 is clearly underpowered here. Total CycD1 levels also correlate with FAM53C levels, which seems to affect the ratios more than the tiny pCycD1 bands. Why is there still a pCycD1 band visible in 4B in the GFP + BTZ + DYRK1Ai condition? And if I look at the data points I honestly don't understand how the authors can conclude from S4C that knockdown of siFAM53C increases (DYRK1A dependent) increases in pCycD1 (relative to total CycD1). In figure 5C, no blot scans are even shown, and again the differences look tiny. So the authors should either find a way to make these assays more robust, or alter their claims appropriately. 

We appreciate these comments from the Reviewer and have significantly revised the manuscript to address them.

The analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knock-down, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.

We removed previous panel 4B from the revised manuscript. For panels 4E and S4B (now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.

The representative Western blot images for 5C-D (now 5F-G) in the original submission are shown in Figure 5E, we apologize if this was not clear. The differences are small, which we acknowledge in the revised manuscript. Note that several factors can affect Cyclin D levels in cells, including the growth rate and the stage of the cell cycle. Our FACS analysis shows that normal organoids have ~63% of cells in G1 and ~13% in S phase; the overall lower proportion of S-phase cells in organoids may make the immunoblot difference appear smaller, with fewer cycling cells resulting in decreased Cyclin D phosphorylation.

Nevertheless, the Reviewer brings up a good point and comments from this Reviewer and the others made us re-think how to best interpret our results. As discussed above, we re-read carefully the Meyer paper and think that FAM53C’s role and DYRK1A activity in cells may be understood when considering levels of both CycD and p21 at the same time in a continuum. While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is likely that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).

(3) The experiments to test if DYRK1A inhibition could rescue the G1 arrest observed upon FAM53C knockdown are not entirely convincing either. It would be much more convincing if they also perform cell counting experiments as they have done in Figures 1F and 1G, to complement the flow cytometry assays. I suggest that the authors do these cell counting experiments in RPE1 +/- P53 cells as well as HCT116 cells. In addition, did the authors test if P21 is induced by DYRK1Ai in HCT116 cells? 

We repeated the experiments with the DYRK1A inhibitor and counted the cells. In p53-null RPE1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide. Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells.

(4) The data in Figure 5C and 5D are identical, although they are supposed to represent either pCycD1 ratios or p21 levels. This is a problem because at least one of the two cannot be true. Please provide the proper data and show (representative) images of both data types.

We apologize for these duplicated panels in the original submission. We now replaced the wrong panel with the correct data (Fig. 5F,G). 

(5) Line 246: "Fam53c knockout mice display developmental and behavioral defects." I don't agree with this claim. The mutant mice are born at almost the expected Mendelian ratios, the body weight development is not consistently altered. But more importantly, no differences in adult survival or microscopic pathology were seen. The authors put strong emphasis on the IMPC behavioral analysis, but they should be more cautious. The IMPC mouse cohorts are tested for many other phenotypes related to behavior and neurological symptoms and apparently none of these other traits were changed in the IMPC Famc53c-/- cohort. Thus, the decreased exploration in a new environment could very well be a chance finding. The authors need to take away claims about developmental and behavioral defects from the abstract, results and discussion sections; the data are just too weak to justify this. 

We agree with the Reviewer that, although we observed significant p-values, this original statement may not be appropriate in the biological sense. We made sure in the revised manuscript to carefully present these data.

Minor comments: 

(6) Can the authors provide a rationale for each of the proteins they chose to generate the list of the 38 proteins in the DepMap analysis? I looked at the list and it seems to me that they do not all have described functions in the G1/S transition. The analysis may thus be biased. 

To address this point, we updated Table S1 (2nd tab) to provide a better rationale for the 38 factors chosen. Our focus was on the canonical RB pathway and we included RB binding proteins whose function had suggested they may also be playing a role in the G1/S transition. We do agree that there is some bias in this selection (e.g., there are more RB binding factors described) but we hope the Reviewer will agree with us that this list and the subsequent analysis identified expected factors, including FAM53C. Future studies using this approach and others will certainly identify new regulators of cell cycle progression.

(7) Figure 1B is confusing to me. Are these just some (arbitrarily) chosen examples? Consider leaving this heatmap out altogether, of explain in more detail. 

We agree with the Reviewer that this panel was not necessarily useful and possibly in the wrong place, and we removed it from the manuscript. We replaced it with a cartoon of top hits in the screen.

(8) The y-axes in Figures 2C, 2D, 2E, and 4D are misleading because they do not start at 0. Please let the axis start at 0, or make axis breaks. 

We re-graphed these panels.

(9) Line 229: " Consequences ... brain development." This subheader is misleading, because the in vitro cortical organoid system is a rather simplistic model for brain development, and far away from physiological brain development. Please alter the header. 

We changed the header to “Consequences of FAM53C inactivation in human cortical organoids in culture”.

(10) Figure S5F: the gating strategy is not clear to me. In particular, how do the authors know the difference between subG1 and G1 DAPI signals? Do they interpret the subG1 as apoptotic cells? If yes, why are there so many? Are the culturing or harvesting conditions of these organoids suboptimal? Perhaps the authors could consider doing IF stainings on EdU or BrdU on paraffin sections of organoids to obtain cleaner data?

Thank you for your feedback. The subG1 population in the original Figure S5F represents cells that died during the dissociation step of the organoids for FACS analysis. To address this point, we performed live & dead staining to exclude dead cells and provide clearer data. We refined gating strategy for better clarity in the new S5F panel.

(11) Figure S6A; the labeling seems incorrect. I would think that red is heterozygous here, and grey mutant. 

We fixed this mistake, thank you. 

Reviewer #1 (Significance): 

The finding that the poorly studied gene FAM53C controls the G1/S transition in cell lines is novel and interesting for the cell cycle field. However, the lack of phenotypes in Famc53-/- mice makes this finding less interesting for a broader audience. Furthermore, the mechanisms are incompletely dissected. The importance of a p53-indepent induction of p21 is not ruled out. And while the direct inhibitory interaction between FAM53C and DYRK1A is convincing (and also reported by others; PMID: 37802655), the authors do not (yet) convincingly show that DYRK1A inhibition can rescue a cell proliferation defect in FAM53C-deficient cells. 

Altogether, this study can be of interest to basic researchers in the cell cycle field. 

I am a cell biologist studying cell cycle fate decisions, and adaptation of cancer cells & stem cells to (drug-induced) stress. My technical expertise aligns well with the work presented throughout this paper, although I am not familiar with biolayer interferometry. 

Reviewer #2 (Evidence, reproducibility and clarity): 

Summary 

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53Cdepleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.

Major points 

(1) Whole study is based on one siRNA to Fam53C and its specificity was not validated. Level of the knock down was shown only in the first figure and not in the other experiments. The observed phenotypes in the cell cycle progression may be affected by variable knock-down efficiency and/or potential off target effects. 

We thank the Reviewer for raising this important point. First, we need to clarify that our experiments were performed with a pool of siRNAs (not one siRNA). Second, commercial antibodies against FAM53C are not of the best quality and it has been challenging to detect FAM53C using these antibodies in our hands – the results are often variable. In addition, to better address the Reviewer’s point and control for the phenotypes we have observed, we performed two additional series of experiments: first, we have confirmed G1 arrest in RPE-1 cells with individual siRNAs, providing more confidence for the specificity of this arrest (Fig. S1B); second, we have new data indicating that other cell lines arrest in G1 upon FAM53C knock-down (Fig. S1E,F and Fig. 4F).

(2) Experiments focusing on the cell cycle progression were done in a single cell line RPE1 that showed a strong sensitivity to FAM53C depletion. In contrast, phenotypes in IPSCs and in mice were only mild suggesting that there might be large differences across various cell types in the expression and function of FAM53C. Therefore, it is important to reproduce the observations in other cell types. 

As mentioned above, we have new data indicating that other cell lines arrest in G1 upon FAM53C knock-down (three cancer cell lines) (Fig. S1E,F and Fig. 4F).

(3) Authors state that FAM53C is a direct inhibitor of DYRK1A kinase activity (Line 203), however this model is not supported by the data in Fig 4A. FAM53C seems to be a good substrate of DYRK1 even at high concentrations when phosphorylations of cyclin D is reduced. It rather suggests that DYRK1 is not inhibited by FAM53C but perhaps FAM53C competes with cyclin D. Further, authors should address if the phosphorylation of cyclin D is responsible for the observed cell cycle phenotype. Is this Cyclin D-Thr286 phosphorylation, or are there other sites involved? 

We revised the text of the manuscript to include the possibility that FAM53C could act as a competitive substrate and/or an inhibitor.

We removed most of the Cyclin D phosphorylation/stability data from the revised manuscript. As the Reviewers pointed out, some of these data were statistically significant but the biological effects were small. As discussed above in our response to Reviewer #1, the analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knockdown, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.

We note, however, that we used specific Thr286 phospho-antibodies, which have been used extensively in the field. Our data in Figure 1 with palbociclib place FAM53C upstream of Cyclin D/CDK4,6. We performed Cyclin D overexpression experiments but RPE-1 cells did not tolerate high expression of Cyclin D1 (T286A mutant) and we have not been able to conduct more ‘genetic’ studies. 

(4) At many places, information on statistical tests is missing and SDs are not shown in the plots. For instance, what statistics was used in Fig 4C? Impact of FAM53C on cyclin D phosphorylation does not seem to be significant. In the same experiment, does DYRK1 inhibitor prevent modification of cyclin D? 

As discussed above, we removed some of these data and re-focused the manuscript on p53-p21 as a second pathway activated by loss of FAM53C.

(5) Validation of SM13797 compound in terms of specificity to DYRK1 was not performed. 

This is an important point. We had cited an abstract from the company (Biosplice) but we agree that providing data is critical. We have now revised the manuscript with a new analysis of the compound’s specificity using kinase assays. These data are shown in Fig. S3F-H.

(6) A fraction of cells in G1 is a very easy readout but it does not measure progression through the G1 phase. Extension of the S phase or G2 delay would indirectly also result in reduction of the G1 fraction. Instead, authors could measure the dynamics of entry to S phase in cells released from a G1 block or from mitotic shake off. 

The Reviewer made a good point. As discussed in our response to Reviewer #1, with p53-null RPE-1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide.

Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells. These data indicate that G1 entry by flow cytometry will not always translate into proliferation.

Other points:

(7) Fig. 2C, 2D, 2E graphs should begin with 0 

We remade these graphs.

(8) Fig. 5D shows that the difference in p21 levels is not significant in FAM53C-KO cells but difference is mentioned in the text. 

We replaced the panel by the correct panel; we apologize for this error.

(9) Fig. 6D comparison of datasets of extremely different sizes does not seem to be appropriate

We agree and revised the text. We hope that the Reviewer will agree with us that it is worth showing these data, which are clearly preliminary but provide evidence of a possible role for FAM53C in the brain.

(10) Could there be alternative splicing in mice generating a partially functional protein without exon 4? Did authors confirm that the animal model does not express FAM53C? 

We performed RNA sequencing of mouse embryonic fibroblasts derived from control and mutant mice. We clearly identified fewer reads in exon 4 in the knockout cells, and no other obvious change in the transcript (data not shown). However, immunoblot with mouse cells for FAM53C never worked well in our hands. We made sure to add this caveat to the revised manuscript.

Reviewer #2 (Significance): 

Main problem of this study is that the advanced experimental models in IPSCs and mice did not confirm the observations in the cell lines and thus the whole manuscript does not hold together. Although I acknowledge the effort the authors invested in these experiments, the data do not contribute to the main conclusion of the paper that FAM53C/DYRK1 regulates G1/S transition. 

Reviewer #3 (Evidence, reproducibility and clarity: 

This paper identifies FAM53C as a novel regulator of cell cycle progression, particularly at the G1/S transition, by inhibiting DYRK1A. Using data from the Cancer Dependency Map, the authors suggest that FAM53C acts upstream of the Cyclin D-CDK4/6-RB axis by inhibiting DYRK1A.  Specifically, their experiments suggest that FAM53C Knockdown induces G1 arrest in cells, reducing proliferation without triggering apoptosis. DYRK1A Inhibition rescues G1 arrest in P53KO cells, suggesting FAM53C normally suppresses DYRK1A activity. Mass Spectrometry and biochemical assays confirm that FAM53C directly interacts with and inhibits DYRK1A. FAM53C Knockout in Human Cortical Organoids and Mice leads to cell cycle defects, growth impairments, and behavioral changes, reinforcing its biological importance. 

Strength of the paper: 

The study introduces a novel cell cycle control signalling module upstream of CDK4/6 in G1/S regulation which could have significant impact. The identification of FAM53C using a depmap correlation analysis is a nice example of the power of this dataset. The experiments are carried out mostly in a convincing manner and support the conclusions of the manuscript. 

Critique: 

(1) The experiments rely heavily on siRNA transfections without the appropriate controls. There are so many cases of off-target effects of siRNA in the literature, and specifically for a strong phenotype on S-phase as described here, I would expect to see solid results by additional experiments. This is especially important since the ko mice do not show any significant developmental cell cycle phenotypes. Moreover, FAM53C does not show a strong fitness effect in the depmap dataset, suggesting that it is largely non-essential in most cancer cell lines. For this paper to reach publication in a high-standard journal, I would expect that the authors show a rescue of the S-phase phenotype using an siRNA-resistant cDNA, and show similar S-phase defects using an acute knock out approach with lentiviral gRNA/Cas9 delivery. 

We thank the Reviewer for this comment. Please refer to the initial response to the three Reviewers, where we discuss our use of single siRNAs and our results in multiple cell lines. Briefly, we can recapitulate the G1 arrest upon FAM53C knock-down using two independent siRNAs in RPE-1 cells. We also observe the same G1 arrest in p53 knockout cells, suggesting it is not due to a non-specific stress response. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype. Human cancer cell lines also arrest in G1 upon FAM53C knock-down, not just RPE-1 cells. Finally, we hope the Reviewer will agree with us that compensatory mechanisms are very common in the cell cycle – which may explain the lack of phenotypes in vivo or upon long-term knockout of FAM53C.

(2) The S-phase phenotype following FAM53C should be demonstrated in a larger variety of TP53WT and mutant cell lines. Given that this paper introduces a new G1/S control element, I think this is important for credibility. Ideally, this should be done with acute gRNA/Cas9 gene deletion using a lentiviral delivery system; but if the siRNA rescue experiments work and validate an on-target effect, siRNA would be an appropriate alternative. 

We now show data with three cancer cell lines (U2OS, A549, and HCT-116 – Fig. S1E,F and Fig. 4F), in addition to our results in RPE-1 cells and in human cortical organoids. We note that the knock-down experiments are complemented by overexpression data (Fig. 1G-I), by genetic data (our original DepMap screen), and our biochemical data (showing direct binding of FAM53C to DYRK1A).

(3) The western blot images shown in the MS appear heavily over-processed and saturated (See for example S4B, 4A, B, and E). Perhaps the authors should provide the original un-processed data of the entire gels? 

For several of our panels (e.g., 4E and S4B, now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.

Data in 4A are also not a western blot but a radiograph.

For immunoblots, we will provide all the source data with uncropped blots with the final submission.

(4) A critical experiment for the proposed mechanism is the rescue of the FAM53C S-phase reduction using DYRK1A inhibition shown in Figure 4. The legend here states that the data were extracted from BrdU incorporation assays, but in Figure S4D only the PI histograms are shown, and the S-phase population is not quantified. The authors should show the BrdU scatterplot and quantify the phenotype using the S-phase population in these plots. G1 measurements from PI histograms are not precise enough to allow for conclusions. Also, why are the intensities of the PI peaks so variable in these plots? Compare, for example, the HCT116 upper and lower panels where the siRNA appears to have caused an increase in ploidy. 

We apologize for the confusion and we fixed these errors, for most of the analyses, we used PI to measure G1 and S-phase entry. We added relevant flow cytometry plots to supplemental figures (Fig. S1G, H, I, as well as Fig. S4E and S4K, and Fig. S5F).

(5) There's an apparent contradiction in how RB deletion rescues the G1 arrest (Figure 2) while p21 seems to maintain the arrest even when DYRK1A is inhibited. Is p21 not induced when FAM53C is depleted in RB ko cells? This should be measured and discussed. 

This comment and comments from the two other Reviewers made us reconsider our model. We re-read carefully the Meyer paper and think that DYRK1A activity may be understood when considering levels of both CycD and p21 at the same time in a continuum (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is obvious that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).

Reviewer #3 (Significance): 

In conclusion, I believe that this MS could potentially be important for the cell cycle field and also provide a new target pathway that could be relevant for cancer therapy. However, the paper has quite a few gaps and inconsistencies that need to be addressed with further experiments. My main worry is that the acute depletion phenotypes appear so strong, while the gene is nonessential in mice and shows only a minor fitness effect in the depmap screens. More convincing controls are necessary to rule out experimental artefacts that misguide the interpretation of the results.

We appreciate this comment and hope that the Reviewer will agree it is still important to share our data with the field, even if the phenotypes in mice are modest.

Range of requests that are excepted in their authority. This depends on the type of power you exert.

eLife Assessment

This fundamental work examines how tRNA modifications influence antibiotic tolerance, providing novel insights that may have therapeutic uses. The evidence supporting the conclusions is convincing. Strengths of the manuscript include the mechanism of tRNA modification influencing antibiotic tolerance and the precise measurement techniques used throughout. Further analysis of growth rate impacts and specific identification of the proteins responsible for the effect would further strengthen the manuscript.

Reviewer #1 (Public review):

Summary:

Cotton et al. investigated the role of tusB in antibiotic tolerance in Yersinia pseudotuberculosis. They used the IP2226 strain and introduced appropriate mutations and complementation constructs. Assays were performed to measure growth rates, antibiotic tolerance, tRNA modification, gene expression and proteomic profiles. In addition, experiments to measure ribosome pausing and bioinformatic analysis of codon usage in ribosomal proteins provided in-depth mechanistic support for the conclusions.

Strengths:

The findings are consistent with the authors having uncovered new mechanistic insights into bacterial antibiotic tolerance mediated by reducing ribosomal protein abundance.

Weaknesses:

Since the WT strain grows faster than the tusB mutant, there is a question of how growth rate, per se, impacts some of the analysis done. The authors should address this issue. In addition, it may not be essential, but would analysis of another slow-growing mutant (in some other antibiotic tolerance pathway if available) serve as a good control in this context?

Reviewer #2 (Public review):

Summary:

This study addresses a critical clinical challenge-bacterial antibiotic tolerance (a key driver of treatment failure distinct from genetic resistance)-by uncovering a novel regulatory role of the conserved s2U tRNA modification in Yersinia pseudotuberculosis. Its strengths are notable and lay a solid foundation for understanding phenotypic drug tolerance. The study is the first to link s2U tRNA modification loss to antibiotic tolerance, specifically targeting translation/transcription-inhibiting antibiotics (doxycycline, gentamicin, rifampicin). By establishing a causal chain - s2U deficiency → codon-specific ribosome pausing (at AAA/CAA/GAA) → reduced ribosomal protein translation → global translational suppression → tolerance - it expands the functional landscape of tRNA modifications beyond canonical translation fidelity, filling a gap in how RNA epigenetics shapes bacterial stress adaptation.

Strengths:

This study makes a valuable contribution to understanding tRNA modification-mediated antibiotic tolerance.

Weaknesses:

There are several limitations that weaken the robustness of the study's mechanistic conclusions. Addressing these gaps would significantly enhance its impact and translational potential.

Reviewer #3 (Public review):

Summary:

In the manuscript of Cotten et al., the authors study the 2-thiolation of tRNA in bacterial antibiotic resistance. The wildtype organism, Yersinia pseudotuberculosis, downregulates 2-thiolation as a response to antibiotics targeting the ribosome. In this manuscript, the authors show that a knockout of tusB causes slower translation. They provide evidence on the mechanisms of the slowing by determining transcription and translation, ribosome profiling and performing codon-usage analysis. They successfully determined that 2 codons are drivers of the translation slowdown, and the data is highly conclusive. Technically, I have nothing to criticize.

Strengths:

All in all, the study is very well made, and the writing is clear and concise. It covers a wide array of state-of-the-art analyses to unravel the interplay of tRNA modifications in translation.

Weaknesses:

The only question that remains to be asked is why the slowed translation leads to a better survival of the bacteria under antibiotic stress. In my opinion, the mechanism itself remains unclear. Thus, the statement that "We expect that this reduction in ribosomal proteins is globally reducing the translational capacity of the cell and is responsible for inducing tolerance to ribosome and RNA polymerase-targeting antibiotics" does not truly emphasize the remaining open question of why slowed translation favors survival. Therefore, I would recommend a minor text revision.

Author response:

Reviewer #1 (Public review): 

Summary: 

Cotton et al. investigated the role of tusB in antibiotic tolerance in Yersinia pseudotuberculosis. They used the IP2226 strain and introduced appropriate mutations and complementation constructs. Assays were performed to measure growth rates, antibiotic tolerance, tRNA modification, gene expression and proteomic profiles. In addition, experiments to measure ribosome pausing and bioinformatic analysis of codon usage in ribosomal proteins provided in-depth mechanistic support for the conclusions. 

Strengths: 

The findings are consistent with the authors having uncovered new mechanistic insights into bacterial antibiotic tolerance mediated by reducing ribosomal protein abundance. 

Weaknesses: 

Since the WT strain grows faster than the tusB mutant, there is a question of how growth rate, per se, impacts some of the analysis done. The authors should address this issue. In addition, it may not be essential, but would analysis of another slow-growing mutant (in some other antibiotic tolerance pathway if available) serve as a good control in this context? 

We would like to thank the reviewer for their time spent reviewing our manuscript and for their positive review. We plan to address their comment as to how growth rate impacts the analyses and plan to incorporate another slow-growing mutant in the revised version of the manuscript.

Reviewer #2 (Public review): 

Summary: 

This study addresses a critical clinical challenge-bacterial antibiotic tolerance (a key driver of treatment failure distinct from genetic resistance)-by uncovering a novel regulatory role of the conserved s2U tRNA modification in Yersinia pseudotuberculosis. Its strengths are notable and lay a solid foundation for understanding phenotypic drug tolerance. The study is the first to link s2U tRNA modification loss to antibiotic tolerance, specifically targeting translation/transcription-inhibiting antibiotics (doxycycline, gentamicin, rifampicin). By establishing a causal chain - s2U deficiency → codon-specific ribosome pausing (at AAA/CAA/GAA) → reduced ribosomal protein translation → global translational suppression → tolerance - it expands the functional landscape of tRNA modifications beyond canonical translation fidelity, filling a gap in how RNA epigenetics shapes bacterial stress adaptation. 

Strengths: 

This study makes a valuable contribution to understanding tRNA modification-mediated antibiotic tolerance. 

Weaknesses: 

There are several limitations that weaken the robustness of the study's mechanistic conclusions. Addressing these gaps would significantly enhance its impact and translational potential. 

We would like to thank the reviewer for their time spent reviewing our manuscript, and for both their positive comments about the significance and novelty of this work as well as their critiques. We plan to address their specific recommendations in the revised manuscript by focusing on the contribution of specific ribosomal proteins (i.e. the 30S subunit protein, S13) through overexpression, codon replacement, and stability experiments. We also plan to design experiments to assess in vivo relevance and assess possible impacts on other pathways involved in antibiotic tolerance.

Reviewer #3 (Public review): 

Summary: 

In the manuscript of Cotten et al., the authors study the 2-thiolation of tRNA in bacterial antibiotic resistance. The wildtype organism, Yersinia pseudotuberculosis, downregulates 2-thiolation as a response to antibiotics targeting the ribosome. In this manuscript, the authors show that a knockout of tusB causes slower translation. They provide evidence on the mechanisms of the slowing by determining transcription and translation, ribosome profiling and performing codon-usage analysis. They successfully determined that 2 codons are drivers of the translation slowdown, and the data is highly conclusive. Technically, I have nothing to criticize. 

Strengths: 

All in all, the study is very well made, and the writing is clear and concise. It covers a wide array of state-of-the-art analyses to unravel the interplay of tRNA modifications in translation. 

Weaknesses: 

The only question that remains to be asked is why the slowed translation leads to a better survival of the bacteria under antibiotic stress. In my opinion, the mechanism itself remains unclear. Thus, the statement that "We expect that this reduction in ribosomal proteins is globally reducing the translational capacity of the cell and is responsible for inducing tolerance to ribosome and RNA polymerase-targeting antibiotics" does not truly emphasize the remaining open question of why slowed translation favors survival. Therefore, I would recommend a minor text revision. 

We would like to thank the reviewer for their time spent reviewing our manuscript and for their positive review of the technical aspects, experimental design, and writing. We will incorporate their suggested text revision into the revised manuscript, and will add to this statement if additional planned experiments shed light on this remaining question.

for - humanities - planetary boundaries - social system tensions - from local to planetary scales - terminology - local to planetary scale

why such huge fluctuations?

Interesting how they'd find ways to get around the moral issues just to conduct this experiment

how much does narrative vs. reality shape social psychology?

It’s disturbing that a single event caused a psychological concept to be used to understand human behavior. It's tragic that a single event leads to decades of thinking about morality and social psychology.

Explore 80+ powerful mobile c for 2026, from global market growth and revenue projections to user behavior, AI trends, and engagement data. Ideal for businesses, developers & marketers planning their next app.

Download page for the environmental omnibus d.d. #2025/12/10

6 legislative proposals (but press release lists 5)

1. Environmental assessments wrt permits
2. industrial emissions directive
3. SCIP database (substances of concern, in the Waste Framework directive) to be replaced with DPP ( #openvraag DPP is not in effect yet, so repeal of SCIP early / protection erosion?)
4. Extended Producer Responsibility req changed for EU producers.
5. INSPIRE

INSPIRE to be aligned with HVD (as expected)

EC press release for the environmental omnibus

eLife Assessment

This valuable study examines how mammals descend effectively and securely along vertical substrates. The conclusions from comparative analyses based on behavioral data and morphological measurements collected from 21 species across a wide range of taxa are convincing, making the work of interest to all biologists studying animal locomotion.

Reviewer #1 (Public review):

Summary:

This unique study reports original and extensive behavioral data collected by the authors on 21 living mammal taxa in zoo conditions (primates, tree shrew, rodents, carnivorans, and marsupials) on how descent along a vertical substrate can be done effectively and securely using gait variables. Ten morphological variables reflecting head size and limb proportions are examined in relationship to vertical descent strategies and then applied to reconstruct modes of vertical descent in fossil mammals.

Strengths:

This is a broad and data-rich comparative study, which requires a good understanding of the mammal groups being compared and how they are interrelated, the kinematic variables that underlie the locomotion used by the animals during vertical descent, and the morphological variables that are associated with vertical descent styles. Thankfully, the study presents data in a cogent way with clear hypotheses at the beginning, followed by results and a discussion that addresses each of those hypotheses using the relevant behavioral and morphological variables, always keeping in mind the relationships of the mammal groups under investigation. As pointed out in the study, there is a clear phylogenetic signal associated with vertical descent style. Strepsirrhine primates much prefer descending tail first, platyrrhine primates descend sideways when given a choice, whereas all other mammals (with the exception of the raccoon) descend head first. Not surprisingly, all mammals descending a vertical substrate do so in a more deliberate way, by reducing speed, and by keeping the limbs in contact for a longer period (i.e., higher duty factors).

Reviewer #2 (Public review):

Summary:

This paper contains kinematic analyses of a large comparative sample of small to medium-sized arboreal mammals (n = 21 species) traveling on near-vertical arboreal supports of varying diameter. This data is paired with morphological measures from the extant sample to reconstruct potential behaviors in a selection of fossil euarchontaglires. This research is valuable to anyone working in mammal locomotion and primate evolution.

Strengths:

The experimental data collection methods align with best research practices in this field and are presented with enough detail to allow for reproducibility of the study as well as comparison with similar datasets. The four predictions in the introduction are well aligned with the design of the study to allow for hypothesis testing. Behaviors are well described and documented, and Figure 1 does an excellent job in conveying the variety of locomotor behaviors observed in this sample. I think the authors took an interesting and unique angle by considering the influence of encephalization quotient on descent and the experience of forward pitch in animals with very large heads.

Comment from the Reviewing Editor on the revised version:

The authors responded to many comments of the reviewers, and I would be happy to see the authors make this version the Version of Record.

Author response:

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

eLife Assessment:

This valuable study examines how mammals descend effectively and securely along vertical substrates. The conclusions from comparative analyses based on behavioral data and morphological measurements collected from 21 species across a wide range of taxa are convincing, making the work of interest to all biologists studying animal locomotion.

We would like to greatly thank the two reviewers for their time in reviewing this work, and for their valuable comments and suggestions that will help to improve this manuscript.

Overall, we agree with the weaknesses raised, which are mainly areas for consideration in future studies: to study more species, and in a natural habitat context.

We will nevertheless add a few modifications to improve the manuscript, notably by making certain figures more readable, and adding definitions and bibliography in the main text concerning gait characteristics.

We also provide brief comments on each point of weakness raised by the reviewers below, in blue.

Reviewer #1 (Public review):

Summary:

This unique study reports original and extensive behavioral data collected by the authors on 21 living mammal taxa in zoo conditions (primates, tree shrew, rodents, carnivorans, and marsupials) on how descent along a vertical substrate can be done effectively and securely using gait variables. Ten morphological variables reflecting head size and limb proportions are examined in relationship to vertical descent strategies and then applied to reconstruct modes of vertical descent in fossil mammals.

Strengths:

This is a broad and data-rich comparative study, which requires a good understanding of the mammal groups being compared and how they are interrelated, the kinematic variables that underlie the locomotion used by the animals during vertical descent, and the morphological variables that are associated with vertical descent styles. Thankfully, the study presents data in a cogent way with clear hypotheses at the beginning, followed by results and a discussion that addresses each of those hypotheses using the relevant behavioral and morphological variables, always keeping in mind the relationships of the mammal groups under investigation. As pointed out in the study, there is a clear phylogenetic signal associated with vertical descent style. Strepsirrhine primates much prefer descending tail first, platyrrhine primates descend sideways when given a choice, whereas all other mammals (with the exception of the raccoon) descend head first. Not surprisingly, all mammals descending a vertical substrate do so in a more deliberate way, by reducing speed, and by keeping the limbs in contact for a longer period (i.e., higher duty factors).

Weaknesses:

The different gait patterns used by mammals during vertical descent are a bit more difficult to interpret. It is somewhat paradoxical that asymmetrical gaits such as bounds, half bounds, and gallops are more common during descent since they are associated with higher speeds and lower duty factors. Also, the arguments about the limb support polygons provided by DSDC vs. LSDC gaits apply for horizontal substrates, but perhaps not as much for vertical substrates.

We analyzed gait patterns using methods commonly found in the literature and discussed our results accordingly. However, the study of limbs support polygons was indeed developed specifically for studying locomotion on horizontal supports, and may not be applicable for studying vertical locomotion, which is in fact a type of locomotion shared by all arboreal species. In the future, it would be interesting to consider new methods for analyzing vertical gaits.

The importance of body mass cannot be overemphasized as it affects all aspects of an animal's biology. In this case, larger mammals with larger heads avoid descending head-first. Variation in trunk/tail and limb proportions also covaries with different vertical descent strategies. For example, a lower intermembral index is associated with tail-first descent. That said, the authors are quick to acknowledge that the five lemur species of their sample are driving this correlation. There is a wide range of intermembral indices among primates, and this simple measure of forelimb over hindlimb has vital functional implications for locomotion: primates with relatively long hindlimbs tend to emphasize leaping, primates with more even limb proportions are typically pronograde quadrupeds, and primates with relatively long forelimbs tend to emphasize suspensory locomotion and brachiation. Equally important is the fact that the intermembral index has been shown to increase with body mass in many primate families as a way to keep functional equivalence for (ascending) climbing behavior (see Jungers, 1985). Therefore, the manner in which a primate descends a vertical substrate may just be a by-product of limb proportions that evolved for different locomotor purposes. Clearly, more vertical descent data within a wider array of primate intermembral indices would clarify these relationships. Similarly, vertical descent data for other primate groups with longer tails, such as arboreal cercopithecoids, and particularly atelines with very long and prehensile tails, should provide more insights into the relationship between longer tail length and tail-first descent observed in the five lemurs. The relatively longer hallux of lemurs correlates with tail-first descent, whereas the more evenly grasping autopods of platyrrhines allow for all four limbs to be used for sideways descent. In that context, the pygmy loris offers a striking contrast. Here is a small primate equipped with four pincer-like, highly grasping autopods and a tail reduced to a short stub. Interestingly, this primate is unique within the sample in showing the strongest preference for head-first descent, just like other non-primate mammals. Again, a wider sample of primates should go a long way in clarifying the morphological and behavioral relationships reported in this study.

We agree with this statement. In the future, we plan to study other species, particularly large-bodied ones with varied intermembral indexes.

Reconstruction of the ancient lifestyles, including preferred locomotor behaviors, is a formidable task that requires careful documentation of strong form-function relationships from extant species that can be used as analogs to infer behavior in extinct species. The fossil record offers challenges of its own, as complete and undistorted skulls and postcranial skeletons are rare occurrences. When more complete remains are available, the entire evidence should be considered to reconstruct the adaptive profile of a fossil species rather than a single ("magic") trait.

We completely agree with this, and we would like to emphasize that our intention here was simply to conduct a modest inference test, the purpose of which is to provide food for thought for future studies, and whose results should be considered in light of a comprehensive evolutionary model.

Reviewer #2 (Public review):

Summary:

This paper contains kinematic analyses of a large comparative sample of small to medium-sized arboreal mammals (n = 21 species) traveling on near-vertical arboreal supports of varying diameter. This data is paired with morphological measures from the extant sample to reconstruct potential behaviors in a selection of fossil euarchontaglires. This research is valuable to anyone working in mammal locomotion and primate evolution.

Strengths:

The experimental data collection methods align with best research practices in this field and are presented with enough detail to allow for reproducibility of the study as well as comparison with similar datasets. The four predictions in the introduction are well aligned with the design of the study to allow for hypothesis testing. Behaviors are well described and documented, and Figure 1 does an excellent job in conveying the variety of locomotor behaviors observed in this sample. I think the authors took an interesting and unique angle by considering the influence of encephalization quotient on descent and the experience of forward pitch in animals with very large heads.

Weaknesses:

The authors acknowledge the challenges that are inherent with working with captive animals in enclosures and how that might influence observed behaviors compared to these species' wild counterparts. The number of individuals per species in this sample is low; however, this is consistent with the majority of experimental papers in this area of research because of the difficulties in attaining larger sample sizes.

Yes, that is indeed the main cost/benefit trade-off with this type of study. Working with captive animals allows for large comparative studies, but there is a risk of variations in locomotor behavior among individuals in the natural environment, as well as few individuals per species in the dataset. That is why we plan and encourage colleagues to conduct studies in the natural environment to compare with these results. However, this type of study is very time-consuming and requires focusing on a single species at a time, which limits the comparative aspect.

Figure 2 is difficult to interpret because of the large amount of information it is trying to convey.

We agree that this figure is dense. One possible solution would be to combine species by phylogenetic groups to reduce the amount of information, as we did with Fig. 3 on the dataset relating to gaits. However, we believe that this would be unfortunate in the case of speed and duty factor because we would have to provide the complete figure in SI anyway, as the species-level information is valuable. We therefore prefer to keep this comprehensive figure here and we will enlarge the data points to improve their visibility, and provide the figure with a sufficiently high resolution to allow zooming in on the details.

Reviewer #1 (Recommendations for the authors):

As indicated in the first section above, this is a strong comparative study that addresses important questions, relative to the evolution of arboreal locomotion in primates and close mammal relatives. My recommendations should be taken in the context of improving a manuscript that is already generally acceptable.

(1) The terms symmetrical and asymmetrical gaits should be briefly defined in the main text (not just in the Methods section) by citing work done by Hildebrand and other relevant studies. To that effect, the statement on lines 96-97 about the convergence of symmetrical gaits is unclear. What does "Symmetrical gaits have evolved convergently in rodents, scandentians, carnivorans, and marsupials" mean? Symmetrical gaits such as the walk, run, trot, etc., are pretty the norm in most mammals and were likely found in metatherians and basal eutherians. This needs clarification. On line 239, the term "ambling" is used in the context of related asymmetrical gaits. To be clear, the amble is a type of running gait involving no whole-body aerial phase and is therefore a symmetrical gait (see Schmitt et al., 2006).

We have added a definition of the terms symmetrical and asymmetrical gaits and added references in the introduction such as: “Symmetrical gaits are defined as locomotor patterns in which the footfalls of a girdle (a pair of fore- or hindlimbs) are evenly spaced in time, with the right and left limbs of a pair of limbs being approximately 50% out of phase with each other (Hildebrand, 1966, 1967). Symmetrical gaits can be further divided into two types: diagonal-sequence gaits, in which a hindlimb footfall is followed by that of the contralateral forelimb, and lateral-sequence gaits, in which a hindlimb footfall is followed by that of the ipsilateral forelimb (Hildebrand, 1967; Shapiro and Raichlen, 2005; Cartmill et al., 2007b). In contrast, asymmetrical gaits are characterized by unevenly spaced footfalls within a girdle, with the right and left limbs moving in near synchrony (Hildebrand, 1977).” Now found in lines 87-94.

We corrected the sentence such as “Symmetrical gaits are also common in rodents, scandentians, etc..” Now found in line 107.

Thank you for pointing this out. We indeed did not use the right term to mention related asymmetrical gaits with increased duty factors. We removed the term « ambling » and the associated reference here. Now found in line 256.

(2) Correlations are used in the paper to examine how brain mass scales with body mass. It is correct to assume that a correlation significantly different from 0 is indicative of allometry (in this case, positive). That said, lines are used in Figure S2 that go through the bivariate scatter plot. The vast majority of scaling studies rely on regression techniques to calculate and compare slopes, which are different statistically from correlations. In this case, a slope not significantly different from 1.0 would support the hypothesis of isometry based on geometric similarity (as brain mass and body mass are two volumes). The authors could refer to the work of Bob Martin and the 1985 edited book by Jungers and contributions therein. These studies should also be cited in the paper.

Thank you for recommending us this better suited method. We replaced the correlations with major axis orthogonal regressions, as recommended by Martin and Barbour 1989. We found a positive slope for all species significantly different from 1 (0.36), indicating a negative allometry (we realized we were mistaken about the allometry terminology, initially reporting a “positive allometry” instead of a positive correlation).

We corrected in the manuscript in the Results and Methods sections, and cited Martin and Barbour 1989 such as:

“To ensure that the EQs of the different species studied are comparable and meaningful, we tested the allometry between the brain and body masses in our dataset following [84] and found a significant and positive slope for all species (major axis orthogonal regression on log transformed values: slope = 0.36, r² = 0.92, p = 5.0.10^-12), indicating a negative allometry (r = 0.97, df = 19, p = 2.0.10^-13), and similar allometric coefficients when restricting the analysis to phylogenetic groups (Fig. S2).” Now found in lines 289-298.

- “To control that brain allometry is homogeneous among all phylogenetic groups, to be able to compare EQ between species, we computed major axis orthogonal regressions, following the recommendation of Martin and Barbour [84], between the Log transformed brain and body masses, over all species and by phylogenetic group using the sma package in R (Fig. S2).” Now found in lines 336-338.

We also changed Figure S2 in Supplementary Information accordingly.

(3) Trunk length is used as the denominator for many of the indices used in the study. In this way, trunk length is considered to be a proxy for body size. There should be a demonstration that trunk length scales isometrically with body mass in all of the mammals compared. If not the case, some of the indices may not be directly comparable.

We did not use trunk length as a proxy for body mass, but to compute geometric body proportions in order to test whether intrinsic body proportions could be related to vertical descent behaviors, namely the length of the tail and of the fore- and hindlimbs relative to the animal. We chose those indices to quantify the capability of limbs to act as levers or counterweights to rotate the animals for this specific question of vertical descent behavior. We therefore do not think that body mass allometry with respect to trunk length is relevant to compare these indices across species here. Also, we don’t expect that trunk length (which is a single dimension) would scale isometrically with body mass, which scales more as a volume.

(4) Given the numerous comparisons done in this study, a Bonferroni correction method should be considered to mitigate type I error (accepting a false positive).

We had already corrected all our statistical tests using the Benjamini-Hochberg method to control for false positives; see the SuppTables Excel file for the complete results of the statistical analyses. We chose this method over the Bonferroni correction because the more modern and balanced Benjamini-Hochberg procedure is better suited for analyses involving a large number of hypotheses.

(5) The terms "arm" and "leg" used in the main text and Table 1 are anatomically incorrect. Instead, the terms "forelimb" and hindlimb" should be used as they include the length sum of the stylopod, zeugopod, and autopod.

Indeed, thank you for pointing that out. We have corrected this error within the manuscript as well as in the figures 4 and S3.

(6) On p. 14, the authors make the statement that the postcranial anatomy of Adapis and Notharctus remains undescribed. The authors should consult the work of Dagosto, Covert, Godinot and others.

We did not state that the postcranial remains of Adapis and Notharctus have not been described. However, we were unfortunately unable to find published illustrations of the known postcranial elements that could be reliably used in this study. To avoid any misunderstanding, we removed the sentence such as: “However, we could not find suitable illustrations of the known postcranial elements of these species in the literature that could be reliably incorporated into this study. Thus, we only included their reconstructed body mass and EQ,..”. Now found in lines 393-397.

Reviewer #2 (Recommendations for the authors):

(1) Line 65/69 - Perchalski et al. 2021 is a single-author publication, so no et al. or w/ colleagues.

Indeed. This has been corrected in the manuscript, now found in lines 65 and 70.

(2) Lines 96-98 - Is it appropriate to say that the use of symmetrical gaits are examples of convergent evolution? There's less burden of evidence to state that these are shared behaviors, rather than suggesting they independently evolved across all those groups.

We agree with this and corrected the sentence such as “Symmetrical gaits are also common in rodents, scandentians, etc..” Now found in line 107.

(3) Line 198 - I am confused by how to interpret (-16,36 %) compared to how other numbers are presented in the rest of the paragraph.

To avoid confusion, we rephrased this sentence such as: “In contrast, primates did not significantly reduce their speed compared to ascents when descending sideways or tail-first (Fig. 2A, SuppTables B).”  Now found in lines 207-209.

eLife Assessment

This valuable study identifies asymmetric dimethylarginine (ADMA) modification of histones as a potential key determinant of the initial genomic binding of Rhino, a Drosophila-specific chromatin protein essential for piRNA cluster specification. The authors provide correlative genomic and imaging data to support their model, although functional validation of the proposed mechanism remains incomplete. Testing the redundancy between dART4 and dART1, which together could affect the prominent piRNA loci, in addition to the minor ones investigated in the manuscript, may change our assessment.

Reviewer #2 (Public review):

The Revision title and abstract are not updated enough to distinguish the special niche piRNA clusters from the more prominent major dual strand piRNA clusters that are widely known in the field for Drosophila, like 42AB and 38C. This revision mainly adds the term "piRNA source loci (piSL)" that is too vague and not a well-accepted name that would distinguish just these particularly niche piRNA clusters from major dual strand piRNA clusters like 42AB and 38C. This piSL term is problematic because it seems to imply these piSL's are connected to or would eventually become major dual strand piRNA clusters, but there is zero evidence in this study for any genetic or evolutionary connection between these two distinct types of piRNA sources. This revision still lacks the necessary changes needed to point out like in the abstract that major dual strand piRNA clusters like 42AB, 38C, 80F, and 102F in Drosophila that make up the bulk of piRNAs cannot be shown to be impacted by changes aimed at depleting ADMA-histones from these loci, and the authors' current evidence is still only limited to showing in these few 'niche' piRNA clusters that ADMA-histones may exhibit a direct interaction with Rhino as supported only by the knockdown of Drosophila Art4.

The author's rebuttal letter argues that 42AB and 38C are just conserved piRNA clusters that may no longer be regulated by ADMA. This is still a weak claim for dismissing the potential genetic redundancy problem when this study can only report strong knockdown of Art4. First, the dual strand 42AB piRNA cluster's conservation as a Drosophilid piRNA cluster is actually still a relatively recent evolutionary innovation in just D.simulans and D.melanogaster that are less than 3MYA diverged. This 42AB cluster is no longer conserved in D.sechelia and is also younger than the uni-strand Flamenco piRNA cluster that is conserve to 7MYA. The evolutionary arguments by the authors are not well-grounded. Second, the 42AB and 38C are the largest major dual strand piRNA clusters with very significant localization of Rhino and impact from Rhino loss of function, and if this paper's central thesis is that ADMA-histones directed by Art1 or Art4 is critical for the expression of dual-strand piRNA cluster loci by impacting Rhino, the current data still remain weak with no new experiments to help bolster their claims.

The author's rebuttal letter argues that the challenges they faced in trying to knock down Art1 in the fly was thwarted by reagent issues, and the explanations are unsatisfactory. They claim they only tested two RNAi cross lines to try to knock down Art1: the strain BDSC #36891, y[1] sc[*] v[1] sev[21]; P{y[+t7.7], v[+t1.8]=TRiP.GL01072}attP2/TM3, Sb[1] that they said they could not obtain this strain to be alive from the stock center? And then testing an alternative line VDRC #v110391P{KK101196}VIE-260B that displayed mediocre knockdown, the authors seemed to suggest they have given up trying to make this very important experiment work? They should have tried to figure out with the BDSC, a venerable stock center for Drosophila genetic tools, why they could not receive that fly strain alive (shipping flies at the economy rate internationally may be cheaper but often is too strenuous for flies to survive), and the authors have not acknowledged testing two other available knockdown lines for Art1: BDSC #31348, y[1] v[1]; P{y[+t7.7] v[+t1.8]=TRiP.JF01306}attP2 dsRNA and VDRC #w1118 P{GD11959}v40388. Trying to get good knockdown of Art1 would be a critical must-have experiment to address whether this arginine methyltransferase has an in vivo impact on ADMA-histones in the Drosophila ovary and showing an impact on 42AB and 38C. The revision does not address this major deficiency in impact on these two major dual strand piRNA clusters, only the very few niche piRNA clusters that are responsive to Art4 knockdown.

The rebuttal letter argues that "Therefore, conserved clusters such as 42AB and 38C may no longer be regulated by ADMA." but then the revision discussion is still speculating much too wildly that the piRNA source loci are then precursors for the eventual large piRNA clusters of 42AB and 38C. This renaming of the term piRNA source loci and the model in Fig. 7C is still misleading because 42AB and 38C are the main largest dual-strand piRNA clusters, and the pictures depict the ADMA-histones as recruiting Rhino and then Kipferl at a piRNA cluster. The term "piRNA source loci" does not sound distinct enough to separate it from the main piRNA clusters of 42AB and 38C, and I had suggested calling them 'niche piRNA clusters' to denote they are very special and distinct to only be responsive to Drosophila Art4 knockdown.

In regards to the revision's changing of gene names, the convention for gene names is to use the previous name designation. Rather than calling the gene DART1, the conventional name of this gene in Flybase is Art1 (CG6554). There is the same problem with using the new name DART4 when in Flybase the gene is called Art4 (CG5358). Alternatively, the authors should clarify the re-naming up front and make it consistent with Drosophila genetics nomenclature, perhaps dArt1 or dArt4 would be more appropriate.